literature review of global warming

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

A review of the global climate change impacts, adaptation, and sustainable mitigation measures

Kashif abbass.

1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094 People’s Republic of China

Muhammad Zeeshan Qasim

2 Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094 People’s Republic of China

Huaming Song

Muntasir murshed.

3 School of Business and Economics, North South University, Dhaka, 1229 Bangladesh

4 Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh

Haider Mahmood

5 Department of Finance, College of Business Administration, Prince Sattam Bin Abdulaziz University, 173, Alkharj, 11942 Saudi Arabia

Ijaz Younis

Associated data.

Data sources and relevant links are provided in the paper to access data.

Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

Introduction

Worldwide observed and anticipated climatic changes for the twenty-first century and global warming are significant global changes that have been encountered during the past 65 years. Climate change (CC) is an inter-governmental complex challenge globally with its influence over various components of the ecological, environmental, socio-political, and socio-economic disciplines (Adger et al.  2005 ; Leal Filho et al.  2021 ; Feliciano et al.  2022 ). Climate change involves heightened temperatures across numerous worlds (Battisti and Naylor  2009 ; Schuurmans  2021 ; Weisheimer and Palmer  2005 ; Yadav et al.  2015 ). With the onset of the industrial revolution, the problem of earth climate was amplified manifold (Leppänen et al.  2014 ). It is reported that the immediate attention and due steps might increase the probability of overcoming its devastating impacts. It is not plausible to interpret the exact consequences of climate change (CC) on a sectoral basis (Izaguirre et al.  2021 ; Jurgilevich et al.  2017 ), which is evident by the emerging level of recognition plus the inclusion of climatic uncertainties at both local and national level of policymaking (Ayers et al.  2014 ).

Climate change is characterized based on the comprehensive long-haul temperature and precipitation trends and other components such as pressure and humidity level in the surrounding environment. Besides, the irregular weather patterns, retreating of global ice sheets, and the corresponding elevated sea level rise are among the most renowned international and domestic effects of climate change (Lipczynska-Kochany  2018 ; Michel et al.  2021 ; Murshed and Dao 2020 ). Before the industrial revolution, natural sources, including volcanoes, forest fires, and seismic activities, were regarded as the distinct sources of greenhouse gases (GHGs) such as CO 2 , CH 4 , N 2 O, and H 2 O into the atmosphere (Murshed et al. 2020 ; Hussain et al.  2020 ; Sovacool et al.  2021 ; Usman and Balsalobre-Lorente 2022 ; Murshed 2022 ). United Nations Framework Convention on Climate Change (UNFCCC) struck a major agreement to tackle climate change and accelerate and intensify the actions and investments required for a sustainable low-carbon future at Conference of the Parties (COP-21) in Paris on December 12, 2015. The Paris Agreement expands on the Convention by bringing all nations together for the first time in a single cause to undertake ambitious measures to prevent climate change and adapt to its impacts, with increased funding to assist developing countries in doing so. As so, it marks a turning point in the global climate fight. The core goal of the Paris Agreement is to improve the global response to the threat of climate change by keeping the global temperature rise this century well below 2 °C over pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5° C (Sharma et al. 2020 ; Sharif et al. 2020 ; Chien et al. 2021 .

Furthermore, the agreement aspires to strengthen nations’ ability to deal with the effects of climate change and align financing flows with low GHG emissions and climate-resilient paths (Shahbaz et al. 2019 ; Anwar et al. 2021 ; Usman et al. 2022a ). To achieve these lofty goals, adequate financial resources must be mobilized and provided, as well as a new technology framework and expanded capacity building, allowing developing countries and the most vulnerable countries to act under their respective national objectives. The agreement also establishes a more transparent action and support mechanism. All Parties are required by the Paris Agreement to do their best through “nationally determined contributions” (NDCs) and to strengthen these efforts in the coming years (Balsalobre-Lorente et al. 2020 ). It includes obligations that all Parties regularly report on their emissions and implementation activities. A global stock-take will be conducted every five years to review collective progress toward the agreement’s goal and inform the Parties’ future individual actions. The Paris Agreement became available for signature on April 22, 2016, Earth Day, at the United Nations Headquarters in New York. On November 4, 2016, it went into effect 30 days after the so-called double threshold was met (ratification by 55 nations accounting for at least 55% of world emissions). More countries have ratified and continue to ratify the agreement since then, bringing 125 Parties in early 2017. To fully operationalize the Paris Agreement, a work program was initiated in Paris to define mechanisms, processes, and recommendations on a wide range of concerns (Murshed et al. 2021 ). Since 2016, Parties have collaborated in subsidiary bodies (APA, SBSTA, and SBI) and numerous formed entities. The Conference of the Parties functioning as the meeting of the Parties to the Paris Agreement (CMA) convened for the first time in November 2016 in Marrakesh in conjunction with COP22 and made its first two resolutions. The work plan is scheduled to be finished by 2018. Some mitigation and adaptation strategies to reduce the emission in the prospective of Paris agreement are following firstly, a long-term goal of keeping the increase in global average temperature to well below 2 °C above pre-industrial levels, secondly, to aim to limit the rise to 1.5 °C, since this would significantly reduce risks and the impacts of climate change, thirdly, on the need for global emissions to peak as soon as possible, recognizing that this will take longer for developing countries, lastly, to undertake rapid reductions after that under the best available science, to achieve a balance between emissions and removals in the second half of the century. On the other side, some adaptation strategies are; strengthening societies’ ability to deal with the effects of climate change and to continue & expand international assistance for developing nations’ adaptation.

However, anthropogenic activities are currently regarded as most accountable for CC (Murshed et al. 2022 ). Apart from the industrial revolution, other anthropogenic activities include excessive agricultural operations, which further involve the high use of fuel-based mechanization, burning of agricultural residues, burning fossil fuels, deforestation, national and domestic transportation sectors, etc. (Huang et al.  2016 ). Consequently, these anthropogenic activities lead to climatic catastrophes, damaging local and global infrastructure, human health, and total productivity. Energy consumption has mounted GHGs levels concerning warming temperatures as most of the energy production in developing countries comes from fossil fuels (Balsalobre-Lorente et al. 2022 ; Usman et al. 2022b ; Abbass et al. 2021a ; Ishikawa-Ishiwata and Furuya  2022 ).

This review aims to highlight the effects of climate change in a socio-scientific aspect by analyzing the existing literature on various sectorial pieces of evidence globally that influence the environment. Although this review provides a thorough examination of climate change and its severe affected sectors that pose a grave danger for global agriculture, biodiversity, health, economy, forestry, and tourism, and to purpose some practical prophylactic measures and mitigation strategies to be adapted as sound substitutes to survive from climate change (CC) impacts. The societal implications of irregular weather patterns and other effects of climate changes are discussed in detail. Some numerous sustainable mitigation measures and adaptation practices and techniques at the global level are discussed in this review with an in-depth focus on its economic, social, and environmental aspects. Methods of data collection section are included in the supplementary information.

Review methodology

Related study and its objectives.

Today, we live an ordinary life in the beautiful digital, globalized world where climate change has a decisive role. What happens in one country has a massive influence on geographically far apart countries, which points to the current crisis known as COVID-19 (Sarkar et al.  2021 ). The most dangerous disease like COVID-19 has affected the world’s climate changes and economic conditions (Abbass et al. 2022 ; Pirasteh-Anosheh et al.  2021 ). The purpose of the present study is to review the status of research on the subject, which is based on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures” by systematically reviewing past published and unpublished research work. Furthermore, the current study seeks to comment on research on the same topic and suggest future research on the same topic. Specifically, the present study aims: The first one is, organize publications to make them easy and quick to find. Secondly, to explore issues in this area, propose an outline of research for future work. The third aim of the study is to synthesize the previous literature on climate change, various sectors, and their mitigation measurement. Lastly , classify the articles according to the different methods and procedures that have been adopted.

Review methodology for reviewers

This review-based article followed systematic literature review techniques that have proved the literature review as a rigorous framework (Benita  2021 ; Tranfield et al.  2003 ). Moreover, we illustrate in Fig.  1 the search method that we have started for this research. First, finalized the research theme to search literature (Cooper et al.  2018 ). Second, used numerous research databases to search related articles and download from the database (Web of Science, Google Scholar, Scopus Index Journals, Emerald, Elsevier Science Direct, Springer, and Sciverse). We focused on various articles, with research articles, feedback pieces, short notes, debates, and review articles published in scholarly journals. Reports used to search for multiple keywords such as “Climate Change,” “Mitigation and Adaptation,” “Department of Agriculture and Human Health,” “Department of Biodiversity and Forestry,” etc.; in summary, keyword list and full text have been made. Initially, the search for keywords yielded a large amount of literature.

Methodology search for finalized articles for investigations.

Source : constructed by authors

Since 2020, it has been impossible to review all the articles found; some restrictions have been set for the literature exhibition. The study searched 95 articles on a different database mentioned above based on the nature of the study. It excluded 40 irrelevant papers due to copied from a previous search after readings tiles, abstract and full pieces. The criteria for inclusion were: (i) articles focused on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures,” and (ii) the search key terms related to study requirements. The complete procedure yielded 55 articles for our study. We repeat our search on the “Web of Science and Google Scholars” database to enhance the search results and check the referenced articles.

In this study, 55 articles are reviewed systematically and analyzed for research topics and other aspects, such as the methods, contexts, and theories used in these studies. Furthermore, this study analyzes closely related areas to provide unique research opportunities in the future. The study also discussed future direction opportunities and research questions by understanding the research findings climate changes and other affected sectors. The reviewed paper framework analysis process is outlined in Fig.  2 .

Framework of the analysis Process.

Natural disasters and climate change’s socio-economic consequences

Natural and environmental disasters can be highly variable from year to year; some years pass with very few deaths before a significant disaster event claims many lives (Symanski et al.  2021 ). Approximately 60,000 people globally died from natural disasters each year on average over the past decade (Ritchie and Roser  2014 ; Wiranata and Simbolon  2021 ). So, according to the report, around 0.1% of global deaths. Annual variability in the number and share of deaths from natural disasters in recent decades are shown in Fig.  3 . The number of fatalities can be meager—sometimes less than 10,000, and as few as 0.01% of all deaths. But shock events have a devastating impact: the 1983–1985 famine and drought in Ethiopia; the 2004 Indian Ocean earthquake and tsunami; Cyclone Nargis, which struck Myanmar in 2008; and the 2010 Port-au-Prince earthquake in Haiti and now recent example is COVID-19 pandemic (Erman et al.  2021 ). These events pushed global disaster deaths to over 200,000—more than 0.4% of deaths in these years. Low-frequency, high-impact events such as earthquakes and tsunamis are not preventable, but such high losses of human life are. Historical evidence shows that earlier disaster detection, more robust infrastructure, emergency preparedness, and response programmers have substantially reduced disaster deaths worldwide. Low-income is also the most vulnerable to disasters; improving living conditions, facilities, and response services in these areas would be critical in reducing natural disaster deaths in the coming decades.

Global deaths from natural disasters, 1978 to 2020.

Source EMDAT ( 2020 )

The interior regions of the continent are likely to be impacted by rising temperatures (Dimri et al.  2018 ; Goes et al.  2020 ; Mannig et al.  2018 ; Schuurmans  2021 ). Weather patterns change due to the shortage of natural resources (water), increase in glacier melting, and rising mercury are likely to cause extinction to many planted species (Gampe et al.  2016 ; Mihiretu et al.  2021 ; Shaffril et al.  2018 ).On the other hand, the coastal ecosystem is on the verge of devastation (Perera et al.  2018 ; Phillips  2018 ). The temperature rises, insect disease outbreaks, health-related problems, and seasonal and lifestyle changes are persistent, with a strong probability of these patterns continuing in the future (Abbass et al. 2021c ; Hussain et al.  2018 ). At the global level, a shortage of good infrastructure and insufficient adaptive capacity are hammering the most (IPCC  2013 ). In addition to the above concerns, a lack of environmental education and knowledge, outdated consumer behavior, a scarcity of incentives, a lack of legislation, and the government’s lack of commitment to climate change contribute to the general public’s concerns. By 2050, a 2 to 3% rise in mercury and a drastic shift in rainfall patterns may have serious consequences (Huang et al. 2022 ; Gorst et al.  2018 ). Natural and environmental calamities caused huge losses globally, such as decreased agriculture outputs, rehabilitation of the system, and rebuilding necessary technologies (Ali and Erenstein  2017 ; Ramankutty et al.  2018 ; Yu et al.  2021 ) (Table ​ (Table1). 1 ). Furthermore, in the last 3 or 4 years, the world has been plagued by smog-related eye and skin diseases, as well as a rise in road accidents due to poor visibility.

Main natural danger statistics for 1985–2020 at the global level

Source: EM-DAT ( 2020 )

Climate change and agriculture

Global agriculture is the ultimate sector responsible for 30–40% of all greenhouse emissions, which makes it a leading industry predominantly contributing to climate warming and significantly impacted by it (Grieg; Mishra et al.  2021 ; Ortiz et al.  2021 ; Thornton and Lipper  2014 ). Numerous agro-environmental and climatic factors that have a dominant influence on agriculture productivity (Pautasso et al.  2012 ) are significantly impacted in response to precipitation extremes including floods, forest fires, and droughts (Huang  2004 ). Besides, the immense dependency on exhaustible resources also fuels the fire and leads global agriculture to become prone to devastation. Godfray et al. ( 2010 ) mentioned that decline in agriculture challenges the farmer’s quality of life and thus a significant factor to poverty as the food and water supplies are critically impacted by CC (Ortiz et al.  2021 ; Rosenzweig et al.  2014 ). As an essential part of the economic systems, especially in developing countries, agricultural systems affect the overall economy and potentially the well-being of households (Schlenker and Roberts  2009 ). According to the report published by the Intergovernmental Panel on Climate Change (IPCC), atmospheric concentrations of greenhouse gases, i.e., CH 4, CO 2 , and N 2 O, are increased in the air to extraordinary levels over the last few centuries (Usman and Makhdum 2021 ; Stocker et al.  2013 ). Climate change is the composite outcome of two different factors. The first is the natural causes, and the second is the anthropogenic actions (Karami 2012 ). It is also forecasted that the world may experience a typical rise in temperature stretching from 1 to 3.7 °C at the end of this century (Pachauri et al. 2014 ). The world’s crop production is also highly vulnerable to these global temperature-changing trends as raised temperatures will pose severe negative impacts on crop growth (Reidsma et al. 2009 ). Some of the recent modeling about the fate of global agriculture is briefly described below.

Decline in cereal productivity

Crop productivity will also be affected dramatically in the next few decades due to variations in integral abiotic factors such as temperature, solar radiation, precipitation, and CO 2 . These all factors are included in various regulatory instruments like progress and growth, weather-tempted changes, pest invasions (Cammell and Knight 1992 ), accompanying disease snags (Fand et al. 2012 ), water supplies (Panda et al. 2003 ), high prices of agro-products in world’s agriculture industry, and preeminent quantity of fertilizer consumption. Lobell and field ( 2007 ) claimed that from 1962 to 2002, wheat crop output had condensed significantly due to rising temperatures. Therefore, during 1980–2011, the common wheat productivity trends endorsed extreme temperature events confirmed by Gourdji et al. ( 2013 ) around South Asia, South America, and Central Asia. Various other studies (Asseng, Cao, Zhang, and Ludwig 2009 ; Asseng et al. 2013 ; García et al. 2015 ; Ortiz et al. 2021 ) also proved that wheat output is negatively affected by the rising temperatures and also caused adverse effects on biomass productivity (Calderini et al. 1999 ; Sadras and Slafer 2012 ). Hereafter, the rice crop is also influenced by the high temperatures at night. These difficulties will worsen because the temperature will be rising further in the future owing to CC (Tebaldi et al. 2006 ). Another research conducted in China revealed that a 4.6% of rice production per 1 °C has happened connected with the advancement in night temperatures (Tao et al. 2006 ). Moreover, the average night temperature growth also affected rice indicia cultivar’s output pragmatically during 25 years in the Philippines (Peng et al. 2004 ). It is anticipated that the increase in world average temperature will also cause a substantial reduction in yield (Hatfield et al. 2011 ; Lobell and Gourdji 2012 ). In the southern hemisphere, Parry et al. ( 2007 ) noted a rise of 1–4 °C in average daily temperatures at the end of spring season unti the middle of summers, and this raised temperature reduced crop output by cutting down the time length for phenophases eventually reduce the yield (Hatfield and Prueger 2015 ; R. Ortiz 2008 ). Also, world climate models have recommended that humid and subtropical regions expect to be plentiful prey to the upcoming heat strokes (Battisti and Naylor 2009 ). Grain production is the amalgamation of two constituents: the average weight and the grain output/m 2 , however, in crop production. Crop output is mainly accredited to the grain quantity (Araus et al. 2008 ; Gambín and Borrás 2010 ). In the times of grain set, yield resources are mainly strewn between hitherto defined components, i.e., grain usual weight and grain output, which presents a trade-off between them (Gambín and Borrás 2010 ) beside disparities in per grain integration (B. L. Gambín et al. 2006 ). In addition to this, the maize crop is also susceptible to raised temperatures, principally in the flowering stage (Edreira and Otegui 2013 ). In reality, the lower grain number is associated with insufficient acclimatization due to intense photosynthesis and higher respiration and the high-temperature effect on the reproduction phenomena (Edreira and Otegui 2013 ). During the flowering phase, maize visible to heat (30–36 °C) seemed less anthesis-silking intermissions (Edreira et al. 2011 ). Another research by Dupuis and Dumas ( 1990 ) proved that a drop in spikelet when directly visible to high temperatures above 35 °C in vitro pollination. Abnormalities in kernel number claimed by Vega et al. ( 2001 ) is related to conceded plant development during a flowering phase that is linked with the active ear growth phase and categorized as a critical phase for approximation of kernel number during silking (Otegui and Bonhomme 1998 ).

The retort of rice output to high temperature presents disparities in flowering patterns, and seed set lessens and lessens grain weight (Qasim et al. 2020 ; Qasim, Hammad, Maqsood, Tariq, & Chawla). During the daytime, heat directly impacts flowers which lessens the thesis period and quickens the earlier peak flowering (Tao et al. 2006 ). Antagonistic effect of higher daytime temperature d on pollen sprouting proposed seed set decay, whereas, seed set was lengthily reduced than could be explicated by pollen growing at high temperatures 40◦C (Matsui et al. 2001 ).

The decline in wheat output is linked with higher temperatures, confirmed in numerous studies (Semenov 2009 ; Stone and Nicolas 1994 ). High temperatures fast-track the arrangements of plant expansion (Blum et al. 2001 ), diminution photosynthetic process (Salvucci and Crafts‐Brandner 2004 ), and also considerably affect the reproductive operations (Farooq et al. 2011 ).

The destructive impacts of CC induced weather extremes to deteriorate the integrity of crops (Chaudhary et al. 2011 ), e.g., Spartan cold and extreme fog cause falling and discoloration of betel leaves (Rosenzweig et al. 2001 ), giving them a somehow reddish appearance, squeezing of lemon leaves (Pautasso et al. 2012 ), as well as root rot of pineapple, have reported (Vedwan and Rhoades 2001 ). Henceforth, in tackling the disruptive effects of CC, several short-term and long-term management approaches are the crucial need of time (Fig.  4 ). Moreover, various studies (Chaudhary et al. 2011 ; Patz et al. 2005 ; Pautasso et al. 2012 ) have demonstrated adapting trends such as ameliorating crop diversity can yield better adaptability towards CC.

Schematic description of potential impacts of climate change on the agriculture sector and the appropriate mitigation and adaptation measures to overcome its impact.

Climate change impacts on biodiversity

Global biodiversity is among the severe victims of CC because it is the fastest emerging cause of species loss. Studies demonstrated that the massive scale species dynamics are considerably associated with diverse climatic events (Abraham and Chain 1988 ; Manes et al. 2021 ; A. M. D. Ortiz et al. 2021 ). Both the pace and magnitude of CC are altering the compatible habitat ranges for living entities of marine, freshwater, and terrestrial regions. Alterations in general climate regimes influence the integrity of ecosystems in numerous ways, such as variation in the relative abundance of species, range shifts, changes in activity timing, and microhabitat use (Bates et al. 2014 ). The geographic distribution of any species often depends upon its ability to tolerate environmental stresses, biological interactions, and dispersal constraints. Hence, instead of the CC, the local species must only accept, adapt, move, or face extinction (Berg et al. 2010 ). So, the best performer species have a better survival capacity for adjusting to new ecosystems or a decreased perseverance to survive where they are already situated (Bates et al. 2014 ). An important aspect here is the inadequate habitat connectivity and access to microclimates, also crucial in raising the exposure to climate warming and extreme heatwave episodes. For example, the carbon sequestration rates are undergoing fluctuations due to climate-driven expansion in the range of global mangroves (Cavanaugh et al. 2014 ).

Similarly, the loss of kelp-forest ecosystems in various regions and its occupancy by the seaweed turfs has set the track for elevated herbivory by the high influx of tropical fish populations. Not only this, the increased water temperatures have exacerbated the conditions far away from the physiological tolerance level of the kelp communities (Vergés et al. 2016 ; Wernberg et al. 2016 ). Another pertinent danger is the devastation of keystone species, which even has more pervasive effects on the entire communities in that habitat (Zarnetske et al. 2012 ). It is particularly important as CC does not specify specific populations or communities. Eventually, this CC-induced redistribution of species may deteriorate carbon storage and the net ecosystem productivity (Weed et al. 2013 ). Among the typical disruptions, the prominent ones include impacts on marine and terrestrial productivity, marine community assembly, and the extended invasion of toxic cyanobacteria bloom (Fossheim et al. 2015 ).

The CC-impacted species extinction is widely reported in the literature (Beesley et al. 2019 ; Urban 2015 ), and the predictions of demise until the twenty-first century are dreadful (Abbass et al. 2019 ; Pereira et al. 2013 ). In a few cases, northward shifting of species may not be formidable as it allows mountain-dwelling species to find optimum climates. However, the migrant species may be trapped in isolated and incompatible habitats due to losing topography and range (Dullinger et al. 2012 ). For example, a study indicated that the American pika has been extirpated or intensely diminished in some regions, primarily attributed to the CC-impacted extinction or at least local extirpation (Stewart et al. 2015 ). Besides, the anticipation of persistent responses to the impacts of CC often requires data records of several decades to rigorously analyze the critical pre and post CC patterns at species and ecosystem levels (Manes et al. 2021 ; Testa et al. 2018 ).

Nonetheless, the availability of such long-term data records is rare; hence, attempts are needed to focus on these profound aspects. Biodiversity is also vulnerable to the other associated impacts of CC, such as rising temperatures, droughts, and certain invasive pest species. For instance, a study revealed the changes in the composition of plankton communities attributed to rising temperatures. Henceforth, alterations in such aquatic producer communities, i.e., diatoms and calcareous plants, can ultimately lead to variation in the recycling of biological carbon. Moreover, such changes are characterized as a potential contributor to CO 2 differences between the Pleistocene glacial and interglacial periods (Kohfeld et al. 2005 ).

Climate change implications on human health

It is an understood corporality that human health is a significant victim of CC (Costello et al. 2009 ). According to the WHO, CC might be responsible for 250,000 additional deaths per year during 2030–2050 (Watts et al. 2015 ). These deaths are attributed to extreme weather-induced mortality and morbidity and the global expansion of vector-borne diseases (Lemery et al. 2021; Yang and Usman 2021 ; Meierrieks 2021 ; UNEP 2017 ). Here, some of the emerging health issues pertinent to this global problem are briefly described.

Climate change and antimicrobial resistance with corresponding economic costs

Antimicrobial resistance (AMR) is an up-surging complex global health challenge (Garner et al. 2019 ; Lemery et al. 2021 ). Health professionals across the globe are extremely worried due to this phenomenon that has critical potential to reverse almost all the progress that has been achieved so far in the health discipline (Gosling and Arnell 2016 ). A massive amount of antibiotics is produced by many pharmaceutical industries worldwide, and the pathogenic microorganisms are gradually developing resistance to them, which can be comprehended how strongly this aspect can shake the foundations of national and global economies (UNEP 2017 ). This statement is supported by the fact that AMR is not developing in a particular region or country. Instead, it is flourishing in every continent of the world (WHO 2018 ). This plague is heavily pushing humanity to the post-antibiotic era, in which currently antibiotic-susceptible pathogens will once again lead to certain endemics and pandemics after being resistant(WHO 2018 ). Undesirably, if this statement would become a factuality, there might emerge certain risks in undertaking sophisticated interventions such as chemotherapy, joint replacement cases, and organ transplantation (Su et al. 2018 ). Presently, the amplification of drug resistance cases has made common illnesses like pneumonia, post-surgical infections, HIV/AIDS, tuberculosis, malaria, etc., too difficult and costly to be treated or cure well (WHO 2018 ). From a simple example, it can be assumed how easily antibiotic-resistant strains can be transmitted from one person to another and ultimately travel across the boundaries (Berendonk et al. 2015 ). Talking about the second- and third-generation classes of antibiotics, e.g., most renowned generations of cephalosporin antibiotics that are more expensive, broad-spectrum, more toxic, and usually require more extended periods whenever prescribed to patients (Lemery et al. 2021 ; Pärnänen et al. 2019 ). This scenario has also revealed that the abundance of resistant strains of pathogens was also higher in the Southern part (WHO 2018 ). As southern parts are generally warmer than their counterparts, it is evident from this example how CC-induced global warming can augment the spread of antibiotic-resistant strains within the biosphere, eventually putting additional economic burden in the face of developing new and costlier antibiotics. The ARG exchange to susceptible bacteria through one of the potential mechanisms, transformation, transduction, and conjugation; Selection pressure can be caused by certain antibiotics, metals or pesticides, etc., as shown in Fig.  5 .

A typical interaction between the susceptible and resistant strains.

Source: Elsayed et al. ( 2021 ); Karkman et al. ( 2018 )

Certain studies highlighted that conventional urban wastewater treatment plants are typical hotspots where most bacterial strains exchange genetic material through horizontal gene transfer (Fig.  5 ). Although at present, the extent of risks associated with the antibiotic resistance found in wastewater is complicated; environmental scientists and engineers have particular concerns about the potential impacts of these antibiotic resistance genes on human health (Ashbolt 2015 ). At most undesirable and worst case, these antibiotic-resistant genes containing bacteria can make their way to enter into the environment (Pruden et al. 2013 ), irrigation water used for crops and public water supplies and ultimately become a part of food chains and food webs (Ma et al. 2019 ; D. Wu et al. 2019 ). This problem has been reported manifold in several countries (Hendriksen et al. 2019 ), where wastewater as a means of irrigated water is quite common.

Climate change and vector borne-diseases

Temperature is a fundamental factor for the sustenance of living entities regardless of an ecosystem. So, a specific living being, especially a pathogen, requires a sophisticated temperature range to exist on earth. The second essential component of CC is precipitation, which also impacts numerous infectious agents’ transport and dissemination patterns. Global rising temperature is a significant cause of many species extinction. On the one hand, this changing environmental temperature may be causing species extinction, and on the other, this warming temperature might favor the thriving of some new organisms. Here, it was evident that some pathogens may also upraise once non-evident or reported (Patz et al. 2000 ). This concept can be exemplified through certain pathogenic strains of microorganisms that how the likelihood of various diseases increases in response to climate warming-induced environmental changes (Table ​ (Table2 2 ).

Examples of how various environmental changes affect various infectious diseases in humans

Source: Aron and Patz ( 2001 )

A recent example is an outburst of coronavirus (COVID-19) in the Republic of China, causing pneumonia and severe acute respiratory complications (Cui et al. 2021 ; Song et al. 2021 ). The large family of viruses is harbored in numerous animals, bats, and snakes in particular (livescience.com) with the subsequent transfer into human beings. Hence, it is worth noting that the thriving of numerous vectors involved in spreading various diseases is influenced by Climate change (Ogden 2018 ; Santos et al. 2021 ).

Psychological impacts of climate change

Climate change (CC) is responsible for the rapid dissemination and exaggeration of certain epidemics and pandemics. In addition to the vast apparent impacts of climate change on health, forestry, agriculture, etc., it may also have psychological implications on vulnerable societies. It can be exemplified through the recent outburst of (COVID-19) in various countries around the world (Pal 2021 ). Besides, the victims of this viral infection have made healthy beings scarier and terrified. In the wake of such epidemics, people with common colds or fever are also frightened and must pass specific regulatory protocols. Living in such situations continuously terrifies the public and makes the stress familiar, which eventually makes them psychologically weak (npr.org).

CC boosts the extent of anxiety, distress, and other issues in public, pushing them to develop various mental-related problems. Besides, frequent exposure to extreme climatic catastrophes such as geological disasters also imprints post-traumatic disorder, and their ubiquitous occurrence paves the way to developing chronic psychological dysfunction. Moreover, repetitive listening from media also causes an increase in the person’s stress level (Association 2020 ). Similarly, communities living in flood-prone areas constantly live in extreme fear of drowning and die by floods. In addition to human lives, the flood-induced destruction of physical infrastructure is a specific reason for putting pressure on these communities (Ogden 2018 ). For instance, Ogden ( 2018 ) comprehensively denoted that Katrina’s Hurricane augmented the mental health issues in the victim communities.

Climate change impacts on the forestry sector

Forests are the global regulators of the world’s climate (FAO 2018 ) and have an indispensable role in regulating global carbon and nitrogen cycles (Rehman et al. 2021 ; Reichstein and Carvalhais 2019 ). Hence, disturbances in forest ecology affect the micro and macro-climates (Ellison et al. 2017 ). Climate warming, in return, has profound impacts on the growth and productivity of transboundary forests by influencing the temperature and precipitation patterns, etc. As CC induces specific changes in the typical structure and functions of ecosystems (Zhang et al. 2017 ) as well impacts forest health, climate change also has several devastating consequences such as forest fires, droughts, pest outbreaks (EPA 2018 ), and last but not the least is the livelihoods of forest-dependent communities. The rising frequency and intensity of another CC product, i.e., droughts, pose plenty of challenges to the well-being of global forests (Diffenbaugh et al. 2017 ), which is further projected to increase soon (Hartmann et al. 2018 ; Lehner et al. 2017 ; Rehman et al. 2021 ). Hence, CC induces storms, with more significant impacts also put extra pressure on the survival of the global forests (Martínez-Alvarado et al. 2018 ), significantly since their influences are augmented during higher winter precipitations with corresponding wetter soils causing weak root anchorage of trees (Brázdil et al. 2018 ). Surging temperature regimes causes alterations in usual precipitation patterns, which is a significant hurdle for the survival of temperate forests (Allen et al. 2010 ; Flannigan et al. 2013 ), letting them encounter severe stress and disturbances which adversely affects the local tree species (Hubbart et al. 2016 ; Millar and Stephenson 2015 ; Rehman et al. 2021 ).

Climate change impacts on forest-dependent communities

Forests are the fundamental livelihood resource for about 1.6 billion people worldwide; out of them, 350 million are distinguished with relatively higher reliance (Bank 2008 ). Agro-forestry-dependent communities comprise 1.2 billion, and 60 million indigenous people solely rely on forests and their products to sustain their lives (Sunderlin et al. 2005 ). For example, in the entire African continent, more than 2/3rd of inhabitants depend on forest resources and woodlands for their alimonies, e.g., food, fuelwood and grazing (Wasiq and Ahmad 2004 ). The livings of these people are more intensely affected by the climatic disruptions making their lives harder (Brown et al. 2014 ). On the one hand, forest communities are incredibly vulnerable to CC due to their livelihoods, cultural and spiritual ties as well as socio-ecological connections, and on the other, they are not familiar with the term “climate change.” (Rahman and Alam 2016 ). Among the destructive impacts of temperature and rainfall, disruption of the agroforestry crops with resultant downscale growth and yield (Macchi et al. 2008 ). Cruz ( 2015 ) ascribed that forest-dependent smallholder farmers in the Philippines face the enigma of delayed fruiting, more severe damages by insect and pest incidences due to unfavorable temperature regimes, and changed rainfall patterns.

Among these series of challenges to forest communities, their well-being is also distinctly vulnerable to CC. Though the detailed climate change impacts on human health have been comprehensively mentioned in the previous section, some studies have listed a few more devastating effects on the prosperity of forest-dependent communities. For instance, the Himalayan people have been experiencing frequent skin-borne diseases such as malaria and other skin diseases due to increasing mosquitoes, wild boar as well, and new wasps species, particularly in higher altitudes that were almost non-existent before last 5–10 years (Xu et al. 2008 ). Similarly, people living at high altitudes in Bangladesh have experienced frequent mosquito-borne calamities (Fardous; Sharma 2012 ). In addition, the pace of other waterborne diseases such as infectious diarrhea, cholera, pathogenic induced abdominal complications and dengue has also been boosted in other distinguished regions of Bangladesh (Cell 2009 ; Gunter et al. 2008 ).

Pest outbreak

Upscaling hotter climate may positively affect the mobile organisms with shorter generation times because they can scurry from harsh conditions than the immobile species (Fettig et al. 2013 ; Schoene and Bernier 2012 ) and are also relatively more capable of adapting to new environments (Jactel et al. 2019 ). It reveals that insects adapt quickly to global warming due to their mobility advantages. Due to past outbreaks, the trees (forests) are relatively more susceptible victims (Kurz et al. 2008 ). Before CC, the influence of factors mentioned earlier, i.e., droughts and storms, was existent and made the forests susceptible to insect pest interventions; however, the global forests remain steadfast, assiduous, and green (Jactel et al. 2019 ). The typical reasons could be the insect herbivores were regulated by several tree defenses and pressures of predation (Wilkinson and Sherratt 2016 ). As climate greatly influences these phenomena, the global forests cannot be so sedulous against such challenges (Jactel et al. 2019 ). Table ​ Table3 3 demonstrates some of the particular considerations with practical examples that are essential while mitigating the impacts of CC in the forestry sector.

Essential considerations while mitigating the climate change impacts on the forestry sector

Source : Fischer ( 2019 )

Climate change impacts on tourism

Tourism is a commercial activity that has roots in multi-dimensions and an efficient tool with adequate job generation potential, revenue creation, earning of spectacular foreign exchange, enhancement in cross-cultural promulgation and cooperation, a business tool for entrepreneurs and eventually for the country’s national development (Arshad et al. 2018 ; Scott 2021 ). Among a plethora of other disciplines, the tourism industry is also a distinct victim of climate warming (Gössling et al. 2012 ; Hall et al. 2015 ) as the climate is among the essential resources that enable tourism in particular regions as most preferred locations. Different places at different times of the year attract tourists both within and across the countries depending upon the feasibility and compatibility of particular weather patterns. Hence, the massive variations in these weather patterns resulting from CC will eventually lead to monumental challenges to the local economy in that specific area’s particular and national economy (Bujosa et al. 2015 ). For instance, the Intergovernmental Panel on Climate Change (IPCC) report demonstrated that the global tourism industry had faced a considerable decline in the duration of ski season, including the loss of some ski areas and the dramatic shifts in tourist destinations’ climate warming.

Furthermore, different studies (Neuvonen et al. 2015 ; Scott et al. 2004 ) indicated that various currently perfect tourist spots, e.g., coastal areas, splendid islands, and ski resorts, will suffer consequences of CC. It is also worth noting that the quality and potential of administrative management potential to cope with the influence of CC on the tourism industry is of crucial significance, which renders specific strengths of resiliency to numerous destinations to withstand against it (Füssel and Hildén 2014 ). Similarly, in the partial or complete absence of adequate socio-economic and socio-political capital, the high-demanding tourist sites scurry towards the verge of vulnerability. The susceptibility of tourism is based on different components such as the extent of exposure, sensitivity, life-supporting sectors, and capacity assessment factors (Füssel and Hildén 2014 ). It is obvious corporality that sectors such as health, food, ecosystems, human habitat, infrastructure, water availability, and the accessibility of a particular region are prone to CC. Henceforth, the sensitivity of these critical sectors to CC and, in return, the adaptive measures are a hallmark in determining the composite vulnerability of climate warming (Ionescu et al. 2009 ).

Moreover, the dependence on imported food items, poor hygienic conditions, and inadequate health professionals are dominant aspects affecting the local terrestrial and aquatic biodiversity. Meanwhile, the greater dependency on ecosystem services and its products also makes a destination more fragile to become a prey of CC (Rizvi et al. 2015 ). Some significant non-climatic factors are important indicators of a particular ecosystem’s typical health and functioning, e.g., resource richness and abundance portray the picture of ecosystem stability. Similarly, the species abundance is also a productive tool that ensures that the ecosystem has a higher buffering capacity, which is terrific in terms of resiliency (Roscher et al. 2013 ).

Climate change impacts on the economic sector

Climate plays a significant role in overall productivity and economic growth. Due to its increasingly global existence and its effect on economic growth, CC has become one of the major concerns of both local and international environmental policymakers (Ferreira et al. 2020 ; Gleditsch 2021 ; Abbass et al. 2021b ; Lamperti et al. 2021 ). The adverse effects of CC on the overall productivity factor of the agricultural sector are therefore significant for understanding the creation of local adaptation policies and the composition of productive climate policy contracts. Previous studies on CC in the world have already forecasted its effects on the agricultural sector. Researchers have found that global CC will impact the agricultural sector in different world regions. The study of the impacts of CC on various agrarian activities in other demographic areas and the development of relative strategies to respond to effects has become a focal point for researchers (Chandioet al. 2020 ; Gleditsch 2021 ; Mosavi et al. 2020 ).

With the rapid growth of global warming since the 1980s, the temperature has started increasing globally, which resulted in the incredible transformation of rain and evaporation in the countries. The agricultural development of many countries has been reliant, delicate, and susceptible to CC for a long time, and it is on the development of agriculture total factor productivity (ATFP) influence different crops and yields of farmers (Alhassan 2021 ; Wu  2020 ).

Food security and natural disasters are increasing rapidly in the world. Several major climatic/natural disasters have impacted local crop production in the countries concerned. The effects of these natural disasters have been poorly controlled by the development of the economies and populations and may affect human life as well. One example is China, which is among the world’s most affected countries, vulnerable to natural disasters due to its large population, harsh environmental conditions, rapid CC, low environmental stability, and disaster power. According to the January 2016 statistical survey, China experienced an economic loss of 298.3 billion Yuan, and about 137 million Chinese people were severely affected by various natural disasters (Xie et al. 2018 ).

Mitigation and adaptation strategies of climate changes

Adaptation and mitigation are the crucial factors to address the response to CC (Jahanzad et al. 2020 ). Researchers define mitigation on climate changes, and on the other hand, adaptation directly impacts climate changes like floods. To some extent, mitigation reduces or moderates greenhouse gas emission, and it becomes a critical issue both economically and environmentally (Botzen et al. 2021 ; Jahanzad et al. 2020 ; Kongsager 2018 ; Smit et al. 2000 ; Vale et al. 2021 ; Usman et al. 2021 ; Verheyen 2005 ).

Researchers have deep concern about the adaptation and mitigation methodologies in sectoral and geographical contexts. Agriculture, industry, forestry, transport, and land use are the main sectors to adapt and mitigate policies(Kärkkäinen et al. 2020 ; Waheed et al. 2021 ). Adaptation and mitigation require particular concern both at the national and international levels. The world has faced a significant problem of climate change in the last decades, and adaptation to these effects is compulsory for economic and social development. To adapt and mitigate against CC, one should develop policies and strategies at the international level (Hussain et al. 2020 ). Figure  6 depicts the list of current studies on sectoral impacts of CC with adaptation and mitigation measures globally.

Sectoral impacts of climate change with adaptation and mitigation measures.

Conclusion and future perspectives

Specific socio-agricultural, socio-economic, and physical systems are the cornerstone of psychological well-being, and the alteration in these systems by CC will have disastrous impacts. Climate variability, alongside other anthropogenic and natural stressors, influences human and environmental health sustainability. Food security is another concerning scenario that may lead to compromised food quality, higher food prices, and inadequate food distribution systems. Global forests are challenged by different climatic factors such as storms, droughts, flash floods, and intense precipitation. On the other hand, their anthropogenic wiping is aggrandizing their existence. Undoubtedly, the vulnerability scale of the world’s regions differs; however, appropriate mitigation and adaptation measures can aid the decision-making bodies in developing effective policies to tackle its impacts. Presently, modern life on earth has tailored to consistent climatic patterns, and accordingly, adapting to such considerable variations is of paramount importance. Because the faster changes in climate will make it harder to survive and adjust, this globally-raising enigma calls for immediate attention at every scale ranging from elementary community level to international level. Still, much effort, research, and dedication are required, which is the most critical time. Some policy implications can help us to mitigate the consequences of climate change, especially the most affected sectors like the agriculture sector;

Warming might lengthen the season in frost-prone growing regions (temperate and arctic zones), allowing for longer-maturing seasonal cultivars with better yields (Pfadenhauer 2020 ; Bonacci 2019 ). Extending the planting season may allow additional crops each year; when warming leads to frequent warmer months highs over critical thresholds, a split season with a brief summer fallow may be conceivable for short-period crops such as wheat barley, cereals, and many other vegetable crops. The capacity to prolong the planting season in tropical and subtropical places where the harvest season is constrained by precipitation or agriculture farming occurs after the year may be more limited and dependent on how precipitation patterns vary (Wu et al. 2017 ).

The genetic component is comprehensive for many yields, but it is restricted like kiwi fruit for a few. Ali et al. ( 2017 ) investigated how new crops will react to climatic changes (also stated in Mall et al. 2017 ). Hot temperature, drought, insect resistance; salt tolerance; and overall crop production and product quality increases would all be advantageous (Akkari 2016 ). Genetic mapping and engineering can introduce a greater spectrum of features. The adoption of genetically altered cultivars has been slowed, particularly in the early forecasts owing to the complexity in ensuring features are expediently expressed throughout the entire plant, customer concerns, economic profitability, and regulatory impediments (Wirehn 2018 ; Davidson et al. 2016 ).

To get the full benefit of the CO 2 would certainly require additional nitrogen and other fertilizers. Nitrogen not consumed by the plants may be excreted into groundwater, discharged into water surface, or emitted from the land, soil nitrous oxide when large doses of fertilizer are sprayed. Increased nitrogen levels in groundwater sources have been related to human chronic illnesses and impact marine ecosystems. Cultivation, grain drying, and other field activities have all been examined in depth in the studies (Barua et al. 2018 ).

• The technological and socio-economic adaptation

The policy consequence of the causative conclusion is that as a source of alternative energy, biofuel production is one of the routes that explain oil price volatility separate from international macroeconomic factors. Even though biofuel production has just begun in a few sample nations, there is still a tremendous worldwide need for feedstock to satisfy industrial expansion in China and the USA, which explains the food price relationship to the global oil price. Essentially, oil-exporting countries may create incentives in their economies to increase food production. It may accomplish by giving farmers financing, seedlings, fertilizers, and farming equipment. Because of the declining global oil price and, as a result, their earnings from oil export, oil-producing nations may be unable to subsidize food imports even in the near term. As a result, these countries can boost the agricultural value chain for export. It may be accomplished through R&D and adding value to their food products to increase income by correcting exchange rate misalignment and adverse trade terms. These nations may also diversify their economies away from oil, as dependence on oil exports alone is no longer economically viable given the extreme volatility of global oil prices. Finally, resource-rich and oil-exporting countries can convert to non-food renewable energy sources such as solar, hydro, coal, wind, wave, and tidal energy. By doing so, both world food and oil supplies would be maintained rather than harmed.

IRENA’s modeling work shows that, if a comprehensive policy framework is in place, efforts toward decarbonizing the energy future will benefit economic activity, jobs (outweighing losses in the fossil fuel industry), and welfare. Countries with weak domestic supply chains and a large reliance on fossil fuel income, in particular, must undertake structural reforms to capitalize on the opportunities inherent in the energy transition. Governments continue to give major policy assistance to extract fossil fuels, including tax incentives, financing, direct infrastructure expenditures, exemptions from environmental regulations, and other measures. The majority of major oil and gas producing countries intend to increase output. Some countries intend to cut coal output, while others plan to maintain or expand it. While some nations are beginning to explore and execute policies aimed at a just and equitable transition away from fossil fuel production, these efforts have yet to impact major producing countries’ plans and goals. Verifiable and comparable data on fossil fuel output and assistance from governments and industries are critical to closing the production gap. Governments could increase openness by declaring their production intentions in their climate obligations under the Paris Agreement.

It is firmly believed that achieving the Paris Agreement commitments is doubtlful without undergoing renewable energy transition across the globe (Murshed 2020 ; Zhao et al. 2022 ). Policy instruments play the most important role in determining the degree of investment in renewable energy technology. This study examines the efficacy of various policy strategies in the renewable energy industry of multiple nations. Although its impact is more visible in established renewable energy markets, a renewable portfolio standard is also a useful policy instrument. The cost of producing renewable energy is still greater than other traditional energy sources. Furthermore, government incentives in the R&D sector can foster innovation in this field, resulting in cost reductions in the renewable energy industry. These nations may export their technologies and share their policy experiences by forming networks among their renewable energy-focused organizations. All policy measures aim to reduce production costs while increasing the proportion of renewables to a country’s energy system. Meanwhile, long-term contracts with renewable energy providers, government commitment and control, and the establishment of long-term goals can assist developing nations in deploying renewable energy technology in their energy sector.

Author contribution

KA: Writing the original manuscript, data collection, data analysis, Study design, Formal analysis, Visualization, Revised draft, Writing-review, and editing. MZQ: Writing the original manuscript, data collection, data analysis, Writing-review, and editing. HS: Contribution to the contextualization of the theme, Conceptualization, Validation, Supervision, literature review, Revised drapt, and writing review and editing. MM: Writing review and editing, compiling the literature review, language editing. HM: Writing review and editing, compiling the literature review, language editing. IY: Contribution to the contextualization of the theme, literature review, and writing review and editing.

Availability of data and material

Declarations.

Not applicable.

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Kashif Abbass, Email: nc.ude.tsujn@ssabbafihsak .

Muhammad Zeeshan Qasim, Email: moc.kooltuo@888misaqnahseez .

Huaming Song, Email: nc.ude.tsujn@gnimauh .

Muntasir Murshed, Email: [email protected] .

Haider Mahmood, Email: moc.liamtoh@doomhamrediah .

Ijaz Younis, Email: nc.ude.tsujn@sinuoyzaji .

• Abbass K, Begum H, Alam ASA, Awang AH, Abdelsalam MK, Egdair IMM, Wahid R (2022) Fresh Insight through a Keynesian Theory Approach to Investigate the Economic Impact of the COVID-19 Pandemic in Pakistan. Sustain 14(3):1054
• Abbass K, Niazi AAK, Qazi TF, Basit A, Song H (2021a) The aftermath of COVID-19 pandemic period: barriers in implementation of social distancing at workplace. Library Hi Tech
• Abbass K, Song H, Khan F, Begum H, Asif M (2021b) Fresh insight through the VAR approach to investigate the effects of fiscal policy on environmental pollution in Pakistan. Environ Scie Poll Res 1–14 [ PubMed ]
• Abbass K, Song H, Shah SM, Aziz B. Determinants of Stock Return for Non-Financial Sector: Evidence from Energy Sector of Pakistan. J Bus Fin Aff. 2019; 8 (370):2167–0234. [ Google Scholar ]
• Abbass K, Tanveer A, Huaming S, Khatiya AA (2021c) Impact of financial resources utilization on firm performance: a case of SMEs working in Pakistan
• Abraham E, Chain E. An enzyme from bacteria able to destroy penicillin. 1940. Rev Infect Dis. 1988; 10 (4):677. [ PubMed ] [ Google Scholar ]
• Adger WN, Arnell NW, Tompkins EL. Successful adaptation to climate change across scales. Glob Environ Chang. 2005; 15 (2):77–86. doi: 10.1016/j.gloenvcha.2004.12.005. [ CrossRef ] [ Google Scholar ]
• Akkari C, Bryant CR. The co-construction approach as approach to developing adaptation strategies in the face of climate change and variability: A conceptual framework. Agricultural Research. 2016; 5 (2):162–173. doi: 10.1007/s40003-016-0208-8. [ CrossRef ] [ Google Scholar ]
• Alhassan H (2021) The effect of agricultural total factor productivity on environmental degradation in sub-Saharan Africa. Sci Afr 12:e00740
• Ali A, Erenstein O. Assessing farmer use of climate change adaptation practices and impacts on food security and poverty in Pakistan. Clim Risk Manag. 2017; 16 :183–194. doi: 10.1016/j.crm.2016.12.001. [ CrossRef ] [ Google Scholar ]
• Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Hogg ET. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag. 2010; 259 (4):660–684. doi: 10.1016/j.foreco.2009.09.001. [ CrossRef ] [ Google Scholar ]
• Anwar A, Sinha A, Sharif A, Siddique M, Irshad S, Anwar W, Malik S (2021) The nexus between urbanization, renewable energy consumption, financial development, and CO2 emissions: evidence from selected Asian countries. Environ Dev Sust. 10.1007/s10668-021-01716-2
• Araus JL, Slafer GA, Royo C, Serret MD. Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci. 2008; 27 (6):377–412. doi: 10.1080/07352680802467736. [ CrossRef ] [ Google Scholar ]
• Aron JL, Patz J (2001) Ecosystem change and public health: a global perspective: JHU Press
• Arshad MI, Iqbal MA, Shahbaz M. Pakistan tourism industry and challenges: a review. Asia Pacific Journal of Tourism Research. 2018; 23 (2):121–132. doi: 10.1080/10941665.2017.1410192. [ CrossRef ] [ Google Scholar ]
• Ashbolt NJ. Microbial contamination of drinking water and human health from community water systems. Current Environmental Health Reports. 2015; 2 (1):95–106. doi: 10.1007/s40572-014-0037-5. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Asseng S, Cao W, Zhang W, Ludwig F (2009) Crop physiology, modelling and climate change: impact and adaptation strategies. Crop Physiol 511–543
• Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Cammarano D. Uncertainty in simulating wheat yields under climate change. Nat Clim Chang. 2013; 3 (9):827–832. doi: 10.1038/nclimate1916. [ CrossRef ] [ Google Scholar ]
• Association A (2020) Climate change is threatening mental health, American Psychological Association, “Kirsten Weir, . from < https://www.apa.org/monitor/2016/07-08/climate-change >, Accessed on 26 Jan 2020.
• Ayers J, Huq S, Wright H, Faisal A, Hussain S. Mainstreaming climate change adaptation into development in Bangladesh. Clim Dev. 2014; 6 :293–305. doi: 10.1080/17565529.2014.977761. [ CrossRef ] [ Google Scholar ]
• Balsalobre-Lorente D, Driha OM, Bekun FV, Sinha A, Adedoyin FF (2020) Consequences of COVID-19 on the social isolation of the Chinese economy: accounting for the role of reduction in carbon emissions. Air Qual Atmos Health 13(12):1439–1451
• Balsalobre-Lorente D, Ibáñez-Luzón L, Usman M, Shahbaz M. The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renew Energy. 2022; 185 :1441–1455. doi: 10.1016/j.renene.2021.10.059. [ CrossRef ] [ Google Scholar ]
• Bank W (2008) Forests sourcebook: practical guidance for sustaining forests in development cooperation: World Bank
• Barua S, Valenzuela E (2018) Climate change impacts on global agricultural trade patterns: evidence from the past 50 years. In Proceedings of the Sixth International Conference on Sustainable Development (pp. 26–28)
• Bates AE, Pecl GT, Frusher S, Hobday AJ, Wernberg T, Smale DA, Colwell RK. Defining and observing stages of climate-mediated range shifts in marine systems. Glob Environ Chang. 2014; 26 :27–38. doi: 10.1016/j.gloenvcha.2014.03.009. [ CrossRef ] [ Google Scholar ]
• Battisti DS, Naylor RL. Historical warnings of future food insecurity with unprecedented seasonal heat. Science. 2009; 323 (5911):240–244. doi: 10.1126/science.1164363. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beesley L, Close PG, Gwinn DC, Long M, Moroz M, Koster WM, Storer T. Flow-mediated movement of freshwater catfish, Tandanus bostocki, in a regulated semi-urban river, to inform environmental water releases. Ecol Freshw Fish. 2019; 28 (3):434–445. doi: 10.1111/eff.12466. [ CrossRef ] [ Google Scholar ]
• Benita F (2021) Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis. Sustain Cities Soc 70:102916 [ PMC free article ] [ PubMed ]
• Berendonk TU, Manaia CM, Merlin C, Fatta-Kassinos D, Cytryn E, Walsh F, Pons M-N. Tackling antibiotic resistance: the environmental framework. Nat Rev Microbiol. 2015; 13 (5):310–317. doi: 10.1038/nrmicro3439. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berg MP, Kiers ET, Driessen G, Van DerHEIJDEN M, Kooi BW, Kuenen F, Ellers J. Adapt or disperse: understanding species persistence in a changing world. Glob Change Biol. 2010; 16 (2):587–598. doi: 10.1111/j.1365-2486.2009.02014.x. [ CrossRef ] [ Google Scholar ]
• Blum A, Klueva N, Nguyen H. Wheat cellular thermotolerance is related to yield under heat stress. Euphytica. 2001; 117 (2):117–123. doi: 10.1023/A:1004083305905. [ CrossRef ] [ Google Scholar ]
• Bonacci O. Air temperature and precipitation analyses on a small Mediterranean island: the case of the remote island of Lastovo (Adriatic Sea, Croatia) Acta Hydrotechnica. 2019; 32 (57):135–150. doi: 10.15292/acta.hydro.2019.10. [ CrossRef ] [ Google Scholar ]
• Botzen W, Duijndam S, van Beukering P (2021) Lessons for climate policy from behavioral biases towards COVID-19 and climate change risks. World Dev 137:105214 [ PMC free article ] [ PubMed ]
• Brázdil R, Stucki P, Szabó P, Řezníčková L, Dolák L, Dobrovolný P, Suchánková S. Windstorms and forest disturbances in the Czech Lands: 1801–2015. Agric for Meteorol. 2018; 250 :47–63. doi: 10.1016/j.agrformet.2017.11.036. [ CrossRef ] [ Google Scholar ]
• Brown HCP, Smit B, Somorin OA, Sonwa DJ, Nkem JN. Climate change and forest communities: prospects for building institutional adaptive capacity in the Congo Basin forests. Ambio. 2014; 43 (6):759–769. doi: 10.1007/s13280-014-0493-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bujosa A, Riera A, Torres CM. Valuing tourism demand attributes to guide climate change adaptation measures efficiently: the case of the Spanish domestic travel market. Tour Manage. 2015; 47 :233–239. doi: 10.1016/j.tourman.2014.09.023. [ CrossRef ] [ Google Scholar ]
• Calderini D, Abeledo L, Savin R, Slafer GA. Effect of temperature and carpel size during pre-anthesis on potential grain weight in wheat. J Agric Sci. 1999; 132 (4):453–459. doi: 10.1017/S0021859699006504. [ CrossRef ] [ Google Scholar ]
• Cammell M, Knight J. Effects of climatic change on the population dynamics of crop pests. Adv Ecol Res. 1992; 22 :117–162. doi: 10.1016/S0065-2504(08)60135-X. [ CrossRef ] [ Google Scholar ]
• Cavanaugh KC, Kellner JR, Forde AJ, Gruner DS, Parker JD, Rodriguez W, Feller IC. Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proc Natl Acad Sci. 2014; 111 (2):723–727. doi: 10.1073/pnas.1315800111. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cell CC (2009) Climate change and health impacts in Bangladesh. Clima Chang Cell DoE MoEF
• Chandio AA, Jiang Y, Rehman A, Rauf A (2020) Short and long-run impacts of climate change on agriculture: an empirical evidence from China. Int J Clim Chang Strat Manag
• Chaudhary P, Rai S, Wangdi S, Mao A, Rehman N, Chettri S, Bawa KS (2011) Consistency of local perceptions of climate change in the Kangchenjunga Himalaya landscape. Curr Sci 504–513
• Chien F, Anwar A, Hsu CC, Sharif A, Razzaq A, Sinha A (2021) The role of information and communication technology in encountering environmental degradation: proposing an SDG framework for the BRICS countries. Technol Soc 65:101587
• Cooper C, Booth A, Varley-Campbell J, Britten N, Garside R. Defining the process to literature searching in systematic reviews: a literature review of guidance and supporting studies. BMC Med Res Methodol. 2018; 18 (1):1–14. doi: 10.1186/s12874-018-0545-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Costello A, Abbas M, Allen A, Ball S, Bell S, Bellamy R, Kett M. Managing the health effects of climate change: lancet and University College London Institute for Global Health Commission. The Lancet. 2009; 373 (9676):1693–1733. doi: 10.1016/S0140-6736(09)60935-1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cruz DLA (2015) Mother Figured. University of Chicago Press. Retrieved from, 10.7208/9780226315072
• Cui W, Ouyang T, Qiu Y, Cui D (2021) Literature Review of the Implications of Exercise Rehabilitation Strategies for SARS Patients on the Recovery of COVID-19 Patients. Paper presented at the Healthcare [ PMC free article ] [ PubMed ]
• Davidson D. Gaps in agricultural climate adaptation research. Nat Clim Chang. 2016; 6 (5):433–435. doi: 10.1038/nclimate3007. [ CrossRef ] [ Google Scholar ]
• Diffenbaugh NS, Singh D, Mankin JS, Horton DE, Swain DL, Touma D, Tsiang M. Quantifying the influence of global warming on unprecedented extreme climate events. Proc Natl Acad Sci. 2017; 114 (19):4881–4886. doi: 10.1073/pnas.1618082114. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dimri A, Kumar D, Choudhary A, Maharana P. Future changes over the Himalayas: mean temperature. Global Planet Change. 2018; 162 :235–251. doi: 10.1016/j.gloplacha.2018.01.014. [ CrossRef ] [ Google Scholar ]
• Dullinger S, Gattringer A, Thuiller W, Moser D, Zimmermann N, Guisan A. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat Clim Chang: Nature Publishing Group; 2012. [ Google Scholar ]
• Dupuis I, Dumas C. Influence of temperature stress on in vitro fertilization and heat shock protein synthesis in maize (Zea mays L.) reproductive tissues. Plant Physiol. 1990; 94 (2):665–670. doi: 10.1104/pp.94.2.665. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Edreira JR, Otegui ME. Heat stress in temperate and tropical maize hybrids: a novel approach for assessing sources of kernel loss in field conditions. Field Crop Res. 2013; 142 :58–67. doi: 10.1016/j.fcr.2012.11.009. [ CrossRef ] [ Google Scholar ]
• Edreira JR, Carpici EB, Sammarro D, Otegui M. Heat stress effects around flowering on kernel set of temperate and tropical maize hybrids. Field Crop Res. 2011; 123 (2):62–73. doi: 10.1016/j.fcr.2011.04.015. [ CrossRef ] [ Google Scholar ]
• Ellison D, Morris CE, Locatelli B, Sheil D, Cohen J, Murdiyarso D, Pokorny J. Trees, forests and water: Cool insights for a hot world. Glob Environ Chang. 2017; 43 :51–61. doi: 10.1016/j.gloenvcha.2017.01.002. [ CrossRef ] [ Google Scholar ]
• Elsayed ZM, Eldehna WM, Abdel-Aziz MM, El Hassab MA, Elkaeed EB, Al-Warhi T, Mohammed ER. Development of novel isatin–nicotinohydrazide hybrids with potent activity against susceptible/resistant Mycobacterium tuberculosis and bronchitis causing–bacteria. J Enzyme Inhib Med Chem. 2021; 36 (1):384–393. doi: 10.1080/14756366.2020.1868450. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• EM-DAT (2020) EMDAT: OFDA/CRED International Disaster Database, Université catholique de Louvain – Brussels – Belgium. from http://www.emdat.be
• EPA U (2018) United States Environmental Protection Agency, EPA Year in Review
• Erman A, De Vries Robbe SA, Thies SF, Kabir K, Maruo M (2021) Gender Dimensions of Disaster Risk and Resilience
• Fand BB, Kamble AL, Kumar M. Will climate change pose serious threat to crop pest management: a critical review. Int J Sci Res Publ. 2012; 2 (11):1–14. [ Google Scholar ]
• FAO (2018).The State of the World’s Forests 2018 - Forest Pathways to Sustainable Development.
• Fardous S Perception of climate change in Kaptai National Park. Rural Livelihoods and Protected Landscape: Co-Management in the Wetlands and Forests of Bangladesh, 186–204
• Farooq M, Bramley H, Palta JA, Siddique KH. Heat stress in wheat during reproductive and grain-filling phases. Crit Rev Plant Sci. 2011; 30 (6):491–507. doi: 10.1080/07352689.2011.615687. [ CrossRef ] [ Google Scholar ]
• Feliciano D, Recha J, Ambaw G, MacSween K, Solomon D, Wollenberg E (2022) Assessment of agricultural emissions, climate change mitigation and adaptation practices in Ethiopia. Clim Policy 1–18
• Ferreira JJ, Fernandes CI, Ferreira FA (2020) Technology transfer, climate change mitigation, and environmental patent impact on sustainability and economic growth: a comparison of European countries. Technol Forecast Soc Change 150:119770
• Fettig CJ, Reid ML, Bentz BJ, Sevanto S, Spittlehouse DL, Wang T. Changing climates, changing forests: a western North American perspective. J Forest. 2013; 111 (3):214–228. doi: 10.5849/jof.12-085. [ CrossRef ] [ Google Scholar ]
• Fischer AP. Characterizing behavioral adaptation to climate change in temperate forests. Landsc Urban Plan. 2019; 188 :72–79. doi: 10.1016/j.landurbplan.2018.09.024. [ CrossRef ] [ Google Scholar ]
• Flannigan M, Cantin AS, De Groot WJ, Wotton M, Newbery A, Gowman LM. Global wildland fire season severity in the 21st century. For Ecol Manage. 2013; 294 :54–61. doi: 10.1016/j.foreco.2012.10.022. [ CrossRef ] [ Google Scholar ]
• Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Clim Chang. 2015; 5 (7):673–677. doi: 10.1038/nclimate2647. [ CrossRef ] [ Google Scholar ]
• Füssel HM, Hildén M (2014) How is uncertainty addressed in the knowledge base for national adaptation planning? Adapting to an Uncertain Climate (pp. 41–66): Springer
• Gambín BL, Borrás L, Otegui ME. Source–sink relations and kernel weight differences in maize temperate hybrids. Field Crop Res. 2006; 95 (2–3):316–326. doi: 10.1016/j.fcr.2005.04.002. [ CrossRef ] [ Google Scholar ]
• Gambín B, Borrás L. Resource distribution and the trade-off between seed number and seed weight: a comparison across crop species. Annals of Applied Biology. 2010; 156 (1):91–102. doi: 10.1111/j.1744-7348.2009.00367.x. [ CrossRef ] [ Google Scholar ]
• Gampe D, Nikulin G, Ludwig R. Using an ensemble of regional climate models to assess climate change impacts on water scarcity in European river basins. Sci Total Environ. 2016; 573 :1503–1518. doi: 10.1016/j.scitotenv.2016.08.053. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• García GA, Dreccer MF, Miralles DJ, Serrago RA. High night temperatures during grain number determination reduce wheat and barley grain yield: a field study. Glob Change Biol. 2015; 21 (11):4153–4164. doi: 10.1111/gcb.13009. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garner E, Inyang M, Garvey E, Parks J, Glover C, Grimaldi A, Edwards MA. Impact of blending for direct potable reuse on premise plumbing microbial ecology and regrowth of opportunistic pathogens and antibiotic resistant bacteria. Water Res. 2019; 151 :75–86. doi: 10.1016/j.watres.2018.12.003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gleditsch NP (2021) This time is different! Or is it? NeoMalthusians and environmental optimists in the age of climate change. J Peace Res 0022343320969785
• Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Toulmin C. Food security: the challenge of feeding 9 billion people. Science. 2010; 327 (5967):812–818. doi: 10.1126/science.1185383. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goes S, Hasterok D, Schutt DL, Klöcking M (2020) Continental lithospheric temperatures: A review. Phys Earth Planet Inter 106509
• Gorst A, Dehlavi A, Groom B. Crop productivity and adaptation to climate change in Pakistan. Environ Dev Econ. 2018; 23 (6):679–701. doi: 10.1017/S1355770X18000232. [ CrossRef ] [ Google Scholar ]
• Gosling SN, Arnell NW. A global assessment of the impact of climate change on water scarcity. Clim Change. 2016; 134 (3):371–385. doi: 10.1007/s10584-013-0853-x. [ CrossRef ] [ Google Scholar ]
• Gössling S, Scott D, Hall CM, Ceron J-P, Dubois G. Consumer behaviour and demand response of tourists to climate change. Ann Tour Res. 2012; 39 (1):36–58. doi: 10.1016/j.annals.2011.11.002. [ CrossRef ] [ Google Scholar ]
• Gourdji SM, Sibley AM, Lobell DB. Global crop exposure to critical high temperatures in the reproductive period: historical trends and future projections. Environ Res Lett. 2013; 8 (2):024041. doi: 10.1088/1748-9326/8/2/024041. [ CrossRef ] [ Google Scholar ]
• Grieg E Responsible Consumption and Production
• Gunter BG, Rahman A, Rahman A (2008) How Vulnerable are Bangladesh’s Indigenous People to Climate Change? Bangladesh Development Research Center (BDRC)
• Hall CM, Amelung B, Cohen S, Eijgelaar E, Gössling S, Higham J, Scott D. On climate change skepticism and denial in tourism. J Sustain Tour. 2015; 23 (1):4–25. doi: 10.1080/09669582.2014.953544. [ CrossRef ] [ Google Scholar ]
• Hartmann H, Moura CF, Anderegg WR, Ruehr NK, Salmon Y, Allen CD, Galbraith D. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 2018; 218 (1):15–28. doi: 10.1111/nph.15048. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hatfield JL, Prueger JH. Temperature extremes: Effect on plant growth and development. Weather and Climate Extremes. 2015; 10 :4–10. doi: 10.1016/j.wace.2015.08.001. [ CrossRef ] [ Google Scholar ]
• Hatfield JL, Boote KJ, Kimball B, Ziska L, Izaurralde RC, Ort D, Wolfe D. Climate impacts on agriculture: implications for crop production. Agron J. 2011; 103 (2):351–370. doi: 10.2134/agronj2010.0303. [ CrossRef ] [ Google Scholar ]
• Hendriksen RS, Munk P, Njage P, Van Bunnik B, McNally L, Lukjancenko O, Kjeldgaard J. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun. 2019; 10 (1):1124. doi: 10.1038/s41467-019-08853-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huang S (2004) Global trade patterns in fruits and vegetables. USDA-ERS Agriculture and Trade Report No. WRS-04–06
• Huang W, Gao Q-X, Cao G-L, Ma Z-Y, Zhang W-D, Chao Q-C. Effect of urban symbiosis development in China on GHG emissions reduction. Adv Clim Chang Res. 2016; 7 (4):247–252. doi: 10.1016/j.accre.2016.12.003. [ CrossRef ] [ Google Scholar ]
• Huang Y, Haseeb M, Usman M, Ozturk I (2022) Dynamic association between ICT, renewable energy, economic complexity and ecological footprint: Is there any difference between E-7 (developing) and G-7 (developed) countries? Tech Soc 68:101853
• Hubbart JA, Guyette R, Muzika R-M. More than drought: precipitation variance, excessive wetness, pathogens and the future of the western edge of the eastern deciduous forest. Sci Total Environ. 2016; 566 :463–467. doi: 10.1016/j.scitotenv.2016.05.108. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hussain M, Butt AR, Uzma F, Ahmed R, Irshad S, Rehman A, Yousaf B. A comprehensive review of climate change impacts, adaptation, and mitigation on environmental and natural calamities in Pakistan. Environ Monit Assess. 2020; 192 (1):48. doi: 10.1007/s10661-019-7956-4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hussain M, Liu G, Yousaf B, Ahmed R, Uzma F, Ali MU, Butt AR. Regional and sectoral assessment on climate-change in Pakistan: social norms and indigenous perceptions on climate-change adaptation and mitigation in relation to global context. J Clean Prod. 2018; 200 :791–808. doi: 10.1016/j.jclepro.2018.07.272. [ CrossRef ] [ Google Scholar ]
• Intergov. Panel Clim Chang 33 from 10.1017/CBO9781107415324
• Ionescu C, Klein RJ, Hinkel J, Kumar KK, Klein R. Towards a formal framework of vulnerability to climate change. Environ Model Assess. 2009; 14 (1):1–16. doi: 10.1007/s10666-008-9179-x. [ CrossRef ] [ Google Scholar ]
• IPCC (2013) Summary for policymakers. Clim Chang Phys Sci Basis Contrib Work Gr I Fifth Assess Rep
• Ishikawa-Ishiwata Y, Furuya J (2022) Economic evaluation and climate change adaptation measures for rice production in vietnam using a supply and demand model: special emphasis on the Mekong River Delta region in Vietnam. In Interlocal Adaptations to Climate Change in East and Southeast Asia (pp. 45–53). Springer, Cham
• Izaguirre C, Losada I, Camus P, Vigh J, Stenek V. Climate change risk to global port operations. Nat Clim Chang. 2021; 11 (1):14–20. doi: 10.1038/s41558-020-00937-z. [ CrossRef ] [ Google Scholar ]
• Jactel H, Koricheva J, Castagneyrol B (2019) Responses of forest insect pests to climate change: not so simple. Current opinion in insect science [ PubMed ]
• Jahanzad E, Holtz BA, Zuber CA, Doll D, Brewer KM, Hogan S, Gaudin AC. Orchard recycling improves climate change adaptation and mitigation potential of almond production systems. PLoS ONE. 2020; 15 (3):e0229588. doi: 10.1371/journal.pone.0229588. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jurgilevich A, Räsänen A, Groundstroem F, Juhola S. A systematic review of dynamics in climate risk and vulnerability assessments. Environ Res Lett. 2017; 12 (1):013002. doi: 10.1088/1748-9326/aa5508. [ CrossRef ] [ Google Scholar ]
• Karami E (2012) Climate change, resilience and poverty in the developing world. Paper presented at the Culture, Politics and Climate change conference
• Kärkkäinen L, Lehtonen H, Helin J, Lintunen J, Peltonen-Sainio P, Regina K, . . . Packalen T (2020) Evaluation of policy instruments for supporting greenhouse gas mitigation efforts in agricultural and urban land use. Land Use Policy 99:104991
• Karkman A, Do TT, Walsh F, Virta MP. Antibiotic-resistance genes in waste water. Trends Microbiol. 2018; 26 (3):220–228. doi: 10.1016/j.tim.2017.09.005. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kohfeld KE, Le Quéré C, Harrison SP, Anderson RF. Role of marine biology in glacial-interglacial CO2 cycles. Science. 2005; 308 (5718):74–78. doi: 10.1126/science.1105375. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kongsager R. Linking climate change adaptation and mitigation: a review with evidence from the land-use sectors. Land. 2018; 7 (4):158. doi: 10.3390/land7040158. [ CrossRef ] [ Google Scholar ]
• Kurz WA, Dymond C, Stinson G, Rampley G, Neilson E, Carroll A, Safranyik L. Mountain pine beetle and forest carbon feedback to climate change. Nature. 2008; 452 (7190):987. doi: 10.1038/nature06777. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lamperti F, Bosetti V, Roventini A, Tavoni M, Treibich T (2021) Three green financial policies to address climate risks. J Financial Stab 54:100875
• Leal Filho W, Azeiteiro UM, Balogun AL, Setti AFF, Mucova SA, Ayal D, . . . Oguge NO (2021) The influence of ecosystems services depletion to climate change adaptation efforts in Africa. Sci Total Environ 146414 [ PubMed ]
• Lehner F, Coats S, Stocker TF, Pendergrass AG, Sanderson BM, Raible CC, Smerdon JE. Projected drought risk in 1.5 C and 2 C warmer climates. Geophys Res Lett. 2017; 44 (14):7419–7428. doi: 10.1002/2017GL074117. [ CrossRef ] [ Google Scholar ]
• Lemery J, Knowlton K, Sorensen C (2021) Global climate change and human health: from science to practice: John Wiley & Sons
• Leppänen S, Saikkonen L, Ollikainen M (2014) Impact of Climate Change on cereal grain production in Russia: Mimeo
• Lipczynska-Kochany E. Effect of climate change on humic substances and associated impacts on the quality of surface water and groundwater: a review. Sci Total Environ. 2018; 640 :1548–1565. doi: 10.1016/j.scitotenv.2018.05.376. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• livescience.com. New coronavirus may have ‘jumped’ to humans from snakes, study finds, live science,. from < https://www.livescience.com/new-coronavirus-origin-snakes.html > accessed on Jan 2020
• Lobell DB, Field CB. Global scale climate–crop yield relationships and the impacts of recent warming. Environ Res Lett. 2007; 2 (1):014002. doi: 10.1088/1748-9326/2/1/014002. [ CrossRef ] [ Google Scholar ]
• Lobell DB, Gourdji SM. The influence of climate change on global crop productivity. Plant Physiol. 2012; 160 (4):1686–1697. doi: 10.1104/pp.112.208298. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ma L, Li B, Zhang T. New insights into antibiotic resistome in drinking water and management perspectives: a metagenomic based study of small-sized microbes. Water Res. 2019; 152 :191–201. doi: 10.1016/j.watres.2018.12.069. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Macchi M, Oviedo G, Gotheil S, Cross K, Boedhihartono A, Wolfangel C, Howell M (2008) Indigenous and traditional peoples and climate change. International Union for the Conservation of Nature, Gland, Suiza
• Mall RK, Gupta A, Sonkar G (2017) Effect of climate change on agricultural crops. In Current developments in biotechnology and bioengineering (pp. 23–46). Elsevier
• Manes S, Costello MJ, Beckett H, Debnath A, Devenish-Nelson E, Grey KA, . . . Krause C (2021) Endemism increases species’ climate change risk in areas of global biodiversity importance. Biol Conserv 257:109070
• Mannig B, Pollinger F, Gafurov A, Vorogushyn S, Unger-Shayesteh K (2018) Impacts of climate change in Central Asia Encyclopedia of the Anthropocene (pp. 195–203): Elsevier
• Martínez-Alvarado O, Gray SL, Hart NC, Clark PA, Hodges K, Roberts MJ. Increased wind risk from sting-jet windstorms with climate change. Environ Res Lett. 2018; 13 (4):044002. doi: 10.1088/1748-9326/aaae3a. [ CrossRef ] [ Google Scholar ]
• Matsui T, Omasa K, Horie T. The difference in sterility due to high temperatures during the flowering period among japonica-rice varieties. Plant Production Science. 2001; 4 (2):90–93. doi: 10.1626/pps.4.90. [ CrossRef ] [ Google Scholar ]
• Meierrieks D (2021) Weather shocks, climate change and human health. World Dev 138:105228
• Michel D, Eriksson M, Klimes M (2021) Climate change and (in) security in transboundary river basins Handbook of Security and the Environment: Edward Elgar Publishing
• Mihiretu A, Okoyo EN, Lemma T. Awareness of climate change and its associated risks jointly explain context-specific adaptation in the Arid-tropics. Northeast Ethiopia SN Social Sciences. 2021; 1 (2):1–18. [ Google Scholar ]
• Millar CI, Stephenson NL. Temperate forest health in an era of emerging megadisturbance. Science. 2015; 349 (6250):823–826. doi: 10.1126/science.aaa9933. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mishra A, Bruno E, Zilberman D (2021) Compound natural and human disasters: Managing drought and COVID-19 to sustain global agriculture and food sectors. Sci Total Environ 754:142210 [ PMC free article ] [ PubMed ]
• Mosavi SH, Soltani S, Khalilian S (2020) Coping with climate change in agriculture: Evidence from Hamadan-Bahar plain in Iran. Agric Water Manag 241:106332
• Murshed M (2020) An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia. Environ Sci Pollut Res 27(29):36254–36281. 10.1007/s11356-020-09497-3 [ PMC free article ] [ PubMed ]
• Murshed M. Pathways to clean cooking fuel transition in low and middle income Sub-Saharan African countries: the relevance of improving energy use efficiency. Sustainable Production and Consumption. 2022; 30 :396–412. doi: 10.1016/j.spc.2021.12.016. [ CrossRef ] [ Google Scholar ]
• Murshed M, Dao NTT. Revisiting the CO2 emission-induced EKC hypothesis in South Asia: the role of Export Quality Improvement. GeoJournal. 2020 doi: 10.1007/s10708-020-10270-9. [ CrossRef ] [ Google Scholar ]
• Murshed M, Abbass K, Rashid S. Modelling renewable energy adoption across south Asian economies: Empirical evidence from Bangladesh, India, Pakistan and Sri Lanka. Int J Finan Eco. 2021; 26 (4):5425–5450. doi: 10.1002/ijfe.2073. [ CrossRef ] [ Google Scholar ]
• Murshed M, Nurmakhanova M, Elheddad M, Ahmed R. Value addition in the services sector and its heterogeneous impacts on CO2 emissions: revisiting the EKC hypothesis for the OPEC using panel spatial estimation techniques. Environ Sci Pollut Res. 2020; 27 (31):38951–38973. doi: 10.1007/s11356-020-09593-4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Murshed M, Nurmakhanova M, Al-Tal R, Mahmood H, Elheddad M, Ahmed R (2022) Can intra-regional trade, renewable energy use, foreign direct investments, and economic growth reduce ecological footprints in South Asia? Energy Sources, Part B: Economics, Planning, and Policy. 10.1080/15567249.2022.2038730
• Neuvonen M, Sievänen T, Fronzek S, Lahtinen I, Veijalainen N, Carter TR. Vulnerability of cross-country skiing to climate change in Finland–an interactive mapping tool. J Outdoor Recreat Tour. 2015; 11 :64–79. doi: 10.1016/j.jort.2015.06.010. [ CrossRef ] [ Google Scholar ]
• npr.org. Please Help Me.’ What people in China are saying about the outbreak on social media, npr.org, . from < https://www.npr.org/sections/goatsandsoda/2020/01/24/799000379/please-help-me-what-people-in-china-are-saying-about-the-outbreak-on-social-medi >, Accessed on 26 Jan 2020.
• Ogden LE. Climate change, pathogens, and people: the challenges of monitoring a moving target. Bioscience. 2018; 68 (10):733–739. doi: 10.1093/biosci/biy101. [ CrossRef ] [ Google Scholar ]
• Ortiz AMD, Outhwaite CL, Dalin C, Newbold T. A review of the interactions between biodiversity, agriculture, climate change, and international trade: research and policy priorities. One Earth. 2021; 4 (1):88–101. doi: 10.1016/j.oneear.2020.12.008. [ CrossRef ] [ Google Scholar ]
• Ortiz R. Crop genetic engineering under global climate change. Ann Arid Zone. 2008; 47 (3):343. [ Google Scholar ]
• Otegui MAE, Bonhomme R. Grain yield components in maize: I. Ear growth and kernel set. Field Crop Res. 1998; 56 (3):247–256. doi: 10.1016/S0378-4290(97)00093-2. [ CrossRef ] [ Google Scholar ]
• Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, . . . Dasgupta P (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change: Ipcc
• Pal JK. Visualizing the knowledge outburst in global research on COVID-19. Scientometrics. 2021; 126 (5):4173–4193. doi: 10.1007/s11192-021-03912-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Panda R, Behera S, Kashyap P. Effective management of irrigation water for wheat under stressed conditions. Agric Water Manag. 2003; 63 (1):37–56. doi: 10.1016/S0378-3774(03)00099-4. [ CrossRef ] [ Google Scholar ]
• Pärnänen KM, Narciso-da-Rocha C, Kneis D, Berendonk TU, Cacace D, Do TT, Jaeger T. Antibiotic resistance in European wastewater treatment plants mirrors the pattern of clinical antibiotic resistance prevalence. Sci Adv. 2019; 5 (3):eaau9124. doi: 10.1126/sciadv.aau9124. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Parry M, Parry ML, Canziani O, Palutikof J, Van der Linden P, Hanson C (2007) Climate change 2007-impacts, adaptation and vulnerability: Working group II contribution to the fourth assessment report of the IPCC (Vol. 4): Cambridge University Press
• Patz JA, Campbell-Lendrum D, Holloway T, Foley JA. Impact of regional climate change on human health. Nature. 2005; 438 (7066):310–317. doi: 10.1038/nature04188. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patz JA, Graczyk TK, Geller N, Vittor AY. Effects of environmental change on emerging parasitic diseases. Int J Parasitol. 2000; 30 (12–13):1395–1405. doi: 10.1016/S0020-7519(00)00141-7. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pautasso M, Döring TF, Garbelotto M, Pellis L, Jeger MJ. Impacts of climate change on plant diseases—opinions and trends. Eur J Plant Pathol. 2012; 133 (1):295–313. doi: 10.1007/s10658-012-9936-1. [ CrossRef ] [ Google Scholar ]
• Peng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, Cassman KG. Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci. 2004; 101 (27):9971–9975. doi: 10.1073/pnas.0403720101. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pereira HM, Ferrier S, Walters M, Geller GN, Jongman R, Scholes RJ, Cardoso A. Essential biodiversity variables. Science. 2013; 339 (6117):277–278. doi: 10.1126/science.1229931. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perera K, De Silva K, Amarasinghe M. Potential impact of predicted sea level rise on carbon sink function of mangrove ecosystems with special reference to Negombo estuary, Sri Lanka. Global Planet Change. 2018; 161 :162–171. doi: 10.1016/j.gloplacha.2017.12.016. [ CrossRef ] [ Google Scholar ]
• Pfadenhauer JS, Klötzli FA (2020) Zonal Vegetation of the Subtropical (Warm–Temperate) Zone with Winter Rain. In Global Vegetation (pp. 455–514). Springer, Cham
• Phillips JD. Environmental gradients and complexity in coastal landscape response to sea level rise. CATENA. 2018; 169 :107–118. doi: 10.1016/j.catena.2018.05.036. [ CrossRef ] [ Google Scholar ]
• Pirasteh-Anosheh H, Parnian A, Spasiano D, Race M, Ashraf M (2021) Haloculture: A system to mitigate the negative impacts of pandemics on the environment, society and economy, emphasizing COVID-19. Environ Res 111228 [ PMC free article ] [ PubMed ]
• Pruden A, Larsson DJ, Amézquita A, Collignon P, Brandt KK, Graham DW, Snape JR. Management options for reducing the release of antibiotics and antibiotic resistance genes to the environment. Environ Health Perspect. 2013; 121 (8):878–885. doi: 10.1289/ehp.1206446. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qasim MZ, Hammad HM, Abbas F, Saeed S, Bakhat HF, Nasim W, Fahad S. The potential applications of picotechnology in biomedical and environmental sciences. Environ Sci Pollut Res. 2020; 27 (1):133–142. doi: 10.1007/s11356-019-06554-4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qasim MZ, Hammad HM, Maqsood F, Tariq T, Chawla MS Climate Change Implication on Cereal Crop Productivity
• Rahman M, Alam K. Forest dependent indigenous communities’ perception and adaptation to climate change through local knowledge in the protected area—a Bangladesh case study. Climate. 2016; 4 (1):12. doi: 10.3390/cli4010012. [ CrossRef ] [ Google Scholar ]
• Ramankutty N, Mehrabi Z, Waha K, Jarvis L, Kremen C, Herrero M, Rieseberg LH. Trends in global agricultural land use: implications for environmental health and food security. Annu Rev Plant Biol. 2018; 69 :789–815. doi: 10.1146/annurev-arplant-042817-040256. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rehman A, Ma H, Ahmad M, Irfan M, Traore O, Chandio AA (2021) Towards environmental Sustainability: devolving the influence of carbon dioxide emission to population growth, climate change, Forestry, livestock and crops production in Pakistan. Ecol Indic 125:107460
• Reichstein M, Carvalhais N. Aspects of forest biomass in the Earth system: its role and major unknowns. Surv Geophys. 2019; 40 (4):693–707. doi: 10.1007/s10712-019-09551-x. [ CrossRef ] [ Google Scholar ]
• Reidsma P, Ewert F, Boogaard H, van Diepen K. Regional crop modelling in Europe: the impact of climatic conditions and farm characteristics on maize yields. Agric Syst. 2009; 100 (1–3):51–60. doi: 10.1016/j.agsy.2008.12.009. [ CrossRef ] [ Google Scholar ]
• Ritchie H, Roser M (2014) Natural disasters. Our World in Data
• Rizvi AR, Baig S, Verdone M. Ecosystems based adaptation: knowledge gaps in making an economic case for investing in nature based solutions for climate change. Gland, Switzerland: IUCN; 2015. p. 48. [ Google Scholar ]
• Roscher C, Fergus AJ, Petermann JS, Buchmann N, Schmid B, Schulze E-D. What happens to the sown species if a biodiversity experiment is not weeded? Basic Appl Ecol. 2013; 14 (3):187–198. doi: 10.1016/j.baae.2013.01.003. [ CrossRef ] [ Google Scholar ]
• Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Khabarov N. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci. 2014; 111 (9):3268–3273. doi: 10.1073/pnas.1222463110. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosenzweig C, Iglesius A, Yang XB, Epstein PR, Chivian E (2001) Climate change and extreme weather events-implications for food production, plant diseases, and pests
• Sadras VO, Slafer GA. Environmental modulation of yield components in cereals: heritabilities reveal a hierarchy of phenotypic plasticities. Field Crop Res. 2012; 127 :215–224. doi: 10.1016/j.fcr.2011.11.014. [ CrossRef ] [ Google Scholar ]
• Salvucci ME, Crafts-Brandner SJ. Inhibition of photosynthesis by heat stress: the activation state of Rubisco as a limiting factor in photosynthesis. Physiol Plant. 2004; 120 (2):179–186. doi: 10.1111/j.0031-9317.2004.0173.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Santos WS, Gurgel-Gonçalves R, Garcez LM, Abad-Franch F. Deforestation effects on Attalea palms and their resident Rhodnius, vectors of Chagas disease, in eastern Amazonia. PLoS ONE. 2021; 16 (5):e0252071. doi: 10.1371/journal.pone.0252071. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sarkar P, Debnath N, Reang D (2021) Coupled human-environment system amid COVID-19 crisis: a conceptual model to understand the nexus. Sci Total Environ 753:141757 [ PMC free article ] [ PubMed ]
• Schlenker W, Roberts MJ. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc Natl Acad Sci. 2009; 106 (37):15594–15598. doi: 10.1073/pnas.0906865106. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schoene DH, Bernier PY. Adapting forestry and forests to climate change: a challenge to change the paradigm. Forest Policy Econ. 2012; 24 :12–19. doi: 10.1016/j.forpol.2011.04.007. [ CrossRef ] [ Google Scholar ]
• Schuurmans C (2021) The world heat budget: expected changes Climate Change (pp. 1–15): CRC Press
• Scott D. Sustainable Tourism and the Grand Challenge of Climate Change. Sustainability. 2021; 13 (4):1966. doi: 10.3390/su13041966. [ CrossRef ] [ Google Scholar ]
• Scott D, McBoyle G, Schwartzentruber M. Climate change and the distribution of climatic resources for tourism in North America. Climate Res. 2004; 27 (2):105–117. doi: 10.3354/cr027105. [ CrossRef ] [ Google Scholar ]
• Semenov MA. Impacts of climate change on wheat in England and Wales. J R Soc Interface. 2009; 6 (33):343–350. doi: 10.1098/rsif.2008.0285. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shaffril HAM, Krauss SE, Samsuddin SF. A systematic review on Asian’s farmers’ adaptation practices towards climate change. Sci Total Environ. 2018; 644 :683–695. doi: 10.1016/j.scitotenv.2018.06.349. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shahbaz M, Balsalobre-Lorente D, Sinha A (2019) Foreign direct Investment–CO2 emissions nexus in Middle East and North African countries: Importance of biomass energy consumption. J Clean Product 217:603–614
• Sharif A, Mishra S, Sinha A, Jiao Z, Shahbaz M, Afshan S (2020) The renewable energy consumption-environmental degradation nexus in Top-10 polluted countries: Fresh insights from quantile-on-quantile regression approach. Renew Energy 150:670–690
• Sharma R. Impacts on human health of climate and land use change in the Hindu Kush-Himalayan region. Mt Res Dev. 2012; 32 (4):480–486. doi: 10.1659/MRD-JOURNAL-D-12-00068.1. [ CrossRef ] [ Google Scholar ]
• Sharma R, Sinha A, Kautish P. Examining the impacts of economic and demographic aspects on the ecological footprint in South and Southeast Asian countries. Environ Sci Pollut Res. 2020; 27 (29):36970–36982. doi: 10.1007/s11356-020-09659-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smit B, Burton I, Klein RJ, Wandel J (2000) An anatomy of adaptation to climate change and variability Societal adaptation to climate variability and change (pp. 223–251): Springer
• Song Y, Fan H, Tang X, Luo Y, Liu P, Chen Y (2021) The effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on ischemic stroke and the possible underlying mechanisms. Int J Neurosci 1–20 [ PMC free article ] [ PubMed ]
• Sovacool BK, Griffiths S, Kim J, Bazilian M (2021) Climate change and industrial F-gases: a critical and systematic review of developments, sociotechnical systems and policy options for reducing synthetic greenhouse gas emissions. Renew Sustain Energy Rev 141:110759
• Stewart JA, Perrine JD, Nichols LB, Thorne JH, Millar CI, Goehring KE, Wright DH. Revisiting the past to foretell the future: summer temperature and habitat area predict pika extirpations in California. J Biogeogr. 2015; 42 (5):880–890. doi: 10.1111/jbi.12466. [ CrossRef ] [ Google Scholar ]
• Stocker T, Qin D, Plattner G, Tignor M, Allen S, Boschung J, . . . Midgley P (2013) Climate change 2013: The physical science basis. Working group I contribution to the IPCC Fifth assessment report: Cambridge: Cambridge University Press. 1535p
• Stone P, Nicolas M. Wheat cultivars vary widely in their responses of grain yield and quality to short periods of post-anthesis heat stress. Funct Plant Biol. 1994; 21 (6):887–900. doi: 10.1071/PP9940887. [ CrossRef ] [ Google Scholar ]
• Su H-C, Liu Y-S, Pan C-G, Chen J, He L-Y, Ying G-G. Persistence of antibiotic resistance genes and bacterial community changes in drinking water treatment system: from drinking water source to tap water. Sci Total Environ. 2018; 616 :453–461. doi: 10.1016/j.scitotenv.2017.10.318. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sunderlin WD, Angelsen A, Belcher B, Burgers P, Nasi R, Santoso L, Wunder S. Livelihoods, forests, and conservation in developing countries: an overview. World Dev. 2005; 33 (9):1383–1402. doi: 10.1016/j.worlddev.2004.10.004. [ CrossRef ] [ Google Scholar ]
• Symanski E, Han HA, Han I, McDaniel M, Whitworth KW, McCurdy S, . . . Delclos GL (2021) Responding to natural and industrial disasters: partnerships and lessons learned. Disaster medicine and public health preparedness 1–4 [ PMC free article ] [ PubMed ]
• Tao F, Yokozawa M, Xu Y, Hayashi Y, Zhang Z. Climate changes and trends in phenology and yields of field crops in China, 1981–2000. Agric for Meteorol. 2006; 138 (1–4):82–92. doi: 10.1016/j.agrformet.2006.03.014. [ CrossRef ] [ Google Scholar ]
• Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA. Going to the extremes. Clim Change. 2006; 79 (3–4):185–211. doi: 10.1007/s10584-006-9051-4. [ CrossRef ] [ Google Scholar ]
• Testa G, Koon E, Johannesson L, McKenna G, Anthony T, Klintmalm G, Gunby R (2018) This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as
• Thornton PK, Lipper L (2014) How does climate change alter agricultural strategies to support food security? (Vol. 1340): Intl Food Policy Res Inst
• Tranfield D, Denyer D, Smart P. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag. 2003; 14 (3):207–222. doi: 10.1111/1467-8551.00375. [ CrossRef ] [ Google Scholar ]
• UNEP (2017) United nations environment programme: frontiers 2017. from https://www.unenvironment.org/news-and-stories/press-release/antimicrobial-resistance - environmental-pollution-among-biggest
• Usman M, Balsalobre-Lorente D (2022) Environmental concern in the era of industrialization: Can financial development, renewable energy and natural resources alleviate some load? Ene Policy 162:112780
• Usman M, Makhdum MSA (2021) What abates ecological footprint in BRICS-T region? Exploring the influence of renewable energy, non-renewable energy, agriculture, forest area and financial development. Renew Energy 179:12–28
• Usman M, Balsalobre-Lorente D, Jahanger A, Ahmad P. Pollution concern during globalization mode in financially resource-rich countries: Do financial development, natural resources, and renewable energy consumption matter? Rene. Energy. 2022; 183 :90–102. doi: 10.1016/j.renene.2021.10.067. [ CrossRef ] [ Google Scholar ]
• Usman M, Jahanger A, Makhdum MSA, Balsalobre-Lorente D, Bashir A (2022a) How do financial development, energy consumption, natural resources, and globalization affect Arctic countries’ economic growth and environmental quality? An advanced panel data simulation. Energy 241:122515
• Usman M, Khalid K, Mehdi MA. What determines environmental deficit in Asia? Embossing the role of renewable and non-renewable energy utilization. Renew Energy. 2021; 168 :1165–1176. doi: 10.1016/j.renene.2021.01.012. [ CrossRef ] [ Google Scholar ]
• Urban MC. Accelerating extinction risk from climate change. Science. 2015; 348 (6234):571–573. doi: 10.1126/science.aaa4984. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vale MM, Arias PA, Ortega G, Cardoso M, Oliveira BF, Loyola R, Scarano FR (2021) Climate change and biodiversity in the Atlantic Forest: best climatic models, predicted changes and impacts, and adaptation options The Atlantic Forest (pp. 253–267): Springer
• Vedwan N, Rhoades RE. Climate change in the Western Himalayas of India: a study of local perception and response. Climate Res. 2001; 19 (2):109–117. doi: 10.3354/cr019109. [ CrossRef ] [ Google Scholar ]
• Vega CR, Andrade FH, Sadras VO, Uhart SA, Valentinuz OR. Seed number as a function of growth. A comparative study in soybean, sunflower, and maize. Crop Sci. 2001; 41 (3):748–754. doi: 10.2135/cropsci2001.413748x. [ CrossRef ] [ Google Scholar ]
• Vergés A, Doropoulos C, Malcolm HA, Skye M, Garcia-Pizá M, Marzinelli EM, Vila-Concejo A. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc Natl Acad Sci. 2016; 113 (48):13791–13796. doi: 10.1073/pnas.1610725113. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Verheyen R (2005) Climate change damage and international law: prevention duties and state responsibility (Vol. 54): Martinus Nijhoff Publishers
• Waheed A, Fischer TB, Khan MI. Climate Change Policy Coherence across Policies, Plans, and Strategies in Pakistan—implications for the China-Pakistan Economic Corridor Plan. Environ Manage. 2021; 67 (5):793–810. doi: 10.1007/s00267-021-01449-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wasiq M, Ahmad M (2004) Sustaining forests: a development strategy: The World Bank
• Watts N, Adger WN, Agnolucci P, Blackstock J, Byass P, Cai W, Cooper A. Health and climate change: policy responses to protect public health. The Lancet. 2015; 386 (10006):1861–1914. doi: 10.1016/S0140-6736(15)60854-6. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weed AS, Ayres MP, Hicke JA. Consequences of climate change for biotic disturbances in North American forests. Ecol Monogr. 2013; 83 (4):441–470. doi: 10.1890/13-0160.1. [ CrossRef ] [ Google Scholar ]
• Weisheimer A, Palmer T (2005) Changing frequency of occurrence of extreme seasonal temperatures under global warming. Geophys Res Lett 32(20)
• Wernberg T, Bennett S, Babcock RC, De Bettignies T, Cure K, Depczynski M, Hovey RK. Climate-driven regime shift of a temperate marine ecosystem. Science. 2016; 353 (6295):169–172. doi: 10.1126/science.aad8745. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• WHO (2018) WHO, 2018. Antimicrobial resistance
• Wilkinson DM, Sherratt TN. Why is the world green? The interactions of top–down and bottom–up processes in terrestrial vegetation ecology. Plant Ecolog Divers. 2016; 9 (2):127–140. doi: 10.1080/17550874.2016.1178353. [ CrossRef ] [ Google Scholar ]
• Wiranata IJ, Simbolon K. Increasing awareness capacity of disaster potential as a support to achieve sustainable development goal (sdg) 13 in lampung province. Jurnal Pir: Power in International Relations. 2021; 5 (2):129–146. doi: 10.22303/pir.5.2.2021.129-146. [ CrossRef ] [ Google Scholar ]
• Wiréhn L. Nordic agriculture under climate change: a systematic review of challenges, opportunities and adaptation strategies for crop production. Land Use Policy. 2018; 77 :63–74. doi: 10.1016/j.landusepol.2018.04.059. [ CrossRef ] [ Google Scholar ]
• Wu D, Su Y, Xi H, Chen X, Xie B. Urban and agriculturally influenced water contribute differently to the spread of antibiotic resistance genes in a mega-city river network. Water Res. 2019; 158 :11–21. doi: 10.1016/j.watres.2019.03.010. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu HX (2020) Losing Steam?—An industry origin analysis of China’s productivity slowdown Measuring Economic Growth and Productivity (pp. 137–167): Elsevier
• Wu H, Qian H, Chen J, Huo C. Assessment of agricultural drought vulnerability in the Guanzhong Plain. China Water Resources Management. 2017; 31 (5):1557–1574. doi: 10.1007/s11269-017-1594-9. [ CrossRef ] [ Google Scholar ]
• Xie W, Huang J, Wang J, Cui Q, Robertson R, Chen K (2018) Climate change impacts on China’s agriculture: the responses from market and trade. China Econ Rev
• Xu J, Sharma R, Fang J, Xu Y. Critical linkages between land-use transition and human health in the Himalayan region. Environ Int. 2008; 34 (2):239–247. doi: 10.1016/j.envint.2007.08.004. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yadav MK, Singh R, Singh K, Mall R, Patel C, Yadav S, Singh M. Assessment of climate change impact on productivity of different cereal crops in Varanasi. India J Agrometeorol. 2015; 17 (2):179–184. doi: 10.54386/jam.v17i2.1000. [ CrossRef ] [ Google Scholar ]
• Yang B, Usman M. Do industrialization, economic growth and globalization processes influence the ecological footprint and healthcare expenditures? Fresh insights based on the STIRPAT model for countries with the highest healthcare expenditures. Sust Prod Cons. 2021; 28 :893–910. [ Google Scholar ]
• Yu Z, Razzaq A, Rehman A, Shah A, Jameel K, Mor RS (2021) Disruption in global supply chain and socio-economic shocks: a lesson from COVID-19 for sustainable production and consumption. Oper Manag Res 1–16
• Zarnetske PL, Skelly DK, Urban MC. Biotic multipliers of climate change. Science. 2012; 336 (6088):1516–1518. doi: 10.1126/science.1222732. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhang M, Liu N, Harper R, Li Q, Liu K, Wei X, Liu S. A global review on hydrological responses to forest change across multiple spatial scales: importance of scale, climate, forest type and hydrological regime. J Hydrol. 2017; 546 :44–59. doi: 10.1016/j.jhydrol.2016.12.040. [ CrossRef ] [ Google Scholar ]
• Zhao J, Sinha A, Inuwa N, Wang Y, Murshed M, Abbasi KR (2022) Does Structural Transformation in Economy Impact Inequality in Renewable Energy Productivity? Implications for Sustainable Development. Renew Energy 189:853–864. 10.1016/j.renene.2022.03.050

• Open access
• Published: 04 January 2021

Climate change and health in North America: literature review protocol

• Sherilee L. Harper   ORCID: orcid.org/0000-0001-7298-8765 1 ,
• Ashlee Cunsolo 2 ,
• Amreen Babujee 1 ,
• Shaugn Coggins 1 ,
• Mauricio Domínguez Aguilar 3 &
• Carlee J. Wright 1  

Systematic Reviews volume  10 , Article number:  3 ( 2021 ) Cite this article

27k Accesses

9 Altmetric

Metrics details

Climate change is a defining issue and grand challenge for the health sector in North America. Synthesizing evidence on climate change impacts, climate-health adaptation, and climate-health mitigation is crucial for health practitioners and decision-makers to effectively understand, prepare for, and respond to climate change impacts on human health. This protocol paper outlines our process to systematically conduct a literature review to investigate the climate-health evidence base in North America.

A search string will be used to search CINAHL®, Web of Science™, Scopus®, Embase® via Ovid, and MEDLINE® via Ovid aggregator databases. Articles will be screened using inclusion/exclusion criteria by two independent reviewers. First, the inclusion/exclusion criteria will be applied to article titles and abstracts, and then to the full articles. Included articles will be analyzed using quantitative and qualitative methods.

This protocol describes review methods that will be used to systematically and transparently create a database of articles published in academic journals that examine climate-health in North America.

Peer Review reports

The direct and indirect impacts of climate change on human health continue to be observed globally, and these wide-ranging impacts are projected to continue to increase and intensify this century [ 1 , 2 ]. The direct climate change effects on health include rising temperatures, which increase heat-related mortality and morbidity [ 3 , 4 , 5 ], and increased frequency and intensity of storms, resulting in increased injury, death, and psychological stressors [ 2 , 6 , 7 , 8 ]. Indirect climate change impacts on health occur via altered environmental conditions, such as climate change impacts on water quality and quantity, which increase waterborne disease [ 9 , 10 , 11 , 12 , 13 ]; shifting ecosystems, which increase the risk of foodborne disease [ 14 , 15 , 16 ], exacerbate food and nutritional security [ 17 , 18 ], and change the range and distribution of vectors that cause vectorborne disease [ 19 , 20 ]; and place-based connections and identities, leading to psycho-social stressors and potential increases in negative mental health outcomes and suicide [ 6 , 8 ]. These wide-ranging impacts are not uniformly or equitably distributed: children, the elderly, those with pre-existing health conditions, those experiencing lower socio-economic conditions, women, and those with close connections to and reliance upon the local environment (e.g. Indigenous Peoples, farmers, fishers) often experience higher burdens of climate-health impacts [ 1 , 2 , 21 ]. Indeed, climate change impacts on human health not only are dependent on exposure to climatic and environmental changes, but also depend on climate change sensitivity and adaptive capacity—both of which are underpinned by the social determinants of health [ 1 , 22 , 23 ].

The inherent complexity, great magnitude, and widespread, inequitable, and intersectional distribution of climate change impacts on health present an urgent and grand challenge for the health sector this century [ 2 , 24 , 25 ]. Climate-health research and evidence is critical for informing effective, equitable, and timely adaptation responses and strategies. For instance, research continues to inform local to international climate change and health vulnerability and adaptation assessments [ 26 ]. However, to create evidence-based climate-health adaptation strategies, health practitioners, researchers, and policy makers must sift and sort through vast and often unmanageable amounts of information. Indeed, the global climate-health evidence base has seen exponential growth in recent years, with tens of thousands of articles published globally this century [ 22 , 25 , 27 , 28 ]. Even when resources are available to parse through the evidence base, the available research evidence may not be locally pertinent to decision-makers, may provide poor quality of evidence, may exclude factors important to decision-makers, may overlook temporal and geographical scales over which decision-makers have impact, and/or may not produce information in a timely manner [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ].

Literature reviews that utilize systematic methods present a tool to efficiently and effectively integrate climate-health information and provide data to support evidence-based decision-making. Furthermore, literature reviews that use systematic methods are replicable and transparent, reduce bias, and are ultimately intended to improve reliability and accuracy of conclusions. As such, systematic approaches to identify, explore, evaluate, and synthesize literature separates insignificant, less rigorous, or redundant literature from the critical and noteworthy studies that are worthy of exploration and consideration [ 38 ]. As such, a systematic approach to synthesizing the climate-health literature provides invaluable information and adds value to the climate-health evidence base from which decision-makers can draw from. Therefore, we aim to systematically and transparently create a database of articles published in academic journals that examine climate-health in North America. As such, we outline our protocol that will be used to systematically identify and characterize literature at the climate-health nexus in North America.

This protocol was designed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) Guidelines [ 39 , 40 ] and presented in accordance with the PRISMA-P checklist.

Research questions

Research on climate change and human health encompasses a diverse range of health outcomes, climate change exposures, populations, and study designs. Given the breadth and depth of information needed by health practitioners and decision-makers, a variety of research questions will be examined (Table 1 ).

Search strategy

The search strategy, including the search string development and selection of databases, was developed in consultation with a research librarian and members of the research team (SLH, AC, and MDA). The search string contains terms related to climate change [ 41 , 42 ], human health outcomes [ 1 , 25 , 43 , 44 ], and study location (Table 2 ). Given the interdisciplinary nature of the climate-health nexus and to ensure that our search is comprehensive, the search string will be used to search five academic databases:

CINAHL® will be searched to capture unique literature not found in other databases on common disease and injury conditions, as well as other health topics;

Web of Science™ will be searched to capture a wide range of multi-disciplinary literature;

Scopus® will be searched to capture literature related to medicine, technology, science, and social sciences;

Embase® via Ovid will be searched to capture a vast range of biomedical sciences journals; and

MEDLINE® via Ovid will be searched to capture literature on biomedical and health sciences.

No language restrictions will be placed on the search. Date restrictions will be applied to capture literature published on or after 01 January 2013, in order to capture literature published after the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (which assessed literature accepted for publication prior to 31 August 2013). An initial test search was conducted on June 10, 2019, and updated on February 14, 2020; however, the search will be updated to include literature published within the most recent full calendar year prior to publication.

To explore the sensitivity of our search and capture any missed articles, (1) a snowball search will be conducted on the reference lists of all the literature that meet the inclusion criteria and (2) a hand search of three relevant disciplinary journals will be conducted:

Environmental Health Perspectives , an open access peer-reviewed journal that is a leading disciplinary journal within environmental health sciences;

The Lancet , a peer-reviewed journal that is the leading disciplinary journal within public health sciences; and

Climatic Change , a peer-reviewed journal covering cross-disciplinary literature that is a leading disciplinary journal for climate change research.

Citations will be downloaded from the databases and uploaded into Mendeley™ reference management software to facilitate reference management, article retrieval, and removal of duplicate citations. Then, de-duplicated citations will be uploaded into DistillerSR® to facilitate screening.

Article selection

Inclusion and exclusion criteria.

To be included, articles must evaluate or examine the intersection of climate change and human health in North America (Fig. 1 ). Health is defined to include physical, mental, emotional, and social health and wellness [ 1 , 25 , 43 , 44 ] (Fig. 1 ). This broad definition will be used to examine the nuanced and complex direct and indirect impacts of climate change on human health. To examine the depth and breadth of climate change impacts on health, climate change contexts are defined to include seasonality, weather parameters, extreme weather events, climate, climate change, climate variability, and climate hazards [ 41 , 42 ] (Fig. 1 ). However, articles that discuss climate in terms of indoor work environments, non-climate hazards due to geologic events (e.g. earthquakes), and non-anthropogenic climate change (e.g. due to volcanic eruptions) will be excluded. This broad definition of climate change contexts will be used in order to examine the wide range and complexity of climate change impacts on human health. To be included, articles need to explicitly link health outcomes to climate change in the goal statement, methods section, and/or results section of the article. Therefore, articles that discuss both human health and climate change—but do not link the two together—will be excluded. The climate-health research has to take place in North America to be included. North America is defined to include Canada, the USA, and Mexico in order to be consistent with the IPCC geographical classifications; that is, in the Fifth Assessment Report, the IPCC began confining North America to include Canada, Mexico, and the USA [ 45 ] (Fig. 1 ). Articles published in any language will be eligible for inclusion. Articles need to be published online on or after 01 January 2013 to be included. No restrictions will be placed on population type (i.e. all human studies will be eligible for inclusion).

Inclusion and exclusion criteria to review climate change and health literature in North America

Level 1 screening

The title and abstract of each citation will be examined for relevance. A stacked questionnaire will be used to screen the titles and abstracts; that is, when a criterion is not met, the subsequent criteria will not be assessed. When all inclusion criteria are met and/or it is unclear whether or not an inclusion criterion is met (e.g. “unsure”), the article will proceed to Level 2 screening. If the article meets any exclusion criteria, it will not proceed to Level 2 screening. Level 1 screening will be completed by two independent reviewers, who will meet to resolve any conflicts via discussion. The level of agreement between reviewers will be evaluated by dividing the total number of conflicts by the total number of articles screened for Level 1.

Level 2 screening

The full text of all potentially relevant articles will be screened for relevance. A stacked questionnaire will also be used to screen the full texts. In Level 2 screening, only articles that meet all the inclusion criteria will be included in the review (i.e. “unsure” will not be an option). Level 2 screening will be completed by two independent reviewers, who will meet to resolve any conflicts via discussion. The level of agreement between reviewers will be evaluated by dividing the total number of conflicts by the total number of articles screened for Level 2 (Fig. 2 ).

Flow chart of screening questions for the literature review on climate change and health in North America

Data extraction and analysis

A data extraction form will be created in DistillerSR® ( Appendix 2 ) and will be tested by three data extractors on a sample of articles to allow for calibration on the extraction process (i.e. 5% of articles if greater than 50 articles, 10% of articles if less than or equal to 50 articles). After completing the calibration process, the form will be adapted based on feedback from the extractors to improve usability and accuracy. The data extractors will then use the data extraction form to complete data extraction. Reviewers will meet regularly to discuss and resolve any further issues in data extraction, in order to ensure the data extraction process remains consistent across reviewers.

Data will be extracted from original research papers (i.e. articles containing data collection and analysis) and review articles that reported a systematic methodology. This data extraction will focus on study characteristics, including the country that the data were collected in, focus of the study (i.e. climate change impact, adaptation, and/or mitigation), weather variables, climatic hazards, health outcomes, social characteristics, and future projections. The categories within each study characteristic will not be mutually exclusive, allowing more than one response/category to be selected under each study characteristic. For the country of study, Canada, the USA, and/or Mexico will be selected if the article describes data collection in each country respectively. Non-North American regions will be selected if the article not only collects data external to North America, but also includes data collection within Canada, the USA, and/or Mexico. For the study focus, data will be extracted on whether the article focuses on climate change impacts, adaptation, and/or mitigation within the goals, methods, and/or results sections of the article. Temperature, precipitation, and/or UV radiation will be selected for weather variables if the article utilizes these data in the goal, methods, and/or results sections. Data will be extracted on the following climatic hazards if the article addresses them in the goal, methods, and/or results sections: heat events (e.g. extreme heat, heat waves), cold events (e.g. extreme cold, winter storms), air quality (e.g. pollution, parts per million (PPM) data, greenhouse gas emissions), droughts, flooding, wildfires, hurricanes, wildlife changes (including changes in disease vectors such as ticks or mosquitos), vegetation changes (including changes in pollen), freshwater (including drinking water), ocean conditions (including sea level rise and ocean acidity/salinity/temperature changes), ice extent/stability/duration (including sea ice and freshwater ice), coastal erosion, permafrost changes, and/or environmental hazards (e.g. exposure to sewage, reduced crop productivity).

Data will be extracted on the following health outcomes if the article focuses on them within the goal, methods, and/or results sections: heat-related morbidity and/or mortality, respiratory outcomes (including asthma, chronic obstructive pulmonary disease), cardiovascular outcomes (including heart attacks or stroke), urinary outcomes (e.g. urinary tract infections, renal failure), dermatologic concerns, mental health and wellness (e.g. suicide, emotional health), fetal health/birth outcomes and/or maternal health, cold exposure, allergies, nutrition (including nutrient deficiency), waterborne disease, foodborne disease, vectorborne disease, injuries (including accidents), and general morbidity and/or mortality. Data on the following social characteristics will also be extracted from the articles if they are included in the goal, methods, and/or results sections of the article: access to healthcare, sex and/or gender, age, income, livelihood (including data on employment, occupation), ethnicity, culture, Indigenous Peoples, rural/remote communities (“rural”, “remote”, or similar terminology must be explicitly mentioned), urban communities (“urban”, “city”, “metropolitan”, or similar terminology must be explicitly used), coastal communities (use of “coastal”, or similar terms must be explicitly mentioned), residence location (zipcode/postal code, neighbourhood, etc.), level of education, and housing (e.g. data on size, age, number of windows, air conditioning). Finally, data will be collected on future projections, including projections that employ qualitative and/or quantitative methods that are included in the goal, methods, and/or results sections of the article.

Descriptive statistics and regression modelling will be used to examine publication trends. Data will be visualized through the use of maps, graphs, and other visualization techniques as appropriate. To enable replicability and transparency, a PRISMA flowchart will be created to illustrate the article selection process and reasons for exclusion. Additionally, qualitative thematic analyses will be conducted. These analyses will utilize constant-comparative approaches to identify patterns across articles through the identification, development, and refinement of codes and themes. Article excerpts will be grouped under thematic categories in order to explore connections in article characteristics, methodologies, and findings.

Quality appraisal of studies included in the systematic scoping review will be performed using a framework based on the Mixed Methods Appraisal Tool (MMAT) [ 46 ] and the Confidence in the Evidence from Reviews of Qualitative Research (CERQual) tool [ 47 ]. This will enable appraisal of evidence in reviews that contain qualitative, quantitative, and mixed methods studies, as well as appraisal of methodological limitations in included qualitative studies. These tools may be adapted to include additional questions as required in order to fit the scope and objectives of the review. A minimum of two reviewers will independently appraise the included articles and discuss judgements as needed. The findings will be made available as supplementary material for the review.

Climate-health literature reviews using systematic methods will be increasingly critical in the health sector, given the depth and breadth of the growing body of climate change and health literature, as well as the urgent need for evidence to inform climate-health adaptation and mitigation strategies. To support and encourage the systematic and transparent identification and synthesis of climate-health information, this protocol describes our approach to systematically and transparently create a database of articles published in academic journals that examine climate-health in North America.

Availability of data and materials

Not applicable.

Abbreviations

Confidence in the Evidence from Reviews of Qualitative Research

Intergovernmental Panel on Climate Change

Mixed Methods Appraisal Tool

Parts per million

Preferred Reporting Items for Systematic review and Meta-Analyses

Preferred Reporting Items for Systematic review and Meta-Analyses, Protocol Extension

• United States of America

Ultraviolet

Smith KR, Woodward A, Campbell-Lendrum D, Chadee DD, Honda Y, Liu Q, et al. Human health: impacts, adaptation, and co-benefits. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, et al., editors. Climate Change 2014: impacts, adaptation, and vulnerability part A: global and sectoral aspects contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press; 2014. p. 709–54.

Google Scholar  

Watts N, Amann M, Ayeb-Karlsson S, Belesova K, Bouley T, Boykoff M, et al. The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health. Lancet. 2018;391(10120):581–630.

Article   Google Scholar  

Son JY, Liu JC, Bell ML. Temperature-related mortality: a systematic review and investigation of effect modifiers. Environ Res Lett. 2019;14:073004.

Campbell S, Remenyi TA, White CJ, Johnston FH. Heatwave and health impact research: a global review. Heal Place. 2018;53:210–8.

Sanderson M, Arbuthnott K, Kovats S, Hajat S, Falloon P. The use of climate information to estimate future mortality from high ambient temperature: a systematic literature review. PLoS One. 2017: e0180369.

Rataj E, Kunzweiler K, Garthus-Niegel S. Extreme weather events in developing countries and related injuries and mental health disorders - a systematic review. BMC Public Health. 2016;16:1020.

Saulnier DD, Brolin Ribacke K, von Schreeb J. No Calm after the storm: a systematic review of human health following flood and storm disasters. Prehosp Disaster Med. 2017;32(5):568–79.

Cunsolo A, Neville E. Ecological grief as a mental health response to climate change-related loss. Nat Clim Chang. 2018;8:275–81.

Levy K, Woster AP, Goldstein RS, Carlton EJ. Untangling the impacts of climate change on waterborne diseases: a systematic review of relationships between diarrheal diseases and temperature, rainfall, flooding, and drought. Environ Sci Technol. 2016;50:4905–22.

Article   CAS   Google Scholar  

Semenza JC, Herbst S, Rechenburg A, Suk JE, Höser C, Schreiber C, et al. Climate change impact assessment of food- and waterborne diseases. Crit Rev Environ Sci Technol. 2012;42(8):857–90.

Cann K, Thomas D, Salmon R, W-J AP, Kay D. Extreme water-related weather events and waterborne disease. Epidemiol Infect. 2013;141:671–86.

Andrade L, O’Dwyer J, O’Neill E, Hynds P. Surface water flooding, groundwater contamination, and enteric disease in developed countries: a scoping review of connections and consequences. Environ Pollut. 2018;236:540–9.

Harper SL, Wright C, Masina S, Coggins S. Climate change, water, and human health research in the Arctic. Water Secur. 2020;10:100062.

Park MS, Park KH, Bahk GJ. Interrelationships between multiple climatic factors and incidence of foodborne diseases. Int J Environ Res Public Health. 2018;15:2482.

Lake IR, Barker GC. Climate change, foodborne pathogens and illness in higher-income countries. Curr Environ Heal Rep. 2018;5(1):187–96.

Lake IR, Gillespie IA, Bentham G, Nichols GL, Lane C, Adak GK, et al. A re-evaluation of the impact of temperature and climate change on foodborne illness. Epidemiol Infect. 2009;137(11):1538–47.

Lake IR, Hooper L, Abdelhamid A, Bentham G, Boxall AB. a. A, Draper A, et al. Climate change and food security: health impacts in developed countries. Environ Health Perspect. 2012;120(11):1520–6.

Springmann M, Mason-D’Croz D, Robinson S, Garnett T, Godfray HCJ, Gollin D, et al. Global and regional health effects of future food production under climate change: a modelling study. Lancet. 2016;387(10031):1937–46.

Campbell-Lendrum D, Manga L, Bagayoko M, Sommerfeld J. Climate change and vector-borne diseases: what are the implications for public health research and policy? Philos Trans R Soc London. 2015;370:20130552.

Sweileh WM. Bibliometric analysis of peer-reviewed literature on climate change and human health with an emphasis on infectious diseases. Glob Health. 2020;16(1):1–17.

Ford J. Indigenous health and climate change. Am J Public Health. 2012;102(7):1260–6.

Butler CD. Climate change, health and existential risks to civilization: a comprehensive review (1989–2013). Int J Environ Res Public Health. 2018;15(10):2266.

Tong S, Ebi K. Preventing and mitigating health risks of climate change. Environ Res. 2019;174:9–13.

Ebi KL, Hess JJ. The past and future in understanding the health risks of and responses to climate variability and change. Int J Biometeorol. 2017;61(S1):71–80.

Hosking J, Campbell-Lendrum D. How well does climate change and human health research match the demands of policymakers? A scoping review. Environ Health Perspect. 2012;120(8):1076–82.

Berry P, Enright PM, Shumake-Guillemot J, Villalobos Prats E, Campbell-Lendrum D. Assessing health vulnerabilities and adaptation to climate change: a review of international progress. Int J Environ Res Public Health. 2018;15(12):2626.

Verner G, Schütte S, Knop J, Sankoh O, Sauerborn R. Health in climate change research from 1990 to 2014: positive trend, but still underperforming. Glob Health Action. 2016;9:30723.

Ebi KL, Hasegawa T, Hayes K, Monaghan A, Paz S, Berry P. Health risks of warming of 1.5 °C, 2 °C, and higher, above pre-industrial temperatures. Environ Res Lett. 2018;13(6):063007.

Bäckstrand K. Civic science for sustainability: reframing the role of experts, policy-makers and citizens in environmental governance. Glob Environ Polit. 2003;3(4):24–41.

Susskind L, Jain R, Martyniuk A. Better environmental policy studies: how to design and conduct more effective analyses. Washington, DC: Island Press; 2001. p. 256.

Holmes J, Clark R. Enhancing the use of science in environmental policy-making and regulation. Environ Sci Policy. 2008;11(8):702–11.

Pearce T, Ford J, Duerden F, Smit B, Andrachuk M, Berrang-Ford L, et al. Advancing adaptation planning for climate change in the Inuvialuit Settlement Region (ISR): a review and critique. Reg Environ Chang. 2011;11(1):1–17.

Gearheard S, Shirley J. Challenges in community-research relationships: learning from natural science in Nunavut. Arctic. 2007;60(1):62–74.

Brownson R, Royer C, Ewing R, McBride T. Researchers and policymakers: travelers in parallel universes. Am J Prev Med. 2006;30(2):164–72.

Feldman P. Improving communication between researchers and policy makers in long-term care or, researchers are from Mars; policy makers are from Venus. Gerontologist. 2001;41(3):312–21.

Pearce TD, Ford JD, Laidler GJ, Smit B, Duerden F, Allarut M, et al. Community collaboration and climate change research in the Canadian Arctic. Polar Res. 2009;28(1):10–27.

Duerden F, Beasley E, Riewe R, Oakes J. Assessing community vulnerabilities to environmental change in the Inuvialuit region. Climate Change: Linking Traditional and Scientific Knowledge. Winnipeg, MB: Aboriginal Issues Press; 2006.

Mulrow CD. Rationale for systematic reviews. BMJ. 1994;309(6954):597–9.

Tricco A, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467–73.

Moher D, Liberati A, Tetzlaff J, Altman D. The Prisma Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9.

IPCC. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, et al., editors. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2013. 1535 pp.

IPCC. Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of Working Groups I and II of the Intergovernmental Panel on Climate Change. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, et al., editors. Cambridge: Cambridge University Press; 2012. p. 582.

Woodward A, Smith KR, Campbell-Lendrum D, Chadee DD, Honda Y, Liu Q, et al. Climate change and health: on the latest IPCC report. Lancet. 2014;383(9924):1185–9.

Confalonieri U, Menne B, Akhtar R, Ebi KL, Hauengue M, Kovats RS, et al. Human Health. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE, editors. Climate change 2007: impacts, adaptation and vulnerability contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2007. p. 391–431.

IPCC. Climate Change 2014: Impacts, adaptation, and vulnerability. Part B: regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, et al., editors. Climate change 2014 impacts, adaptation and vulnerability: part A: global and sectoral aspects. Cambridge, UK and New York, USA: Cambridge University Press; 2014. p. 688.

Hong Q, Pluye P, Fàbregues S, Bartlett G, Boardman F, Cargo M, et al. Mixed Methods Appraisal Tool (MMAT), Version 2018. Registration of copyright (#1148552), Canadian Intellectual Property Office, Industry Canada; 2018. p. 1–11.

Lewin S, Glenton C, Munthe-Kaas H, Carlsen B, Colvin CJ, Gülmezoglu M, et al. Using qualitative evidence in decision making for health and social interventions: an approach to assess confidence in findings from qualitative evidence syntheses (GRADE-CERQual). PLoS Med. 2015;12(10):1001895.

Download references

Acknowledgements

We would like to thank Maria Tan at the University of Alberta Library for the advice, expertise and guidance provided in developing the search strategy for this protocol. Special thanks to those who assisted with methodology refinement, including Etienne de Jongh, Katharine Neale, and Tianna Rusnak.

Funding was provided by the Canadian Institutes for Health Research (to SLH and AC). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Authors and affiliations.

School of Public Health, University of Alberta, ECHA, 11405 87 Ave NW, Edmonton, AB, T6G 1C9, Canada

Sherilee L. Harper, Amreen Babujee, Shaugn Coggins & Carlee J. Wright

School of Arctic & Subarctic Studies, Labrador Institute of Memorial University, 219 Hamilton River Road, Stn B, PO Box 490, Happy Valley-Goose Bay, NL, A0P 1E0, Canada

Ashlee Cunsolo

Unidad de Ciencias Sociales, Universidad Autónoma de Yucatán, Calle 61 x 66 # 525. Col. Centro, Mérida, Yucatán, México

Mauricio Domínguez Aguilar

You can also search for this author in PubMed   Google Scholar

Contributions

SLH, AC, and MDA contributed to the conceptualization, methodology, writing, and editing of the manuscript. AB contributed to the methodology, writing, and editing of the manuscript. SC contributed to the writing and editing of the manuscript. CJW contributed to visualization, writing, and editing of the manuscript. The authors have read and approved the final manuscript.

Corresponding author

Correspondence to Sherilee L. Harper .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

Search strategy for CINAHL®, Web of Science™, Scopus®, Embase® via Ovid, and MEDLINE® via Ovid.

Data extraction form

• *Categories were not mutually exclusive; that is, more than one category could be selected

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Harper, S.L., Cunsolo, A., Babujee, A. et al. Climate change and health in North America: literature review protocol. Syst Rev 10 , 3 (2021). https://doi.org/10.1186/s13643-020-01543-y

Download citation

Received : 30 October 2020

Accepted : 23 November 2020

Published : 04 January 2021

DOI : https://doi.org/10.1186/s13643-020-01543-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Climate Change
• Human Health
• Mental Health
• North America

Systematic Reviews

ISSN: 2046-4053

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• For authors
• Browse by collection
• BMJ Journals More You are viewing from: Google Indexer

You are here

• Volume 11, Issue 6
• Health effects of climate change: an overview of systematic reviews
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• http://orcid.org/0000-0003-4548-2229 Rhea J Rocque 1 ,
• Caroline Beaudoin 2 ,
• http://orcid.org/0000-0002-4716-6505 Ruth Ndjaboue 2 , 3 ,
• Laura Cameron 1 ,
• Louann Poirier-Bergeron 2 ,
• Rose-Alice Poulin-Rheault 2 ,
• Catherine Fallon 2 , 4 ,
• http://orcid.org/0000-0002-4114-8971 Andrea C Tricco 5 , 6 ,
• http://orcid.org/0000-0003-4192-0682 Holly O Witteman 2 , 3
• 1 Prairie Climate Centre , The University of Winnipeg , Winnipeg , Manitoba , Canada
• 2 Faculty of Medicine , Université Laval , Quebec , QC , Canada
• 3 VITAM Research Centre for Sustainable Health , Quebec , QC , Canada
• 4 CHUQ Research Centre , Quebec , QC , Canada
• 5 Li Ka Shing Knowledge Institute , Toronto , Ontario , Canada
• 6 Dalla Lana School of Public Health , University of Toronto , Toronto , Ontario , Canada
• Correspondence to Dr Rhea J Rocque; rhea.rocque{at}gmail.com

Objectives We aimed to develop a systematic synthesis of systematic reviews of health impacts of climate change, by synthesising studies’ characteristics, climate impacts, health outcomes and key findings.

Design We conducted an overview of systematic reviews of health impacts of climate change. We registered our review in PROSPERO (CRD42019145972). No ethical approval was required since we used secondary data. Additional data are not available.

Data sources On 22 June 2019, we searched Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Cochrane and Web of Science.

Eligibility criteria We included systematic reviews that explored at least one health impact of climate change.

Data extraction and synthesis We organised systematic reviews according to their key characteristics, including geographical regions, year of publication and authors’ affiliations. We mapped the climate effects and health outcomes being studied and synthesised major findings. We used a modified version of A MeaSurement Tool to Assess systematic Reviews-2 (AMSTAR-2) to assess the quality of studies.

Results We included 94 systematic reviews. Most were published after 2015 and approximately one-fifth contained meta-analyses. Reviews synthesised evidence about five categories of climate impacts; the two most common were meteorological and extreme weather events. Reviews covered 10 health outcome categories; the 3 most common were (1) infectious diseases, (2) mortality and (3) respiratory, cardiovascular or neurological outcomes. Most reviews suggested a deleterious impact of climate change on multiple adverse health outcomes, although the majority also called for more research.

Conclusions Most systematic reviews suggest that climate change is associated with worse human health. This study provides a comprehensive higher order summary of research on health impacts of climate change. Study limitations include possible missed relevant reviews, no meta-meta-analyses, and no assessment of overlap. Future research could explore the potential explanations between these associations to propose adaptation and mitigation strategies and could include broader sociopsychological health impacts of climate change.

• public health
• social medicine

Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study. All data relevant to the study are included in the article or uploaded as supplementary information. Additional data are not available.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2020-046333

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Strengths and limitations of this study

A strength of this study is that it provides the first broad overview of previous systematic reviews exploring the health impacts of climate change. By targeting systematic reviews, we achieve a higher order summary of findings than what would have been possible by consulting individual original studies.

By synthesising findings across all included studies and according to the combination of climate impact and health outcome, we offer a clear, detailed and unique summary of the current state of evidence and knowledge gaps about how climate change may influence human health.

A limitation of this study is that we were unable to access some full texts and therefore some studies were excluded, even though we deemed them potentially relevant after title and abstract inspection.

Another limitation is that we could not conduct meta-meta-analyses of findings across reviews, due to the heterogeneity of the included systematic reviews and the relatively small proportion of studies reporting meta-analytic findings.

Finally, the date of the systematic search is a limitation, as we conducted the search in June 2019.

Introduction

The environmental consequences of climate change such as sea-level rise, increasing temperatures, more extreme weather events, increased droughts, flooding and wildfires are impacting human health and lives. 1 2 Previous studies and reviews have documented the multiple health impacts of climate change, including an increase in infectious diseases, respiratory disorders, heat-related morbidity and mortality, undernutrition due to food insecurity, and adverse health outcomes ensuing from increased sociopolitical tension and conflicts. 2–5 Indeed, the most recent Lancet Countdown report, 2 which investigates 43 indicators of the relationship between climate change and human health, arrived at their most worrisome findings since the beginning of their on-going annual work. This report underlines that the health impacts of climate change continue to worsen and are being felt on every continent, although they are having a disproportionate and unequal impact on populations. 2 Authors caution that these health impacts will continue to worsen unless we see an immediate international response to limiting climate change.

To guide future research and action to mitigate and adapt to the health impacts of climate change and its environmental consequences, we need a complete and thorough overview of the research already conducted regarding the health impacts of climate change. Although the number of original studies researching the health impacts of climate change has greatly increased in the recent decade, 2 these do not allow for an in-depth overview of the current literature on the topic. Systematic reviews, on the other hand, allow a higher order overview of the literature. Although previous systematic reviews have been conducted on the health impacts of climate change, these tend to focus on specific climate effects (eg, impact of wildfires on health), 6 7 health impacts (eg, occupational health outcomes), 8 9 countries, 10–12 or are no longer up to date, 13 14 thus limiting our global understanding of what is currently known about the multiple health impacts of climate change across the world.

In this study, we aimed to develop such a complete overview by synthesising systematic reviews of health impacts of climate change. This higher order overview of the literature will allow us to better prepare for the worsening health impacts of climate change, by identifying and describing the diversity and range of health impacts studied, as well as by identifying gaps in previous research. Our research objectives were to synthesise studies’ characteristics such as geographical regions, years of publication, and authors’ affiliations, to map the climate impacts, health outcomes, and combinations of these that have been studied, and to synthesise key findings.

We applied the Cochrane method for overviews of reviews. 15 This method is designed to systematically map the themes of studies on a topic and synthesise findings to achieve a broader overview of the available literature on the topic.

Research questions

Our research questions were the following: (1) What is known about the relationship between climate change and health, as shown in previous systematic reviews? (2) What are the characteristics of these studies? We registered our plan (CRD42019145972 16 ) in PROSPERO, an international prospective register of systematic reviews and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 17 to report our findings, as a reporting guideline for overviews is still in development. 18

Search strategy and selection criteria

To identify relevant studies, we used a systematic search strategy. There were two inclusion criteria. We included studies in this review if they (1) were systematic reviews of original research and (2) reported at least one health impact as it related (directly or indirectly) to climate change.

We defined a systematic review, based on Cochrane’s definition, as a review of the literature in which one ‘attempts to identify, appraise and synthesize all the empirical evidence that meets pre-specified eligibility criteria to answer a specific research question [by] us[ing] explicit, systematic methods that are selected with a view aimed at minimizing bias, to produce more reliable findings to inform decision making’. 19 We included systematic reviews of original research, with or without meta-analyses. We excluded narrative reviews, non-systematic literature reviews and systematic reviews of materials that were not original research (eg, systematic reviews of guidelines.)

We based our definition of health impacts on the WHO’s definition of health as, ‘a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity’. 20 Therefore, health impacts included, among others, morbidity, mortality, new conditions, worsening/improving conditions, injuries and psychological well-being. Included studies could refer to climate change or global warming directly or indirectly, for instance, by synthesising the direct or indirect health effects of temperature rises or of natural conditions/disasters made more likely by climate change (eg, floods, wildfires, temperature variability, droughts.) Although climate change and global warming are not equivalent terms, in an effort to avoid missing relevant literature, we included studies using either term. We included systematic reviews whose main focus was not the health impacts of climate change, providing they reported at least one result regarding health effects related to climate change (or consequences of climate change.) We excluded studies if they did not report at least one health effect of climate change. For instance, we excluded studies which reported on existing measures of health impacts of climate change (and not the health impact itself) and studies which reported on certain health impacts without a mention of climate change, global warming or environmental consequences made more likely by climate change.

On 22 June 2019, we retrieved systematic reviews regarding the health effects of climate change by searching from inception the electronic databases Medline, CINAHL, Embase, Cochrane, Web of Science using a structured search (see online supplemental appendix 1 for final search strategy developed by a librarian.) We did not apply language restrictions. After removing duplicates, we imported references into Covidence. 21

Supplemental material

Screening process and data extraction.

To select studies, two trained analysts first screened independently titles and abstracts to eliminate articles that did not meet our inclusion criteria. Next, the two analysts independently screened the full text of each article. A senior analyst resolved any conflict or disagreement.

Next, we decided on key information that needed to be extracted from studies. We extracted the first author’s name, year of publication, number of studies included, time frame (in years) of the studies included in the article, first author’s institution’s country affiliation, whether the systematic review included a meta-analysis, geographical focus, population focus, the climate impact(s) and the health outcome(s) as well as the main findings and limitations of each systematic review.

Two or more trained analysts (RR, CB, RN, LC, LPB, RAPR) independently extracted data, using Covidence and spreadsheet software (Google Sheets). An additional trained analyst from the group or senior research team member resolved disagreements between individual judgments.

Coding and data mapping

To summarise findings from previous reviews, we first mapped articles according to climate impacts and health outcomes. To develop the categories of climate impacts and health outcomes, two researchers (RR and LC) consulted the titles and abstracts of each article. We started by identifying categories directly based on our data and finalised our categories by consulting previous conceptual frameworks of climate impacts and health outcomes. 1 22 23 The same two researchers independently coded each article according to their climate impact and health outcome. We then compared coding and resolved disagreements through discussion.

Next, using spreadsheet software, we created a matrix to map articles according to their combination of climate impacts and health outcomes. Each health outcome occupied one row, whereas climate impacts each occupied one column. We placed each article in the matrix according to the combination(s) of their climate impact(s) and health outcome(s). For instance, if we coded an article as ‘extreme weather’ for climate and ‘mental health’ for health impact, we noted the reference of this article in the cell at the intersection of these two codes. We calculated frequencies for each cell to identify frequent combinations and gaps in literature. Because one study could investigate more than one climate impact and health outcome, the frequency counts for each category could exceed the number of studies included in this review.

Finally, we re-read the Results and Discussion sections of each article to summarise findings of the studies. We first wrote an individual summary for each study, then we collated the summaries of all studies exploring the same combination of categories to develop an overall summary of findings for each combination of categories.

Quality assessment

We used a modified version of AMSTAR-2 to assess the quality of the included systematic reviews ( online supplemental appendix 2 ). The purpose of this assessment was to evaluate the quality of the included studies as a whole to get a sense of the overall quality of evidence in this field. Therefore, individual quality scores were not compiled for each article, but scores were aggregated according to items. Since AMSTAR-2 was developed for syntheses of systematic reviews of randomised controlled trials, working with a team member with expertise in knowledge synthesis (AT), we adapted it to suit a research context that is not amenable to randomised controlled trials. For instance, we changed assessing and accounting for risk of bias in studies’ included randomised controlled trials to assessing and accounting for limitations in studies’ included articles. Complete modifications are presented in online supplemental appendix 2 .

Patient and public involvement

Patients and members of the public were not involved in this study.

Articles identified

As shown in the PRISMA diagram in figure 1 , from an initial set of 2619 references, we retained 94 for inclusion. More precisely, following screening of titles and abstracts, 146 studies remained for full-text inspection. During full-text inspection, we excluded 52 studies, as they did not report a direct health effect of climate change (n=17), did not relate to climate change (n=15), were not systematic reviews (n=10), or we could not retrieve the full text (n=10).

• Download figure
• Open in new tab
• Download powerpoint

The flow chart for included articles in this review.

Study descriptions

A detailed table of all articles and their characteristics can be found in online supplemental appendix 3 . Publication years ranged from 2007 to 2019 (year of data extraction), with the great majority of included articles (n=69; 73%) published since 2015 ( figure 2 ). A median of 30 studies had been included in the systematic reviews (mean=60; SD=49; range 7–722). Approximately one-fifth of the systematic reviews included meta-analyses of their included studies (n=18; 19%). The majority of included systematic reviews’ first authors had affiliations in high-income countries, with the largest representations by continent in Europe (n=30) and Australia (n=24) ( figure 3 ). Countries of origin by continents include (from highest to lowest frequency, then by alphabetical order): Europe (30); UK (9), Germany (6), Italy (4), Sweden (4), Denmark (2), France (2), Georgia (1), Greece (1) and Finland (1); Australia (24); Asia (21); China (11), Iran (4), India (1), Jordan (1), Korea (1), Nepal (1), Philippines (1), Taiwan (1); North America (16); USA (15), Canada (1); Africa (2); Ethiopia (1), Ghana (1), and South America (1); Brazil (1).

Number of included systematic reviews by year of publication.

Number of publications according to geographical affiliation of the first author.

Regarding the geographical focus of systematic reviews, most of the included studies (n=68; 72%) had a global focus or no specified geographical limitations and therefore included studies published anywhere in the world. The remaining systematic reviews either targeted certain countries (n=12) (1 for each Australia, Germany, Iran, India, Ethiopia, Malaysia, Nepal, New Zealand and 2 reviews focused on China and the USA), continents (n=5) (3 focused on Europe and 2 on Asia), or regions according to geographical location (n=6) (1 focused on Sub-Saharan Africa, 1 on Eastern Mediterranean countries, 1 on Tropical countries, and 3 focused on the Arctic), or according to the country’s level of income (n=3) (2 on low to middle income countries, 1 on high income countries).

Regarding specific populations of interest, most of the systematic reviews did not define a specific population of interest (n=69; 73%). For the studies that specified a population of interest (n=25; 26.6%), the most frequent populations were children (n=7) and workers (n=6), followed by vulnerable or susceptible populations more generally (n=4), the elderly (n=3), pregnant people (n=2), people with disabilities or chronic illnesses (n=2) and rural populations (n=1).

We assessed studies for quality according to our revised AMSTAR-2. Complete scores for each article and each item are available in online supplemental appendix 4 . Out of 94 systematic reviews, the most commonly fully satisfied criterion was #1 (Population, Intervention, Comparator, Outcome (PICO) components) with 81/94 (86%) of included systematic reviews fully satisfying this criterion. The next most commonly satisfied criteria were #16 (potential sources of conflict of interest reported) (78/94=83% fully), #13 (account for limitations in individual studies) (70/94=75% fully and 2/94=2% partially), #7 (explain both inclusion and exclusion criteria) (64/94=68% fully and 19/94=20% partially), #8 (description of included studies in adequate detail) (36/94=38% fully and 41/94=44% partially), and #4 (use of a comprehensive literature search strategy) (0/94=0% fully and 80/94=85% partially). For criteria #11, #12, and #15, which only applied to reviews including meta-analyses, 17/18 (94%) fully satisfied criterion #11 (use of an appropriate methods for statistical combination of results), 12/18 (67%) fully satisfied criterion #12 (assessment of the potential impact of Risk of Bias (RoB) in individual studies) (1/18=6% partially), and 11/18 (61%) fully satisfied criterion #15 (an adequate investigation of publication bias, small study bias).

Climate impacts and health outcomes

Regarding climate impacts, we identified 5 mutually exclusive categories, with 13 publications targeting more than one category of climate impacts: (1) meteorological (n=71 papers) (eg, temperature, heat waves, humidity, precipitation, sunlight, wind, air pressure), (2) extreme weather (n=24) (eg, water-related, floods, cyclones, hurricanes, drought), (3) air quality (n=7) (eg, air pollution and wildfire smoke exposure), (4) general (n=5), and (5) other (n=3). Although heat waves could be considered an extreme weather event, papers investigating heat waves’ impact on health were classified in the meteorological impact category, since some of these studies treated them with high temperature. ‘General’ climate impacts included articles that did not specify climate change impacts but stated general climate change as their focus. ‘Other’ climate impacts included studies investigating other effects indirectly related to climate change (eg, impact of environmental contaminants) or general environmental risk factors (eg, environmental hazards, sanitation and access to clean water.)

We identified 10 categories to describe the health outcomes studied by the systematic reviews, and 29 publications targeted more than one category of health outcomes: (1) infectious diseases (n=41 papers) (vector borne, food borne and water borne), (2) mortality (n=32), (3) respiratory, cardiovascular and neurological (n=23), (4) healthcare systems (n=16), 5) mental health (n=13), (6) pregnancy and birth (n=11), 7) nutritional (n=9), (8) skin diseases and allergies (n=8), (9) occupational health and injuries (n=6) and (10) other health outcomes (n=17) (eg, sleep, arthritis, disability-adjusted life years, non-occupational injuries, etc)

Figure 4 depicts the combinations of climate impact and health outcome for each study, with online supplemental appendix 5 offering further details. The five most common combinations are studies investigating the (1) meteorological impacts on infectious diseases (n=35), (2) mortality (n=24) and (3) respiratory, cardiovascular and neurological outcomes (n=17), (4) extreme weather events’ impacts on infectious diseases (n=14), and (5) meteorological impacts on health systems (n=11).

Summary of the combination of climate impact and health outcome (frequencies). The total frequency for one category of health outcome could exceed the number of publications included in this health outcome, since one publication could explore the health impact according to more than one climate factor (eg, one publication could explore both the impact of extreme weather events and temperature on mental health).

For studies investigating meteorological impacts on health, the three most common health outcomes studied were impacts on (1) infectious diseases (n=35), (2) mortality (n=24) and (3) respiratory, cardiovascular and neurological outcomes (n=17). Extreme weather event studies most commonly reported health outcomes related to (1) infectious diseases (n=14), (2) mental health outcomes (n=9) and (3) nutritional outcomes (n=6) and other health outcomes (eg, injuries, sleep) (n=6). Studies focused on the impact of air quality were less frequent and explored mostly health outcomes linked to (1) respiratory, cardiovascular and neurological outcomes (n=6), (2) mortality (n=5) and (3) pregnancy and birth outcomes (n=3).

Summary of findings

Most reviews suggest a deleterious impact of climate change on multiple adverse health outcomes, with some associations being explored and/or supported with consistent findings more often than others. Some reviews also report conflicting findings or an absence of association between the climate impact and health outcome studied (see table 1 for a detailed summary of findings according to health outcomes).

• View inline

Summary of findings from systematic reviews according to health outcome and climate impact

Notable findings of health outcomes according to climate impact include the following. For meteorological factors (n=71), temperature and humidity are the variables most often studied and report the most consistent associations with infectious diseases and respiratory, cardiovascular, and neurological outcomes. Temperature is also consistently associated with mortality and healthcare service use. Some associations are less frequently studied, but remain consistent, including the association between some meteorological factors (eg, temperature and heat) and some adverse mental health outcomes (eg, hospital admissions for mental health reasons, suicide, exacerbation of previous mental health conditions), and the association between heat and adverse occupational outcomes and some adverse birth outcomes. Temperature is also associated with adverse nutritional outcomes (likely via crop production and food insecurity) and temperature and humidity are associated with some skin diseases and allergies. Some health outcomes are less frequently studied, but studies suggest an association between temperature and diabetes, impaired sleep, cataracts, heat stress, heat exhaustion and renal diseases.

Extreme weather events (n=24) are consistently associated with mortality, some mental health outcomes (eg, distress, anxiety, depression) and adverse nutritional outcomes (likely via crop production and food insecurity). Some associations are explored less frequently, but these studies suggest an association between drought and respiratory and cardiovascular outcomes (likely via air quality), between extreme weather events and an increased use of healthcare services and some adverse birth outcomes (likely due to indirect causes, such as experiencing stress). Some health outcomes are less frequently studied, but studies suggest an association between extreme weather events and injuries, impaired sleep, oesophageal cancer and exacerbation of chronic illnesses. There are limited and conflicting findings for the association between extreme weather events and infectious diseases, as well as for certain mental health outcomes (eg, suicide and substance abuse). At times, different types of extreme weather events (eg, drought vs flood) led to conflicting findings for some health outcomes (eg, mental health outcomes, infectious diseases), but for other health outcomes, the association was consistent independently of the extreme weather event studied (eg, mortality, healthcare service use and nutritional outcomes).

The impact of air quality on health (n=7) was less frequently studied, but the few studies exploring this association report consistent findings regarding an association with respiratory-specific mortality, adverse respiratory outcomes and an increase in healthcare service use. There is limited evidence regarding the association between air quality and cardiovascular outcomes, limited and inconsistent evidence between wildfire smoke exposure and adverse birth outcomes, and no association is found between exposure to wildfire smoke and increase in use of health services for mental health reasons. Only one review explored the impact of wildfire smoke exposure on ophthalmic outcomes, and it suggests that it may be associated with eye irritation and cataracts.

Reviews which stated climate change as their general focus and did not specify the climate impact(s) under study were less frequent (n=5), but they suggest an association between climate change and pollen allergies in Europe, increased use of healthcare services, obesity, skin diseases and allergies and an association with disability-adjusted life years. Reviews investigating the impact of other climate-related factors (n=3) show inconsistent findings concerning the association between environmental pollutant and adverse birth outcomes, and two reviews suggest an association between environmental risk factors and pollutants and childhood stunting and occupational diseases.

Most reviews concluded by calling for more research, noting the limitations observed among the studies included in their reviews, as well as limitations in their reviews themselves. These limitations included, among others, some systematic reviews having a small number of publications, 24 25 language restrictions such as including only papers in English, 26 27 arriving at conflicting evidence, 28 difficulty concluding a strong association due to the heterogeneity in methods and measurements or the limited equipment and access to quality data in certain contexts, 24 29–31 and most studies included were conducted in high-income countries. 32 33

Previous authors also discussed the important challenge related to exploring the relationship between climate change and health. Not only is it difficult to explore the potential causal relationship between climate change and health, mostly due to methodological challenges, but there are also a wide variety of complex causal factors that may interact to determine health outcomes. Therefore, the possible causal mechanisms underlying these associations were at times still unknown or uncertain and the impacts of some climate factors were different according to geographical location and specificities of the context. Nonetheless, some reviews offered potential explanations for the climate-health association, with the climate factor at times, having a direct impact on health (eg, flooding causing injuries, heat causing dehydration) and in other cases, having an indirect impact (eg, flooding causing stress which in turn may cause adverse birth outcomes, heat causing difficulty concentrating leading to occupational injuries.)

Principal results

In this overview of systematic reviews, we aimed to develop a synthesis of systematic reviews of health impacts of climate change by mapping the characteristics and findings of studies exploring the relationship between climate change and health. We identified four key findings.

First, meteorological impacts, mostly related to temperature and humidity, were the most common impacts studied by included publications, which aligns with findings from a previous scoping review on the health impacts of climate change in the Philippines. 10 Indeed, meteorological factors’ impact on all health outcomes identified in this review are explored, although some health outcomes are more rarely explored (eg, mental health and nutritional outcomes). Although this may not be surprising given that a key implication of climate change is the long-term meteorological impact of temperature rise, this finding suggests we also need to undertake research focused on other climate impacts on health, including potential direct and indirect effects of temperature rise, such as the impact of droughts and wildfire smoke. This will allow us to better prepare for the health crises that arise from these ever-increasing climate-related impacts. For instance, the impacts of extreme weather events and air quality on certain health outcomes are not explored (eg, skin diseases and allergies, occupational health) or only rarely explored (eg, pregnancy outcomes).

Second, systematic reviews primarily focus on physical health outcomes, such as infectious diseases, mortality, and respiratory, cardiovascular and neurological outcomes, which also aligns with the country-specific previous scoping review. 10 Regarding mortality, we support Campbell and colleagues’ 34 suggestion that we should expand our focus to include other types of health outcomes. This will provide better support for mitigation policies and allow us to adapt to the full range of threats of climate change.

Moreover, it is unclear whether the distribution of frequencies of health outcomes reflects the actual burden of health impacts of climate change. The most commonly studied health outcomes do not necessarily reflect the definition of health presented by the WHO as, ‘a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity’. 20 This suggests that future studies should investigate in greater depth the impacts of climate change on mental and broader social well-being. Indeed, some reviews suggested that climate change impacts psychological and social well-being, via broader consequences, such as political instability, health system capacity, migration, and crime, 3 4 35 36 thus illustrating how our personal health is determined not only by biological and environmental factors but also by social and health systems. The importance of expanding our scope of health in this field is also recognised in the most recent Lancet report, which states that future reports will include a new mental health indicator. 2

Interestingly, the reviews that explored the mental health impacts of climate change were focused mostly on the direct and immediate impacts of experiencing extreme weather events. However, psychologists are also warning about the long-term indirect mental health impacts of climate change, which are becoming more prevalent for children and adults alike (eg, eco-anxiety, climate depression). 37 38 Even people who do not experience direct climate impacts, such as extreme weather events, report experiencing distressing emotions when thinking of the destruction of our environment or when worrying about one’s uncertain future and the lack of actions being taken. To foster emotional resilience in the face of climate change, these mental health impacts of climate change need to be further explored. Humanity’s ability to adapt to and mitigate climate change ultimately depends on our emotional capacity to face this threat.

Third, there is a notable geographical difference in the country affiliations of first authors, with three quarters of systematic reviews having been led by first authors affiliated to institutions in Europe, Australia, or North America, which aligns with the findings of the most recent Lancet report. 2 While perhaps unsurprising given the inequalities in research funding and institutions concentrated in Western countries, this is of critical importance given the significant health impacts that are currently faced (and will remain) in other parts of the world. Research funding organisations should seek to provide more resources to authors in low-income to middle-income countries to ensure their expertise and perspectives are better represented in the literature.

Fourth, overall, most reviews suggest an association between climate change and the deterioration of health in various ways, illustrating the interdependence of our health and well-being with the well-being of our environment. This interdependence may be direct (eg, heat’s impact on dehydration and exhaustion) or indirect (eg, via behaviour change due to heat.) The most frequently explored and consistently supported associations include an association between temperature and humidity with infectious diseases, mortality and adverse respiratory, cardiovascular and neurological outcomes. Other less frequently studied but consistent associations include associations between climate impacts and increased use of healthcare services, some adverse mental health outcomes, adverse nutritional outcomes and adverse occupational health outcomes. These associations support key findings of the most recent Lancet report, in which authors report, among others, increasing heat exposure being associated with increasing morbidities and mortality, climate change leading to food insecurity and undernutrition, and to an increase in infectious disease transmission. 2

That said, a number of reviews included in this study reported limited, conflicting and/or an absence of evidence regarding the association between the climate impact and health outcome. For instance, there was conflicting or limited evidence concerning the association between extreme weather events and infectious diseases, cardiorespiratory outcomes and some mental health outcomes and the association between air quality and cardiovascular-specific mortality and adverse birth outcomes. These conflicting and limited findings highlight the need for further research. These associations are complex and there exist important methodological challenges inherent to exploring the causal relationship between climate change and health outcomes. This relationship may at times be indirect and likely determined by multiple interacting factors.

The climate-health link has been the target of more research in recent years and it is also receiving increasing attention from the public and in both public health and climate communication literature. 2 39–41 However, the health framing of climate change information is still underused in climate communications, and researchers suggest we should be doing more to make the link between human health and climate change more explicit to increase engagement with the climate crisis. 2 41–43 The health framing of climate communication also has implications for healthcare professionals 44 and policy-makers, as these actors could play a key part in climate communication, adaptation and mitigation. 41 42 45 These key stakeholders’ perspectives on the climate-health link, as well as their perceived role in climate adaptation and mitigation could be explored, 46 since research suggests that health professionals are important voices in climate communications 44 and especially since, ultimately, these adverse health outcomes will engender pressure on and cost to our health systems and health workers.

Strengths and limitations

To the best of our knowledge, the current study provides the first broad overview of previous systematic reviews exploring the health impacts of climate change. Our review has three main strengths. First, by targeting systematic reviews, we achieve a higher order summary of findings than what would have been possible by consulting individual original studies. Second, by synthesising findings across all included studies and according to the combination of climate impact and health outcome, we offer a clear, detailed and unique summary of the current state of evidence and knowledge gaps about how climate change may influence human health. This summary may be of use to researchers, policy-makers and communities. Third, we included studies published in all languages about any climate impact and any health outcome. In doing so, we provide a comprehensive and robust overview.

Our work has four main limitations. First, we were unable to access some full texts and therefore some studies were excluded, even though we deemed them potentially relevant after title and abstract inspection. Other potentially relevant systematic reviews may be missing due to unseen flaws in our systematic search. Second, due to the heterogeneity of the included systematic reviews and the relatively small proportion of studies reporting meta-analytic findings, we could not conduct meta-meta-analyses of findings across reviews. Future research is needed to quantify the climate and health links described in this review, as well as to investigate the causal relationship and other interacting factors. Third, due to limited resources, we did not assess overlap between the included reviews concerning the studies they included. Frequencies and findings should be interpreted with potential overlap in mind. Fourth, we conducted the systematic search of the literature in June 2019, and it is therefore likely that some recent systematic reviews are not included in this study.

Conclusions

Overall, most systematic reviews of the health impacts of climate change suggest an association between climate change and the deterioration of health in multiple ways, generally in the direction that climate change is associated with adverse human health outcomes. This is worrisome since these outcomes are predicted to rise in the near future, due to the rise in temperature and increase in climate-change-related events such as extreme weather events and worsened air quality. Most studies included in this review focused on meteorological impacts of climate change on adverse physical health outcomes. Future studies could fill knowledge gaps by exploring other climate-related impacts and broader psychosocial health outcomes. Moreover, studies on health impacts of climate change have mostly been conducted by first authors affiliated with institutions in high-income countries. This inequity needs to be addressed, considering that the impacts of climate change are and will continue to predominantly impact lower income countries. Finally, although most reviews also recommend more research to better understand and quantify these associations, to adapt to and mitigate climate change’s impacts on health, it will also be important to unpack the ‘what, how, and where’ of these effects. Health effects of climate change are unlikely to be distributed equally or randomly through populations. It will be important to mitigate the changing climate’s potential to exacerbate health inequities.

Ethics statements

Patient consent for publication.

Not required.

Acknowledgments

The authors gratefully acknowledge the contributions of Selma Chipenda Dansokho, as research associate, and Thierry Provencher, as research assistant, to this project, and of Frederic Bergeron, for assistance with search strategy, screening and selection of articles for the systematic review.

• Portier C ,
• Arnell N , et al
• Hsiang SM ,
• Frumkin H ,
• Holloway T , et al
• Alderman K ,
• Turner LR ,
• Coates SJ ,
• Davis MDP ,
• Andersen LK
• Jin H , et al
• Azizi M , et al
• Dorotan MM ,
• Sigua JA , et al
• Mcintyre M , et al
• Liu J , et al
• Herlihy N ,
• Bar-Hen A ,
• Verner G , et al
• Hosking J ,
• Campbell-Lendrum D
• Pollock M ,
• Fernandes RM ,
• Witteman HO ,
• Dansokho SC ,
• McKenzie J ,
• Pieper D , et al
• ↵ About Cochrane reviews . Available: https://www.cochranelibrary.com/about/about-cochrane-reviews [Accessed 14 Sept 2020 ].
• World Health Organization
• ↵ Covidence systematic review software . Available: www.covidence.org
• Schlosberg D , et al
• Amegah AK ,
• Jaakkola JJK
• Zheng S , et al
• Leyva EWA ,
• Davidson PM
• Phalkey RK ,
• Aranda-Jan C ,
• Marx S , et al
• Porpora MG ,
• Piacenti I ,
• Scaramuzzino S , et al
• Morton LC ,
• Zhu R , et al
• Rutherford S , et al
• Klinger C ,
• Xu D , et al
• Campbell S ,
• Remenyi TA ,
• White CJ , et al
• Palmer J , et al
• Benevolenza MA ,
• Davenport L
• Maibach EW ,
• Baldwin P , et al
• Schütte S ,
• Nisbet MC ,
• Maibach EW , et al
• Costello A ,
• Montgomery H ,
• Hess J , et al
• Berhane K ,
• Bernhardt V ,
• Finkelmeier F ,
• Verhoff MA , et al
• de Sousa TCM ,
• Amancio F ,
• Hacon SdeS , et al
• Bai Z , et al
• Roda Gracia J ,
• Schumann B ,
• Hedlund C ,
• Blomstedt Y ,
• Aghamohammadi N , et al
• Khader YS ,
• Abdelrahman M ,
• Abdo N , et al
• Matysiak A ,
• Mackenzie JS , et al
• Nichols A ,
• Maynard V ,
• Goodman B , et al
• Tong S , et al
• Swynghedauw B
• Emelyanova A ,
• Oksanen A , et al
• Su H , et al
• Mengersen K ,
• Dale P , et al
• Veenema TG ,
• Thornton CP ,
• Lavin RP , et al
• Gatton ML ,
• Ghazani M ,
• FitzGerald G ,
• Hu W , et al
• Lu Y , et al
• Fearnley E ,
• Woster AP ,
• Goldstein RS , et al
• Philipsborn R ,
• Brosi BJ , et al
• Semenza JC ,
• Rechenburg A , et al
• Stensgaard A-S ,
• Vounatsou P ,
• Sengupta ME , et al
• Shipp-Hilts A ,
• Eidson M , et al
• Thomas DR ,
• Salmon RL , et al
• Prudhomme C , et al
• Wildenhain J ,
• Vandenbergh A , et al
• Barnett AG ,
• Wang X , et al
• Ghanizadeh G ,
• Heidari M ,
• Lawton EM ,
• Liang R , et al
• Moghadamnia MT ,
• Ardalan A ,
• Mesdaghinia A , et al
• Parthasarathy R ,
• Krishnan A , et al
• Sanderson M ,
• Arbuthnott K ,
• Kovats S , et al
• Schubert AJ ,
• Jehn M , et al
• Sheffield PE ,
• Guo Y , et al
• Daniels A , et al
• Madaniyazi L ,
• Yu W , et al
• Pereira G ,
• Uhl SA , et al
• Johnston FH , et al
• Youssouf H ,
• Liousse C ,
• Roblou L , et al
• Agnew M , et al
• Zhang Y , et al
• Crooks JL ,
• Davies JM , et al
• Sawatzky A ,
• Cunsolo A ,
• Jones-Bitton A , et al
• Duan J , et al
• Kunzweiler K ,
• Garthus-Niegel S
• Fernandez A ,
• Jones M , et al
• Saha S , et al
• Carolan-Olah M ,
• Frankowska D
• McCormick S
• Poursafa P ,
• Kelishadi R
• Vilcins D ,
• Huang K-C ,
• Yang T-Y , et al
• Augustin J ,
• Franzke N ,
• Augustin M , et al
• Binazzi A ,
• Bonafede M , et al
• Bonafede M ,
• Marinaccio A ,
• Asta F , et al
• Flouris AD ,
• Ioannou LG , et al
• Kjellstrom T ,
• Baldasseroni A
• Varghese BM ,
• Bi P , et al
• Wimalawansa SA ,
• Wimalawansa SJ
• Ahn HS , et al
• Otte im Kampe E ,
• Rifkin DI ,

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2
• Data supplement 3
• Data supplement 4
• Data supplement 5

Twitter @RutNdjab, @ATricco, @hwitteman

Contributors RN, CF, ACT, HOW contributed to the design of the study. CB, RN, LPB, RAPR and HOW contributed to the systematic search of the literature and selection of studies. RR, HOW, LC conducted data analysis and interpretation. RR and HOW drafted the first version of the article with early revision by CB, LC and RN. All authors critically revised the article and approved the final version for submission for publication. RR and HOW had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Funding This study was funded by the Canadian Institutes of Health Research (CIHR) FDN-148426. The CIHR had no role in determining the study design, the plans for data collection or analysis, the decision to publish, nor the preparation of this manuscript. ACT is funded by a Tier 2 Canada Research Chair in Knowledge Synthesis. HOW is funded by a Tier 2 Canada Research Chair in Human-Centred Digital Health.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Read the full text or download the PDF:

Global Warming: A Review of the Debates on the Causes, Consequences and Politics of Global Response

• Arega Bazezew Berlie University of Ghana

A review of the causes, consequences and political responses to global warming is the focus of this paper. The term global warming is now popularly used to refer to the concentration of greenhouse gases (carbon dioxide, methane and nitrous oxide) in the atmosphere attributed mainly to human activities. Evidence show that, there has been an intense and often emotional debate on the causes and consequences of global warming for many years. Though, the causes are still widely disputed and lack consensuses among proponents, much of the evidence prove to be increasing global warming. It is no longer a prediction— it is actually happening. Major indicators include extinction of many species, population displacement/migration, desertification, famine, drought and chronic food insecurity. Governments, the scientific community and politicians are not unanimous to reduce global warming which emanate from their political positions and conflict of interests. The center of the debate is what causes global warming. In the scientific literature, there is a strong argument that global warming has intensified in recent decades and the changes are more of human-induced greenhouse gases emissions. However, opponents of anthropogenic global warming at the other extreme strongly argue that the cause of global warming is natural and the contribution of humans is minimal. These project the issue of global warming at the forefront of the international political agenda and make it a major political, institutional and environmental challenge of our time.  The general objective of the study is to discuss the debates among the politicians and scientific communities on the causes and consequences of global warming. In this regard, the relevant literature in relation to the debates on global warming are reviewed. Finally, global warming is inevitable and no longer a prediction. Alternative actions such as climate change adaptation and/or mitigation measures have to be given top priority besides the reduction of dangerous greenhouse gas emissions. 

Author Biography

Department of Geography and Environmental Studies

• For Readers
• For Authors
• For Librarians

About | ORID | Contact Us | UG Home

Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.

We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.

Brief introduction to this section that descibes Open Access especially from an IntechOpen perspective

Want to get in touch? Contact our London head office or media team here

Our team is growing all the time, so we’re always on the lookout for smart people who want to help us reshape the world of scientific publishing.

Home > Books > The Nature, Causes, Effects and Mitigation of Climate Change on the Environment

Global Warming and Climate Change (GWCC) Realities

Submitted: 09 March 2021 Reviewed: 21 April 2021 Published: 24 June 2021

DOI: 10.5772/intechopen.97820

Cite this chapter

There are two ways to cite this chapter:

From the Edited Volume

The Nature, Causes, Effects and Mitigation of Climate Change on the Environment

Edited by Stuart A. Harris

To purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www.cbspd.com | [email protected]

Chapter metrics overview

852 Chapter Downloads

Impact of this chapter

Total Chapter Downloads on intechopen.com

Total Chapter Views on intechopen.com

Overall attention for this chapters

The study attempted to investigate the urgency of the global warming and climate change by analyzing the available data from the secondary sources. The document analysis technique was used to examine the available literature. When it comes to the urgency of global warming and climate change, the study showed that there are two schools of thought. One is in support of the motion, claiming that global warming is a real phenomenon triggered by anthropogenic behavior, while the other is opposed to the motion, claiming that global warming and climate change are complicated phenomena, and that forecasting future climates is difficult due to the various players involved, about which climate specialists know little or nothing. Based on document analysis, study infers that there is certain uncertainty about the future of the climate, because climate always changes, and it cannot be certainly affirmed that the climate change is man- made (anthropogenic activities) or is due to natural occurrence. However, it is evident that the global surface temperature, borehole temperature, sea surface temperature, and the sea level is increasing over the years. The study suggests that for the humanity to be certain about their future, treating the global warming and climate change as an act of urgency and working towards prevention and mitigation by limiting the production of greenhouse gases and mindfully consuming the natural resources would be the plausible solution for the larger problem of Global Warming and Climate change.

• Global warming
• Climate change
• Global surface temperature
• Borehole temperature
• Sea surface temperature

Author Information

Yonten chophel *.

• Daga Central School, Dagana, Bhutan

*Address all correspondence to: [email protected]

1. Introduction

Global warming is a disastrous phenomenon compounded by the human activities often drilled by the greed and corporate paradigm of thinking. Its totaling effect in the long run might threaten the survival of mankind and any other species for that matter. Many nations recognize it as serious immediate threat and they forms the associations and organizations to combat against it. However, there are few cynical nations, owing to several reason, economic reason being dominant. But, with passage of time, it has become so apparent that even a rational high school student can comprehend the status and need of action, it is very evident to a layman, that the temperature of the earth, be it in any season is gradually increasing and the people working in the field are awe struck by the crops ability to grow in new higher unnatural altitudes. The lexicon “global warming” might be new to them but they have already felt its consequences.

Climate is very essential concept, owing to dependence of existence of life on it. Living beings survive on food, food is produced by plants and growth of plants depends on climate. So, erratic change in the climate will put billions of lives at stake. Therefore, it is of paramount importance that we discuss, monitor and make sure that the climate for different regions remains at its natural range. Many literatures suggest that global warming caused by the global warming gases emitted enormously by the manufacturing factories and automobiles industries are the cause of erratic climate [ 1 , 2 , 3 , 4 , 5 , 6 ]. Global warming if unchecked will influence the climate of world to the point that earth tipping point is reached, where the surviving earth cannot further sustain any lives in it, changing the course of history preceded by mass extinction of species in the world. Owing to the likelihood of paying a bigger price latter due the simple negligence in time, it is duties of all the global citizens to be mindful about it and do come together to find the effective solutions to mitigate the problem that is ever going to be bigger as it gets delayed. It would be grave mistake to decide not to heed on this global immediate call, for it has the ability to either make or break our collective future.

There is two school of thoughts with regard to urgency of the matter, where one school of thought believes that understanding the climate change is complex and it is extremely difficult to predict the future of climate in this complex universe, and are skeptical about the anthropogenic global warming, suggesting that the information that is known about weather and its causes is not enough to predict the future climate and the climate model are far from precision [ 7 ]. However, another school of thought, expresses its concern and urgency to save the earth form untimely destruction, advocating the need to limit the production of the global warming gases that enhance the chances and intensity of occurrence of the erratic weather [ 7 ,  8 ]. In this modern era, people spent more time looking into screen than looking into sky for weather. As a responsible global citizen, it is our moral obligation to explore the underlying truth and make informed decisions locally. Thus, this study explored the available literature on the global warming and climate change in terms of its urgency.

2. Literature review

Global warming is the term that was introduced or used for the first time by climatologist Wallace Broecker in his article “Climate Change: Are we on the Brink of pronounced Global warming?” Global warming is observable increase in the global temperature of earth (both land and water) and climate change is the effect brought about by the process of warming globally or in general, overall long-term change in our climate, including sea level rising, extreme weather, and ocean acidification. However, the term global warming and climate change are used interchangeably but there is difference in it, global warming is more sensitive and more diverging which results in less advocacy by some subpopulations [ 9 ]. Further, Krauss [ 3 ] clearly puts it in the context when he quotes Lorenz, “Climate is what you expect; weather is what you get.” (p.158). Krauss [ 3 ] argues that just because there is an anomalous cold day in Washington, DC does not mean that global warming is not happening. Likewise, just because there is an ultra-hot week in Washington, DC also does not validate global warming.

The solar radiation from the sun is balanced by the thermal radiations reflecting from the earth; this interaction balance and determines the surface temperature of the earth. The incoming solar radiation from the sun is independent but the outgoing thermal radiations depends on the earth’s surface temperature and the presence of greenhouse gases, which absorbs some of the thermal radiations. Greenhouse gasses (GHG), such as carbon dioxide, methane, nitrous oxides, water vapor, ozone and chlorofluorocarbon (CFC) are responsible for trapping of heat. For instance, water vapor (40%) is responsible for absorbing majority of thermal radiation from earth, followed by carbon dioxide (30%), methane (20%) and other gases (5%) [ 7 ]. So, it indicates that outgoing thermal radiation is mostly absorbed by water vapor and carbon dioxide. Likewise, the change in composition of water vapor due to human activities are negligible, so it implies that the greenhouse gases produced by human activities are likely responsible for the most of the trapping of heat [ 10 , 11 ].

However, some authors [ 12 ] argues that the heat trapping by the CO 2 is not significant and rather it is likely that sun radiations are responsible for the global temperature rise. Again, the debate on whether the sun radiations or GHG is responsible for the global temperature continues, recently, Herring [ 13 ] refuted the claim that likeliness of sun radiation as the cause of global warming might not be true. He argues that, it is possible that sun can warm the earth provided that the pattern of the solar intensity increases over the years. Likewise, the sunspot data do indicate that there was a small increase in the amount of sunlight from late 1800s to the mid-1900s which experts estimates that it could have contributed at the most up to 0.1°C of the 1.0°C (1.8°F) of warming observed since the pre-industrial era. However, there has been no significant net change in the sun’s energy output from the late 1970s to the present (see Figure 1 ), which is when the most rapid global warming was observed. Further, scientists rule out the significant role of sun in global warming due to the fact that if the sun energy output had intensified then it is logical to expect all the layers of earth’s atmosphere to be warmed, which is not the case that is been observed. Rather, satellite and weather balloons observation showed that more warming in the lower atmosphere (troposphere) and cooling in the upper atmosphere (stratosphere) [ 13 ]. This pattern of differential warming is what is been expected due to result of increasing GHG trapping heat.

The peaks and valleys in solar geomagnetic activity since 1900, based on the number of sunspots observed on the face of the sun each day (orange dots). Graph by NOAA Climate.gov , based on data from the WDC-SILSO, Royal Observatory of Belgium (source: Herring [ 13 ]).

The main source of global warming gases is the burning of the fossil fuels and it is observed that a nation with the abundance of the availability of the fossils fuels tends to depend more on fossil fuels than the nations with low abundance of fossils resources [ 5 ]. Global energy consumption with regard to fossil fuels are staggeringly high, and its prediction for the coming years are also projected very high, as reflected in the world energy outlook report of 2012 and 2013. Thereby the production of greenhouse gases will be soared and ultimately it might bring about the increase in the global temperature of the earth. Likewise, the rising population’s demand for fuel, as well as the need for economic growth and a higher standard of living, are both factors to consider. Lack of political will and institutional failure in to make and enforce effective environmental policies, as well as a lack of related knowledge and Rampant disinformation, have all hampered action against global warming and reduction of the global warming gases [ 7 ]. Furthermore, the global temperature rises correlates with the rising pattern in the consumption of the fossil fuel, indicating the likeliness of the GHG warming the earth [ 3 ].

With the increase in the concentration of the greenhouse gases it will act like a thick blanket in the atmosphere where it will absorb the solar radiation. The carbon molecules and the oxygen molecule in the carbon dioxide undergoes vibration like stretching, bending, and this action absorbs the solar radiations [ 1 , 2 ]. Thereby hindering the reflection of solar radiation by the earth making the earth surface temperature warmer.

Many studies affirms that the GWCC is a worrying thing. For example, a study done by Suonan et al. [ 14 ] suggests that due to global warming there will be more than worse phenological alteration in some places like in Tibetan Plateau, more than what is predicated or known. This idea makes us think of worst situation if the temperature of the earth keeps warming up. Moreover, the global sea level has risen by about 8 inches since 1880, and it’s projected to rise another 1 to 4 feet by 2100 (as a result of melting of ice), thus the danger of storm surges and high tides would increase the flooding in many regions [ 8 ].

Shrinking of glaciers, early breaking up of ice on rivers and lakes, shifting of plant and animal and premature flowering are some of the observable effects global climate change had on environment. Moreover, effects that scientists had predicted in the past are now occurring: loss of sea ice, accelerated sea level rise, more intense heat waves. Scientists have high confidence that global temperatures will continue to rise for decades to come, largely due to greenhouse gases produced by human activities. The Intergovernmental Panel on Climate Change (IPCC) which includes more than 1,300 scientists from the United States and other countries, forecasts a temperature rise of 2.5 to 10 degrees Fahrenheit over the next century [ 15 ]. Moreover, the IPCC predicts that increases in global mean temperature of less than 1.8 to 5.4 degrees Fahrenheit (1 to 3 degrees Celsius) above sea levels will produce beneficial impacts in some regions and harmful ones in others. Net annual costs will increase over time as global temperatures increases [ 8 ].

However, not everyone believes that the earth’s surface temperature is rising, and even if global warming is true, not everyone accepts that human activities are the primary cause. Also, not everyone agrees that climate change is a problem. As a result, critics and deniers of global warming and climate change do not see the need to take steps to delay or reverse these trends [ 5 , 7 ]. USA disengaging from Kyoto protocol and Paris agreement on climate showed the polarity of attitude of nations and people towards the GWCC issue. Moreover, at number of climate summit, member countries failed to agree to number of “Environmental targets” in recent years (Kyoto, Copenhagen, so on) [ 5 ].

Extend to which the idea of global warming and climate change have reached to the mankind largely depends on their exposure to the main stream medias and social media. It is found out that the social media network does plays vital role in spreading the knowledge and awareness of the GWCC and it is also indicated that they understand the concept better when they are expose to those terms in positive or negative light [ 16 ]. However, as watchdog it is responsibility of media and social media to uncover the truth, but some studies suggest that newspapers aren’t doing much to convince the health impact of GWCC to the public [ 17 ]. Moreover, in the study done by Shapiro and Park [ 18 ], they found out that people responses to the GWCC in social media particularly their reactions to YouTube video of GWCC are general and shows little or no concern about GWCC, which indicates many people aren’t so convince about the reality of GWCC.

On the contrary, the very existence of 175 plus active organization on climate change [ 19 ], and 100 plus top websites on climate change [ 20 ], indicates the concern and the urgency expressed by the people around the world. Study done by Liu et al. [ 21 ] also affirms that many congressmen do believe GWCC as real thing. Further, IPCC asserts that scientific evidence for warming of the climate system is unequivocal, global temperature rise, warming of oceans, shrinking of ice sheets, rising of sea level, acidification of ocean, and declining of Arctic sea ice are some of the events that conveys the story of the happening event so called Global warming and climate change [ 8 ]. In addition, study done by Allen and McAleer [ 22 ] suggests that the of negative emotions or indifference to global warming might be due to lack of clear logical framework and confusion of short-term variations in localized weather with the long-term global average climate change.

The happenings of global warming can be traced through using the technique of observation and examining the rise in the land surface temperature, borehole temperature profile, sea surface temperature, and sea level [ 7 , 8 , 23 ]. If there are increase in all those four independent parameters, it is the indication that the global warming is occurring [ 7 ]. This increase is attributed to the increase in the global warming gases, CO 2 concentrations in the atmosphere have risen from 0.028 percent in pre-industrial 1750 to 0.043 percent today. Until recently, it was thought that stabilizing CO2 levels in the atmosphere at about 0.055 percent by 2035 will be enough to keep global warming below 2°C. However, 3°C is becoming more possible, which will induce wreaking havoc on human colonies, coral reefs, rain forests, and polar ice caps. To keep temperature rises below 2°C, urgent international action is required, which means keeping CO 2 levels below 0.045 percent. Only if governments can negotiate on cooperative national and international action can this become a possibility [ 23 ].

Despite the two school of thought on the urgency and status of GWCC, the evidences from the surface temperature, sea level rise, sea surface temperature, and borehole temperature profile indicate that the global warming is happening and it is going to be a major threat in the future, which will be discussed in the result and discussion section in detail.

3. Methodology

The study utilized the qualitative design and uses document analysis approach of the data collected from the existing documented secondary sources. Resources were collected via use of Research for Life search engine, mostly peer-review journals and accredited resources were used in finding the useable data. Data were collected from more than fifteen different sources. Primarily the main sources of the collected data included Journal article titled “Global warming and climate change: Realities, Uncertainties and Measures” by A. P. Alzebeokhai, published in 2009, data from the NASA-Global climate change, data from NOAA (National centers for environmental information: National oceanic and atmospheric administration), and Intergovernmental Panel on Climate Change (IPCC) (latest and updated information as of 2019).

The data collection and analysis are done in line with the research question on “Is Global warming and climate change (GWCC) really occurring, and how urgent it is?” Data from several climate expert organizations such as NASA-Global climate change, IPCC, NOAA and other accredited scientific journals are used for the same. The data from those separate sources were compared and analyzed. Owing to the lack of primary data, no statistical software was employed for the analysis.

4. Result and discussion

The result from the analysis of four separate sets of observations, including surface temperature measurements, sea surface temperature, sea level changes, and temperature profiles in boreholes, all indicate that the earth’s surface temperature is increasing, suggesting that it is warming. Each of these separate sets of observations yields findings that overlap and complement each other, suggesting that the GWCC is a real phenomenon.

4.1 Surface temperature

The surface of the earth has warmed by an average of 1.0°C (1.8°F) in the last 100 years, according to regular measurements of the earth’s surface temperature recorded daily from thousands of weather stations around the world, both ashore and stumped. Mean weekly, monthly, and annual temperatures are calculated using daily temperature measurements. As a result, the average annual temperature change can be easily monitored from year to year. The global mean temperature has risen by 0.1°C per decade over the last two decades, with 2005 being the warmest year on record [ 7 ]. The effects of large population centers on global mean temperature, referred to as the “urban heat island effect,” are calculated and corrected for; however, this accounts for less than 15% of observed global warming. Global warming is not constant across the globe, both in terms of time and space; high latitude regions warm more than low latitude regions [ 7 ]. Human activities are estimated to have caused approximately 1.0°C of global warming above pre-industrial levels, with a likely range of 0.8°C to 1.2°C. Global temperature rise is likely to reach 1.5°C between 2030 and 2052, if it continues to increase at the current rate [ 15 ]. Moreover, according to the Global climate change report 2018 [ 23 ], complied by NOAA, shows that the surface temperature is increasing, the data compared the temperature recorded from 1880 till 2018 which showed that, 2016 is the warmest recorded temperature with 0.95 temperature anomaly degree Celsius, followed by 2015 with 0.91 anomaly degree Celsius, and 2017 with 0.85 anomaly degree Celsius [ 23 ].

Additionally, the data from three major compilations based on measured surface temperatures: from GISS (Goddard institute for space studies), HadCRU (global temperature dataset) and NCDC (national oceanic and atmospheric administration) showed upward trend (see Figure 2 ). They have expressed the trend as the temperature difference (“anomaly”) with respect to the 1901–2000 average as the baseline [ 24 ].

Comparison of three data set on surface temperature (source: Verheggen [ 24 ]).

The comparison of the three different dataset form three climate recoding source showed that the temperatures juggle up and down, but the overall trend is upward meaning the globe is warming.

Upon analysing trend through the average of the three datasets over the period 1975–2009 (during which greenhouse gas forcing was the dominant driver of climate change), the following are (see Figure 3 ).

Showing average temperature from three dataset (source: Verheggen [ 24 ]).

For all three-temperature series, the trend from 1975 to 2009 is about the same (0.17 +/− 0.03 degrees per decade). The error reflects the trend’s 95 percent confidence interval, i.e., if the trend analysis were repeated a hundred times on the actual underlying results, the trend will be within the range of 0.14 to 0.20 degrees per decade 95 times out of 100 (see Figure 3 ) [ 24 ].

The thin black lines (see Figure 3 ) represent the 95% confidence “predictions bands” for the data. Based on the observed variability, 95% of the data are expected to fall within these lines. The observed yearly variability in global temperatures (occasionally exceeding 0.2 degrees) is such that 10 years is too short to discern the underlying long-term trend (0.17 degrees per decade) [ 24 ]. Thus, data from all different sources shows and depicts that the surface temperature of the earth has increased over the decade, with different data source agreeing to the value range of 0.1°C increase per decade [ 24 ]. This indicates the occurrence of global warming.

4.2 Sea level rise

Another predictor of global warming and climate change comes from a completely different series of findings (the measurements of water level changes). The amount of water in the oceans is rising as a result of thermal expansion of water within the oceans and, as well as due to, melting of glaciers and polar ice as the earth warms. Regular water level observations are taken at various sites, equivalent to temperature measurements; daily water level variations, mostly due to tides and storms, are averaged to obtain mean sea level for a given period of time. Figure 4 depicts the average annual change in sea level between 1880 and 2008. Over the last century, the average water level has risen by around 18 cm [ 7 ]. Between 1880 and 1990, it increased by an estimated 2 mm per year on average (left chart in Figure 4 ) and is now growing at a rate of about 3.4 mm per year (right chart in Figure 4 ). Similar to global temperature changes, sea level changes, aren’t constant, so the detailed changes aren’t always in line with surface temperature measurements. The thermal expansion of the water column occurs later than the associated change in surface temperature, with ocean currents influencing the timing. Global temperature changes, as well as changes in sea level, are not constant, and the details of these changes are not always in line with surface temperature measurements. The water column’s thermal expansion occurs later than the related change in surface temperature; the differences are affected by ocean currents ( Figure 5 ) [ 7 ].

Mean annual sea level rise associated with the thermal expansion of sea water due to warming and widespread melting of ice sheets (source: Aizebeokhai [ 7 ]).

Trend of increase in sea level (satellite data) (source: NASA-global climate change [ 25 ]).

Furthermore, the latest data from the NASA-Global Climate Change, shows that the trend of sea level is upward and increasing. The increase rate of 3.3 mm per year is recorded [ 25 ]. Thus, the data from the existing sources both form past and the recent, indicates the rise in the sea level, agreeing to rise value range of 2 to 3.4 mm per year. Which hints to the occurrence of the global warming and retreat of the ice sheets and glaciers.

4.3 Borehole temperature profile

The thermal history of the earth’s subsurface offers a third evidence of global warming and global climate change. The subsurface stores temperature records over time that are related to the prevailing environment at the time. Responsive thermometers are used to calculate temperature profiles with depth in boreholes, caves, and deep mines. Temperature anomalies due to geological features, upward flow of warmth from the earth’s interior, heat produced by crustal rocks, and variations in groundwater movement are generally adjusted for. Surface temperature oscillations propagate downward with depth, with shorter duration fluctuations attenuating more than longer period fluctuations. As a result, only long-term fluctuations in temperature penetrate great depths, with seasonal changes penetrating around 15 m until the signals fade. Century-long variations, in contrast to seasonal variations, can be observed to depths of about 150 m, and millennial cycles can be observed to depths of 500 m or more. These depths are easily attained by low-cost drilling. The subsurface serves as a selective filter, eliminating short-term temperature fluctuations and maintaining excellent records of global warming and, as a result, climate change ( Figure 6 ) [ 7 , 26 ].

Borehole temperature profile from sites in North America showing warmer temperatures within near-surface depths of 100–150 meters (source: Aizebeokhai [ 7 ]).

The temperature profiles suggest substantial warming in the last century from 0.6°C in southeast Utah to more than 2.0°C in Alaska. Curves are arbitrarily offset for display purpose (see Figure 6 ). The temperature profiles of boreholes spread across a length of about 500 km of northern Alaska shows anomalous warming of 2 to 5°C in the upper 100 to 150 m of the permafrost and rocks [ 7 ]. Similarly, borehole temperature profiles in eastern Canada shows a less rapid warming of about 1.0°C. A warming of about 0.5 and 1.0°C were observed in Nebraska sites and Utah sites, respectively (see Figure 6 ). These results indicate that geothermal data mimicked the geographic variations of warming observed in weather station data. Baseline temperatures from previous centuries are often inferred from geothermal evidence, enabling researchers to date the start of the industrial revolution to this century and thus determine the effect of industrialization on global warming and climate change [ 7 ]. As a result of the data from the borehole temperature profile from various places, the temperature rises in the range of 0.5 to 5°C at 100 to 150 m. This hints to the likelihood of global warming.

4.4 Sea surface temperature

The thermometer measurements made on water samples taken by merchant and navy ships as they sailed the world’s oceans date back to about 1850, and constitute the instrumental record of natural processes within the oceans. The data is best for parts of the oceans along major trading routes, and it’s understandable that they are scarcer further back in time. These readings, like those from land-based meteorological observation posts, must be gridded to provide a global average sea surface temperature. Since the oceans cover about 75% of the earth’s surface, the sea surface temperature record is close to global temperature records, as one would expect ( Figure 7 ).

Comparison of Sea surface temperature and land surface temperature (source: Brawlower and Bice [ 26 ]).

The two records are identical, but the SST (sea surface temperature) varies across a narrower spectrum than the land surface temperature, and the land temperature are subjected to dramatic swings. This disparity is primarily due to the oceans’ higher heat potential than the air (it takes a long time to heat and cool the oceans).

The measurement and data collected from hundreds of buoys stationed across the ocean at the depth range of about 2000 m collected over the years as early as 1955, showed that not just the surface of the oceans but the whole upper half of the ocean is gradually warming. Over the past 50 years average of 0.1 to 0.2°C was recorded. So, while the whole ocean has absorbed a huge amount of heat, its overall temperature has changed little. Nevertheless, the very surface of the ocean has warmed almost as much as the rest of earth’s surface [ 26 ]. Thus, form the past data and the present data it shows that the oceans are warming up slowly which almost resonates with the increase in land surface temperature, indicating the occurrence of global warming.

Analysis of four dimension (indictors) indicates the happening of global warming. The connection between the global warming and climate change is well documented. For instance, according to IPCC [ 27 ] “Changes in many extreme weather and climate events have been observed since about 1950. Some of these changes have been linked to human influences, including a decrease in cold temperature extremes, an increase in warm temperature extremes, an increase in extreme high sea levels and an increase in the number of heavy precipitation events in a number of regions” (p.7). Moreover, IPCC [ 15 ] predicts differences in mean temperature in most land and ocean regions, hot extremes in most inhabited regions, heavy precipitation in several regions, and the probability of drought and precipitation deficits in some regions, caused by the phenomenon of global warming.

5. Conclusion

Earth is facing the global warming. Scientifically, there is no longer any doubt that the surface temperature of land, sea surface temperature, and sea levels are increasing. The logical reason of this increasing trend is far beyond the occurrence of natural cause. Despite the different propaganda between the global warming and climate change denier and the advocates, it is of paramount importance to heed for the interest of the humanity and survival of humankind. There is still considerable confusion regarding the exact timing and scale of global warming, as well as the impacts that would result from it, although this is also due as much to human responses to the issue as it is to scientific uncertainties. Growing temperatures, changes in rainfall levels and seasonality; increased frequency of severe weather events such as droughts, floods, and hurricanes; sea level rise; melting of polar ice and glaciers are only a few of the consequences that can be expected. Desertification, loss of tropical forests and coral reefs, decreases in agricultural production, extinction of species, water scarcity, increasing natural disaster losses, and the spread of tropical diseases are likely to be among the ecological and human effects of these changes. Whether the severity of these impacts results in only a slight deterioration in environmental quality and social well-being, or a truly catastrophic collapse that leads to famine, mass displacement, and resource wars, will be determined by how we behave in the coming decades, as well as the probability of an unanticipated response within the climate system to rising temperatures and greenhouse gas emissions.

The polarity of the views with regard to the urgency is demystified by a study in China [ 28 ]. The authors reported that on the global scale the average public concern about the GWCC among Chinese citizens are relatively low, further analysis revealed that youth and women with greater post-materialist values had more concern about GWCC than that of their counterparts. Likewise, citizens from provinces with higher economic dependency on carbon-intensive industries were found to have less concern about GWCC than people from provinces with lower carbon dependency. Their study revealed the underlining motive of the GWCC deniers and skeptics. Perhaps, the polarity in views between two school of thought has much to do with national or regional benefit than the actual truth.

Skeptics of global warming are not very convinced of the fact that the change in the climate and the extreme climate, periodically experienced, are the result of the effect of global warming, enraged by the production of greenhouse gases like carbon dioxide. However, they are also equally not able to disregard the voluminous literature which suggests that the climate change is primarily linked with the global warming. Understanding the climate change in totality is sophisticated, owing to the number of players involved in it, which aren’t yet fully understood by the experts themselves.

However, the existing literature suggests based on the evidences of increase in the surface temperature, sea surface temperature, borehole temperature, and sea level , that it would be wise on the part of humanity to act to minimize the global warming – to which burning of fossil fuels is main culprit. Cause of global warming is well documented and known by majority of the people, what is apparently failing, is in the action to minimize the production of global warming gases and excessive natural resources consumption. Humans are most intelligent species, yet most skeptical and cynical creature who ever walked the earth, choice are-to scum to habit of cynicism, or accept the fact for real and act to maintain the balance in nature for the sake of humanity.

• 1. Agarwal SK. Global warming and climate change. New Delhi: APH Publishing Corporation; 2011.
• 2. Shrivastava A. Global Warming. New Delhi: APH Publishing Corporation; 2009.
• 3. Krauss LM. The physics of climate change. London: Head of Zeus Ltd; 2021.
• 4. Budzianowski W. Time delay of global warming. International Journal of Global Warming. 2011;3(3):289.
• 5. Johnsson F, Kjärstad J, Rootzén J. The threat to climate change mitigation posed by the abundance of fossil fuels. Climate Policy. 2018;19(2):258-274.
• 6. Henderson RM, Reinert SA, Dekhtyar P, Migdal A. Climate Change In 2017: Implications for Business [Internet]. Hbs.edu. 2017 [cited 20 March 2019]. Available from: https://www.hbs.edu/environment/Documents/Climate_Change_2017.pdf
• 7. Aizebeokhai AP. Global Warming and Climate Change: Realities, Uncertainties and Measures. International Journal of Physical Science. 2009;4(13):868-879.
• 8. Jackson R. The Effects of Climate Change [Internet]. Climate Change: Vital Signs of the Planet. 2019 [cited 10 March 2020]. Available from: https://climate.nasa.gov/effects/
• 9. Benjamin D, Por H, Budescu D. Climate Change Versus Global Warming: Who Is Susceptible to the Framing of Climate Change? Environment and Behaviour. 2016;49(7):745-770.
• 10. Agarwal A, Narain S. Global Warming: in an Unequal World [Internet]. Indiaenvironmentportal.org.in . 2003 [cited 17 February 2020]. Available from: http://www.indiaenvironmentportal.org.in/files/GlobalWarming%20Book.pdf
• 11. Climate change: Evidence, Impacts, and Choices. [Internet]. Ipclimatechange.trg-learning.com . 2013 [cited 4 March 2020]. Available from: http://ipclimatechange.trg-learning.com/wp-content/uploads/2013/11/NAS-Climate-Change-Evidence-Impacts-and-Choices.pdf
• 12. Florides L. A., Christodoulides, P. and Messaritis, V., 2010. Global warming and CO2 Vs Sun. In: Harris, S. A. (Ed.). Global Warming, Chapter 3: 23-62. Croatia, IntechOpen. 249p. Doi: 10.5772/10283.
• 13. Herring D. Couldn't the Sun be the cause of global warming? | NOAA Climate.gov [Internet]. Climate.gov . 2020 [cited 12 March 2021]. Available from: https://www.climate.gov/news-features/climate-qa/couldnt-sun-be-cause-global-warming
• 14. Suonan J, Classen AT, Sanders NJ, He J. Plant phenological sensitivity to climate change on the Tibetan Plateau and relative to other areas of the world. Ecosphere. 2019;10(1).
• 15. IPCC Special report “Global Warming of 1.5 degree Celsius- summary for teachers. [Internet]. Ipcc.ch. 2018 [cited 13 March 2020]. Available from: https://www.ipcc.ch/site/assets/uploads/sites/2/2018/12/ST1.5_OCE_LR.pdf
• 16. Lineman M, Do Y, Kim JY, Joo GJ. Talking about Climate Change and Global Warming [Internet]. 2015 [cited 16 March 2020]. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138996
• 17. Weathers MR. Newspaper Coverage of Global Warming and Climate Change (GWCC) as a Public Health Issue. Applied Environmental Education & Communication. 2013;12(1):19-28.
• 18. Shapiro MA, Park HW. More than entertainment: YouTube and public responses to the science of global warming and climate change - Matthew A. Shapiro, Han Woo Park, 2015 [Internet]. SAGE Journals. 2014 [cited 11 March 2020]. Available from: https://journals.sagepub.com/doi/abs/10.1177/0539018414554730?journalCode=ssic
• 19. Member Organizations [Internet]. US Climate Action Network. 2019 [cited 12 March 2020]. Available from: https://www.usclimatenetwork.org/member-organizations
• 20. 100 Top Climate Change Sites [Internet]. World.org . 2013 [cited 23 March 2020]. Available from: http://www.world.org/weo/climate
• 21. Liu X, Vedlitz A, Stoutenborough J, Robinson S. Scientists’ views and positions on global warming and climate change: A content analysis of congressional testimonies [Internet]. 2015 [cited 17 March 2020]. Available from: https://link.springer.com/article/10.1007%2Fs10584-015-1390-6
• 22. Allen DE, McAleer M. Fake news and indifference to scientific fact: President Trump’s confused tweets on global warming, climate change and weather [Internet]. Eprints.ucm.es . 2018 [cited 12 January 2020]. Available from: http://eprints.ucm.es/47902/1/1817.pdf
• 23. Global Climate Report - Annual 2018 | State of the Climate | National Centers for Environmental Information (NCEI) [Internet]. Ncdc.noaa.gov . 2018 [cited 17 March 2020]. Available from: https://www.ncdc.noaa.gov/sotc/global/201813
• 24. Verheggen B. Global Average temperature increases. GISS, HadCRU and NCDC compared. [Internet]. My view on climate change. 2010 [cited 19 March 2020]. Available from: https://ourchangingclimate.wordpress.com/tag/ncdc/
• 25. Sea Level | NASA Global Climate Change [Internet]. Climate Change: Vital Signs of the Planet. 2020 [cited 17 February 2020]. Available from: https://climate.nasa.gov/vital-signs/sea-level/
• 26. Brawlower T, Bice D. Temperature: Ocean Warming | EARTH 103: Earth in the Future [Internet]. E- education.psu.edu . 2019 [cited 10 February 2020]. Available from: https://www.e-education.psu.edu/earth103/node/753
• 27. Climate Change 2014 Synthesis Report Summary for Policymaker [Internet]. Ipcc.ch. 2014 [cited 1 March 2020]. Available from: https://www.ipcc.ch/site/assets/uploads/2018/02/AR5_SYR_FINAL_SPM.pdf
• 28. Liu X, Hao F, Portney K, Liu Y. Examining Public Concern about Global Warming and Climate Change in China. The China Quarterly. 2019; 242:460-486.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Continue reading from the same book

Edited by Stuart Harris

Published: 09 March 2022

By Stuart A. Harris

870 downloads

By Amor Escoz-Roldán, Pablo Ángel Meira-Cartea and Jo...

333 downloads

By Pawan Thapa

1684 downloads

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Talking about Climate Change and Global Warming

Contributed equally to this work with: Maurice Lineman, Yuno Do

Affiliation College of Natural Sciences, Department of Biological Sciences, Pusan National University, Busan, South Korea

* E-mail: [email protected]

• Maurice Lineman, 
• Yuno Do, 
• Ji Yoon Kim, 
• Gea-Jae Joo

• Published: September 29, 2015
• https://doi.org/10.1371/journal.pone.0138996
• Reader Comments

The increasing prevalence of social networks provides researchers greater opportunities to evaluate and assess changes in public opinion and public sentiment towards issues of social consequence. Using trend and sentiment analysis is one method whereby researchers can identify changes in public perception that can be used to enhance the development of a social consciousness towards a specific public interest. The following study assessed Relative search volume (RSV) patterns for global warming (GW) and Climate change (CC) to determine public knowledge and awareness of these terms. In conjunction with this, the researchers looked at the sentiment connected to these terms in social media networks. It was found that there was a relationship between the awareness of the information and the amount of publicity generated around the terminology. Furthermore, the primary driver for the increase in awareness was an increase in publicity in either a positive or a negative light. Sentiment analysis further confirmed that the primary emotive connections to the words were derived from the original context in which the word was framed. Thus having awareness or knowledge of a topic is strongly related to its public exposure in the media, and the emotional context of this relationship is dependent on the context in which the relationship was originally established. This has value in fields like conservation, law enforcement, or other fields where the practice can and often does have two very strong emotive responses based on the context of the problems being examined.

Citation: Lineman M, Do Y, Kim JY, Joo G-J (2015) Talking about Climate Change and Global Warming. PLoS ONE 10(9): e0138996. https://doi.org/10.1371/journal.pone.0138996

Editor: Hayley J. Fowler, Newcastle University, UNITED KINGDOM

Received: August 18, 2014; Accepted: September 8, 2015; Published: September 29, 2015

Copyright: © 2015 Lineman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper.

Funding: This study was financially supported by the 2015 Post-Doc Development Program of Pusan National University.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Identifying trends in the population, used to be a long and drawn out process utilizing surveys and polls and then collating the data to determine what is currently most popular with the population [ 1 , 2 ]. This is true for everything that was of merit to the political organizations present, regarding any issue of political or public interest.

Recently, the use of the two terms ‘Climate Change’ and ‘Global Warming’ have become very visible to the public and their understanding of what is happening with respect to the climate [ 3 ]. The public response to all of the news and publicity about climate has been a search for understanding and comprehension, leading to support or disbelief. The two terms while having similarity in meaning are used in slightly different semantic contexts. The press in order to expand their news readership/viewer lists has chosen to use this ambiguity to their favor in providing news to the public [ 4 ]. Within the news releases, the expression ‘due to climate change’ has been used to explain phenomological causality.

These two terms “global warming–(GW)” and “climate change–(CC)” both play a role in how the public at large views the natural world and the changes occurring in it. They are used interactively by the news agencies, without a thought towards their actual meaning [ 3 , 4 ]. Therefore, the public in trying to identify changes in the news and their understanding of those changes looks for the meaning of those terms online. The extent of their knowledge can be examined by assessing the use of the terms in online search queries. Information searches using the internet are increasing, and therefore can indicate public or individual interest.

Internet search queries can be tracked using a variety of analytic engines that are independent of, or embedded into, the respective search engines (google trend, naver analytics) and are used to determine the popularity of a topic in terms of internet searches [ 5 ]. The trend engines will look for selected keywords from searches, keywords chosen for their relevance to the field or the query being performed.

The process of using social media to obtain information on public opinion is a practice that has been utilized with increasing frequency in modern research for subjects ranging from politics [ 6 , 7 ] to linguistics [ 8 – 10 ] complex systems [ 11 , 12 ] to environment [ 13 ]. This variety of research belies the flexibility of the approach, the large availability of data availability for mining in order to formulate a response to public opinion regarding the subject being assessed. In modern society understanding how the public responds regarding complex issues of societal importance [ 12 ].

While the two causally connected terms GW and CC are used interchangeably, they describe entirely different physical phenomena [ 14 ]. These two terms therefore can be used to determine how people understand the parallel concepts, especially if they are used as internet search query terms in trend analysis. However, searching the internet falls into two patterns, searches for work or for personal interest, neither of which can be determined from the trend engines. The By following the searches, it is possible to determine the range of public interest in the two terms, based on the respective volumes of the search queries. Previously in order to mine public opinion on a subject, government agencies had to revert to polling and surveys, which while being effective did not cover a very large component of the population [ 15 – 17 ].

Google trend data is one method of measuring popularity of a subject within the population. Individuals searching for a topic use search keywords to obtain the desired information [ 5 , 18 ]. These keywords are topic sensitive, and therefore indicate the level of knowledge regarding the searched topic. The two primary word phrases here “climate change” and “global warming” are unilateral terms that indicate a level of awareness about the issue which is indicative of the individuals interest in that subject [ 5 , 19 , 20 ]. Google trend data relates how often a term is searched, that is the frequency of a search term can be identified from the results of the Google® trend analysis. While frequency is not a direct measure of popularity, it does indicate if a search term is common or uncommon and the value of that term to the public at large. The relationship between frequency and popularity lies in the volume of searches by a large number of individuals over specific time duration. Therefore, by identifying the number of searches during a specific period, it is possible to come to a proximate understanding of how popular or common a term is for the general population [ 21 ]. However, the use of trend data is more appropriately used to identify awareness of an issue rather than its popularity.

This brings us to sentiment analysis. Part of the connection between the search and the populations’ awareness of an issue can be measured using how they refer to the subject in question. This sentiment, is found in different forms of social media, or social networking sites sites i.e. twitter®, Facebook®, linked in® and personal blogs [ 7 , 22 – 24 ]. Thus, the original information, which was found on the internet, becomes influenced by personal attitudes and opinions [ 25 ]and then redistributed throughout the internet, accessible to anyone who has an internet connection and the desire to search. This behavior affects the information that now provides the opportunity to assess public sentiment regarding the prevailing attitudes regarding environmental issues [ 26 , 27 ]. To assess this we used Google® and Twitter® data to understand public concerns related to climate change and global warming. Google trend was used to trace changes in interest between the two phenomena. Tweets (comments made on Twitter®) were analyzed to identify negative or positive emotional responses.

Comparatively, twitter data is more indicative of how people refer to topics of interest [ 28 – 31 ], in a manner that is very linguistically restricted. As well, twitter is used as a platform for verbal expression of emotional responses. Due to the restrictions on tweet size (each tweet can only be 140 characters in length), it is necessary to be more direct in dealing with topics of interest to the tweeter. Therefore, the tweets are linguistically more emotionally charged and can be used to define a level of emotional response by the tweeter.

The choice of target words for the tweets and for the Google trend searches were the specific topic phrases [ 32 , 33 ]. These were chosen because of the descriptive nature of the phrases. Scientific literature is very specific in its use and therefore has very definitive meanings. The appropriation of these words by the population as a method for describing their response to the variation in the environment provides the basis for the choice as target words for the study. The classification of the words as being positive versus negative lies in the direction provided by Frank Lutz. This politicization of a scientific word as a means of directing public awareness, means the prescription of one phrase (climate change) as being more positive than the other (global warming).

Global warming is defined as the long-term trend of increasing average global temperatures; alternatively, climate change is defined as a change in global or regional climate patterns, in particular a change apparent from the mid to late 20 th century onwards and attributed to the increased levels of atmospheric carbon dioxide arising from the use of fossil fuels. Therefore, the search keywords were chosen based on their scientific value and their public visibility. What is important about the choice of these search terms is that due to their scientific use, they describe a distinctly identifiable state. The more specific these words are, the less risk of the algorithm misinterpreting the keyword and thus having the results misinterpreted [ 34 – 36 ].

The purpose of the following study was to identify trends within search parameters for two specific sets of trend queries. The second purpose of the study was to identify how the public responds emotionally to those same queries. Finally, the purpose of the study was to determine if the two had any connections.

Data Collection

Public awareness of the terms climate change and global warming was identified using Google Trends (google.com/trends) and public databases of Google queries [ 37 ]. To specify the exact searches we used the two terms ‘climate change’ and ‘global warming’ as query phrases. Queries were normalized using relative search volume (RSV) to the period with the highest proportion of searches going to the focal terms (i.e. RSV = 100 is the period with the highest proportion for queries within a category and RSV = 50 when 50% of that is the highest search proportion). Two assumptions were necessary for this study. The first is, of the two terms, climate change and global warming, that which draws more search results is considered more interesting to the general population. The second assumption is that changes in keyword search patterns are indicators of the use of different forms of terminology used by the public. To analyze sentiments related to climate change and global warming, tweets containing acronyms for climate change and global warming were collected from Twitter API for the period from October 12 to December 12, 2013. A total of 21,182 and 26,462 tweets referencing the terms climate change and global warming were collected respectively. When duplicated tweets were identified, they were removed from the analysis. The remaining tweets totaled 8,465 (climate change) and 8,263 (global warming) were compiled for the sentiment analysis.

Data Analysis

In Twitter® comments are emotionally loaded, due to their textually shortened nature. Sentiment analysis, which is in effect opinion mining, is how opinions in texts are assessed, along with how they are expressed in terms of positive, neutral or negative content [ 36 ]. Nasukawa and Yi [ 10 ]state that sentiment analysis identifies statements of sentiment and classifies those statements based on their polarity and strength along with their relationship to the topic.

Sentiment analysis was conducted using Semantria® software ( www.semantria.com ), which is available as an MS Excel spreadsheet application plugin. The plugin is broken into parts of speech (POS), the algorithm within the plugin then identifies sentiment-laden phrases and then scores them from -10 to 10 on a logarithmic scale, and finally the scores for each POS are tabulated to identify the final score for each phrase. The tweets are then via statistical inferences tagged with a numerical value from -2 to 2 and given a polarity, which is classified as positive, neutral or negative [ 36 ]. Semantria®, the program utilized for this study, has been used since 2011 to perform sentiment analyses [ 7 , 22 ].

For the analysis, an identity column was added to the dataset to enable analysis of individual tweets with respect to sentiment. A basic sentiment analysis was conducted on the dataset using the Semantria® plugin. The plugin uses a cloud based corpus of words tagged with sentimental connotations to analyze the dataset. Through statistical inference, each tweet is tagged with a sentiment value from -2 to +2 and a polarity of (i) negative, (ii) neutral, or (iii) positive. Positive nature increases with increasing positive sentiment. The nature of the language POS assignation is dependent upon the algorithmic classification parameters defined by the Semantria® program. Determining polarity for each POS is achieved using the relationship between the words as well as the words themselves. By assigning negative values to specific negative phrases, it limits the use of non-specific negation processes in language; however, the program has been trained to assess non-specific linguistic negations in context.

A tweet term frequency dictionary was computed using the N-gram method from the corpus of climate change and global warming [ 38 ]. We used a combination of unigrams and bigrams, which has been reported to be effective [ 39 ]. Before using the N-gram method, typological symbols were removed using the open source code editor (i.e. Notepad) or Microsoft Words’ “Replace” function.

Differences in RSV’s for the terms global warming and climate change for the investigation period were identified using a paired t-test. Pettitt and Mann-Kendall tests were used to identify changes in distribution, averages and the presence of trends within the weekly RSV’s. The Pettitt and MK tests, which assume a stepwise shift in the mean (a break point) and are sensitive to breaks in the middle of a time series, were applied to test for homogeneity in the data [ 40 ]. Temporal trends within the time series were analyzed with Spearman’s non-parametric correlation analysis. A paired t-test and Spearman’s non-parametric correlation analysis were conducted using SPSS software (version 17.0 SPSS In corp. Chicago IL) and Pettitt and MK tests were conducted using XLSTAT (version 7.0).

To determine the accuracy and reliability of the Sentiment analysis, a Pearson’s chi-square analysis was performed. This test identifies the difference ratio for each emotional response group, and then compares them to determine reliance and probability of interactions between the variables, in this case the terms global warming and climate change.

According to Google trend ( Fig 1 ) from 2004–2014, people searched for the term global warming (n = 8,464; mean ± S.D = 25.33 ± 2.05) more frequently than climate change (n = 8,283; mean ± S.D. = 7.97±0.74). Although the Intergovernmental Panel on Climate Change (IPCC) published its Fourth Assessment Report in 2007 and was awarded the Nobel Prize, interest in the term global warming as used in internet searches has decreased significantly since 2010 (K = 51493, t = 2010-May-23, P<0.001). Further the change in RSV also been indicative of the decreased pattern (Kendall’s tau = -0.336, S = -44563, P<0.001). The use of the term “climate change” has risen marginally since 2006 (K = 38681, t = 2006-Oct-08, P<0.001), as indicated by a slight increase (Kendall’s tau = -0.07, S = 9068, P<0.001). These findings show that the difference in usage of the two terms climate change and global warming has recently been reduced.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0138996.g001

The sentiment analysis of tweets ( Fig 2 ) shows that people felt more negative about the term global warming (sentiment index = -0.21±0.34) than climate change (-0.068±0.36). Global warming tweets reflecting negative sentiments via descriptions such as, “bad, fail, crazy, afraid and catastrophe,” represented 52.1% of the total number of tweets. As an example, the tweet, “Supposed to snow here in the a.m.! OMG. So sick of already, but Saturday says 57 WTF!” had the lowest score at -1.8. Another observation was that 40.7% of tweets, including “agree, recommend, rescue, hope, and contribute,” were regarded as neutral. While 7.2% of tweets conveyed positive messages such as, “good, accept, interesting, and truth.” One positive global warming tweet, read, “So if we didn’t have global warming, would all this rain be snow!”. The results from the Pearson’s chi-square analysis showed that the relationship between the variables was significant (Pearson’s chi-square –763.98, d.f. = 2, P<0.001). Negative climate change tweets represented 33.1% of the total while neutral tweets totaled 49.8%, while positive climate change tweets totaled 17.1%.

https://doi.org/10.1371/journal.pone.0138996.g002

Understandably, global warming and climate change are the terms used most frequently to describe each phenomenon, respectively, as revealed by the N-gram analysis ( Table 1 ). When people tweeted about global warming, they repeatedly used associated such as, “ice, snow, Arctic, and sea.” In contrast, tweets referring to climate change commonly used, “report, IPCC, world, science, environment, and scientist.” People seem to think that climate change as a phenomenon is revealed by scientific investigation.

https://doi.org/10.1371/journal.pone.0138996.t001

Internet searches are one way of understanding the popularity of an idea or meme within the public at large. Within that frame of reference, the public looks at these two terms global warming and climate change and their awareness of the roles of the two phenomena [ 41 ]. From 2004 to 2008, the search volumes for the term global warming far exceeded the term climate change. The range for the term global warming in Relative search volumes (RSV) was more than double that of climate change in this period ( Fig 1 ). From 2008 on the RSV’s began to steadily decrease until in 2014 when the RSV’s for the term global warming were nearly identical to those for the term climate change. From 2008 there was an increase in the RSVs for CC until 2010 at which point the RSVs also began to decline for the term climate change. The decline in the term climate change for the most part paralleled that of the term global warming from 2010 on to the present.

While we are seeing the increases and decreases in RSVs for both the terms global warming and climate change, the most notable changes occur when the gap between the terms was the greatest, from 2008 through to 2010. During this period, there was a very large gap found between the RSVs for the terms global warming and climate change; however, searches for the term climate change was increasing while searches for the tem global warming were decreasing. The counter movement of the RSV’s for the two terms shows that there is a trend happening with respect to term recognition. At this point, there was an increase in the use of the CC term while there was a corresponding decrease in the use of the GW term. The change in the use of the term could have been due to changes in the publicity of the respective terms, since at this point, the CC term was being used more visibly in the media, and therefore the CC term was showing up in headlines and the press, resulting in a larger number of searches for the CC term. Correspondingly, the decrease in the use of the GW term is likely due to the changes in how the term was perceived by the public. The public press determines how a term is used, since they are the body that consistently utilizes a term throughout its visible life. The two terms, regardless of how they differ in meaning, are used with purpose in a scientific context, yet the public at large lacks this definition and therefore has no knowledge of the variations in the terms themselves [ 42 ]. Therefore, when searching for a term, the public may very well, choose the search term that they are more comfortable with, resulting in a search bias, since they do not know the scientific use of the term.

The increase in the use of the CC term, could be a direct result of the release of the fourth assessment report for the IPCC in 2007 [ 43 ]. The publicity related to the release of this document, which was preceded by the release of the Al Gore produced documentary “An Inconvenient Truth”, both of which were followed by the selection by the Nobel committee of Al Gore and the IPCC scientists for the Nobel Prize in 2007 [ 43 ]. These three acts individually may not have created the increased media presence of the CC term; however, at the time the three events pushed the CC term and increased its exposure to the public which further drove the public to push for positive environmental change at the political level [ 44 , 45 ]. This could very well have resulted in the increases in RSV’s for the CC term. This point is more likely to depict accurately the situation, since in 2010 the use of the two terms decline at almost the same rate, with nearly the same patterns.

Thus with respect to trend analysis, what is interesting is that RSVs are paralleling the press for specific environmental events that have predetermined value according to the press. The press in increasing the visibility of the term may drive the increases in the RSV’s for that term. Prior to 2007, the press was using the GW term indiscriminately whenever issues affecting the global climate arose; however, after the movie, the report and then the Nobel prize the terminology used by the press switched and the CC term became the word du jour. This increased the visibility of the word to the public, thereby it may be that increasing public awareness of the word, but not necessarily its import, is the source for the increases in RSV’s between 2008 and 2010.

The decline in the RSV’s then is a product of the lack of publicity about the issue. As the terms become more familiar, there would be less necessity to drive the term publicly into the spotlight; however, occasionally events/situations arise that refocus the issue creating a resurgence in the terms even though they have reached their peak visibility between 2008 and 2010.

Since these terms have such an impact on the daily lives of the public via local regional national and global weather it is understandable that they have an emotional component to them [ 46 ]. Every country has its jokes about the weather, where they come up with cliché’s about the weather (i.e. if you don’t like the weather wait 10minutes) that often show their discord and disjunction with natural climatological patterns [ 47 ]. Furthermore, some sectors of society (farmers) have a direct relationship with the climate and their means of living; bad weather is equal to bad harvests, which means less money. To understand how society represents this love hate relationship with the weather, the twitter analysis was performed. Twitter, a data restricted social network system, has a limited character count to relay information about any topic the sender chooses to relate. These tweets can be used to assess the sentiment of the sender towards a certain topic. As stated previously, the sentiment is defined by the language of the tweet within the twitter system. Sentiment analysis showed that the two terms differed greatly. Based on the predefined algorithm for the sentiment analysis, certain language components carried a positive sentiment, while others carried a negative sentiment. Tweets about GW and CC were subdivided based on their positive, neutral and negative connotations within the tweet network. These emotions regardless of their character still play a role in how humans interacts with surroundings including other humans [ 48 , 49 ] As seen in Fig 2 the different terms had similar distributions, although with different ranges in the values. Global warming showed a much smaller positive tweet value than did climate change. Correspondent to this the respective percentage of positive sentiments for CC was more than double that of GW. Comparatively, the neutral percentiles were more similar for each term with a small difference. However, the negative sentiments for the two terms again showed a greater disparity, with negative statements about GW nearly double those of climate change.

These differences show that there is a perceptive difference in how the public relates to the two terms Global Warming and Climate Change [ 50 , 51 ]. Climate change is shown in a more positive light than global warming simply based on the tweets produced by the public. The difference in how people perceive climate change and global warming is possibly due to the press, personal understanding of the terms, or level of education. While this in itself is indefinable, since by nature tweets are linguistically restrictive, the thing to take from it is that there is a measurable difference in how individuals respond to climatological changes that they are experiencing daily. These changes have a describable effect on how the population is responding to the publicity surrounding the two terms to the point where it can be used to manipulate governmental policy [ 52 ].

Sentiment analysis is a tool that can be used to determine how the population feels about a topic; however, the nature of the algorithm makes it hard to effectively determine how this is being assessed. For the current study, the sentiment analysis showed that there was a greater negative association with the term global warming than with the term climate change. This difference, which while being an expression of individual like or dislike at the time the tweet was created, denotes that the two terms were either not understood in their true form, or that individuals may have a greater familiarity with one term over the other, which may be due to a longer exposure to the term (GW) or the negative press associated with the term (GW).

Conclusions

Trend analysis identified that the public is aware of the terminology used to describe climatological variation. The terminology showed changes in use over time with global warming starting as the more well-known term, and then its use decreased over time. At the same time, the more definitive term climate change had less exposure early on; however, with the increase of press exposure, the public became increasingly aware of the term and its more accurate definition. This increase appeared to be correspondent with the increasing publicity around three very powerful press exposure events (a documentary, a scientific report release and a Nobel Prize). The more the term was used the more people came to use it, this included searches on the internet.

Comparatively sentiment analysis showed that the two terms had differential expressions in the population. With climate change being seen in a more positive frame than global warming. The use of sentiment analysis as a tool to evaluate how the population is responding to a feature is an important tool. However, it is a tool that measures, it does not define.

Social network systems and internet searches are effective tools in identifying changes in both public awareness and public perception of an issue. However, in and of itself, these are bell ringers they can be used to determine the importance of an issue, but not the rationale behind the why it is important. This is an important fact to remember when using analytical tools that evaluate social network systems and their use by the public.

Acknowledgments

This study was financially supported by the 2015 Post-Doc. Development Program of Pusan National University

Author Contributions

Conceived and designed the experiments: YD GJJ. Performed the experiments: ML YD. Analyzed the data: ML YD. Contributed reagents/materials/analysis tools: JK YD. Wrote the paper: ML YD GJJ.

• 1. Motteux NMG. Evaluating people-environment relationships: developing appropriate research methodologies for sustainable management and 2003.
• View Article
• Google Scholar
• 7. Abeywardena IS. Public opinion on OER and MOCC: a sentiment analysis of twitter data. 2014.
• 8. Choi Y, Cardie C, editors. Learning with compositional semantics as structural inference for subsentential sentiment analysis. Proceedings of the Conference on Empirical Methods in Natural Language Processing; 2008: Association for Computational Linguistics.
• 9. Wiegand M, Balahur A, Roth B, Klakow D, Montoyo A, editors. A survey on the role of negation in sentiment analysis. Proceedings of the workshop on negation and speculation in natural language processing; 2010: Association for Computational Linguistics.
• 10. Nasukawa T, Yi J, editors. Sentiment analysis: Capturing favorability using natural language processing. Proceedings of the 2nd international conference on Knowledge capture; 2003: ACM.
• 13. De Longueville B, Smith RS, Luraschi G, editors. Omg, from here, i can see the flames!: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. Proceedings of the 2009 international workshop on location based social networks; 2009: ACM.
• PubMed/NCBI
• 23. Wang X, Wei F, Liu X, Zhou M, Zhang M, editors. Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. Proceedings of the 20th ACM international conference on Information and knowledge management; 2011: ACM.
• 27. Zhao D, Rosson MB, editors. How and why people Twitter: the role that micro-blogging plays in informal communication at work. Proceedings of the ACM 2009 international conference on Supporting group work; 2009: ACM.
• 30. Phelan O, McCarthy K, Smyth B, editors. Using twitter to recommend real-time topical news. Proceedings of the third ACM conference on Recommender systems; 2009: ACM.
• 31. Bruns A, Burgess J. # ausvotes: How Twitter covered the 2010 Australian federal election. 2011.
• 32. Pinkerton B, editor Finding what people want: Experiences with the WebCrawler. Proceedings of the Second International World Wide Web Conference; 1994: Chicago.
• 36. Lawrence L. Reliability of Sentiment Mining Tools: A comparison of Semantria and Social Mention. 2014.
• 43. Neverla I, editor The IPCC-reports 1990–2007 in the media. A case-study on the dialectics between journalism and natural sciences. ICA-Conference, Global Communication and Social Change, Montreal: International Communications Association; 2008.
• 44. Giddens A. The politics of climate change. Cambridge, UK. 2009.
• 46. Doherty TJ, Clayton S. The Psychological Impacts of Global Climate Change. 2011.
• 51. Lazarus RS. Emotion and adaptation: Oxford University Press; 1991.

Moving Trend Analysis Methodology for Hydro-meteorology Time Series Dynamic Assessment

• Open access
• Published: 15 May 2024

Cite this article

You have full access to this open access article

• Zekâi Şen 1  

In the last 30 years, there are many publications in the literature due to global warming and climate change impacts exhibiting non-stationary behaviors in hydro-meteorology time series records especially in the forms of increasing or decreasing trends. The conventional trend analyzes cover the entire recording time with a single straight-line trend and slope. These methods do not provide information about up and down partial moving trends evolution at shorter durations along the entire record length. This paper proposes a dynamic methodology for identifying such evolutionary finite duration moving trend method (MTM) identifications and interpretations. The purpose of choosing MTM was to investigate the dynamic partial trend evolution over the recording period so that dry (decreasing trend) and wet (increasing trend) segments could be objectively identified and these trends could assist in water resources management in the study area. The moving trend analysis is like the classical moving average methodology with one important digression that instead of arithmetic averages and their horizontal line representations, a series of finite duration successive increasing and decreasing trends are identified over a given hydro-meteorology time series record. In general, partial moving trends of 10-year, 20-year, 30-year and 40-year occur above or below the overall trend and thus provide practical insight into the dynamic trend pattern with important implications. The moving trend methodology is applied to annual records of Danube River discharges, New Jersey state wise temperatures and precipitation time series from the City of Istanbul.

Avoid common mistakes on your manuscript.

1 Introduction

Global warming due to greenhouse gas (GHG) released into the troposphere causes climate change, which can reflect itself as partial and overall increasing or decreasing trends in any hydro-meteorological time series. It has already been proven by Milly et al. ( 2008 ) that all hydro-meteorological records fall into non-stationary realm with the inclusion of trends. Hydro-meteorological records have systematic components such as trends and variability (Burn and Hag Elnur 2002 ). There are basic methodological contributions that provide probabilistic or statistical trending tests (Mann 1945 ; Kendal 1970 ; Spearman 1904 ; Sen 1968 ; IPCC 2007 ; Şen 2012 ; Alashan 2018 ; Güçlü 2018 ; Ashraf et al. 2021 ). The applications of these methods have increased in an unprecedented way and therefore there are many studies in different disciplines that try to determine the overall (holistic) trend. Hamed ( 2008 ) has proposed different trend detection methodologies in hydro-meteorology records. A review of current trend methodologies is presented by Sonali and Kumar ( 2013 ) and applied these methodologies to temperature records. One of the major problems in the Mann and Kendall (MK) trend identification methodology is the assumption that time series must be serially independent, which cannot be met with hydro-meteorological data and hence pre-whitening (Yue and Wang 2002 ) or over-whitening (Şen 2017 procedures are offered to alleviate this requirement. In different water related disciplines many researchers applied the MK methodology holistically (overall) on a given time series records (Hirsch et al. 1982 ; van Belle and Hughes 1984 ; Hirsch and Slack 1984 ; Cailas et al. 1986 ; Hipel et al. 1988 ; Demaree and Nicolis 1990 ; Yu et al. 1993 ; Gan 1998 ; Taylor and Loftis 1989 ; Lins and Slack 1999 ; Douglas et al. 2000 ; Hamilton et al. 2001 ; Kalra et al. 2008 ; O’Brien et al. 2021 ; Mateus and Pitoto 2022 ).

The averaging of a partially fixed number of sub-time series is the basis of the moving average procedure, which can be weighted or unweighted. The average amount moves over time, repeatedly deleting old data points, leaving the average levels in that order. The unweighted alternative is a simple moving average procedure, where all observations are given the same weight. The weighted moving average, on the other hand, assigns different weights in the mean calculations. The basic idea in weights is that the newest data has more weights for prediction than the old ones, so exponentially increasing weights can be added towards the newest records. Such assignments may dependent on expert opinions, for example Yeh et al. ( 2003 ) proposed exponential weighted moving average control charts to detect small changes in process variability. Lee and Apley ( 2011 ) proposed another graph for the design of the residual-based weighted moving average for autoregressive moving average (ARMA) models (Box and Jenkins 1976 ).

The unweighted moving average method has advantages such as easy understanding and computation, small data requirement from the past, and removal of outliers after certain periods. On the other hand, disadvantages include that the estimate depends on the average length, requires all data from the past and gives the same weight or importance to all data used in the calculations.

The moving trend method (MTM) in this paper is based on the application of the innovative trend analysis (ITA) procedure proposed by Şen ( 2012 ) and used by several authors for hydro-meteorological datasets. Recently, ITA methodology is used along with traditional trend identification methodologies by many authors (Achite et al. 2021 ; Esit et al. 2021 ; Ullah et al. 2022 ; Hirca et al. 2022 ; Fanta et al. 2022 ; Pastagia and Mehta 2022 ; Xie et al. 2022 ; Abbas et al. 2023 ; Birpınar et al. 2023 ).

The main purpose of this paper is to explore the possibility of determining moving trend in a series of sub-times (10-year, 20-year, 30-year and 40-year) within hydro-meteorological time series. This methodology is called the moving trend method (MTM). Alongside the overall (holistic) trend across all records, a set of sub-time trends are presented to recognize the dynamic development behavior of trends. In this way, one can appreciate how trends have developed throughout the practically entire recording with scientific commentary. Sub-times are considered as repetition periods (recurrence intervals) and accordingly probabilities are associated with a set of partial trend slopes. MTM application is made for annual hydro-meteorological records from Danube River discharges, New Jersey state-wise temperatures and Istanbul City precipitation values.

Following a brief literature review in the Section 1 , this article consists subsequent 5 sections covering theoretical and practical aspects of MTM analysis methodology. The importance of the traditional statistical moving average procedure is explained in Section 2 . In connection with this, Section 3 explains the theoretical characteristics of the proposed MTM approach. Section 4 includes three different hydrometeorology recording applications with figures, tables, implications, and inferences. Section 5 presents the pros and cons of the proposed MTM methodology. Finally, Section 6 provides conclusive information about the limitations of the MTM approach and its useful aspects for further research.

2 Moving Average Significance

A series of arithmetic averages with a fixed number of data in a time series is called the moving average procedure, which moves through the series adjacent to the consecutive average. Prior to formal pattern identification in any hydro-meteorology time series, visual inspection reveals several short-term embedded features such as partial trends. Generally, within short periods, a time series has a few statistical and probabilistic components, including possible cascade of spikes (jumps), trend, periodicity, short- and long-term serial dependencies, and random variations. Numerous articles focus on holistic trend determination methodology such as the Mann-Kendal trend test (Mann 1955 ; Sen 1968 ; Kendall 1973 ), over a long-term systematic variation that is a linear function and shows the general trend.

The moving average method provides a series of consecutive averages over a period of m for which the number of data is smaller than whole data length, n. The moving average over m period represents the time series change as a horizontal line. There are several problems with the moving average procedure, such as determining the extension of the moving average to eliminate the original fluctuations in the time series. The resulting moving average time series cannot be used for future trend prediction, which is the main target for objective trend determination. Another problem is with horizontal lines, which can be replaced by straight lines that linearly increase or decrease linearly as partial and local trends; this is the main purpose of this paper recommendation as moving trend methodology (MTM).

3 Moving Trend Method (MTM)

It is well-known that global warming refers to the increasing temperature trend prevailing throughout the Earth; the effects of climate in any hydro-meteorology record series are not global, but local and regional in the forms of either increasing or decreasing trends. Like spatial globalism and regionalism, temporally any given hydro-meteorological time series can have trends over the entire recording period or over desired sub-periods in a series. As mentioned earlier in the Section 1 , MTM is based on innovative trend analysis modified for sub-period trend identification along the following points.

Decide on the duration of a series of periods to determine the trend series record. It is adapted in this paper for 10-year, 20-year, 30-year and 40-year. The last two periods are in line with the World Meteorological Organization’s recommendation for climate change trend identification studies (WHO 2017 ). Meanwhile, the holistic trend for the entire record is also considered for comparison purposes,

Starting from the first record, the trend component of each period is determined according to the innovative trend analysis approach. For this purpose, the trend slope, S T , for each period is calculated according to the following expression.

where m is the duration of period, \({\overline{\text{X}}}_{\text{S}\text{H}}\) ( \({\overline{\text{X}}}_{\text{F}\text{H}}\) ) is the second (first) half of the original time series data during the period,

To plot the linear trend over the corresponding period, the crossing point is taken as the period mid-time, t mp , (abscissa), and the arithmetic mean value of the as \({\overline{\text{X}}}_{\text{P}}\) (ordinate) Thus, moving trend sequences are obtained within the hydro-meteorological data.

4 Application

The Danube River is the second longest river in Europe after the Volga in Russia, with a length of approximately 2,850 km (1,770 mi) and is a river in many parts of Europe, including Austria, Slovakia, Hungary, Croatia, Serbia, Romania, Bulgaria, Moldova and Ukraine. It has a record starting from 1840. Another very long temperature record exists from the USA state of New Jersey, starting from 1895. Rainfall records on the European side of the city of Istanbul in Turkey start from 1936. The application of the MTM is performed for different long-term annual hydro-meteorology variables, including records for Danube River discharges (1840–2010), New Jersey state-wise temperature (1895–2010) and Istanbul precipitation (1939–2020) with their statistical parameters in Table 1 .

For the Danube River annual discharge records, there are 4 graphs in Fig. 1 for the 10-year, 20-year, 30-year and 40-year sequences of moving trend components. The same graphs also show the overall holistic trend component of the records for comparison. The moving trend component of each period fluctuates around the holistic trend, which is an overall approach and does not give detailed information as the serial trend evolution across the whole records in shorter periods than the number of records. The following points are important in the interpretation of comparative trend study.

In all Graphs, Holistic Trend has a Slightly Increasing Slope of 79 m 3 /year,

In Fig. 1 a, there are two extremely wet periods in the past between 1842 and 1852 and 1872–1882, respectively in the 10-year consecutive periods. Since then all 10-year moving trends are in decreasing form and in continuous reduction and the most decreasing 20-year trend appeared between 1982 and 2002. This irrespective or increasing or decreasing tendencies. There are several increasing trends around the overall (holistic) trend an especially in the most recent 10-year (1992–2002) it is also very close to general tendency,

As for the 20-year moving trends, there was only one increasing trend in the past between 1842 and 1862 (see Fig. 1 b). Since then all consecutive 20-year moving trends are in decreasing form and their collective appearance is also in decrease until the end of record where the most decreasing moving trend slope is during 1982–2002 period,

Figure 1 c includes moving trends for 30-year periods, which is the World Meteorological Organization’s proposed standard duration for climate change studies (WHO 2017 ). The only increasing 30-year moving trend is between 1972 and 2002 period, all other moving trends have decreasing tendency around the holistic trend,

Danube River annual discharge trends for a 10-year, b 20-year, c 30-year, d 40-year

The common conclusion from all graphs is that the most dynamic water formations occurred around 1900. Each of these graphs has distinct moving average trends bouncing from increasing (wet) to the decreasing (dry). It is not possible to obtain interconnected moving trend series.

Each graph in Fig. 1 reveals the relative position of different duration MTM components relative to the holistic trend. It is obvious that there are increasing and decreasing finite-period trends above and below, which provide detailed dynamic changes to make the future finite-trend forecast of water resources planning, operation and management. The following points are important for this type of works.

In Fig. 1 a, the 10-year periods represent 10 increasing moving trends, of which 3 are above the overall trend, 5 are below. The number of decreasing trends is 5 and only 2 of them are above the overall trend. This information implies that increasing trends are more effective than decreasing alternatives throughout the entire recording period. Especially after 1980, increasing trends are observed. In the light of these explanations, it can be predicted that the 10-year moving averages of the annual discharges of the Danube River are bound to increase.

Comparing the 10-year moving trends with the 20-year trends in Fig. 1 b shows that the latter trends are closer to the general holistic trend, and most of the trends in this period tend to decrease with increasing tendency throughout the entire recording period,

The 30-year moving trends in Fig. 1 c are closer to the overall holistic trend than the 20-year duration trends. Although there have been two decreasing trends in the past 60 years, there is an increasing trend in the last 30 years,

In Fig. 1 d, the 40-year moving trends are almost like the 30-year trends, there are two trends that have increasing appearance in the last 80 years.

Figure 2 shows all moving trends from different periods collectively to make comparison of the most severe situations dynamically throughout the record length. Among all these periods 30-year moving trends attract attention because of the WHO ( 2017 ) report recommendation. The most variation domain of moving trends is attached with 10-year period. Although short period but has the biggest increasing and decreasing trend variation domain from 4400 m 3 /sec to 6400 m 3 /sec.

Danube River discharge records moving trend collection

As for the New Jersey annual temperature record MTM, the overall trend and moving trends are shown in Fig. 3 . There is a holistic trend that increases with a slope of 0.0175 o F/year. It is worth paying attention to the following points.

In Figs. 3 a and 10-year consecutive moving trends show continuously increasing and decreasing tendencies that take place around the holistic trend. The general tendency of moving trends follows overall temperature increase with ups and downs,

Figure 3 b shows the 20-year consecutive moving trends that follow the overall (holistic) increasing tendency. Especially, between 1970 and 2010 20-year trends are in the form of decreasing trends, but their positions show temperature increases,

Three 30-year periods moving trends are shown in Fig. 3 c, which have standard lengths of records for climate change impact interpretations as recommended by World Meteorological Organization. Increasing and decreasing moving trends are in good accord with the holistic trend. The middle point of each moving trend shown increase in the position of increasing and decreasing trends,

There are two 40-year moving trends in Fig. 3 d. Just opposite the holistic trend, 1930–1970 trend has ignorable slope, whereas the recent moving trend during 1970–2010 period has decreasing trend but its middle point position is higher than the previous one. This point indicates that whether increasing or decreasing 40-year trends they have increasing middle point location by time,

New Jersey annual temperature trends, a 10-year, b 20-year, c 30-year, d 40-year

In Fig. 4 all period moving average trends are shown collectively, which shows that whatever is the moving trend period there is in all increasing trend position irrespective of increasing or decreasing tendency. Although there appear decreasing moving trends in the most recent periods compared to the same set moving trend position is in increase.

New Jersey State wise temperature records moving trend collection

Annual precipitation records of Istanbul City from 1940 to 2012 are shown in Fig. 5 with moving trend components over each periods. The overall trend tends to increase with slope 0.0507 cm/year, which means a very small increase in precipitation record moving means.

For 10-year moving trends below the overall trend indicates possible water shortages that have occurred at various time durations in the past and it is clear from Fig. 5 a that decadal shortages are about 86% (see Fig. 5 a). The only decade with increasing precipitation seems from 1962 to 1972,

20-year moving trends during 1972–1992 and 1992–2002 periods are the same with the holistic trend that shows quite stationary rainfall regime on the average in the last 40 years of the record,

As for the 30-year period during 1982–2002 there appears a slight decrease around the holistic trend. The middle point position of this moving trend lies on the holistic trend line. This means that during the first (second) half of this period moving trend decrease is above (below) the holistic trend (see Fig. 5 c),

Figure 5 d is for 40-year period decreasing moving trend component, which has almost the same slope as the holistic trend again with half slightly over and the next half below the holistic trend.

Istanbul annual precipitation trends, a 10-year, b 20-year, c 30-year, d 40-year

Moving trends of different periods are shown collectively in Fig. 6 , where only 10-year period moving trends expose quite sharp increasing and decreasing trends. Especially, the last 30-year trend indicates a decreasing trend of which the middle point lies on the holistic trend with early (late) half is above (below) the holistic trend.

Istanbul/Florya precipitation records moving trend collection

A set of slope values for moving trends are given in Table 2 for each data set including different periods 10-year, 20-year, 30-year and 40-year, respectively. A wide variety of moving trend slope variations can be noticed for each period. The mean and the standard deviations of each period are also given in the same table.

Of course, the average values are based on positive (increasing) and negative (decreasing) trend values and thus generally show the general trend towards overall positive or negative moving trend values. A common point as for the 30-year period is concerned that all the hydro-meteorology records have decreasing trend slopes. For example, Istanbul moving trend average values are negative for all periods, indicating that it is possible to expect precipitation reduction in future time periods.

5 Discussion

There are different trend identification methodologies in the open literature and some of them require restrictive assumptions that are not very satisfactory for natural hydro-meteorological recording time series, for which the assumption of serial independence is most important. Some are interested in non-parametric procedures in which the natural order of hydro-meteorology time series is replaced, for example, by orders as ranks (Spearman 1904 ). Most of these trending tests require long time series as their results are in biased for short samples. In this article, innovative trend analysis (ITA) methodology is applied as a different approach to overall trend determination, because moving trend components are valid even for time series samples that are significantly shorter than the World Meteorology Organization recommendation of 30 years (WHO 2017 ). Overwhelmingly, theoretical trend identification or practical procedures predominantly consider the entire sample length of a given time series. Many are biased because they do not meet key constraining assumptions such as serial independence, normal (Gaussian) probability distribution function (PDF) or long sample lengths.

Comparing the results of MTM procedure for different sub-periods of a hydro-meteorology time series provides the dynamic behavior of finite length trend development over the recording period. Generally, moving trends fluctuate around the overall trend line. Comparing up and down moving trend numbers to understand the frequency of short-term percentages result in higher (lower) values than the overall trend. Instead of taking future action according to the overall full recording time trend, making dynamic comments considering the sequence of MTM trends, and accordingly, better trending probability in the future.

The innovative aspect of this paper is the dynamic description of the desired partial-time trend analysis instead of holistic classical trend trends that do not show cycles of increasing and decreasing trend evolution over the entire length of the record. Once possible finite-term trend slopes have been identified, it is possible to make same-term future trend slope forecasts for improved water resources management planning and operation.

The MTM methodology provides information about trend slopes, so that during which moving trend duration the maximum and minimum increasing and decreasing slopes occur and, if necessary, average slopes for these two types of trends can be determined.

6 Conclusions

As a result of the greenhouse gas (GHG) emissions into the troposphere, global warming has led to climate change at different increasing or decreasing rates in different parts of the world. In climate change impact studies, trend analysis procedures are important to decide whether there is an increasing or decreasing overall trend for a hydro-meteorological variable at a location, unlike general circulation (climate) models (GCMs). This paper provides moving trend analysis for several sub-periods in a given time series records and their comparison with classical holistic trend procedures in the literature. For this purpose, a dynamic trend evaluation study is carried out by investigating subsequent special moving trend for 10-year, 20-year, 30-year and 40-year periods. The application of the proposed moving trend methodology is given for annual records of Danube River discharges, New Jersey state-wise temperatures and Istanbul City precipitation. Moving trending also provides percentages of decreasing (increasing) trend activity over the entire time series. It is recommended that the application of moving trend analysis sheds light on more dynamic structural behavior of hydro-meteorological records. The only limitation for holistic trend analysis is at least 30 years of data, which is recommended by the World Meteorological Organization and is also valid for the moving trend analysis methodology. There has not been yet any other research using MTM analysis, but in future water resources management studies such part-time trends tend to provide important information about dry and wet trend tendencies and their likely duration.

Data Availability

Data can be provided upon request from the corresponding author.

Abbas M, Arshad M, Shahid MA (2023) Zoning of groundwater level using innovative trend analysis: case study at Rechna Doab, Pakistan. Water Resour Irrig Manage - WRIM 12:64–80. https://doi.org/10.19149/wrim.v12i1-3.3155

Article   Google Scholar  

Achite M, Ceribasi G, Ceyhunlu AI et al (2021) The innovative polygon trend analysis (IPTA) as a simple qualitative method to detect changes in Environment—Example detecting trends of the total monthly precipitation in semiarid area. Sustainability 13:12674. https://doi.org/10.3390/su132212674

Alashan S (2018) An improved version of innovative trend analyses. Arab J Geosci 11(3):50

Ashraf MS, Ahmad I, Khan NM, Zhang F, Bilal A, Guo J (2021) Streamflow variations in monthly, seasonal, annual and extreme values using Mann-Kendall, Spearmen’s rho and innovative trend analysis. Water Resour Manag 35:243–261

Birpınar ME, Kızılöz B, Şişman E (2023) Classic trend analysis methods’ paradoxical results and innovative trend analysis methodology with percentile ranges. Theor Appl Climatol. https://doi.org/10.1007/s00704-023-04449-6

Box GEP, Jenkins GM (1976) Time series analysis, forecasting and control. Holden-Day, San Francisco

Google Scholar  

Burn DH, Hag Elnur MA (2002) Detection of hydrologic trends and variability. J Hydrol 255:107–122

Cailas MD, Cavadias G, Gehr R (1986) Application of a non-parametric approach for monitoring and detecting trends in water quality data of the St. Lawrence River. Water Pollut Res J Can 21(2):153–167

Article   CAS   Google Scholar  

Demaree GR, Nicolis C (1990) Onset of Sahelian drought viewed as a fluctuation-induced transition. Q J R Meteorol Soc 116:221–238

Douglas EM, Vogel R, Kroll CN (2000) Trends in floods and low flows in the United States: impact of spatial correlation. J Hydrol 240:90–105

Esit M, Kumar S, Pandey A et al (2021) Seasonal to multi-year soil moisture drought forecasting. Clim Atmospheric Sci 4:1–8. https://doi.org/10.1038/s41612-021-00172-z

Fanta SS, Yesuf MB, Saeed S et al (2022) Analysis of precipitation and temperature trends under the impact of climate change over ten districts of Jimma Zone, Ethiopia. Earth Syst Environ. https://doi.org/10.1007/s41748-022-00322-0

Gan TY (1998) Hydro-climatic trends and possible climatic warming in the Canadian prairies. Water Resour Res 34(11):3009–3015

Güçlü YS (2018) Multiple Şen-innovative trend analyses and partial Mann-Kendall test. J Hydrol Elsevier 566:685–704

Hamed KH (2008) Trend detection in hydrologic data: the Mann–Kendall trend test under the scaling hypothesis. J Hydrol 349:350–363

Hamilton JP, Whitelaw GS, Fenech A (2001) Mean annual temperature and annual precipitation trends at Canadian biosphere reserves. Environ Monit Assess 67:239–275

Hipel KW, McLeod AI, Weiler RR (1988) Data analysis of water quality time series in Lake Erie, Water Resour. Bull 24(3):533–544

CAS   Google Scholar  

Hirca T, Eryılmaz TG, Niazkar M (2022) Applications of innovative polygonal trend analyses to precipitation series of Eastern Black Sea Basin, Turkey. Theor Appl Climatol 147:651–667. https://doi.org/10.1007/s00704-021-03837-0

Hirsch RM, Slack JR (1984) A nonparametric trend test for seasonal data with serial dependence. Water Resour Res 20(1):727–732

Hirsch RM, Slack JR, Smith RA (1982) Techniques of trend analysis for monthly water quality analysis. Water Resource Res 18(1):107–121

IPCC (2007) Climate Change 2007: impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

Kalra A, Piechota TC, Davies R, Tootle GA (2008) Changes in U.S. streamflow and western U.S. snowpack. J Hydrol Eng 13:156–163

Kendall MG (1970) Rank correlation methods, 4th edn. Griffin, London

Kendall MG (1973) Rank correlation methods. Charles Griffin Book Series, London 

Lee H, Apley DW (2011) Improved design of robust exponentially weighted moving average control charts for autocorrelated processes. Qual Reliab Eng Int 27(3):337–352

Lins HF, Slack JR (1999) Streamflow trends in the United States. Geophys Res Lett 26(2):227–230

Mann HB (1945) Nonparametric tests against trend. Econometrica 13(3):245–259

Mann HB (1955) Non-parametric tests against trend Econometrica. J Econom Soc 13(3):245–259

Mateus C, Potito A (2022) Long-term trends in daily extreme air temperature indices in Ireland from 1885 to 2018. Weather Clim Extrem 36:100464

Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management? Science 319:573–574

O’Brien NL, Burn DH, Annable WK, Thompson PJ (2021) Trend detection in the presence of positive and negative serial correlation: a comparison of block maxima and peaks-over-threshold data. Water Resour Res 57:e2020WR028886

Pastagia J, Mehta D (2022) Application of innovative trend analysis on rainfall time series over Rajsamand district of Rajasthan state. Water Supply 22:7189–7196. https://doi.org/10.2166/ws.2022.276

Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389

Şen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17(9):1042–1046

Şen Z (2017) Hydrological trend analysis with innovative and over-whitening procedures. Hydrol Sci J 62(2):294–305

Sonali P, Kumar DN (2013) Review of trend detection methods and their application to detect temperature changes in India. J Hydrol 476:212–227

Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15:72–101. https://doi.org/10.2307/1412159

Taylor CH, Loftis JC (1989) Testing for trend in lake and groundwater quality time series. Water Resour Bull 25(4):715–726

Ullah I, Ma X, Yin J, Omer A, Habtemicheal BA, Saleem F, Iyakaremye V, Syed S, Arshad M, Liu M (2022) Spatiotemporal characteristics of meteorological drought variability and trends (1981–2020) over South Asia and the associated large-scale circulation patterns. Clim Dyn 60:2261–2284

Van Belle VG, Hughes JP (1984) Nonparametric tests for trends in water quality. Water Resour Res 20(1):127–136

WHO (2017) WMO Guidelines on the Calculation of Climate Normals. WMO-No. 1203. p 18

Xie Y, Liu S, Huang S, Fang H, Ding M, Huang C, Shen T (2022) Local trend analysis method of hydrological time series based on piecewise linear representation and hypothesis test. J Clean Prod 339:130695

Yeh AB, Lin DK, Zhou H, Venkataramani CH (2003) A multivariate exponentially weighted moving average control chart for monitoring process 52 variability. J Applied Statistics 30(5):507–536

Yu YS, Zou S, Whittemore D (1993) Non-parametric trend analysis of water quality data of river in Kansas. J Hydrol 150:61–80

Yue S, Wang CY (2002) Applicability of pre-whitening to eliminate the influence of serial correlation on the Mann-Kendall test. Water Resour Res 38. https://doi.org/10.1029/2001WR000861

Download references

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). The research received no funds.

Author information

Authors and affiliations.

Faculty of Engineering and Natural Sciences, İstanbul Medipol University, Beykoz, 84810, Turkey

You can also search for this author in PubMed   Google Scholar

Contributions

All parts are contributed by the single author.

Corresponding author

Correspondence to Zekâi Şen .

Ethics declarations

Ethical approval.

The manuscript is conducted in the ethical manner advised by the targeted journal.

Consent to Participate

Not applicable.

Consent to Publish

The research is scientifically consented to be published.

Conflict of Interest

The author declares no conflict of interest to any party.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Şen, Z. Moving Trend Analysis Methodology for Hydro-meteorology Time Series Dynamic Assessment. Water Resour Manage (2024). https://doi.org/10.1007/s11269-024-03872-2

Download citation

Received : 01 April 2024

Accepted : 23 April 2024

Published : 15 May 2024

DOI : https://doi.org/10.1007/s11269-024-03872-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Hydro-meteorology
• Find a journal
• Publish with us
• Track your research

Search Icon

Events See all →

At-home anthro live.

1:00 p.m. - 1:45 p.m.

Alumni Weekend 2024

Various locations

268th Commencement

Franklin Field

Wawa Welcome America Day

10:00 a.m. - 5:00 p.m.

Penn Museum, 3260 South St.

Arts, Humanities, & Social Sciences

What predicts human behavior and how to change it

In the largest quantitative synthesis to date, dolores albarracín and her team dig through years of research on the science behind behavior change to determine the best ways to promote changes in behavior..

Pandemics, global warming, and rampant gun violence are all clear lessons in the need to move large groups of people to change their behavior. When a crisis hits, researchers, policymakers, health officials, and community leaders have to know how best to encourage people to change en masse and quickly. Each crisis leads to rehashing the same strategies, even those that have not worked in the past, due to the lack of definitive science of what interventions work across the board combined with well intended but erroneous intuitions.

To produce evidence on what determines and changes behavior, Dolores Albarracín and her colleagues from Penn’s Social Action Lab undertook a review of all of the available meta-analyses—a synthesis of the results from multiple studies—to determine what interventions work best when trying to change people’s behavior. What results is a new classification of predictors of behavior and a novel, empirical model for understanding the different ways to change behavior by targeting either individual or social/structural factors.

A new paper published in Nature Reviews Psychology reports that the strategies that people assume will work—like giving people accurate information or trying to change their beliefs—do not. At the same time, others like providing social support and tapping into individuals’ behavioral skills and habits as well as removing practical obstacles to behavior (e.g., providing health insurance to encourage health behaviors) can have more sizable impacts.

“Interventions targeting knowledge, general attitudes, beliefs, administrative and legal sanctions, and trustworthiness—these factors researchers and policymakers put so much weight on—are actually quite ineffective,” says Albarracín, the Alexandra Heyman Nash University Professor in the Annenberg School for Communication and director of the Science of Science Communication Division of the Annenberg Public Policy Center, who also has an appointment in Penn’s School of Arts & Sciences. “They have negligible effects.”

Unfortunately, many policies and reports are centered around goals like increasing vaccine confidence (an attitude) or curbing misinformation. Policymakers must look at evidence to determine what factors will return the investment, Albarracín says.

Co-author Javier Granados Samayoa, the Vartan Gregorian Postdoctoral Fellow at the Annenberg Public Policy Center , has noticed researchers’ tendency to target knowledge and beliefs when creating behavior change interventions.

“There’s something about it that seems so straightforward—you think and therefore you do. But what the literature suggests is that there are a lot of intervening processes that have to line up for people to actually act on those beliefs, so it’s not that easy,” he says.

Read more at Annenberg School for Communication.

Class of 2025 relishes time together at Hey Day

Picturing artistic pursuits

Hundreds of undergraduates take classes in the fine arts each semester, among them painting and drawing, ceramics and sculpture, printmaking and animation, photography and videography. The courses, through the School of Arts & Sciences and the Stuart Weitzman School of Design, give students the opportunity to immerse themselves in an art form in a collaborative way.

Campus & Community

Penn celebrates operation and benefits of largest solar power project in Pennsylvania

Solar production has begun at the Great Cove I and II facilities in central Pennsylvania, the equivalent of powering 70% of the electricity demand from Penn’s academic campus and health system in the Philadelphia area.

Education, Business, & Law

Investing in future teachers and educational leaders

The Empowerment Through Education Scholarship Program at Penn’s Graduate School of Education is helping to prepare and retain teachers and educational leaders.

‘The Illuminated Body’ fuses color, light, and sound

A new Arthur Ross Gallery exhibition of work by artist Barbara Earl Thomas features cut-paper portraits reminiscent of stained glass and an immersive installation constructed with intricately cut material lit from behind.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Open access
• Published: 06 December 2023

Divergent data-driven estimates of global soil respiration

• Shoji Hashimoto   ORCID: orcid.org/0000-0003-3022-7495 1 , 2 ,
• Akihiko Ito   ORCID: orcid.org/0000-0001-5265-0791 2 , 3 &
• Kazuya Nishina   ORCID: orcid.org/0000-0002-8820-1282 3  

Communications Earth & Environment volume  4 , Article number:  460 ( 2023 ) Cite this article

1664 Accesses

13 Altmetric

Metrics details

• Carbon cycle

The release of carbon dioxide from the soil to the atmosphere, known as soil respiration, is the second largest terrestrial carbon flux after photosynthesis, but the convergence of the data-driven estimates is unclear. Here we collate all historical data-driven estimates of global soil respiration to analyze convergence and uncertainty in the estimates. Despite the development of a dataset and advanced scaling techniques in the last two decades, we find that inter-model variability has increased. Reducing inter-model variability of global soil respiration is not an easy task, but when the puzzle pieces of the carbon cycle fit together perfectly, climate change prediction will be more reliable.

Similar content being viewed by others

Temporal changes in global soil respiration since 1987

Global patterns of geo-ecological controls on the response of soil respiration to warming

Past climate conditions predict the influence of nitrogen enrichment on the temperature sensitivity of soil respiration

Introduction.

The increase in atmospheric carbon dioxide (CO 2 ) concentration caused by human activity since the Industrial Revolution has resulted in climate change, making the global terrestrial carbon cycle a major concern 1 . Land absorbs and emits about 10 times more carbon than anthropogenic emissions 2 . Terrestrial vegetation takes in atmospheric CO 2 through photosynthesis, while respiration by vegetation and soil releases almost the same amount of CO 2 back into the atmosphere. The size of land uptake and emission fluxes is estimated to be ~130 Pg C yr −1 2 . The difference between uptake and emissions represents the net carbon uptake by land, and maintaining and even enhancing this uptake is critical for mitigating climate change. The net carbon uptake by plants, which is the difference of photosynthetic carbon assimilation and autotrophic respiration by plants, is referred to as net primary productivity. The mean ± standard deviation and median of the estimates are 56.2 ± 14.3 and 56.4 Pg C yr –1 , respectively, based on an intensive review 3 .

The primary flux from the land to the atmosphere is the soil-to-atmosphere CO 2 flux, also known as soil respiration 2 , 4 . Soil respiration comprises two sources: heterotrophic respiration, which is the decomposition of soil organic matter by microbes, and belowground autotrophic respiration, which is plant root respiration 5 , 6 , 7 . The total soil CO 2 production is the sum of these two sources, and it can be measured as soil-to-atmospheric CO 2 flux on the soil surface. Since the 1980s, this flux has been intensively observed 8 . Soil respiration is a key flux of the global carbon cycle because it is large and potentially increases with climate change due to the accelerated decomposition of stored soil organic carbon caused by warming, as well as potential disturbances caused by changes in precipitation. Therefore, soil respiration flux is as important as the uptake flux by vegetation 9 , 10 , 11 .

Accurately quantifying global carbon fluxes is essential to understanding the global carbon cycle and predicting future climate more accurately. In 2011, an intensive literature review found 251 estimates of total terrestrial net primary productivity using various methodologies (i.e., inventory aggregation, modeling, remote sensing, etc.), demonstrating that uptake by vegetation has been well-studied 3 . However, despite its importance, global soil respiration estimates have been limited; for instance, the number of data-driven estimates is less than half of the net primary productivity.

The development of the global Soil Respiration Database (SRDB) was a key milestone for soil respiration studies 8 . Soil respiration is generally measured using the chamber method, which monitors changes in CO 2 concentration in a chamber placed on the soil surface. Due to the large spatio-temporal variability of soil respiration across sites, landscapes, biomes, climates, and globally, a substantial amount of data is essential to comprehensively understand this phenomenon and to obtain accurate scaled-up global estimates. In general, observed data at various biomes, climate, and soil are used to develop a data-driven model and the model is applied on a global scale to obtain a global estimate of soil respiration. The SRDB collated observed soil respiration values published in the literature, providing easy access to a global dataset essential for upscaling field scale soil respiration data to a global scale. Dozens of global estimates have been reported 12 , 13 , 14 , but it is still unclear whether the total sum of global soil respiration is consistent among various estimates, and how much uncertainty remains in its spatio–temporal distributions. To constrain the global carbon budget, an accurate estimate of global soil respiration is crucial.

This Perspective aims to present the spatio–temporal uncertainty of global soil respiration, discuss the potential causes of this uncertainty and future research directions. First, we summarize the methodologies and historical estimates. Next, we compile and quantitively analyze available data-driven spatio–temporal estimates of global soil respiration. We compared the spatial distributions of soil respiration in these estimates and identified areas with notable inter-model variability, and quantified the inter-model variability in temporal trends. Finally, we reflect on the achievement of historical estimates and address the remaining challenges.

Reviewing historical data-driven estimates of global soil respiration estimates

We collected all global estimates of total soil respiration from various sources, including literature surveys, data-repositories, and direct contact with authors (Supplementary Table  1 ). We identified 23 studies of global estimates of total soil respiration, spanning from the first estimate by Schlesinger in 1977 to a recent estimate by Jian in 2022. Out of these, map data were available for 14 studies. We recalculated global estimates using the available map data, and selected datasets with appropriate grid areas for analysis (see the Method section). After screening, we obtained 11 spatial estimates of global soil respiration. In addition to data-driven soil respiration data, we analyzed heterotrophic respiration data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) 15 , specifically rh (soil heterotrophic respiration of CMIP6 variables). More details of the methods are described in Supplementary information.

Methodology of upscaling—data, techniques, resolutions

Table  1 provides an overview of the upscaling methodology. The compilation showed that since the release of the first SRDB in 2010, most estimates were based on the SRDB, including solely the SRDB or extended SRDB. The SRDB has been updated, and the latest version is version 5, which includes 10,366 observations. From the late 2010s, machine learning approaches replaced other methods. Before that, semi-empirical modeling was the primary approach. Regarding resolution, the most commonly used resolution was 0.5°, and the very fine spatial resolution of 30 seconds (~1 km) was also adopted from the late 2010s. Both 0.5° and 30 s have been the most common resolutions in the past two decades. In contrast to spatial resolution, the adopted temporal resolution has been annual in the past decade, while older estimates with semi-empirical models often used monthly time steps.

Is the total sum converging?

Over the last 50 years, a total of 23 studies have reported global estimates of soil respiration (Fig.  1 and Table  1 ). Global soil respiration ranged from less than 70 Pg C yr −1 to more than 100 Pg C yr −1 (range: 68–101 Pg C yr −1 , excluding screened estimates, see the Methods section). Before 2010, the majority of estimates were below 80 Pg C yr −1 (mean value 74 Pg C yr −1 , range: 68–80 Pg C yr −1 ), and after 2010, much higher estimates were often reported. The mean estimate published after 2010 is 89 Pg C yr −1 (range: 68–101 Pg C yr −1 ). The higher values reported after 2010 do not mean that the global estimates converged, but rather that the variability increased (standard deviation before/after 2010: 5.2, 9.0 Pg C yr −1 , respectively). The global estimates after 2010 contain both higher and lower values (i.e., below 80 Pg C yr −1 ). While machine learning approaches using the SRDB are more common (Table  1 ), the difference between semi-empirical (including linear model; mean value 90 Pg C yr −1 , range: 80–98 Pg C yr −1 ) and machine-learning estimates (mean value: 89 Pg C yr −1 , range: 73–101 Pg C yr −1 ) was not significant (Supplementary Fig.  1 ).

Temporal change ( a ) and violine plots for before 2010 and after 2010 ( b ). Each number label represents a different study (see Table  1 for details). The blue line shows the cumulative mean for all data, while the purple line shows the cumulative mean for data published since 2010. Note that some studies reported multiple estimates using different methodologies, and the estimates are plotted according to the year of publication, as some studies did not specify the exact year for the estimates.

Which regions have higher inter-model variability?

Figure  2 illustrates the spatial distributions of soil respiration estimated in 11 studies. While soil respiration is generally high in warm, humid regions and low in dry and/or cold regions, the maps demonstrate varying magnitudes and spatial patterns of soil respiration. We divided the land into 14 regions based on the continents, latitudes, and primary biomes to identify areas with high inter-model variability, evaluated the regional inter-model variability using the coefficient of variation (CV) (Fig.  3 and Supplementary Fig.  2 ). The analysis showed that most regions had CV values below 25%, but regions B2, C2, and C5 had higher values (>25%) (Fig.  3 ). These regions include the north African dry area (e.g., the Sahara Desert, B2), deserted areas in the central Eurasian continent (e.g., Gobi and Taklamakan Deserts, C2), and the islands in Southeast Asia, such as the Malay Peninsula, Sumatra, Java, Borneo, and New Guinea (C5). Notably, the number of data points in each region did not always correlate with CV (Supplementary Fig.  3 ). While regions B2, C2, and C5 had fewer data points, CVs were smaller for many regions with similar data points.

The number indicated in brackets is the study ID (see Table  1 ).

Refer to the map panel for the region label. Color indicates the mean annual temperature of the region.

How the global soil respiration is changing with time?

Time series estimates demonstrated clear interannual variability (Fig.  4 ). The anomaly suggests that the interannual variability of soil respiration varies from −5 Pg C yr −1 to +5 Pg C yr −1 , and the magnitude of anomaly differs among estimates (Fig.  4 ). While all estimates synchronized in some years, such as low in around 1984–1985 and 1992 and high in 1998 and 2010, which are correlated with El Nino-Southern Oscillation and global temperature (1991 eruption of Mount Pinatubo for 1992 in part), the anomaly varied in other years.

The number label indicates the study ID in panel ( a ). Refer to Table  1 for the study ID. The red dashed line in panel ( b ) is the global mean temperature.

All but one estimate showed an increasing trend over time, but the slope of the linear trend differed between estimates. Examining the time period from 1980 to 2010, during which multiple estimates were available, the trends ranged from 0.038 Pg C yr −1 ( p  = 0.13, R 2  = 0.05) to 0.23 Pg C yr −1 ( p  = 0.003, R 2  = 0.61) with a mean of 0.11 Pg C yr −1 . Although no negative trend was found, the trends for some estimates were considerably smaller than for others.

The global scale temperature sensitivity of soil respiration against the anomaly of the global land mean annual temperature ranged from 1.9 Pg C yr −1 °C −1 ( p  = 0.005, R 2  = 0.23) to 5.0 Pg C yr −1 °C −1 ( p  = 0.004, R 2  = 0.58), with a mean of 3.5 Pg C yr −1 °C −1 (Supplementary Fig.  4 ).

Monthly seasonality was evaluated to a lesser extent (Supplementary Fig.  5 ). Only four estimates were found, and they demonstrated that global soil respiration was highest in July and August or northern summer (8–9 Pg C mo −1 ) and lowest in February (4–6 Pg C mo −1 ). Soil respiration in July was slightly higher than that in August, but the values were almost comparable within each study. The amplitude of the monthly seasonal soil respiration ranged from 2 to 4 Pg C mo −1 .

Comparing the data-driven estimates with the output of Earth System Models (CMIP6)

We compared the spatial inter-model uncertainty of the data-driven global soil respiration estimates with the heterotrophic respiration from Earth system models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Fig.  5 ). The comparison showed that the inter-model variability for the ESMs was, on the whole, twice as high as that for the data-driven soil respiration estimates. This is mainly because ESMs calculate heterotrophic respirations using process-based carbon cycle models with varying parameter settings while data-driven estimates are directly generated from observed soil respiration. Moreover, the CVs for the B2 and C2 regions were also higher for CMIP6, while that for C5 was of the same magnitude as that of the data-driven estimates.

Comparison between the inter-model coefficient of variation (CV) values for data-driven soil respiration estimates and those for heterotrophic respiration of the outputs of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The x -axis shows the amount of soil/heterotrophic respiration per region. Refer to the map panel in Fig.  3 for the region label.

History of global soil respiration estimates

The earliest studies of soil respiration date back to the late 19th and early 20th centuries 16 , 17 , 18 , with the first attempt to estimate global soil respiration made by Schlesinger in 1977 19 , more than a century after the first attempt to estimate global net primary productivity by von Liebig 3 , 20 . While the history of global soil respiration estimates is shorter and fewer in number than estimates of net primary productivity, progress has been rapid in recent decades due to the development of databases and machine learning techniques. In the last decade, there has been a notable increase in the number of studies as a result of the urgent need to understand the global carbon cycle. However, surprisingly, the efforts to estimate global soil respiration have not yet led to a convergence of estimates. Instead, the divergence appears to be increasing. Our comprehensive review uncovered several key findings: (1) There is an increase in inter-model variability in the total sum despite the development of a global dataset and advanced scaling techniques. (2) The inter-model variability varies across different regions, with some regions having CV values exceeding 25%. (3) There is substantial inter-model variability in the temporal changes, as well as in their sensitivity to temperature, which is the primary driver of these changes. To converge estimates, what do we need (Fig.  6 )?

These directions are categorized into four fundamental pillars: data-driven modeling, observation data, mechanisms, and mutual, multiple constraints.

To converge estimates

The creation of the global SRDB has been the most outstanding factor in boosting the study of global soil respiration estimates 8 . While tower flux data (FLUXNET) 21 have a standardized data network, there was previously no such dataset for soil respiration data. All recent global estimates of soil respiration are now based on the SRDB, which has tripled the amount of observed soil respiration data in the last 20 years, allowing for more data-driven global estimates. However, the uneven spatial coverage of data remains an issue, and has been identified as a critical problem in estimating global soil respiration 14 , 22 , 23 . The data are biased toward temperate regions and sparse in dry areas, northern areas, the African continent, and South America 14 , 23 . This problem was noted as early as the 1990s 24 , 25 , suggesting that despite early cautions and the rapid increase in data over the last two decades, the problem has not been resolved. The finer spatio–temporal resolution of data is also crucial. The SRDB has data with an annual time step, which affects estimates. Ongoing efforts to develop high-resolution temporal resolution data (COSORE (COntinuous SOil REspiration)) 26 and advances in high-precision Global Navigation Satellite System technology 27 could help to log more precise location of observations at lower cost, making it easier to match observational data with high-resolution forcing data 28 , 29 , 30 , 31 . Advances in remote sensing of the terrestrial carbon cycle are also promising 32 . However, scaling up from the field scale to the global scale continues to be a source of uncertainty due to the higher spatial heterogeneity of soil respiration at the field scale in nature 33 , and variability caused by different observation methods 34 , 35 .

In the past two decades, the use of machine learning has been implemented to upscale global soil respiration estimates 36 . This approach has shown higher performance in reproducing observed data and extracting important explaining variables. However, even with the same dataset and similar techniques, there has been an increase in spatio–temporal inter-model variability. This variability is partly due to different protocols and variations of forcing data, such as land use, climate, and area size, indicating the need for an international Model Intercomparison Project with a standardized protocol (i.e., CMIP6) 15 to evaluate uncertainty rooted in the scale-up methodology. Analyzing the causes of this variability of data-driven estimates is challenging when relying solely on outputs like this study. Therefore, such intercomparisons will also be instrumental in identifying the key factors influencing global soil respiration.

Heterotrophic and autotrophic respiration are based on different processes, but measuring each separately has been challenging. This has hindered our understanding and ability to constrain soil respiration in the past 7 . However, in the past two decades, many studies have disentangled the complexity of soil respiration through experiments, observations, modeling, and synthesis. The SRDB contains both heterotrophic and autotrophic respiration, and has contributed to global estimates of each respiration 37 , 38 , although additional observational data for each respiration are necessary. Some new processes are already incorporated in process-based carbon cycle models (CCMs) (e.g., microbes) 39 , and newly developed datasets on soil carbon 28 , minerals 3 , microbes 40 , and fungi 41 , 42 can improve the performance of machine learning approaches. Although their effectiveness is unknown, some soil-based functional types (e.g., soil functional type, decomposition functional type, etc.) have been proposed 43 , 44 , which may contribute to a better understanding and constraint of global soil respiration. Isotopic studies ( 14 C and 13 C) are also essential to separate autotrophic and heterotrophic respiration and disentangle soil carbon processes 45 , 46 , 47 , 48 through field observation and model evaluation.

Temperature sensitivity and its impact on soil respiration have been studied extensively, but they remain one of the most important yet unsettled issues 9 , 49 . Recent studies have shed new light on this topic, and ongoing discussions among researchers 50 , 51 , 52 are expected to lead to further insights. Changes in precipitation patterns also play a crucial role in the sensitivity of soil respiration to climate 14 . More refined temporal observations and modeling would incorporate short-term responses of soil respiration to climate events such as droughts, freeze/thaw cycles, precipitation events, and priming 53 into global estimates. The development of a new soil moisture dataset may replace the use of precipitation as a proxy for moisture conditions in the future 54 . These spatio–temporal changes in forcing data (e.g., climate) and sensitivity contribute to inter-model variability in temporal trends of global soil respiration. In particular, deserted areas in the B2 and C2 regions show different responses to precipitation and variability of precipitation, which may have caused the higher CV, while the reason for C5 is unclear. The mix of heterotrophic and autotrophic respiration processes makes the climate responses of soil respiration even more complex. Recent studies suggest that each component may contribute differently to total soil respiration based on observational data synthesis 13 , 55 , 56 . Therefore, it is important to evaluate each type of respiration as well as the total soil respiration.

In an effort to constrain global estimates based on the bottom-up approach, another important way of constraint is mutual, multiple constraints using other fluxes and stocks in global carbon cycle. Global carbon fluxes and stocks on land are interconnected and are often spatio–temporally estimated based on independent observed data (e.g., gross primary productivity, net primary productivity, soil carbon stock, net ecosystem exchange). These mutual multiple constraints would not decisively constrain all carbon fluxes and stocks, but would work to lessen the uncertainty and find inconsistencies 57 . Like fitting puzzle pieces together, constraining global soil respiration estimates with multiple other fluxes and stocks are an essential process.

Benchmarking of carbon cycle models and ESMs

The primary purpose of global data-driven estimates of soil respiration is to allow for benchmarking of carbon cycle models and ESMs. To predict future climate, process-based carbon cycle models are necessary, and it is essential to constrain them properly. Soil respiration consists of heterotrophic and autotrophic respiration, so constraining models with each respiration component is ideal. However, for data-driven global estimates, total soil respiration is the most intensively studied, while most ESMs only output heterotrophic respiration. To improve the constraint on soil processes in ESMs, more data-driven global estimates of both total soil respiration and its individual components are needed. ESMs should output separate estimates for heterotrophic and belowground autotrophic respiration and use both the total soil respiration and its individual components for constraining model simulations 58 , 59 .

Comparing the total and heterotrophic respirations, our analysis suggested that the inter-model variability from CMIP6 can potentially be reduced through processes, parameterization, and constraints. Recent studies also suggest the importance of selecting model predictions with comparable model performance with data (emergent constraint) 60 , 61 , 62 , which can further reduce inter-model variability in future predictions.

We have traced back historical estimates of global soil respiration in the last half-century to demonstrate the progress made and remaining uncertainty. Future efforts to better constrain global soil respiration estimates can be categorized into four fundamental pillars: data-driven modeling, observation data, mechanisms, and mutual, multiple constraints. Reducing inter-model variability is not an easy task, but when the puzzle pieces of the carbon cycle fit together perfectly, climate change prediction will be more reliable. Refining estimates of critical components like soil respiration is a step towards ensuring all pieces of the global carbon cycle fit together.

Data availability

The datasets used in this study are available from each repository or supplementary information of each study or direct request to the authors of the original paper (see Supplementary Table  1 ). The map data converted to NetCDF format is also available in the ZENODO repository https://doi.org/10.5281/ZENODO.8404747 63 .

Code availability

The scripts used are available from the corresponding author on reasonable request.

Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408 , 184–187 (2000).

Article   CAS   Google Scholar  

Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14 , 4811–4900 (2022).

Article   Google Scholar  

Ito, A. A historical meta-analysis of global terrestrial net primary productivity: are estimates converging? Glob. Change Biol. 17 , 3161–3175 (2011).

Schlesinger, W. H. & Andrews, J. A. Soil respiration and the global carbon cycle. Biogeochemistry 48 , 7–20 (2000).

Bond-Lamberty, B., Wang, C. & Gower, S. T. A global relationship between the heterotrophic and autotrophic components of soil respiration? Glob. Change Biol. 10 , 1756–1766 (2004).

Ryan, M. G. & Law, B. E. Interpreting, measuring, and modeling soil respiration. Biogeochemistry 73 , 3–27 (2005).

Subke, J.-A. & Bahn, M. On the ‘temperature sensitivity’ of soil respiration: Can we use the immeasurable to predict the unknown? Soil Biol. Biochem. 42 , 1653–1656 (2010).

Bond-Lamberty, B. & Thomson, A. A global database of soil respiration data. Biogeosciences 7 , 1915–1926 (2010).

Jenkinson, D. S. The turnover of organic carbon and nitrogen in soil. Phil. Trans. R. Soc. Lond. B 329 , 361–368 (1990).

Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540 , 104–108 (2016).

van Gestel, N. et al. Predicting soil carbon loss with warming. Nature 554 , E4–E5 (2018).

Hashimoto, S. et al. Global spatiotemporal distribution of soil respiration modeled using a global database. Biogeosciences 12 , 4121–4132 (2015).

Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M. & Vargas, R. Globally rising soil heterotrophic respiration over recent decades. Nature 560 , 80–83 (2018).

Stell, E., Warner, D., Jian, J., Bond‐Lamberty, B. & Vargas, R. Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions? Glob. Change Biol. 27 , 3923–3938 (2021).

Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9 , 1937–1958 (2016).

Deherain, P. P. & Demoussy, E. Sur l’oxydation de la matiere organique du sol. Ann. Agron. 22 , 305–337 (1886).

Google Scholar  

Gainey, P. L. Parallel formation of carbon dioxide, ammonia and nitrate in soil. Soil Sci 7 , 293–312 (1919).

Lundegardh, H. Carbon dioxide evolution of soil and crop growth. Soil Sci 23 , 417–453 (1927).

Schlesinger, W. H. Carbon balance in terrestrial detritus. Annu. Rev. Ecol. Syst. 8 , 51–81 (1977).

von Liebig, J. Der chemische Process der Erna ̈hrung der Vegetabilien. (Friedrich Vieweg und Sohn, 1862).

Baldocchi, D. et al. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem–scale carbon dioxide, water vapor, and energy flux densities. Bull. Amer. Meteorol. Soc. 82 , 2415–2434 (2001).

Lu, H. et al. Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models. Environ. Res. Lett. 16 , 054048 (2021).

Jian, J. et al. A restructured and updated global soil respiration database (SRDB-V5). Earth Syst. Sci. Data 13 , 255–267 (2021).

Peng, C. & Apps, M. J. Simulating global soil-CO 2 flux and its response to climate change. J Environ. Sci. 12 , 257–265 (2000).

CAS   Google Scholar  

Raich, J. W. & Potter, C. S. Global patterns of carbon dioxide emissions from soils. Global Biogeochem. Cy. 9 , 23–36 (1995).

Bond‐Lamberty, B. et al. COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data. Glob. Change Biol. 26 , 7268–7283 (2020).

Krofcheck, D. J., Litvak, M. E. & Hurteau, M. D. Allometric relationships for Quercus gambelii and Robinia neomexicana for biomass estimation following disturbance. Ecosphere 10 , e02905 (2019).

Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12 , e0169748 (2017).

Yamazaki, D. et al. A high-accuracy map of global terrain elevations: Accurate Global Terrain Elevation map. Geophys. Res. Lett. 44 , 5844–5853 (2017).

Zhou, T. et al. Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: a comparison between Sentinel-2, Sentinel-3 and Landsat-8 images. Sci. Total Environ. 755 , 142661 (2021).

Rossiter, D. G. Past, present & future of information technology in pedometrics. Geoderma 324 , 131–137 (2018).

Xiao, J. et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sens. Environ. 233 , 111383 (2019).

Ohashi, M., Kume, T., Yamane, S. & Suzuki, M. Hot spots of soil respiration in an Asian tropical rainforest. Geophys. Res. Lett . 34 , L08705 (2007).

Pumpanen, J. et al. Comparison of different chamber techniques for measuring soil CO 2 efflux. Agri. For. Meteorol. 123 , 159–176 (2004).

Davidson, E. A., Savage, K., Verchot, L. V. & Navarro, R. Minimizing artifacts and biases in chamber-based measurements of soil respiration. Agri. For. Meteorol. 113 , 21–37 (2002).

Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566 , 195–204 (2019).

Tang, X. et al. Global variability in belowground autotrophic respiration in terrestrial ecosystems. Earth Syst. Sci. Data 11 , 1839–1852 (2019).

Tang, X. et al. Spatial and temporal patterns of global soil heterotrophic respiration in terrestrial ecosystems. Earth Syst. Sci. Data 12 , 1037–1051 (2020).

Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3 , 909–912 (2013).

Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365 , eaav0550 (2019).

Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346 , 1256688 (2014).

Yu, K. et al. The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil. Earth Syst. Sci. Data 14 , 4339–4350 (2022).

Petrakis, S., Barba, J., Bond-Lamberty, B. & Vargas, R. Using greenhouse gas fluxes to define soil functional types. Plant Soil 423 , 285–294 (2018).

Bond‐Lamberty, B. et al. Estimating heterotrophic respiration at large scales: challenges, approaches, and next steps. Ecosphere 7 , e01380 (2016).

Hahn, V., Högberg, P. & Buchmann, N. 14 C - a tool for separation of autotrophic and heterotrophic soil respiration. Glob. Change Biol. 12 , 972–982 (2006).

Camino‐Serrano, M. et al. Including stable carbon isotopes to evaluate the dynamics of soil carbon in the land‐surface model ORCHIDEE. J. Adv. Model Earth Syst. 11 , 3650–3669 (2019).

Koarashi, J. et al. Quantitative aspects of heterogeneity in soil organic matter dynamics in a cool-temperate Japanese beech forest: a radiocarbon-based approach. Glob. Change Biol. 15 , 631–642 (2009).

Hanson, P. J., Edwards, N. T., Garten, C. T. & Andrews, J. A. Separating root and soil microbial contributions to soil respiration: a review of methods and observations. Biogeochemistry 48 , 115–146 (2000).

Boone, R. D., Nadelhoffer, K. J., Canary, J. D. & Kaye, J. P. Roots exert a strong influence on the temperature sensitivity of soil respiration. Nature 396 , 570–572 (1998).

Nottingham, A. T., Meir, P., Velasquez, E. & Turner, B. L. Soil carbon loss by experimental warming in a tropical forest. Nature 584 , 234–237 (2020).

Carey, J. C. et al. Temperature response of soil respiration largely unaltered with experimental warming. Proc. Natl Acad. Sci. USA 113 , 13797–13802 (2016).

Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Change 3 , 395–398 (2013).

Guenet, B. et al. Impact of priming on global soil carbon stocks. Glob. Change Biol. 24 , 1873–1883 (2018).

Bogena, H. R. et al. COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth Syst. Sci. Data 14 , 1125–1151 (2022).

Tang, X. et al. A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems. Sci. Total Environ. 798 , 149273 (2021).

Jian, J. et al. The global contribution of roots to total soil respiration. Glob. Ecol. Biogeogr. 31 , 685–699 (2022).

Jian, J. et al. Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle. Nat. Commun. 13 , 1733 (2022).

Kim, D., Lee, M.-I. & Seo, E. Improvement of soil respiration parameterization in a dynamic global vegetation model and its impact on the simulation of terrestrial carbon fluxes. J. Clim. 32 , 127–143 (2019).

Guenet, B. et al. Spatial biases reduce the ability of earth system models to simulate soil heterotrophic respiration fluxes. EGUsphere https://doi.org/10.5194/egusphere-2023-922 (2023).

Shiogama, H., Watanabe, M., Kim, H. & Hirota, N. Emergent constraints on future precipitation changes. Nature 602 , 612–616 (2022).

Varney, R. M. et al. A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming. Nat. Commun. 11 , 5544 (2020).

Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10 , 1717–1736 (2013).

Hashimoto, S., Ito, A. & Nishina, K. Map data of historical global estimates of soil respiration. https://doi.org/10.5281/ZENODO.8404747 (2023).

Raich, J. W. & Schlesinger, W. H. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B 44 , 81–99 (1992).

Raich, J. & Potter, C. Global patterns of carbon dioxide emissions from soils on a 0.5 degree grid cell basis. https://doi.org/10.3334/CDIAC/LUE.DB1015 (1996).

Raich, J. W., Potter, C. S. & Bhagawati, D. Interannual variability in global soil respiration, 1980-94. Glob. Change Biol. 8 , 800–812 (2002).

Raich, J., Potter, C. & Bhagawat, D. Interannual variability in global soil respiration on a 0.5 degree grid cell basis (1980-1994). https://doi.org/10.3334/CDIAC/LUE.NDP081 (2003).

Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464 , 579–582 (2010).

Hashimoto, S. A new estimation of global soil greenhouse gas fluxes using a simple data-oriented model. PLoS ONE 7 , e41962 (2012).

Chen, S. et al. A new estimate of global soil respiration from 1970 to 2008. Chin. Sci. Bull. 58 , 4153–4160 (2013).

Hashimoto, S. Global gridded soil respiration, heterotrophic respiration, autotrophic respiration, and Q 10 value (Hashimoto et al. 2015, Biogeosciences). https://doi.org/10.5281/ZENODO.4708444 (2021).

Oertel, C., Matschullat, J., Zurba, K., Zimmermann, F. & Erasmi, S. Greenhouse gas emissions from soils—A review. Geochemistry 76 , 327–352 (2016).

Xu, M. & Shang, H. Contribution of soil respiration to the global carbon equation. J. Plant Physiol. 203 , 16–28 (2016).

Adachi, M., Ito, A., Yonemura, S. & Takeuchi, W. Estimation of global soil respiration by accounting for land-use changes derived from remote sensing data. J. Environ. Manage. 200 , 97–104 (2017).

Zhao, Z. et al. Model prediction of biome-specific global soil respiration from 1960 to 2012. Earth’s Future 5 , 715–729 (2017).

Hursh, A. et al. The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale. Glob. Change Biol. 23 , 2090–2103 (2017).

Jian, J., Steele, M. K., Thomas, R. Q., Day, S. D. & Hodges, S. C. Constraining estimates of global soil respiration by quantifying sources of variability. Glob. Change Biol. 24 , 4143–4159 (2018).

Jian, J., Steele, M. K., Day, S. D. & Thomas, R. Q. Future global soil respiration rates will swell despite regional decreases in temperature sensitivity caused by rising temperature. Earth’s Future 6 , 1539–1554 (2018).

Warner, D. L., Bond‐Lamberty, B., Jian, J., Stell, E. & Vargas, R. Spatial predictions and associated uncertainty of annual soil respiration at the global scale. Glob. Biogeochem. Cy. 33 , 1733–1745 (2019).

Warner, D. L., Bond-Lamberty, B. P., Jian, J., Stell, E. & Vargas, R. Global gridded 1-km annual soil respiration and uncertainty derived from SRDB V3. https://doi.org/10.3334/ORNLDAAC/1736 (2019).

Tang, X. et al. A globally gridded heterotrophic respiration dataset based on field observations. https://doi.org/10.6084/M9.FIGSHARE.8882567 (2019).

Tang, X. et al. A gridded dataset of belowground autotrophic respiration from 1980 to 2012 in global terrestrial ecosystems upscaling of observations. https://doi.org/10.6084/M9.FIGSHARE.7636193 (2019).

Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv 6 , eabb8508 (2020).

Huang, N. & Wang, L. Global annual soil respiration from 2000 to 2014. https://doi.org/10.5061/DRYAD.W3R2280NQ (2020).

Lei, J. et al. Temporal changes in global soil respiration since 1987. Nat. Commun. 12 , 403 (2021).

Lu, H. Global soil respiration and its components dataset derived from the Random Forest method. https://doi.org/10.5281/ZENODO.4686669 (2021).

Stell, E., Warner, D. L., Jian, J., Bond-Lamberty, B. P. & Vargas, R. Global gridded 1-km soil and soil heterotrophic respiration derived from SRDB v5. https://doi.org/10.3334/ORNLDAAC/1928 (2021).

Epule, T. A new compendium of soil respiration data for Africa. Challenges 6 , 88–97 (2015).

Song, X. et al. Quantification of soil respiration in forest ecosystems across China. Atmos. Environ. 94 , 546–551 (2014).

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP19H03008, JP21H03580, and JP22H02400. We greatly thank B. Bond-Lamberty, M. Adachi, and J. Jian for sharing their spatio–temporal data for soil respiration estimates with us. The authors acknowledge the use of the ChatGPT language model, developed by OpenAI, for providing language assistance in preparing the manuscript. We acknowledge two editors, M. Li and C. Davis, and three anonymous reviewers for their helpful comments on the manuscript.

Author information

Authors and affiliations.

Department of Forest Soils, Forestry and Forest Products Research Institute, 1, Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan

Shoji Hashimoto

Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan

Shoji Hashimoto & Akihiko Ito

Earth System Division, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki, 305-8506, Japan

Akihiko Ito & Kazuya Nishina

You can also search for this author in PubMed   Google Scholar

Contributions

S.H.: Conceptualization, methodology, software, data curation, writing, visualization A.I.: Methodology, supervision, writing, funding acquisition K.N.: Methodology, writing.

Corresponding author

Correspondence to Shoji Hashimoto .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Communications Earth & Environment thanks Daniel Warner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Mengze Li and Clare Davis. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Peer review file, supplementary information, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Hashimoto, S., Ito, A. & Nishina, K. Divergent data-driven estimates of global soil respiration. Commun Earth Environ 4 , 460 (2023). https://doi.org/10.1038/s43247-023-01136-2

Download citation

Received : 02 July 2023

Accepted : 21 November 2023

Published : 06 December 2023

DOI : https://doi.org/10.1038/s43247-023-01136-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

The Federal Register

The daily journal of the united states government, request access.

Due to aggressive automated scraping of FederalRegister.gov and eCFR.gov, programmatic access to these sites is limited to access to our extensive developer APIs.

If you are human user receiving this message, we can add your IP address to a set of IPs that can access FederalRegister.gov & eCFR.gov; complete the CAPTCHA (bot test) below and click "Request Access". This process will be necessary for each IP address you wish to access the site from, requests are valid for approximately one quarter (three months) after which the process may need to be repeated.

An official website of the United States government.

If you want to request a wider IP range, first request access for your current IP, and then use the "Site Feedback" button found in the lower left-hand side to make the request.