journal of scientific research and reports impact factor

Journal of Scientific Research and Reports

ISSN: 2320-0227

Journal of Scientific Research and Reports (ISSN: 2320-0227) aims to publish high quality papers ( Click here for Types of paper ) in all areas of ‘scientific research’. By not excluding papers based on novelty, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer-reviewed, open-access INTERNATIONAL journal.

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Betacyanin, antioxidant activity and shelf-life evaluation of thermally, microwave, and chemically processed lime-flavored dragon fruit ready to serve beverage.

Dharmendrakumar Patel, Suresh Bhise

Assessment of Alcohol Use Control Programs in Secondary Schools in Lofa County, Liberia

Macklin Marvin Korvah

An Impact Study on the Pattern of Groundwater Use by Farmers and Commercial Sectors in Tirupur District, Textile City, India

Venketesa Palanichamy N, Kalpana M

An Analysis of Energy Input and Output Dynamics in Redgram Production of Vikarabad District, Telangana, India

Sravan Kumar , K. Sridhar

Economics of Capsicum and Tomato Crops Production under Protected Cultivation in the Regions of Kalyana-Karnataka, India

Shashikala S. Ruli, Ganeshagouda I. Patil, Kadli Veeresh , Goudappa S. B.

Economic Viability and Financial Feasibility of Secondary Processing Unit of Turmeric in Chamarajanagar District of Karnataka, India

Navyashree, B. M., K. B. Vedamurthy , Vaishnavi, Venkataramana, M. N., Ujjwal, B.

A Study on the Psychological Impact of Covid-19 on Farmers

C. Vaishnavi, Shivanand K. Kammar , S. B. Goudappa , Vijaya B. Wali , Prashanth. B

Corporate Governance and Challenges of Financial Leverage of Privatised Cement Industry in Nigeria

Bappayo Masu Gombe, Mustapha Mukhtar

Understanding the Sustainability and Growth Dynamics of Shrimp Aquaculture in Odisha, India: A Decomposition Approach

Pritam Tripathy, Swadesh Prakash , Sivaramane, N., Neha W. Qureshi, Arpita Sharma

Enhancing Maize (Zea mays L.) Yield and Zn Content with Zn Application through Seed, Soil and Foliar Methods

Kiranpreet Kaur, Gurbax Singh Chhina , Mandeep Kaur , Amritpal Kaur

Autoregressive Integrated Moving Average (ARIMA) Model with Genetic Algorithm to Forecast the Chilli and Turmeric Productions in India

Elakkiya N, Banjul Bhattacharyya , Sathees Kumar K

Growth and Yield of Summer Squash (Cucurbita pepo L.) as Influenced by Different Coloured Plastic Mulches in the Tarai Region of Uttarakhand

Vatsala Tewari, Swagat Ranjan Behera, Riya Pandey, Priyanka Panwar

Impact Assessment of Integrated Farming System Project on the Socio-economic Status of the Beneficiary Farmers of Bidar District of Karnataka

Ganeshagouda I. Patil , Srinivas N., Dhananjaya P., Shashikala Ruli, Kadli Veeresh

Integrated Crop Management Practices on Yield and Economics of Brinjal at Tiruvallur District of Tamil Nadu, India

G. Sathish, K. Sundharaiya, C. Tamilselvi , V.A. Vijayashanthi , P. Yogameenakshi, P.Arul Arasu , V. Sivakumar, I. Geethalakshmi

Study on the Challenges Encountered by Eri Silkworm Rearers in Tapioca Cultivation and Eri Rearing in Udalguri District of Assam

Jugabrat Sarma , Dipankar Brahma , Prety Rekha Narzary , Abhigyan Rajkhowa , Santanu Kaushik Bora, Dhiraj Saikia, Bitopan Das

Review Article

Accessing role of urea briquettes fertilizer for enhancing nitogen use efficiency and crop productivity.

Hridesh Harsha Sarma , Akash Paul, Ayekpam Robertson , Shahin Shobnur, Sonam Lhamu , Anjali Taku

Lilium: A High-Value Cut Flower Production Guide for Lucrative Return

Prachurjya Samantaray, Aradhana Nayak, Itishree Pradhan , Akanshya Pattanaik, Reshma Sahoo , Sukirti Mohanty

A Comprehensive Review on Evolution, Challenges of Models, Opportunities in Urban Farming Practices in the World

Anushi, A. Krishnamoorthi, Jitendra Chaurasia, Bijay Kumar Baidya, Abhishek Singh, Sapna, Vijay Kumar, Anushka Singh

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What is Journal Impact Factor?

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Daunted by the idea of choosing the right journal for your paper? Don’t be. Metrics have become an everyday word in scholarship, in general. Within its many fields of research – if not all of them – they provide important data about a journal’s impact and relevance among its readers. In an era of information proliferation, it has become increasingly important to know where to capture the most attention and interest of your target audience.

So, whenever you are in doubt about which journal suits you better, don’t forget to browse its metrics; they will certainly help you with the decision-making process. Start, for example, with the Journal Impact Factor.

Impact factor (IF) is a measure of the number of times an average paper in a journal is cited, during a year. Clarivate Analytics releases the Journal Impact Factors annually as part of the Web of Science Journal Citation Reports®. Only journals listed in the Science Citation Index Expanded® (SCIE) and Social Sciences Citation Index® (SSCI) receive an Impact Factor.

What is a good impact factor for a scientific journal?

Impact Factors are used to measure the importance of a journal by calculating the number of times selected articles are cited within a particular year. Hence, the higher the number of citations or articles coming from a particular journal, or impact factor, the higher it is ranked. IF is also a powerful tool if you want to compare journals in the subject category.

Measuring a Journal Impact Factor:

• CiteScore metrics – helps to measure journal citation impact. Free, comprehensive, transparent and current metrics calculated using data from Scopus®, the largest abstract and citation database of peer-reviewed literature.
• SJR – or SCImago Journal Rank, is based on the concept of a transfer of prestige between journals via their citation links.
• SNIP – or Source Normalized Impact per Paper, is a sophisticated metric that accounts for field-specific differences in citation practices.
• JIF – or Journal Impact Factor is calculated by Clarivate Analytics as the average of the sum of the citations received in a given year to a journal’s previous two years of publications, divided by the sum of “citable” publications in the previous two years.
• H-index – Although originally conceived as an author-level metric, the H -index has been being applied to higher-order aggregations of research publications, including journals.

Deciding the perfect journal for your paper is an important step. Metrics are excellent tools to guide you through the process. However, we also recommend you not neglect a perfectly written text, not only scientific and grammatically but also fitting the chosen journal’s requirements and scope. At Elsevier, we provide text-editing services that aim to amend and adjust your manuscript, to increase its chances of a successful acceptance by your target journal. Although each journal has its own editorial team, the overall quality, language and whether the article is innovative may also play a role.

Language Editing Services by Elsevier Author Services:

We know that, as an academic researcher, you have many things to do to stay relevant.

Writing relevant manuscripts is a crucial part of your endeavors.

That’s why we, at Elsevier Author Service s, support you throughout your publication journey with a suite of products and services to help improve your manuscript before submission.

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Articles Journal Citation Reports: Annual Impact Factor

Journal citation reports: annual impact factor, may 9, 2022 • knowledge, information.

• The Clarivate Analytics Impact Factor
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• The Agony and the Ecstasy: the history and the meaning of the Journal Impact Factor

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Journal Impact Factor: Its Use, Significance and Limitations

Mohit sharma.

Department of Conservative Dentistry, Shree Bankey Bihari Dental College and Research Centre, Ghaziabad, India

Anurag Sarin

Priyanka gupta.

1 Department of Conservative Dentistry, Maharana Pratap College of Dentistry and Research Centre, Gwalior, India

Shobhit Sachdeva

2 Department of Pedodontics, ITS Dental College, Ghaziabad, India

Ankur V. Desai

3 Department of Conservative Dentistry, Vaidik Dental College and Research Centre, Daman, India

Dear Editor,

The impact factor (IF) is frequently used as an indicator of the importance of a journal to its field. It was first introduced by Eugene Garfield, the founder of the Institute for Scientific Information.[ 1 ] Although IF is widely used by institutions and clinicians, people have widespread misconception regarding the method for calculating the journal IF, its significance and how it can be utilized. The IF of a journal is not associated to the factors like quality of peer review process and quality of content of the journal, but is a measure that reflects the average number of citations to articles published in journals, books, thesis, project reports, newspapers, conference/seminar proceedings, documents published in internet, notes, and any other approved documents (by Indian Council of Medical Research or similar body).[ 2 ]

Impact factor is commonly used to evaluate the relative importance of a journal within its field and to measure the frequency with which the “average article” in a journal has been cited in a particular time period. Journal which publishes more review articles will get highest IFs. Journals with higher IFs believed to be more important than those with lower ones.[ 3 ] According to Eugene Garfield “impact simply reflects the ability of the journals and editors to attract the best paper available.”[ 4 ] Journal which publishes more review articles will get maximum IFs.

Impact factor can be calculated after completing the minimum of 3 years of publication; for that reason journal IF cannot be calculated for new journals. The journal with the highest IF is the one that published the most commonly cited articles over a 2-year period. The IF applies only to journals, not to individual articles or individual scientists unlike the “H-index.” The relative number of citations an individual article receives is better evaluated as “citation impact.” In a given year, the IF of a journal is the average number of citations received per article published in that journal during the 2 preceding years. IFs are calculated each year by Thomson scientific for those journals that it indexes, and are published in Journal Citation Reports ( http://www.thomsonreuters.com/products_services/science/science_products/a-z/journal_citation_reports/ ). For example, if a journal has an IF of 3 in 2008, then its papers published in 2006 and 2007 received three citations each on average in 2008. The 2008 IFs are actually published in 2009; they cannot be calculated until all of the 2008 publications have been processed by the indexing agency (Thomson Reuters). The IF for the biomedical journals may range up to 5-8%.[ 5 ] The IF of any journal may be calculated by the formula;

2012 impactfactor =A/B

Where A is the number of times articles published in 2010 and 2011 were cited by indexed journals during 2012. B is the total number of citable items like articles and reviews published by that journal in 2010 and 2011.

The calculation of IF for the journal where in a person has published articles is a contentious issue. Nevertheless, this have been already warned; “misuse in evaluating individuals” because there is “a wide variation from article to article within a single journal” therefore, “In an ideal world, evaluators would read each article and make personal judgments,” said by Eugene Garfield.[ 1 ]

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The Journal of Materials Science is a key publication exploring the intricate relationship between structure, properties, and uses of various materials.

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• Published: 19 April 2024

A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact

• Aklilu Endalamaw 1 , 2 ,
• Resham B Khatri 1 , 3 ,
• Tesfaye Setegn Mengistu 1 , 2 ,
• Daniel Erku 1 , 4 , 5 ,
• Eskinder Wolka 6 ,
• Anteneh Zewdie 6 &
• Yibeltal Assefa 1  

BMC Health Services Research volume  24 , Article number:  487 ( 2024 ) Cite this article

674 Accesses

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The growing adoption of continuous quality improvement (CQI) initiatives in healthcare has generated a surge in research interest to gain a deeper understanding of CQI. However, comprehensive evidence regarding the diverse facets of CQI in healthcare has been limited. Our review sought to comprehensively grasp the conceptualization and principles of CQI, explore existing models and tools, analyze barriers and facilitators, and investigate its overall impacts.

This qualitative scoping review was conducted using Arksey and O’Malley’s methodological framework. We searched articles in PubMed, Web of Science, Scopus, and EMBASE databases. In addition, we accessed articles from Google Scholar. We used mixed-method analysis, including qualitative content analysis and quantitative descriptive for quantitative findings to summarize findings and PRISMA extension for scoping reviews (PRISMA-ScR) framework to report the overall works.

A total of 87 articles, which covered 14 CQI models, were included in the review. While 19 tools were used for CQI models and initiatives, Plan-Do-Study/Check-Act cycle was the commonly employed model to understand the CQI implementation process. The main reported purposes of using CQI, as its positive impact, are to improve the structure of the health system (e.g., leadership, health workforce, health technology use, supplies, and costs), enhance healthcare delivery processes and outputs (e.g., care coordination and linkages, satisfaction, accessibility, continuity of care, safety, and efficiency), and improve treatment outcome (reduce morbidity and mortality). The implementation of CQI is not without challenges. There are cultural (i.e., resistance/reluctance to quality-focused culture and fear of blame or punishment), technical, structural (related to organizational structure, processes, and systems), and strategic (inadequate planning and inappropriate goals) related barriers that were commonly reported during the implementation of CQI.

Conclusions

Implementing CQI initiatives necessitates thoroughly comprehending key principles such as teamwork and timeline. To effectively address challenges, it’s crucial to identify obstacles and implement optimal interventions proactively. Healthcare professionals and leaders need to be mentally equipped and cognizant of the significant role CQI initiatives play in achieving purposes for quality of care.

Peer Review reports

Continuous quality improvement (CQI) initiative is a crucial initiative aimed at enhancing quality in the health system that has gradually been adopted in the healthcare industry. In the early 20th century, Shewhart laid the foundation for quality improvement by describing three essential steps for process improvement: specification, production, and inspection [ 1 , 2 ]. Then, Deming expanded Shewhart’s three-step model into ‘plan, do, study/check, and act’ (PDSA or PDCA) cycle, which was applied to management practices in Japan in the 1950s [ 3 ] and was gradually translated into the health system. In 1991, Kuperman applied a CQI approach to healthcare, comprising selecting a process to be improved, assembling a team of expert clinicians that understands the process and the outcomes, determining key steps in the process and expected outcomes, collecting data that measure the key process steps and outcomes, and providing data feedback to the practitioners [ 4 ]. These philosophies have served as the baseline for the foundation of principles for continuous improvement [ 5 ].

Continuous quality improvement fosters a culture of continuous learning, innovation, and improvement. It encourages proactive identification and resolution of problems, promotes employee engagement and empowerment, encourages trust and respect, and aims for better quality of care [ 6 , 7 ]. These characteristics drive the interaction of CQI with other quality improvement projects, such as quality assurance and total quality management [ 8 ]. Quality assurance primarily focuses on identifying deviations or errors through inspections, audits, and formal reviews, often settling for what is considered ‘good enough’, rather than pursuing the highest possible standards [ 9 , 10 ], while total quality management is implemented as the management philosophy and system to improve all aspects of an organization continuously [ 11 ].

Continuous quality improvement has been implemented to provide quality care. However, providing effective healthcare is a complicated and complex task in achieving the desired health outcomes and the overall well-being of individuals and populations. It necessitates tackling issues, including access, patient safety, medical advances, care coordination, patient-centered care, and quality monitoring [ 12 , 13 ], rooted long ago. It is assumed that the history of quality improvement in healthcare started in 1854 when Florence Nightingale introduced quality improvement documentation [ 14 ]. Over the passing decades, Donabedian introduced structure, processes, and outcomes as quality of care components in 1966 [ 15 ]. More comprehensively, the Institute of Medicine in the United States of America (USA) has identified effectiveness, efficiency, equity, patient-centredness, safety, and timeliness as the components of quality of care [ 16 ]. Moreover, quality of care has recently been considered an integral part of universal health coverage (UHC) [ 17 ], which requires initiatives to mobilise essential inputs [ 18 ].

While the overall objective of CQI in health system is to enhance the quality of care, it is important to note that the purposes and principles of CQI can vary across different contexts [ 19 , 20 ]. This variation has sparked growing research interest. For instance, a review of CQI approaches for capacity building addressed its role in health workforce development [ 21 ]. Another systematic review, based on random-controlled design studies, assessed the effectiveness of CQI using training as an intervention and the PDSA model [ 22 ]. As a research gap, the former review was not directly related to the comprehensive elements of quality of care, while the latter focused solely on the impact of training using the PDSA model, among other potential models. Additionally, a review conducted in 2015 aimed to identify barriers and facilitators of CQI in Canadian contexts [ 23 ]. However, all these reviews presented different perspectives and investigated distinct outcomes. This suggests that there is still much to explore in terms of comprehensively understanding the various aspects of CQI initiatives in healthcare.

As a result, we conducted a scoping review to address several aspects of CQI. Scoping reviews serve as a valuable tool for systematically mapping the existing literature on a specific topic. They are instrumental when dealing with heterogeneous or complex bodies of research. Scoping reviews provide a comprehensive overview by summarizing and disseminating findings across multiple studies, even when evidence varies significantly [ 24 ]. In our specific scoping review, we included various types of literature, including systematic reviews, to enhance our understanding of CQI.

This scoping review examined how CQI is conceptualized and measured and investigated models and tools for its application while identifying implementation challenges and facilitators. It also analyzed the purposes and impact of CQI on the health systems, providing valuable insights for enhancing healthcare quality.

Protocol registration and results reporting

Protocol registration for this scoping review was not conducted. Arksey and O’Malley’s methodological framework was utilized to conduct this scoping review [ 25 ]. The scoping review procedures start by defining the research questions, identifying relevant literature, selecting articles, extracting data, and summarizing the results. The review findings are reported using the PRISMA extension for a scoping review (PRISMA-ScR) [ 26 ]. McGowan and colleagues also advised researchers to report findings from scoping reviews using PRISMA-ScR [ 27 ].

Defining the research problems

This review aims to comprehensively explore the conceptualization, models, tools, barriers, facilitators, and impacts of CQI within the healthcare system worldwide. Specifically, we address the following research questions: (1) How has CQI been defined across various contexts? (2) What are the diverse approaches to implementing CQI in healthcare settings? (3) Which tools are commonly employed for CQI implementation ? (4) What barriers hinder and facilitators support successful CQI initiatives? and (5) What effects CQI initiatives have on the overall care quality?

Information source and search strategy

We conducted the search in PubMed, Web of Science, Scopus, and EMBASE databases, and the Google Scholar search engine. The search terms were selected based on three main distinct concepts. One group was CQI-related terms. The second group included terms related to the purpose for which CQI has been implemented, and the third group included processes and impact. These terms were selected based on the Donabedian framework of structure, process, and outcome [ 28 ]. Additionally, the detailed keywords were recruited from the primary health framework, which has described lists of dimensions under process, output, outcome, and health system goals of any intervention for health [ 29 ]. The detailed search strategy is presented in the Supplementary file 1 (Search strategy). The search for articles was initiated on August 12, 2023, and the last search was conducted on September 01, 2023.

Eligibility criteria and article selection

Based on the scoping review’s population, concept, and context frameworks [ 30 ], the population included any patients or clients. Additionally, the concepts explored in the review encompassed definitions, implementation, models, tools, barriers, facilitators, and impacts of CQI. Furthermore, the review considered contexts at any level of health systems. We included articles if they reported results of qualitative or quantitative empirical study, case studies, analytic or descriptive synthesis, any review, and other written documents, were published in peer-reviewed journals, and were designed to address at least one of the identified research questions or one of the identified implementation outcomes or their synonymous taxonomy as described in the search strategy. Based on additional contexts, we included articles published in English without geographic and time limitations. We excluded articles with abstracts only, conference abstracts, letters to editors, commentators, and corrections.

We exported all citations to EndNote x20 to remove duplicates and screen relevant articles. The article selection process includes automatic duplicate removal by using EndNote x20, unmatched title and abstract removal, citation and abstract-only materials removal, and full-text assessment. The article selection process was mainly conducted by the first author (AE) and reported to the team during the weekly meetings. The first author encountered papers that caused confusion regarding whether to include or exclude them and discussed them with the last author (YA). Then, decisions were ultimately made. Whenever disagreements happened, they were resolved by discussion and reconsideration of the review questions in relation to the written documents of the article. Further statistical analysis, such as calculating Kappa, was not performed to determine article inclusion or exclusion.

Data extraction and data items

We extracted first author, publication year, country, settings, health problem, the purpose of the study, study design, types of intervention if applicable, CQI approaches/steps if applicable, CQI tools and procedures if applicable, and main findings using a customized Microsoft Excel form.

Summarizing and reporting the results

The main findings were summarized and described based on the main themes, including concepts under conceptualizing, principles, teams, timelines, models, tools, barriers, facilitators, and impacts of CQI. Results-based convergent synthesis, achieved through mixed-method analysis, involved content analysis to identify the thematic presentation of findings. Additionally, a narrative description was used for quantitative findings, aligning them with the appropriate theme. The authors meticulously reviewed the primary findings from each included material and contextualized these findings concerning the main themes1. This approach provides a comprehensive understanding of complex interventions and health systems, acknowledging quantitative and qualitative evidence.

Search results

A total of 11,251 documents were identified from various databases: SCOPUS ( n  = 4,339), PubMed ( n  = 2,893), Web of Science ( n  = 225), EMBASE ( n  = 3,651), and Google Scholar ( n  = 143). After removing duplicates ( n  = 5,061), 6,190 articles were evaluated by title and abstract. Subsequently, 208 articles were assessed for full-text eligibility. Following the eligibility criteria, 121 articles were excluded, leaving 87 included in the current review (Fig.  1 ).

Article selection process

Operationalizing continuous quality improvement

Continuous Quality Improvement (CQI) is operationalized as a cyclic process that requires commitment to implementation, teamwork, time allocation, and celebrating successes and failures.

CQI is a cyclic ongoing process that is followed reflexive, analytical and iterative steps, including identifying gaps, generating data, developing and implementing action plans, evaluating performance, providing feedback to implementers and leaders, and proposing necessary adjustments [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].

CQI requires committing to the philosophy, involving continuous improvement [ 19 , 38 ], establishing a mission statement [ 37 ], and understanding quality definition [ 19 ].

CQI involves a wide range of patient-oriented measures and performance indicators, specifically satisfying internal and external customers, developing quality assurance, adopting common quality measures, and selecting process measures [ 8 , 19 , 35 , 36 , 37 , 39 , 40 ].

CQI requires celebrating success and failure without personalization, leading each team member to develop error-free attitudes [ 19 ]. Success and failure are related to underlying organizational processes and systems as causes of failure rather than blaming individuals [ 8 ] because CQI is process-focused based on collaborative, data-driven, responsive, rigorous and problem-solving statistical analysis [ 8 , 19 , 38 ]. Furthermore, a gap or failure opens another opportunity for establishing a data-driven learning organization [ 41 ].

CQI cannot be implemented without a CQI team [ 8 , 19 , 37 , 39 , 42 , 43 , 44 , 45 , 46 ]. A CQI team comprises individuals from various disciplines, often comprising a team leader, a subject matter expert (physician or other healthcare provider), a data analyst, a facilitator, frontline staff, and stakeholders [ 39 , 43 , 47 , 48 , 49 ]. It is also important to note that inviting stakeholders or partners as part of the CQI support intervention is crucial [ 19 , 38 , 48 ].

The timeline is another distinct feature of CQI because the results of CQI vary based on the implementation duration of each cycle [ 35 ]. There is no specific time limit for CQI implementation, although there is a general consensus that a cycle of CQI should be relatively short [ 35 ]. For instance, a CQI implementation took 2 months [ 42 ], 4 months [ 50 ], 9 months [ 51 , 52 ], 12 months [ 53 , 54 , 55 ], and one year and 5 months [ 49 ] duration to achieve the desired positive outcome, while bi-weekly [ 47 ] and monthly data reviews and analyses [ 44 , 48 , 56 ], and activities over 3 months [ 57 ] have also resulted in a positive outcome.

Continuous quality improvement models and tools

There have been several models are utilized. The Plan-Do-Study/Check-Act cycle is a stepwise process involving project initiation, situation analysis, root cause identification, solution generation and selection, implementation, result evaluation, standardization, and future planning [ 7 , 36 , 37 , 45 , 47 , 48 , 49 , 50 , 51 , 53 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. The FOCUS-PDCA cycle enhances the PDCA process by adding steps to find and improve a process (F), organize a knowledgeable team (O), clarify the process (C), understand variations (U), and select improvements (S) [ 55 , 71 , 72 , 73 ]. The FADE cycle involves identifying a problem (Focus), understanding it through data analysis (Analyze), devising solutions (Develop), and implementing the plan (Execute) [ 74 ]. The Logic Framework involves brainstorming to identify improvement areas, conducting root cause analysis to develop a problem tree, logically reasoning to create an objective tree, formulating the framework, and executing improvement projects [ 75 ]. Breakthrough series approach requires CQI teams to meet in quarterly collaborative learning sessions, share learning experiences, and continue discussion by telephone and cross-site visits to strengthen learning and idea exchange [ 47 ]. Another CQI model is the Lean approach, which has been conducted with Kaizen principles [ 52 ], 5 S principles, and the Six Sigma model. The 5 S (Sort, Set/Straighten, Shine, Standardize, Sustain) systematically organises and improves the workplace, focusing on sorting, setting order, shining, standardizing, and sustaining the improvement [ 54 , 76 ]. Kaizen principles guide CQI by advocating for continuous improvement, valuing all ideas, solving problems, focusing on practical, low-cost improvements, using data to drive change, acknowledging process defects, reducing variability and waste, recognizing every interaction as a customer-supplier relationship, empowering workers, responding to all ideas, and maintaining a disciplined workplace [ 77 ]. Lean Six Sigma, a CQI model, applies the DMAIC methodology, which involves defining (D) and measuring the problem (M), analyzing root causes (A), improving by finding solutions (I), and controlling by assessing process stability (C) [ 78 , 79 ]. The 5 C-cyclic model (consultation, collection, consideration, collaboration, and celebration), the first CQI framework for volunteer dental services in Aboriginal communities, ensures quality care based on community needs [ 80 ]. One study used meetings involving activities such as reviewing objectives, assigning roles, discussing the agenda, completing tasks, retaining key outputs, planning future steps, and evaluating the meeting’s effectiveness [ 81 ].

Various tools are involved in the implementation or evaluation of CQI initiatives: checklists [ 53 , 82 ], flowcharts [ 81 , 82 , 83 ], cause-and-effect diagrams (fishbone or Ishikawa diagrams) [ 60 , 62 , 79 , 81 , 82 ], fuzzy Pareto diagram [ 82 ], process maps [ 60 ], time series charts [ 48 ], why-why analysis [ 79 ], affinity diagrams and multivoting [ 81 ], and run chart [ 47 , 48 , 51 , 60 , 84 ], and others mentioned in the table (Table  1 ).

Barriers and facilitators of continuous quality improvement implementation

Implementing CQI initiatives is determined by various barriers and facilitators, which can be thematized into four dimensions. These dimensions are cultural, technical, structural, and strategic dimensions.

Continuous quality improvement initiatives face various cultural, strategic, technical, and structural barriers. Cultural dimension barriers involve resistance to change (e.g., not accepting online technology), lack of quality-focused culture, staff reporting apprehensiveness, and fear of blame or punishment [ 36 , 41 , 85 , 86 ]. The technical dimension barriers of CQI can include various factors that hinder the effective implementation and execution of CQI processes [ 36 , 86 , 87 , 88 , 89 ]. Structural dimension barriers of CQI arise from the organization structure, process, and systems that can impede the effective implementation and sustainability of CQI [ 36 , 85 , 86 , 87 , 88 ]. Strategic dimension barriers are, for example, the inability to select proper CQI goals and failure to integrate CQI into organizational planning and goals [ 36 , 85 , 86 , 87 , 88 , 90 ].

Facilitators are also grouped to cultural, structural, technical, and strategic dimensions to provide solutions to CQI barriers. Cultural challenges were addressed by developing a group culture to CQI and other rewards [ 39 , 41 , 80 , 85 , 86 , 87 , 90 , 91 , 92 ]. Technical facilitators are pivotal to improving technical barriers [ 39 , 42 , 53 , 69 , 86 , 90 , 91 ]. Structural-related facilitators are related to improving communication, infrastructure, and systems [ 86 , 92 , 93 ]. Strategic dimension facilitators include strengthening leadership and improving decision-making skills [ 43 , 53 , 67 , 86 , 87 , 92 , 94 , 95 ] (Table  2 ).

Impact of continuous quality improvement

Continuous quality improvement initiatives can significantly impact the quality of healthcare in a wide range of health areas, focusing on improving structure, the health service delivery process and improving client wellbeing and reducing mortality.

Structure components

These are health leadership, financing, workforce, technology, and equipment and supplies. CQI has improved planning, monitoring and evaluation [ 48 , 53 ], and leadership and planning [ 48 ], indicating improvement in leadership perspectives. Implementing CQI in primary health care (PHC) settings has shown potential for maintaining or reducing operation costs [ 67 ]. Findings from another study indicate that the costs associated with implementing CQI interventions per facility ranged from approximately $2,000 to $10,500 per year, with an average cost of approximately $10 to $60 per admitted client [ 57 ]. However, based on model predictions, the average cost savings after implementing CQI were estimated to be $5430 [ 31 ]. CQI can also be applied to health workforce development [ 32 ]. CQI in the institutional system improved medical education [ 66 , 96 , 97 ], human resources management [ 53 ], motivated staffs [ 76 ], and increased staff health awareness [ 69 ], while concerns raised about CQI impartiality, independence, and public accountability [ 96 ]. Regarding health technology, CQI also improved registration and documentation [ 48 , 53 , 98 ]. Furthermore, the CQI initiatives increased cleanliness [ 54 ] and improved logistics, supplies, and equipment [ 48 , 53 , 68 ].

Process and output components

The process component focuses on the activities and actions involved in delivering healthcare services.

Service delivery

CQI interventions improved service delivery [ 53 , 56 , 99 ], particularly a significant 18% increase in the overall quality of service performance [ 48 ], improved patient counselling, adherence to appropriate procedures, and infection prevention [ 48 , 68 ], and optimised workflow [ 52 ].

Coordination and collaboration

CQI initiatives improved coordination and collaboration through collecting and analysing data, onsite technical support, training, supportive supervision [ 53 ] and facilitating linkages between work processes and a quality control group [ 65 ].

Patient satisfaction

The CQI initiatives increased patient satisfaction and improved quality of life by optimizing care quality management, improving the quality of clinical nursing, reducing nursing defects and enhancing the wellbeing of clients [ 54 , 76 , 100 ], although CQI was not associated with changes in adolescent and young adults’ satisfaction [ 51 ].

CQI initiatives reduced medication error reports from 16 to 6 [ 101 ], and it significantly reduced the administration of inappropriate prophylactic antibiotics [ 44 ], decreased errors in inpatient care [ 52 ], decreased the overall episiotomy rate from 44.5 to 33.3% [ 83 ], reduced the overall incidence of unplanned endotracheal extubation [ 102 ], improving appropriate use of computed tomography angiography [ 103 ], and appropriate diagnosis and treatment selection [ 47 ].

Continuity of care

CQI initiatives effectively improve continuity of care by improving client and physician interaction. For instance, provider continuity levels showed a 64% increase [ 55 ]. Modifying electronic medical record templates, scheduling, staff and parental education, standardization of work processes, and birth to 1-year age-specific incentives in post-natal follow-up care increased continuity of care to 74% in 2018 compared to baseline 13% in 2012 [ 84 ].

The CQI initiative yielded enhanced efficiency in the cardiac catheterization laboratory, as evidenced by improved punctuality in procedure starts and increased efficiency in manual sheath-pulls inside [ 78 ].

Accessibility

CQI initiatives were effective in improving accessibility in terms of increasing service coverage and utilization rate. For instance, screening for cigarettes, nutrition counselling, folate prescription, maternal care, immunization coverage [ 53 , 81 , 104 , 105 ], reducing the percentage of non-attending patients to surgery to 0.9% from the baseline 3.9% [ 43 ], increasing Chlamydia screening rates from 29 to 60% [ 45 ], increasing HIV care continuum coverage [ 51 , 59 , 60 ], increasing in the uptake of postpartum long-acting reversible contraceptive use from 6.9% at the baseline to 25.4% [ 42 ], increasing post-caesarean section prophylaxis from 36 to 89% [ 62 ], a 31% increase of kangaroo care practice [ 50 ], and increased follow-up [ 65 ]. Similarly, the QI intervention increased the quality of antenatal care by 29.3%, correct partograph use by 51.7%, and correct active third-stage labour management, a 19.6% improvement from the baseline, but not significantly associated with improvement in contraceptive service uptake [ 61 ].

Timely access

CQI interventions improved the time care provision [ 52 ], and reduced waiting time [ 62 , 74 , 76 , 106 ]. For instance, the discharge process waiting time in the emergency department decreased from 76 min to 22 min [ 79 ]. It also reduced mean postprocedural length of stay from 2.8 days to 2.0 days [ 31 ].

Acceptability

Acceptability of CQI by healthcare providers was satisfactory. For instance, 88% of the faculty, 64% of the residents, and 82% of the staff believed CQI to be useful in the healthcare clinic [ 107 ].

Outcome components

Morbidity and mortality.

CQI efforts have demonstrated better management outcomes among diabetic patients [ 40 ], patients with oral mucositis [ 71 ], and anaemic patients [ 72 ]. It has also reduced infection rate in post-caesarean Sect. [ 62 ], reduced post-peritoneal dialysis peritonitis [ 49 , 108 ], and prevented pressure ulcers [ 70 ]. It is explained by peritonitis incidence from once every 40.1 patient months at baseline to once every 70.8 patient months after CQI [ 49 ] and a 63% reduction in pressure ulcer prevalence within 2 years from 2008 to 2010 [ 70 ]. Furthermore, CQI initiatives significantly reduced in-hospital deaths [ 31 ] and increased patient survival rates [ 108 ]. Figure  2 displays the overall process of the CQI implementations.

The overall mechanisms of continuous quality improvement implementation

In this review, we examined the fundamental concepts and principles underlying CQI, the factors that either hinder or assist in its successful application and implementation, and the purpose of CQI in enhancing quality of care across various health issues.

Our findings have brought attention to the application and implementation of CQI, emphasizing its underlying concepts and principles, as evident in the existing literature [ 31 , 32 , 33 , 34 , 35 , 36 , 39 , 40 , 43 , 45 , 46 ]. Continuous quality improvement has shared with the principles of continuous improvement, such as a customer-driven focus, effective leadership, active participation of individuals, a process-oriented approach, systematic implementation, emphasis on design improvement and prevention, evidence-based decision-making, and fostering partnership [ 5 ]. Moreover, Deming’s 14 principles laid the foundation for CQI principles [ 109 ]. These principles have been adapted and put into practice in various ways: ten [ 19 ] and five [ 38 ] principles in hospitals, five principles for capacity building [ 38 ], and two principles for medication error prevention [ 41 ]. As a principle, the application of CQI can be process-focused [ 8 , 19 ] or impact-focused [ 38 ]. Impact-focused CQI focuses on achieving specific outcomes or impacts, whereas process-focused CQI prioritizes and improves the underlying processes and systems. These principles complement each other and can be utilized based on the objectives of quality improvement initiatives in healthcare settings. Overall, CQI is an ongoing educational process that requires top management’s involvement, demands coordination across departments, encourages the incorporation of views beyond clinical area, and provides non-judgemental evidence based on objective data [ 110 ].

The current review recognized that it was not easy to implement CQI. It requires reasonable utilization of various models and tools. The application of each tool can be varied based on the studied health problem and the purpose of CQI initiative [ 111 ], varied in context, content, structure, and usability [ 112 ]. Additionally, overcoming the cultural, technical, structural, and strategic-related barriers. These barriers have emerged from clinical staff, managers, and health systems perspectives. Of the cultural obstacles, staff non-involvement, resistance to change, and reluctance to report error were staff-related. In contrast, others, such as the absence of celebration for success and hierarchical and rational culture, may require staff and manager involvement. Staff members may exhibit reluctance in reporting errors due to various cultural factors, including lack of trust, hierarchical structures, fear of retribution, and a blame-oriented culture. These challenges pose obstacles to implementing standardized CQI practices, as observed, for instance, in community pharmacy settings [ 85 ]. The hierarchical culture, characterized by clearly defined levels of power, authority, and decision-making, posed challenges to implementing CQI initiatives in public health [ 41 , 86 ]. Although rational culture, a type of organizational culture, emphasizes logical thinking and rational decision-making, it can also create challenges for CQI implementation [ 41 , 86 ] because hierarchical and rational cultures, which emphasize bureaucratic norms and narrow definitions of achievement, were found to act as barriers to the implementation of CQI [ 86 ]. These could be solved by developing a shared mindset and collective commitment, establishing a shared purpose, developing group norms, and cultivating psychological preparedness among staff, managers, and clients to implement and sustain CQI initiatives. Furthermore, reversing cultural-related barriers necessitates cultural-related solutions: development of a culture and group culture to CQI [ 41 , 86 ], positive comprehensive perception [ 91 ], commitment [ 85 ], involving patients, families, leaders, and staff [ 39 , 92 ], collaborating for a common goal [ 80 , 86 ], effective teamwork [ 86 , 87 ], and rewarding and celebrating successes [ 80 , 90 ].

The technical dimension barriers of CQI can include inadequate capitalization of a project and insufficient support for CQI facilitators and data entry managers [ 36 ], immature electronic medical records or poor information systems [ 36 , 86 ], and the lack of training and skills [ 86 , 87 , 88 ]. These challenges may cause the CQI team to rely on outdated information and technologies. The presence of barriers on the technical dimension may challenge the solid foundation of CQI expertise among staff, the ability to recognize opportunities for improvement, a comprehensive understanding of how services are produced and delivered, and routine use of expertise in daily work. Addressing these technical barriers requires knowledge creation activities (training, seminar, and education) [ 39 , 42 , 53 , 69 , 86 , 90 , 91 ], availability of quality data [ 86 ], reliable information [ 92 ], and a manual-online hybrid reporting system [ 85 ].

Structural dimension barriers of CQI include inadequate communication channels and lack of standardized process, specifically weak physician-to-physician synergies [ 36 ], lack of mechanisms for disseminating knowledge and limited use of communication mechanisms [ 86 ]. Lack of communication mechanism endangers sharing ideas and feedback among CQI teams, leading to misunderstandings, limited participation and misinterpretations, and a lack of learning [ 113 ]. Knowledge translation facilitates the co-production of research, subsequent diffusion of knowledge, and the developing stakeholder’s capacity and skills [ 114 ]. Thus, the absence of a knowledge translation mechanism may cause missed opportunities for learning, inefficient problem-solving, and limited creativity. To overcome these challenges, organizations should establish effective communication and information systems [ 86 , 93 ] and learning systems [ 92 ]. Though CQI and knowledge translation have interacted with each other, it is essential to recognize that they are distinct. CQI focuses on process improvement within health care systems, aiming to optimize existing processes, reduce errors, and enhance efficiency.

In contrast, knowledge translation bridges the gap between research evidence and clinical practice, translating research findings into actionable knowledge for practitioners. While both CQI and knowledge translation aim to enhance health care quality and patient outcomes, they employ different strategies: CQI utilizes tools like Plan-Do-Study-Act cycles and statistical process control, while knowledge translation involves knowledge synthesis and dissemination. Additionally, knowledge translation can also serve as a strategy to enhance CQI. Both concepts share the same principle: continuous improvement is essential for both. Therefore, effective strategies on the structural dimension may build efficient and effective steering councils, information systems, and structures to diffuse learning throughout the organization.

Strategic factors, such as goals, planning, funds, and resources, determine the overall purpose of CQI initiatives. Specific barriers were improper goals and poor planning [ 36 , 86 , 88 ], fragmentation of quality assurance policies [ 87 ], inadequate reinforcement to staff [ 36 , 90 ], time constraints [ 85 , 86 ], resource inadequacy [ 86 ], and work overload [ 86 ]. These barriers can be addressed through strengthening leadership [ 86 , 87 ], CQI-based mentoring [ 94 ], periodic monitoring, supportive supervision and coaching [ 43 , 53 , 87 , 92 , 95 ], participation, empowerment, and accountability [ 67 ], involving all stakeholders in decision-making [ 86 , 87 ], a provider-payer partnership [ 64 ], and compensating staff for after-hours meetings on CQI [ 85 ]. The strategic dimension, characterized by a strategic plan and integrated CQI efforts, is devoted to processes that are central to achieving strategic priorities. Roles and responsibilities are defined in terms of integrated strategic and quality-related goals [ 115 ].

The utmost goal of CQI has been to improve the quality of care, which is usually revealed by structure, process, and outcome. After resolving challenges and effectively using tools and running models, the goal of CQI reflects the ultimate reason and purpose of its implementation. First, effectively implemented CQI initiatives can improve leadership, health financing, health workforce development, health information technology, and availability of supplies as the building blocks of a health system [ 31 , 48 , 53 , 68 , 98 ]. Second, effectively implemented CQI initiatives improved care delivery process (counselling, adherence with standards, coordination, collaboration, and linkages) [ 48 , 53 , 65 , 68 ]. Third, the CQI can improve outputs of healthcare delivery, such as satisfaction, accessibility (timely access, utilization), continuity of care, safety, efficiency, and acceptability [ 52 , 54 , 55 , 76 , 78 ]. Finally, the effectiveness of the CQI initiatives has been tested in enhancing responses related to key aspects of the HIV response, maternal and child health, non-communicable disease control, and others (e.g., surgery and peritonitis). However, it is worth noting that CQI initiative has not always been effective. For instance, CQI using a two- to nine-times audit cycle model through systems assessment tools did not bring significant change to increase syphilis testing performance [ 116 ]. This study was conducted within the context of Aboriginal and Torres Strait Islander people’s primary health care settings. Notably, ‘the clinics may not have consistently prioritized syphilis testing performance in their improvement strategies, as facilitated by the CQI program’ [ 116 ]. Additionally, by applying CQI-based mentoring, uptake of facility-based interventions was not significantly improved, though it was effective in increasing community health worker visits during pregnancy and the postnatal period, knowledge about maternal and child health and exclusive breastfeeding practice, and HIV disclosure status [ 117 ]. The study conducted in South Africa revealed no significant association between the coverage of facility-based interventions and Continuous Quality Improvement (CQI) implementation. This lack of association was attributed to the already high antenatal and postnatal attendance rates in both control and intervention groups at baseline, leaving little room for improvement. Additionally, the coverage of HIV interventions remained consistently high throughout the study period [ 117 ].

Regarding health care and policy implications, CQI has played a vital role in advancing PHC and fostering the realization of UHC goals worldwide. The indicators found in Donabedian’s framework that are positively influenced by CQI efforts are comparable to those included in the PHC performance initiative’s conceptual framework [ 29 , 118 , 119 ]. It is clearly explained that PHC serves as the roadmap to realizing the vision of UHC [ 120 , 121 ]. Given these circumstances, implementing CQI can contribute to the achievement of PHC principles and the objectives of UHC. For instance, by implementing CQI methods, countries have enhanced the accessibility, affordability, and quality of PHC services, leading to better health outcomes for their populations. CQI has facilitated identifying and resolving healthcare gaps and inefficiencies, enabling countries to optimize resource allocation and deliver more effective and patient-centered care. However, it is crucial to recognize that the successful implementation of Continuous Quality Improvement (CQI) necessitates optimizing the duration of each cycle, understanding challenges and barriers that extend beyond the health system and settings, and acknowledging that its effectiveness may be compromised if these challenges are not adequately addressed.

Despite abundant literature, there are still gaps regarding the relationship between CQI and other dimensions within the healthcare system. No studies have examined the impact of CQI initiatives on catastrophic health expenditure, effective service coverage, patient-centredness, comprehensiveness, equity, health security, and responsiveness.

Limitations

In conducting this review, it has some limitations to consider. Firstly, only articles published in English were included, which may introduce the exclusion of relevant non-English articles. Additionally, as this review follows a scoping methodology, the focus is on synthesising available evidence rather than critically evaluating or scoring the quality of the included articles.

Continuous quality improvement is investigated as a continuous and ongoing intervention, where the implementation time can vary across different cycles. The CQI team and implementation timelines were critical elements of CQI in different models. Among the commonly used approaches, the PDSA or PDCA is frequently employed. In most CQI models, a wide range of tools, nineteen tools, are commonly utilized to support the improvement process. Cultural, technical, structural, and strategic barriers and facilitators are significant in implementing CQI initiatives. Implementing the CQI initiative aims to improve health system blocks, enhance health service delivery process and output, and ultimately prevent morbidity and reduce mortality. For future researchers, considering that CQI is context-dependent approach, conducting scale-up implementation research about catastrophic health expenditure, effective service coverage, patient-centredness, comprehensiveness, equity, health security, and responsiveness across various settings and health issues would be valuable.

Availability of data and materials

The data used and/or analyzed during the current study are available in this manuscript and/or the supplementary file.

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Endalamaw, A., Khatri, R.B., Mengistu, T.S. et al. A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact. BMC Health Serv Res 24 , 487 (2024). https://doi.org/10.1186/s12913-024-10828-0

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Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Climate economics support for the UN climate targets

Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Received : 25 January 2023

Accepted : 21 February 2024

Published : 17 April 2024

Issue Date : 18 April 2024

DOI : https://doi.org/10.1038/s41586-024-07219-0

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