bmi research on renewable energy

Notification: View the latest site access restrictions, updates, and resources related to the coronavirus (COVID-19) »

100% Clean Electricity by 2035 Study

An NREL study shows there are multiple pathways to 100% clean electricity by 2035 that would produce significant benefits exceeding the additional power system costs.

For the study, funded by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, NREL modeled technology deployment, costs, benefits, and challenges to decarbonize the U.S. power sector by 2035, evaluating a range of future scenarios to achieve a net-zero power grid by 2035.

The exact technology mix and costs will be determined by research and development, among other factors, over the next decade. The results are published in Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 .

Scenario Approach

To examine what it would take to achieve a net-zero U.S. power grid by 2035, NREL leveraged decades of research on high-renewable power systems, from the Renewable Electricity Futures Study , to the Storage Futures Study , to the Los Angeles 100% Renewable Energy Study , to the Electrification Futures Study , and more.

NREL used its publicly available flagship  Regional Energy Deployment System   capacity expansion model to study supply-side scenarios representing a range of possible pathways to a net-zero power grid by 2035—from the most to the least optimistic availability and costs of technologies.

The scenarios apply a carbon constraint to:

• Achieve 100% clean electricity by 2035 under accelerated demand electrification
• Reduce economywide, energy-related emissions by 62% in 2035 relative to 2005 levels—a steppingstone to economywide decarbonization by 2050.

For each scenario, NREL modeled the least-cost option to maintain safe and reliable power during all hours of the year.

Key Findings

Technology deployment must rapidly scale up.

In all modeled scenarios, new clean energy technologies are deployed at an unprecedented scale and rate to achieve 100% clean electricity by 2035. As modeled, wind and solar energy provide 60%–80% of generation in the least-cost electricity mix in 2035, and the overall generation capacity grows to roughly three times the 2020 level by 2035—including a combined 2 terawatts of wind and solar.

To achieve those levels would require rapid and sustained growth in installations of solar and wind generation capacity. If there are challenges with siting and land use to be able to deploy this new generation capacity and associated transmission, nuclear capacity helps make up the difference and more than doubles today’s installed capacity by 2035.

Across the four scenarios, 5–8 gigawatts of new hydropower and 3–5 gigawatts of new geothermal capacity are also deployed by 2035. Diurnal storage (2–12 hours of capacity) also increases across all scenarios, with 120–350 gigawatts deployed by 2035 to ensure demand for electricity is met during all hours of the year.

Seasonal storage becomes important when clean electricity makes up about 80%–95% of generation and there is a multiday to seasonal mismatch of variable renewable supply and demand. Across the scenarios, seasonal capacity in 2035 ranges about 100–680 gigawatts.

Significant additional research is needed to understand the manufacturing and supply chain associated with the unprecedent deployment envisioned in the scenarios.

Significant Additional Transmission Capacity

In all scenarios, significant transmission is also added in many locations, mostly to deliver energy from wind-rich regions to major load centers in the eastern United States. As modeled, the total transmission capacity in 2035 is one to almost three times today’s capacity, which would require between 1,400 and 10,100 miles of new high-capacity lines per year, assuming new construction starts in 2026.

Climate and Health Benefits of Decarbonization Offset the Costs

NREL finds in all modeled scenarios the health and climate benefits associated with fewer emissions offset the power system costs to get to 100% clean electricity.

Decarbonizing the power grid by 2035 could total $330 billion to $740 billion in additional power system costs, depending on restrictions on new transmission and other infrastructure development. However, there is substantial reduction in petroleum use in transportation and natural gas in buildings and industry by 2035. As a result, up to 130,000 premature deaths are avoided by 2035, which could save between $390 billion to $400 billion in avoided mortality costs.

When factoring in the avoided cost of damage from floods, drought, wildfires, and hurricanes due to climate change, the United States could save over an additional $1.2 trillion—totaling an overall net benefit to society ranging from $920 billion to $1.2 trillion.

Necessary Actions To Achieve 100% Clean Electricity

The transition to a 100% clean electricity U.S. power system will require more than reduced technology costs. Several key actions will need to take place in the coming decade:

• Dramatic acceleration of electrification and increased efficiency in demand
• New energy infrastructure installed rapidly throughout the country
• Expanded clean technology manufacturing and the supply chain
• Continued research, development, demonstration, and deployment to bring emerging technologies to the market.

Failing to achieve any of the key actions could increase the difficulty of realizing the scenarios outlined in the study.

Study Resources

Full report, supporting materials.

Download the technical report, Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 .

Download the report overview infographic and a 1-slide summary brief deck or a 10-slide summary brief deck .

Paul Denholm

Principal Energy Analyst

Energy Analysis Delivered to Your Inbox

Your personal data will only be used for as long as you are subscribed. For more information, review the  NREL security and privacy policy .

Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Create an account

Create a free IEA account to download our reports or subcribe to a paid service.

• Key Findings
• 2021: A year of global economic recovery?
• Energy demand
• CO2 emissions
• Natural gas
• Electricity demand
• Electricity supply
• Methodological note
• Acknowledgements

Cite report

IEA (2021), Global Energy Review 2021 , IEA, Paris https://www.iea.org/reports/global-energy-review-2021, Licence: CC BY 4.0

Share this report

• Share on Twitter Twitter
• Share on Facebook Facebook
• Share on LinkedIn LinkedIn
• Share on Email Email
• Share on Print Print

Report options

Renewables bucked the trend in 2020.

Renewable energy use increased 3% in 2020 as demand for all other fuels declined. The primary driver was an almost 7% growth in electricity generation from renewable sources. Long-term contracts, priority access to the grid, and continuous installation of new plants underpinned renewables growth despite lower electricity demand, supply chain challenges, and construction delays in many parts of the world. Accordingly, the share of renewables in global electricity generation jumped to 29% in 2020, up from 27% in 2019. Bioenergy use in industry grew 3%, but was largely offset by a decline in biofuels as lower oil demand also reduced the use of blended biofuels.

Renewables are on track to set new records in 2021

Renewable electricity generation in 2021 is set to expand by more than 8% to reach 8 300 TWh, the fastest year-on-year growth since the 1970s. Solar PV and wind are set to contribute two-thirds of renewables growth. China alone should account for almost half of the global increase in renewable electricity in 2021, followed by the United States, the European Union and India. 

Renewable electricity generation increase by technology, 2019-2020 and 2020-2021

Renewable electricity generation increase by technology, country and region, 2020-2021.

Wind is set for the largest increase in renewable generation, growing by 275 TWh, or almost 17%, which is significantly greater than 2020 levels. Policy deadlines in China and the United States drove developers to complete a record amount of capacity late in the fourth quarter of 2020, leading to notable increases in generation already from the first two months of 2021. Over the course of 2021, China is expected to generate 600 TWh and the United States 400 TWh, together representing more than half of global wind output.

While China will remain the largest PV market, expansion will continue in the United States with ongoing policy support at the federal and state level. Having experienced a significant decline in new solar PV capacity additions in 2020 as a result of Covid-related delays, India’s PV market is expected to recover rapidly in 2021, while increases in generation in Brazil and Viet Nam are driven by strong policy supports for distributed solar PV applications. Globally, solar PV electricity generation is expected to increase by 145 TWh, almost 18%, to approach 1 000 TWh in 2021.

We expect hydropower generation to increase further in 2021 through a combination of economic recovery and new capacity additions from large projects in China. Energy from waste electricity projects in Asia will drive growth of bioenergy, thanks to incentives.

Increases in electricity generation from all renewable sources should push the share of renewables in the electricity generation mix to an all-time high of 30% in 2021. Combined with nuclear, low-carbon sources of generation well and truly exceed output from the world’s coal plants in 2021.

Share of low-carbon sources and coal in world electricity generation, 1971-2021

In 2021, the biofuels market is likely to recover and approach 2019 production levels as transportation activity slowly resumes and biofuel blending rates increase. Biofuels are consumed mostly in road transportation, blended with gasoline and diesel fuels, and thus are less affected by continued depressed activity in the aviation sector. 

Subscription successful

Thank you for subscribing. You can unsubscribe at any time by clicking the link at the bottom of any IEA newsletter.

Technology Solutions » Electricity

Renewable energy, at-a-glance.

• Renewable energy is the fastest-growing energy source in the United States, increasing 42 percent from 2010 to 2020 (up 90 percent from 2000 to 2020).
• Renewables made up nearly 20 percent of utility-scale U.S. electricity generation in 2020, with the bulk coming from hydropower (7.3 percent) and wind power (8.4 percent).
• Solar generation (including distributed), which made up 3.3 percent of total U.S. generation in 2020, is the fastest-growing electricity source.
• Globally, renewables made up 29 percent of electricity generation in 2020, much of it from hydropower (16.8 percent).
• A record amount of over 256 GW of renewable power capacity was added globally during 2020.
• Renewable ethanol and biodiesel transportation fuels made up more than 17 percent of total U.S. renewable energy consumption in 2020, a decrease from recent years, likely due to the COVID-19 pandemic.

Renewable Supply and Demand

Renewable energy is the fastest-growing energy source globally and in the United States.

• About 11.2 percent of the energy consumed globally for heating,  power , and transportation came from modern renewables in 2019 (i.e., biomass, geothermal, solar, hydro, wind, and biofuels), up from 8.7 percent a decade prior (see figure below).
• Renewables made up 29  percent of global electricity generation by the end of 2020. Led by wind power and solar PV, more than 256 GW of capacity was added in 2020, an increase of nearly 10 percent in total installed renewable power capacity.

The  International Energy Agency  notes that the development and deployment of renewable electricity technologies are projected to continue to be deployed at record levels, but government policies and financial support are needed to incentivize even greater deployments of clean electricity (and supporting infrastructure) to give the world a chance to achieve its net zero climate goals.

Estimated Global Renewable Energy Share of Total Final Energy Consumption (2009-2019)

Renewable Energy Policy Network for the 21 st Century , p. 31. (2019)

In the United States:

• Almost 5 percent of the energy consumed across sectors in the United States was from renewable sources in 2020 (11.6 quadrillion Btu out of a total of 92.9 quadrillion Btu). U.S. consumption of renewables is expected to grow over the next 30 years at an average annual rate of 2.4 percent, higher than the overall growth rate in energy consumption (0.5 percent per year) under a business-as-usual scenario.
• Renewables made up 19.8 percent of electricity generation in 2020, with hydro and wind making up the majority. That’s expected to rise to 35 percent by 2030. Most of the increase is expected to come from wind and solar. Non-hydro renewables have increased their share of electric power generation from less than 1 percent in 2005 to over 12.5 percent at the end of 2020 while demand for electricity has remained relatively stable.

In the transportation sector, renewable fuels, such as ethanol and biodiesel, have increased significantly during the past decade. However, slower growth (i.e., 0.6 – 0.7 percent annual growth) is expected out to mid-century.

In the  industrial sector , biomass makes up 98 percent of the renewable energy use with roughly 60 percent derived from biomass wood, 31 percent from biofuels, and nearly 7 percent from biomass waste.

Uncertainty about federal tax credits (e.g., Renewable Fuel Standard), California’s Low Carbon fuel standard, fuel prices, and economic growth will influence the pace of U.S. renewable energy source development.

Renewable Energy Drivers

Factors affecting renewable energy deployment include market conditions (e.g., cost, diversity, proximity to demand or transmission, and resource availability), policy decisions, (e.g., tax credits, feed-in tariffs, and renewable portfolio standards) as well as specific regulations. Nearly all countries had renewable energy policy targets in place at the end of 2020.

Businesses with sustainability goals are also driving renewable energy development by building their own facilities (e.g., solar roofs and wind farms), procuring renewable electricity through power purchase agreements, and purchasing renewable energy certificates (RECs).

Wind and solar renewable energy technologies have seen substantial cost declines over the past decade. Between 2010 and 2019, the cost of utility-scale solar photovoltaics fell 82 percent, and the cost of onshore wind fell 39 percent. Increased demand and procurement requires more of these technologies to be manufactured and developed, causing reduced costs due to learning and economies of scale, which increases the incentive for additional procurement.

Global weighted average levelized cost of electricity from utility-scale power generation technologies, 2010 and 2019

Policy Drivers

Two federal tax credits have encouraged renewable energy in the United States:

• The production tax credit (PTC), first enacted in 1992 and subsequently amended, was a corporate tax credit available to a wide range of renewable technologies including wind, landfill gas, geothermal, and small hydroelectric. For eligible technologies, the utility received a 2.2 ¢/kWh ($22/MWh) credit for all electricity generated during the first 10 years of operation. The PTC is currently being phased out; at the end of December 2020, the PTC was extended for another year at 60 percent of the full credit amount, and facilities beginning construction after December 31, 2021 will no longer be able to claim this credit.
• The investment tax credit (ITC) is earned when qualifying equipment, including solar hot water, photovoltaics, and small wind turbines, are placed into service. The credit reduces installation costs and shortens the payback time of these technologies. The Consolidated Appropriations Act (2016) extended the ITC for three years, but Congress then passed a two year delay in 2020. It will phase down to 10 percent in 2024 (from 26 percent in 2021).

States  offer added incentives, making renewables even easier to implement from a cost perspective.  

A renewable portfolio standard requires electric utilities to deliver a certain amount of electricity from renewable or alternative energy sources by a given date. State standards range from modest to ambitious, and qualifying energy sources vary. Some states also include “carve-outs” (requirements that a certain percentage of the portfolio be generated from a specific energy source, such as solar power) or other incentives to encourage the development of particular resources. Although climate change may not be the prime motivation behind these standards, they can deliver significant greenhouse gas reductions and other benefits, including job creation, energy security, and cleaner air. Most states allow utilities to comply with the renewable portfolio standard through tradeable credits that utilities can sell for additional revenue.

In  states with a renewable portfolio standard , utilities consider cost, intermittency and resource availability in choosing technologies that satisfy this requirement.

In the U.S. transportation sector,  The Energy Policy Act of 2005  created a Renewable Fuel Standard that required 2.78 percent of gasoline consumed in the United States in 2006 to be renewable fuel.

The Energy Independence and Security Act of 2007  created a new Renewable Fuel Standard, which increased the required volumes of to 36 billion gallons by 2022, or about 7 percent of expected annual gasoline and diesel consumption above a business-as-usual scenario.

Types of Renewable Energy

Renewable energy comes from sources that can be regenerated or naturally replenished. The main sources are:

• Water (hydropower and hydrokinetic)
• Solar (power and hot water)
• Biomass (biofuel and biopower)
• Geothermal (power and heating)

All sources of renewable energy are used to generate electric power. In addition, geothermal steam is used directly for heating and cooking. Biomass and solar sources are also used for space and water heating. Ethanol and biodiesel (and to a lesser extent, gaseous biomethane) are used for transportation.

Renewable energy sources are considered to be zero (wind, solar, and water), low (geothermal) or neutral (biomass) with regard to greenhouse gas emissions during their operation. A neutral source has emissions that are balanced by the amount of carbon dioxide absorbed during the growing process. However, each source’s overall environmental impact depends on its overall lifecycle emissions, including manufacturing of equipment and materials, installation as well as land-use impacts.

Large conventional hydropower projects currently provide the majority of renewable electric power generation worldwide. With about 1,170 gigawatts (GW) of global capacity,  hydropower produced  an estimated 4,370 terawatt hours (TWh) of the roughly 26,000 TWh total global electricity in 2020.

The United States is the fourth-largest producer of hydropower after China, Brazil, and Canada. In 2011, a much wetter than average year in the U.S. Northwest, the United States generated 7.9 percent of its total electricity from hydropower. The Department of Energy has found that the  untapped generation potential at existing U.S. dams  designed for purposes other than power production (i.e., water supply, flood control, and inland navigation) represents 12 GW, roughly 15 percent of current hydropower capacity.

Hydropower operational costs are relatively low, and hydropower generates little to no greenhouse gas emissions. The main environmental impact is that a dam to create a reservoir or divert water to a hydropower plant changes the ecosystem and physical characteristic of the river.

Waterpower captures the energy of flowing water in rivers, streams, and waves to generate electricity. Conventional hydropower plants can be built in rivers with no water storage (known as “run-of-the-river” units) or in conjunction with reservoirs that store water, which can be used on an as-needed basis. As water travels downstream, it is channeled down through a pipe or other intake structure in a dam (penstock). The flowing water turns the blades of a turbine, generating electricity in the powerhouse, located at the base of the dam.

Other Hydroelectric Power Generation

Small hydropower projects, generally less than 10 megawatts (MW), and micro-hydropower (less than 1 MW) are less costly to develop and have a lower environmental impact than large conventional hydropower projects. In 2019, the total amount of small hydro installed worldwide was 78 GW.  China had the largest share  at 54 percent. China, Italy, Japan, Norway and the United States are the top five small hydro countries by installed capacity. Many countries have renewable energy targets that include the development of small hydro projects.

Hydrokinetic electric power, including wave and tidal power, is a form of unconventional hydropower that captures energy from waves or currents and does not require dam construction. These technologies are in various stages of research, development, and deployment. In 2011, a 254 MW  tidal power plant in South Korea  began operation, doubling the global capacity to 527 MW. By the end of 2018, global capacity was about 532 MW.

Low-head hydro is a commercially available source of hydrokinetic electric power that has been used in farming areas for more than 100 years. Generally, the capacity of these devices is small, ranging from 1kW to 250kW.

Pumped storage hydropower plants use inexpensive electricity (typically overnight during periods of low demand) to pump water from a lower-lying storage reservoir to a storage reservoir located above the power house for later use during periods of peak electricity demand. Although economically viable, this strategy is not considered renewable since it uses more electricity than it generates.

Hydroelectric Power Generation

Environment Canada, 2012

Wind was the second largest renewable energy source worldwide (after hydropower) for power generation. Wind power produced more than 6 percent of global electricity in 2020 with 743 GW of  global capacity  (707.4 GW is onshore). Capacity is indicative of the maximum amount of electricity that can be generated when the wind is blowing at sufficient levels for a turbine. Because the wind is not always blowing, wind farms do not always produce as much as their capacity. With around 290 MW,  China  had the largest installed capacity of wind generation in 2020. The United States, with 122.5 GW, had the second-largest capacity; Texas, Oklahoma, Iowa, and Kansas provide more than half of U.S. wind generation, with Texas greatly leading all other states in installed capacity, at 27 percent of the U.S. total. In 2019, wind energy overtook hydropower for the largest share of renewable generation in the U.S., providing 8.4 percent of electricity in 2020.

Although people have harnessed the energy generated by the movement of air for hundreds of years, modern turbines reflect significant technological advances over early windmills and even over turbines from just 10 years ago. Generating electric power using wind turbines creates no greenhouse gases, but since a wind farm includes dozens or more turbines, widely-spaced, it requires thousands of acres of land. For example, Lone Star is a 200 MW wind farm on approximately 36,000 acres in Texas. However, most of the land in between turbines can still be utilized for farming or grazing.

Average turbine size has been steadily increasing over the past 30 years. Today, new onshore turbines are typically in the range of 2 – 5 MW. The largest production models, designed for off-shore use can generate 12 MW; some innovative turbine models under development are expected to generate more than 14 MW in offshore projects in the coming years. Due to higher costs and technology constraints, off-shore capacity, approximately 35.6 GW in 2020, is only a small share (about 5 percent) of total installed wind generation capacity.

Wind Turbine Sizes

GE, Vox, 2019

Solar energy resources are massive and widespread, and they can be harnessed anywhere that receives sunlight. The amount of solar radiation, also known as insolation, reaching  the Earth’s surface  every hour is more than all the energy currently consumed by all human activities each year. A number of factors, including geographic location, time of day, and weather conditions, all affect the amount of energy that can be harnessed for electricity production or heating purposes.

Solar photovoltaics are the fastest growing electricity source. In 2020, around 139 GW of global capacity was added, bringing the total to about 760 GW and producing almost 3 percent of the world’s electricity.

Solar energy can be captured for electricity production using:

• A solar or photovoltaic cell, which converts sunlight into electricity using the photoelectric effect. Typically, photovoltaics are found on the roofs of residential and commercial buildings. Additionally, utilities have constructed large (greater than 100 MW) photovoltaic facilities that require anywhere from  5 to 13 acres per MW , depending on the technologies used. In the United States, non-residential solar (e.g. utility-scale) installations made up 16.7 GW, while residential solar (e.g. rooftop) installations made up 19.1 GW.
• Concentrating solar power (CSP), which uses lenses or mirrors to concentrate sunlight into a narrow beam that heats a fluid, producing steam to drive a turbine that generates electricity. Concentrating solar power projects are larger-scale than residential or commercial PV and are often owned and operated by electric utilities.
• Although utility-scale CSP plants were in operation long before solar photovoltaics became widely commercialized, solar photovoltaics have largely taken over this market, due to their declining costs. Global CSP capacity grew only 1.6 percent in 2020 to 6.2 GW.

Solar hot water heaters, typically found on the roofs of homes and apartments, provide residential hot water by using a solar collector, which absorbs solar energy, that in turn heats a conductive fluid, and transfers the heat to a water tank. Modern collectors are designed to be functional even in cold climates and on overcast days.

Electricity generated from solar energy emits no greenhouse gases. The main  environmental impacts  of solar energy come from the use of some hazardous materials (arsenic and cadmium) in the manufacturing of PV and the large amount of land required, hundreds of acres, for a utility-scale solar project.

Concentrating Solar Power

Solar collectors (i.e., parabolic troughs) capture and concentrate sunlight to heat a synthetic oil called therminol, which then heats water to create steam. The steam is piped to an onsite turbine-generator to produce electricity, which is then transmitted over power lines. On cloudy days, the plant has a supplementary natural gas boiler.

U.S. Department of Energy, 2019

Biomass energy sources are used to generate electricity and provide direct heating, and can be converted into biofuels as a direct substitute for fossil fuels used in transportation. Unlike intermittent wind and solar energy, biomass can be used continuously or according to a schedule.  Biomass is derived  from wood, waste, landfill gas, crops, and alcohol fuels. Traditional biomass, including waste wood, charcoal, and manure, has been a source of energy for domestic cooking and heating throughout human history. In rural areas of the developing world, it remains the dominant fuel source. Globally in 2019, bioenergy accounted for about 11.6 percent of total energy consumption. The growing use of biomass has resulted in increasing international trade in biomass fuels in recent years; wood pellets, biodiesel, and ethanol are the main fuels traded internationally.

In 2020, global biomass electric power capacity stood at 145 GW, increasing 5.8 percent from the previous year. The United States had 16 GW of installed biomass-fueled  electric generation capacity . In the United States, most of the electricity from wood biomass is generated at lumber and paper mills using their own wood waste; in addition, wood waste is used to generate the heat for drying wood products and other manufacturing processes. Biomass waste is mostly  municipal solid waste , i.e., garbage, which is burned as a fuel to run power plants. On average,  a ton of garbage  generates 550 to 750 kWh of electricity. Landfill gas contains methane that can be captured, processed and used to fuel power plants, manufacturing facilities, vehicles and homes. In the United States, there is currently more than 2 GW of installed  landfill gas-fired generation capacity  at more than 600 projects.

In addition to landfill gas, biofuels can be synthesized from dedicated crops, trees and grasses, agricultural waste, and algae feedstock; these include renewable forms of diesel, ethanol, butanol, methane, and other hydrocarbons. Corn ethanol is the most widely used biofuel in the United States. Roughly 39 percent of the  U.S. corn crop  was diverted to the production of ethanol for gasoline in 2019, up from 20 percent in 2006. Gasoline with up to 10 percent ethanol (E10) can be used in most vehicles without further modification, while special flexible fuel vehicles can use a gasoline-ethanol blend that has up to 85 percent ethanol (E85).

Closed-loop biomass, where power is generated using feedstocks grown specifically for the purpose of energy production, is generally considered to be carbon dioxide neutral because the carbon dioxide emitted during combustion of the fuel was previously captured during the growth of the feedstock. While biomass can avoid the use of fossil fuels, the net effect of biopower and biofuels on greenhouse gas emissions will depend on full lifecycle emissions for the biomass source, how it is used, and indirect land-use effects. Overall, however, biomass energy can have varying impacts on the environment. Wood biomass, for example, contains sulfur and nitrogen, which yield air pollutants sulfur dioxide and nitrogen oxides, though in much lower quantities than coal combustion.

Geothermal provided an estimated 225 TWh globally in 2020, with 97 TWh in the form of electricity (with an estimated 14.1 GW of capacity) and the remaining half in the form of heat. ( Total global electricity  generation in 2020 was 26,000 TWh).

In the United States, nearly 17 TWh of  geothermal electricity  was generated in 2020, making up about 3.4 percent of non-hydroelectric renewable electricity generation, but only 0.4 percent of total electricity generation.  Seven states  generated electricity from geothermal energy: California, Hawaii, Idaho, Nevada, New Mexico, Oregon and Utah. Of these, California accounted for 80 percent of this generation.

Traditional geothermal energy exploits naturally occurring high temperatures, located relatively close to the Earth’s surface in some areas, to generate electric power and for direct uses such as heating and cooking. Geothermal areas are generally located near tectonic plate boundaries, where there are earthquakes and volcanoes. In some places, hot springs and geysers have been used for bathing, cooking and heating for centuries

Generating geothermal electric power typically involves drilling a well, perhaps a mile or two in depth, in search of rock temperatures in the range of 300 to 700°F. Water is pumped down this well, where it is reheated by hot rocks. It travels through natural fissures and rises up a second well as steam, which can be used to spin a turbine and generate electricity or be used for heating or other purposes. Several wells may have to be drilled before a suitable one is in place and the size of the resource cannot be confirmed until after drilling. Additionally, some water is lost to evaporation in this process, so new water is added to maintain the continuous flow of steam. Like biopower and unlike intermittent wind and solar power, geothermal electricity can be used continuously. Very small quantities of carbon dioxide trapped below the Earth’s surface are released during this process.

Enhanced geothermal systems  use advanced, often experimental, drilling and fluid injection techniques to augment and expand the availability of geothermal resources.

Geothermal Power Station

BBC Science

Renewable Energy Indicators, 2020

Renewable Energy Policy Network for the 21 st  Century (REN21)

U.S. Renewable Resource Availability

The following maps from the DOE National Renewable Energy Laboratory depict the relative availability of renewable energy resources throughout the United States.

• Wind resources are abundant in the Great Plains, Iowa, Minnesota, along the spine of Appalachian Mountains, in the Western Mountains, and many off-shore locations.
• Solar photovoltaic and concentrating solar power resources are the highest in the desert Southwest and diminish in intensity in a northward direction.
• The best biomass resources are in the upper central plains (corn) and forests of the Pacific Northwest.
• Traditional geothermal resources are concentrated in the Western United States.

U.S. Wind Resource Map

U.S. National Renewable Energy Laboratories

U.S. Photovoltaic Solar Resources

U.S. Biomass Resource

U.S. Geothermal Resource

Related Content

Tags Clean Energy Electricity Renewables Energy

No (supply) chain, no gain: How offshore wind can boost local economies

August 24, 2023

1:00 pm – 2:15 pm

Unlocking the climate benefits of new grid technologies

December 6, 2022

Other Resources

• Renewable Energy Policy Network for the 21st Century REN21
• Status of Renewables
• Renewable & Alternative Fuels, Energy Information Agency
• Hydropower Vision: A New Chapter for America’s 1st Renewable Electricity Source, U.S. Department of Energy
• An Assessment of Energy Potential at Non-Powered Dams in the United States, U.S. Department of Energy
• Renewable Energy Credits Factsheet, World Resources Institute

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

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 18 October 2022

Machine learning for a sustainable energy future

• Zhenpeng Yao   ORCID: orcid.org/0000-0001-8286-8257 1 , 2 , 3 , 4   na1 ,
• Yanwei Lum   ORCID: orcid.org/0000-0001-7261-2098 5 , 6   na1 ,
• Andrew Johnston 6   na1 ,
• Luis Martin Mejia-Mendoza 2 ,
• Xin Zhou 7 ,
• Yonggang Wen 7 ,
• Alán Aspuru-Guzik   ORCID: orcid.org/0000-0002-8277-4434 2 , 8 ,
• Edward H. Sargent   ORCID: orcid.org/0000-0003-0396-6495 6 &
• Zhi Wei Seh   ORCID: orcid.org/0000-0003-0953-567X 5  

Nature Reviews Materials volume  8 ,  pages 202–215 ( 2023 ) Cite this article

29k Accesses

84 Citations

25 Altmetric

Metrics details

• Computer science

Electrocatalysis

• Energy grids and networks
• Solar cells

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances — at the materials, devices and systems levels — for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

Similar content being viewed by others

A review of the recent progress in battery informatics

Predicting the state of charge and health of batteries using data-driven machine learning

Man-Fai Ng, Jin Zhao, … Zhi Wei Seh

Fundamental theory on multiple energy resources and related case studies

Introduction.

The combustion of fossil fuels, used to fulfill approximately 80% of the world’s energy needs, is the largest single source of rising greenhouse gas emissions and global temperature 1 . The increased use of renewable sources of energy, notably solar and wind power, is an economically viable path towards meeting the climate goals of the Paris Agreement 2 . However, the rate at which renewable energy has grown has been outpaced by ever-growing energy demand, and as a result the fraction of total energy produced by renewable sources has remained constant since 2000 (ref. 3 ). It is thus essential to accelerate the transition towards sustainable sources of energy 4 . Achieving this transition requires energy technologies, infrastructure and policies that enable and promote the harvest, storage, conversion and management of renewable energy.

In sustainable energy research, suitable material candidates (such as photovoltaic materials) must first be chosen from the combinatorial space of possible materials, then synthesized at a high enough yield and quality for use in devices (such as solar panels). The time frame of a representative materials discovery process is 15–20 years 5 , 6 , leaving considerable room for improvement. Furthermore, the devices have to be optimized for robustness and reproducibility to be incorporated into energy systems (such as in solar farms) 7 , where management of energy usage and generation patterns is needed to further guarantee commercial success.

Here we explore the extent to which machine learning (ML) techniques can help to address many of these challenges 8 , 9 , 10 . ML models can be used to predict specific properties of new materials without the need for costly characterization; they can generate new material structures with desired properties; they can understand patterns in renewable energy usage and generation; and they can help to inform energy policy by optimizing energy management at both device and grid levels.

In this Perspective, we introduce Acc(X)eleration Performance Indicators (XPIs), which can be used to measure the effectiveness of platforms developed for accelerated energy materials discovery. Next, we discuss closed-loop ML frameworks and evaluate the latest advances in applying ML to the development of energy harvesting, storage and conversion technologies, as well as the integration of ML into a smart power grid. Finally, we offer an overview of energy research areas that stand to benefit further from ML.

Performance indicators

Because many reports discuss ML-accelerated approaches for materials discovery and energy systems management, we posit that there should be a consistent baseline from which these reports can be compared. For energy systems management, performance indicators at the device, plant and grid levels have been reported 11 , 12 , yet there are no equivalent counterparts for accelerated materials discovery.

The primary goal in materials discovery is to develop efficient materials that are ready for commercialization. The commercialization of a new material requires intensive research efforts that can span up to two decades: the goal of every accelerated approach should be to accomplish commercialization an order-of-magnitude faster. The materials science field can benefit from studying the case of vaccine development. Historically, new vaccines take 10 years from conception to market 13 . However, after the start of the COVID-19 pandemic, several companies were able to develop and begin releasing vaccines in less than a year. This achievement was in part due to an unprecedented global research intensity, but also to a shift in the technology: after a technological breakthrough in 2008, the cost of sequencing DNA began decreasing exponentially 14 , 15 , enabling researchers to screen orders-of-magnitude more vaccines than was previously possible.

ML for energy technologies has much in common with ML for other fields like biomedicine, sharing the same methodology and principles. However, in practice, ML models for different technologies are exposed to additional unique requirements. For example, ML models for medical applications usually have complex structures that take into account regulatory oversight and ensure the safe development, use and monitoring of systems, which usually does not happen in the energy field 16 . Moreover, data availability varies substantially from field to field; biomedical researchers can work with a relatively large amount of data that energy researchers usually lack. This limited data accessibility can constrain the usage of sophisticated ML models (such as deep learning models) in the energy field. However, adaptation has been quick in all energy subfields, with a rapidly increased number of groups recognizing the importance of statistical methods and starting to use them for various problems. We posit that the use of high-throughput experimentation and ML in materials discovery workflows can result in breakthroughs in accelerating development, but the field first needs a set of metrics with which ML models can be evaluated and compared.

Accelerated materials discovery methods should be judged based on the time it takes for a new material to be commercialized. We recognize that this is not a useful metric for new platforms, nor is it one that can be used to decide quickly which platform is best suited for a particular scenario. We therefore propose here XPIs that new materials discovery platforms should report.

Acceleration factor of new materials, XPI-1

This XPI is evaluated by dividing the number of new materials that are synthesized and characterized per unit time with the accelerated platform by the number of materials that are synthesized and characterized with traditional methods. For example, an acceleration factor of ten means that for a given time period, the accelerated platform can evaluate ten times more materials than a traditional platform. For materials with multiple target properties, researchers should report the rate-limiting acceleration factor.

Number of new materials with threshold performance, XPI-2

This XPI tracks the number of new materials discovered with an accelerated platform that have a performance greater than the baseline value. The selection of this baseline value is critical: it should be something that fairly captures the standard to which new materials need to be compared. As an example, an accelerated platform that seeks to discover new perovskite solar cell materials should track the number of devices made with new materials that have a better performance than the best existing solar cell 17 .

Performance of best material over time, XPI-3

This XPI tracks the absolute performance — whether it is Faradaic efficiency, power conversion efficiency or other — of the best material as a function of time. For the accelerated framework, the evolution of the performance should increase faster than the performance obtained by traditional methods 18 .

Repeatability and reproducibility of new materials, XPI-4

This XPI seeks to ensure that the new materials discovered are consistent and repeatable: this is a key consideration to screen out materials that would fail at the commercialization stage. The performance of a new material should not vary by more than x % of its mean value (where x is the standard error): if it does, this material should not be included in either XPI-2 (number of new materials with threshold performance) or XPI-3 (performance of best material over time).

Human cost of the accelerated platform, XPI-5

This XPI reports the total costs of the accelerated platform. This should include the total number of researcher hours needed to design and order the components for the accelerated system, develop the programming and robotic infrastructure, develop and maintain databases used in the system and maintain and run the accelerated platform. This metric would provide researchers with a realistic estimate of the resources required to adapt an accelerated platform for their own research.

Use of the XPIs

Each of these XPIs can be measured for computational, experimental or integrated accelerated systems. Consistently reporting each of these XPIs as new accelerated platforms are developed will allow researchers to evaluate the growth of these platforms and will provide a consistent metric by which different platforms can be compared. As a demonstration, we applied the XPIs to evaluate the acceleration performance of several typical platforms: Edisonian-like trial-test, robotic photocatalysis development 19 and design of a DNA-encoded-library-based kinase inhibitor 20 (Table  1 ). To obtain a comprehensive performance estimate, we define one overall acceleration score S adhering to the following rules. The dependent acceleration factors (XPI-1 and XPI-2), which function in a synergetic way, are added together to reflect their contribution as a whole. The independent acceleration factors (XPI-3, XPI-4 and XPI-5), which may function in a reduplicated way, are multiplied together to value their contributions respectively. As a result, the overall acceleration score can be calculated as S  = (XPI-1 + XPI-2) × XPI-3 × XPI-4 ÷ XPI-5. As the reference, the Edisonian-like approach has a calculated overall XPIs score of around 1, whereas the most advanced method, the DNA-encoded-library-based drug design, exhibits an overall XPIs score of 10 7 . For the sustainability field, the robotic photocatalysis platform has an overall XPIs score of 10 5 .

For energy systems, the most frequently reported XPI is the acceleration factor, in part because it is deterministic, but also because it is easy to calculate at the end of the development of a workflow. In most cases, we expect that authors report the acceleration factor only after completing the development of the platform. Reporting the other suggested XPIs will provide researchers with a better sense of both the time and human resources required to develop the platform until it is ready for publication. Moving forward, we hope that other researchers adopt the XPIs — or other similar metrics — to allow for fair and consistent comparison between the different methods and algorithms that are used to accelerate materials discovery.

Closed-loop ML for materials discovery

The traditional approach to materials discovery is often Edisonian-like, relying on trial and error to develop materials with specific properties. First, a target application is identified, and a starting pool of possible candidates is selected (Fig.  1a ). The materials are then synthesized and incorporated into a device or system to measure their properties. These results are then used to establish empirical structure–property relationships, which guide the next round of synthesis and testing. This slow process goes through as many iterations as required and each cycle can take several years to complete.

a | Traditional Edisonian-like approach, which involves experimental trial and error. b | High-throughput screening approach involving a combination of theory and experiment. c | Machine learning (ML)-driven approach whereby theoretical and experimental results are used to train a ML model for predicting structure–property relationships. d | ML-driven approach for property-directed and automatic exploration of the chemical space using optimization ML (such as genetic algorithms or generative models) that solve the ‘inverse’ design problem.

A computation-driven, high-throughput screening strategy (Fig.  1b ) offers a faster turnaround. To explore the overall vast chemical space (~10 60 possibilities), human intuition and expertise can be used to create a library with a substantial number of materials of interest (~10 4 ). Theoretical calculations are carried out on these candidates and the top performers (~10 2 candidates) are then experimentally verified. With luck, the material with the desired functionality is ‘discovered’. Otherwise, this process is repeated in another region of the chemical space. This approach can still be very time-consuming and computationally expensive and can only sample a small region of the chemical space.

ML can substantially increase the chemical space sampled, without costing extra time and effort. ML is data-driven, screening datasets to detect patterns, which are the physical laws that govern the system. In this case, these laws correspond to materials structure–property relationships. This workflow involves high-throughput virtual screening (Fig.  1c ) and begins by selecting a larger region (~10 6 ) of the chemical space of possibilities using human intuition and expertise. Theoretical calculations are carried out on a representative subset (~10 4 candidates) and the results are used for training a discriminative ML model. The model can then be used to make predictions on the other candidates in the overall selected chemical space 9 . The top ~10 2 candidates are experimentally verified, and the results are used to improve the predictive capabilities of the model in an iterative loop. If the desired material is not ‘discovered’, the process is repeated on another region of the chemical space.

An improvement on the previous approaches is a framework that requires limited human intuition or expertise to direct the chemical space search: the automated virtual screening approach (Fig.  1d ). To begin with, a region of the chemical space is picked at random to initiate the process. Thereafter, this process is similar to the previous approach, except that the computational and experimental data is also used to train a generative learning model. This generative model solves the ‘inverse’ problem: given a required property, the goal is to predict an ideal structure and composition in the chemical space. This enables a directed, automated search of the chemical space, towards the goal of ‘discovering’ the ideal material 8 .

ML for energy

ML has so far been used to accelerate the development of materials and devices for energy harvesting (photovoltaics), storage (batteries) and conversion (electrocatalysis), as well as to optimize power grids. Besides all the examples discussed here, we summarize the essential concepts in ML (Box  1 ), the grand challenges in sustainable materials research (Box  2 ) and the details of key studies (Table  2 ).

Box 1 Essential concepts in ML

With the availability of large datasets 122 , 125 and increased computing power, various machine learning (ML) algorithms have been developed to solve diverse problems in energy. Below, we provide a brief overview of the types of problem that ML can solve in energy technology, and we then summarize the status of ML-driven energy research. More detailed information about the nuts and bolts of ML techniques can be found in previous reviews 173 , 174 , 175 .

Property prediction

Supervised learning models are predictive (or discriminative) models that are given a datapoint x , and seek to predict a property y (for example, the bandgap 27 ) after being trained on a labelled dataset. The property y can be either continuous or discrete. These models have been used to aid or even replace physical simulations or measurements under certain circumstances 176 , 177 .

Generative materials design

Unsupervised learning models are generative models that can generate or output new examples x ′ (such as new molecules 104 ) after being trained on an unlabelled dataset. This generation of new examples can be further enhanced with additional information (physical properties) to condition or bias the generative process, allowing the models to generate examples with improved properties and leading to the property-to-structure approach called inverse design 52 , 178 .

Self-driving laboratories

Self-driving or autonomous laboratories 19 use ML models to plan and perform experiments, including the automation of retrosynthesis analysis (such as in reinforcement-learning-aided synthesis planning 124 , 179 ), prediction of reaction products (such as in convolutional neural networks (CNNs) for reaction prediction 137 , 138 ) and reaction condition optimization (such as in robotic workflows optimized by active learning 19 , 160 , 180 , 181 , 182 , 183 ). Self-driving laboratories, which use active learning for iterating through rounds of synthesis and measurements, are a key component in the closed-loop inverse design 52 .

Aiding characterization

ML models have been used to aid the quantitative or qualitative analysis of experimental observations and measurements, including assisting in the determination of crystal structure from transmission electron microscopy images 184 , identifying coordination environment 81 and structural transition 83 from X-ray absorption spectroscopy and inferring crystal symmetry from electron diffraction 176 .

Accelerating theoretical computations

ML models can enable otherwise intractable simulations by reducing the computational cost (processor core amount and time) for systems with increased length and timescales 69 , 70 and providing potentials and functionals for complex interactions 68 .

Optimizing system management

ML models can aid the management of energy systems at the device or grid power level by predicting lifetimes (such as battery life 43 , 44 ), adapting to new loads (such as in long short-term memory for building load prediction 95 ) and optimizing performance (such as in reinforcement learning for smart grid control 94 ).

Box 2 Grand challenges in energy materials research

Photovoltaics.

Discover non-toxic (Pd- and Cd-free) materials with good optoelectronic properties

Identify and minimize materials defects in light-absorbing materials

Design effective recombination-layer materials for tandem solar cells

Develop materials design strategies for long-term operational stability 125

Develop (hole/electron) transport materials with high carrier mobility 125

Optimize cell structure for maximum light absorption and minimum use of active materials

Tune materials bandgaps for optimal solar-harvesting performance under complex operation conditions 21 , 22

Develop Earth-abundant cathode materials (Co-free) with high reversibility and charge capacity 4

Design electrolytes with wider electrochemical windows and high conductivity 4

Identify electrolyte systems to boost battery performance and lifetime 4

Discover new molecules for redox flow batteries with suitable voltage 4

Understand correlation between defect growth in battery materials and overall degradation process of battery components

Tune operando (dis)charging protocol for minimized capacity loss, (dis)charging rate and optimal battery life under diversified conditions 7 , 53

Design materials with optimal adsorption energy for maximized catalytic activity 60 , 61

Identify and study active sites on catalytic materials 58

Engineer catalytic materials for extended durability 58 , 60 , 61

Identify a fuller set of materials descriptors that relate to catalytic activity 60 , 61

Engineer multiple catalytic functionalities into the same material 60 , 61

Design multiscale electrode structures for optimized catalytic activity

Correlate atomistic contamination and growth of catalyst particles with electrode degradation process

Tune operando (dis)charging protocol for minimized capacity loss and optimal cell life

ML is accelerating the discovery of new optoelectronic materials and devices for photovoltaics, but major challenges are still associated with each step.

Photovoltaics materials discovery

One materials class for which ML has proved particularly effective is perovskites, because these materials have a vast chemical space from which the constituents may be chosen. Early representations of perovskite materials for ML were atomic-feature representations, in which each structure is encoded as a fixed-length vector comprised of an average of certain atomic properties of the atoms in the crystal structure 21 , 22 . A similar technique was used to predict new lead-free perovskite materials with the proper bandgap for solar cells 23 (Fig.  2a ). These representations allowed for high accuracy but did not account for any spatial relation between atoms 24 , 25 . Materials systems can also be represented as images 26 or as graphs 27 , enabling the treatment of systems with diverse number of atoms. The latter representation is particularly compelling, as perovskites, particularly organic–inorganic perovskites, have crystal structures that incorporate a varying number of atoms, and the organic molecules can vary in size.

a | Energy harvesting 23 . b | Energy storage 38 . c | Energy conversion 76 . d | Energy management 93 . ICSD, Inorganic Crystal Structure Database; ML, machine learning.

Although bandgap prediction is an important first step, this parameter alone is not sufficient to indicate a useful optoelectronic material; other parameters, including electronic defect density and stability, are equally important. Defect energies are addressable with computational methods, but the calculation of defects in structures is extremely computationally expensive, which inhibits the generation of a dataset of defect energies from which an ML model can be trained. To expedite the high-throughput calculation of defect energies, a Python toolkit has been developed 28 that will be pivotal in building a database of defect energies in semiconductors. Researchers can then use ML to predict both the formation energy of defects and the energy levels of these defects. This knowledge will ensure that the materials selected from high-throughput screening will not only have the correct bandgap but will also either be defect-tolerant or defect-resistant, finding use in commercial optoelectronic devices.

Even without access to a large dataset of experimental results, ML can accelerate the discovery of optoelectronic materials. Using a self-driving laboratory approach, the number of experiments required to optimize an organic solar cell can be reduced from 500 to just 60 (ref. 29 ). This robotic synthesis method accelerates the learning rate of the ML models and drastically reduces the cost of the chemicals needed to run the optimization.

Solar device structure and fabrication

Photovoltaic devices require optimization of layers other than the active layer to maximize performance. One component is the top transparent conductive layer, which needs to have both high optical transparency and high electronic conductivity 30 , 31 . A genetic algorithm that optimized the topology of a light-trapping structure enabled a broadband absorption efficiency of 48.1%, which represents a more than threefold increase over the Yablonovitch limit, the 4 n 2 factor (where n is the refractive index of the material) theoretical limit for light trapping in photovoltaics 32 .

A universal standard irradiance spectrum is usually used by researchers to determine optimal bandgaps for solar cell operation 33 . However, actual solar irradiance fluctuates based on factors such as the position of the Sun, atmospheric phenomena and the season. ML can reduce yearly spectral sets into a few characteristic spectra 33 , allowing for the calculation of optimal bandgaps for real-world conditions.

To optimize device fabrication, a CNN was used to predict the current–voltage characteristics of as-cut Si wafers based on their photoluminescence images 34 . Additionally, an artificial neural network was used to predict the contact resistance of metallic front contacts for Si solar cells, which is critical for the manufacturing process 35 .

Although successful, these studies appear to be limited to optimizing structures and processes that are already well established. We suggest that, in future work, ML could be used to augment simulations, such as the multiphysics models for solar cells. Design of device architecture could begin from such simulation models, coupled with ML in an iterative process to quickly optimize design and reduce computational time and cost. In addition, optimal conditions for the scaling-up of device area and fabrication processes are likely to be very different from those for laboratory-scale demonstrations. However, determining these optimal conditions could be expensive in terms of materials cost and time, owing to the need to construct much larger devices. In this regard, ML, together with the strategic design of experiments, could greatly accelerate the optimization of process conditions (such as the annealing temperatures and solvent choice).

Electrochemical energy storage

Electrochemical energy storage is an essential component in applications such as electric vehicles, consumer electronics and stationary power stations. State-of-the-art electrochemical energy storage solutions have varying efficacy in different applications: for example, lithium-ion batteries exhibit excellent energy density and are widely used in electronics and electric vehicles, whereas redox flow batteries have drawn substantial attention for use in stationary power storage. ML approaches have been widely employed in the field of batteries, including for the discovery of new materials such as solid-state ion conductors 36 , 37 , 38 (Fig.  2b ) and redox active electrolytes for redox flow batteries 39 . ML has also aided battery management, for example, through state-of-charge determination 40 , state-of-health evaluation 41 , 42 and remaining-life prediction 43 , 44 .

Electrode and electrolyte materials design

Layered oxide materials, such as LiCoO 2 or LiNi x Mn y Co 1- x - y O 2 , have been used extensively as cathode materials for alkali metal-ion (Li/Na/K) batteries. However, developing new Li-ion battery materials with higher operating voltages, enhanced energy densities and longer lifetimes is of paramount interest. So far, universal design principles for new battery materials remain undefined, and hence different approaches have been explored. Data from the Materials Project have been used to model the electrode voltage profile diagrams for different materials in alkali metal-ion batteries (Na and K) 45 , leading to the proposition of 5,000 different electrode materials with appropriate moderate voltages. ML was also employed to screen 12,000 candidates for solid Li-ion batteries, resulting in the discovery of ten new Li-ion conducting materials 46 , 47 .

Flow batteries consist of active materials dissolved in electrolytes that flow into a cell with electrodes that facilitate redox reactions. Organic flow batteries are of particular interest. In flow batteries, the solubility of the active material in the electrolyte and the charge/discharge stability dictate performance. ML methods have explored the chemical space to find suitable electrolytes for organic redox flow batteries 48 , 49 . Furthermore, a multi-kernel-ridge regression method accelerated the discovery of active organic molecules using multiple feature training 48 . This method also helped in predicting the solubility dependence of anthraquinone molecules with different numbers and combinations of sulfonic and hydroxyl groups on pH. Future opportunities lie in the exploration of large combinatorial spaces for the inverse design of high-entropy electrodes 50 and high-voltage electrolytes 51 . To this end, deep generative models can assist the discovery of new materials based on the simplified molecular input line entry system (SMILES) representation of molecules 52 .

Battery device and stack management

A combination of mechanistic and semi-empirical models is currently used to estimate capacity and power loss in lithium-ion batteries. However, the models are applicable only to specific failure mechanisms or situations and cannot predict the lifetimes of batteries at the early stages of usage. By contrast, mechanism-agnostic models based on ML can accurately predict battery cycle life, even at an early stage of a battery’s life 43 . A combined early-prediction and Bayesian optimization model has been used to rapidly identify the optimal charging protocol with the longest cycle life 44 . ML can be used to accelerate the optimization of lithium-ion batteries for longer lifetimes 53 , but it remains to be seen whether these models can be generalized to different battery chemistries 54 .

ML methods can also predict important properties of battery storage facilities. A neural network was used to predict the charge/discharge profiles in two types of stationary battery systems, lithium iron phosphate and vanadium redox flow batteries 55 . Battery power management techniques must also consider the uncertainty and variability that arise from both the environment and the application. An iterative Q -learning ( reinforcement learning ) method was also designed for battery management and control in smart residential environments 56 . Given the residential load and the real-time electricity rate, the method is effective at optimizing battery charging/discharging/idle cycles. Discriminative neural network-based models can also optimize battery usage in electric vehicles 57 .

Although ML is able to predict the lifetime of batteries, the underlying degradation mechanisms are difficult to identify and correlate to the state of health and lifetime. To this end, incorporation of domain knowledge into a hybrid physics-based ML model can provide insight and reduce overfitting 53 . However, incorporating the physics of battery degradation processes into a hybrid model remains challenging; representation of electrode materials that encode both compositional and structural information is far from trivial. Validation of these models also requires the development of operando characterization techniques, such as liquid-phase transmission electron microscopy and ambient-pressure X-ray absorption spectroscopy (XAS), that reflect true operating conditions as closely as possible 54 . Ideally, these characterization techniques should be carried out in a high-throughput manner, using automated sample changers, for example, in order to generate large datasets for ML.

Electrocatalysts

Electrocatalysis enables the conversion of simple feedstocks (such as water, carbon dioxide and nitrogen) into valuable chemicals and/or fuels (such as hydrogen, hydrocarbons and ammonia), using renewable energy as an input 58 . The reverse reactions are also possible in a fuel cell, and hydrogen can be consumed to produce electricity 59 . Active and selective electrocatalysts must be developed to improve the efficiency of these reactions 60 , 61 . ML has been used to accelerate electrocatalyst development and device optimization.

Electrocatalyst materials discovery

The most common descriptor of catalytic activity is the adsorption energy of intermediates on a catalyst 61 , 62 . Although these adsorption energies can be calculated using density functional theory (DFT), catalysts possess multiple surface binding sites, each with different adsorption energies 63 . The number of possible sites increases dramatically if alloys are considered, and thus becomes intractable with conventional means 64 .

DFT calculations are critical for the search of electrocatalytic materials 65 and efforts have been made to accelerate the calculations and to reduce their computational cost by using surrogate ML models 66 , 67 , 68 , 69 . Complex reaction mechanisms involving hundreds of possible species and intermediates can also be simplified using ML, with a surrogate model predicting the most important reaction steps and deducing the most likely reaction pathways 70 . ML can also be used to screen for active sites across a random, disordered nanoparticle surface 71 , 72 . DFT calculations are performed on only a few representative sites, which are then used to train a neural network to predict the adsorption energies of all active sites.

Catalyst development can benefit from high-throughput systems for catalyst synthesis and performance evaluation 73 , 74 . An automatic ML-driven framework was developed to screen a large intermetallic chemical space for CO 2 reduction and H 2 evolution 75 . The model predicted the adsorption energy of new intermetallic systems and DFT was automatically performed on the most promising candidates to verify the predictions. This process went on iteratively in a closed feedback loop. 131 intermetallic surfaces across 54 alloys were ultimately identified as promising candidates for CO 2 reduction. Experimental validation 76 with Cu–Al catalysts yielded an unprecedented Faradaic efficiency of 80% towards ethylene at a high current density of 400 mA cm – 2 (Fig.  2c ).

Because of the large number of properties that electrocatalysts may possess (such as shape, size and composition), it is difficult to do data mining on the literature 77 . Electrocatalyst structures are complex and difficult to characterize completely; as a result, many properties may not be fully characterized by research groups in their publications. To avoid situations in which potentially promising compositions perform poorly as a result of non-ideal synthesis or testing conditions, other factors (such as current density, particle size and pH value) that affect the electrocatalyst performance must be kept consistent. New approaches such as carbothermal shock synthesis 78 , 79 may be a promising avenue, owing to its propensity to generate uniformly sized and shaped alloy nanoparticles, regardless of composition.

XAS is a powerful technique, especially for in situ measurements, and has been widely employed to gain crucial insight into the nature of active sites and changes in the electrocatalyst over time 80 . Because the data analysis relies heavily on human experience and expertise, there has been interest in developing ML tools for interpreting XAS data 81 . Improved random forest models can predict the Bader charge (a good approximation of the total electronic charge of an atom) and nearest-neighbour distances, crucial factors that influence the catalytic properties of the material 82 . The extended X-ray absorption fine structure (EXAFS) region of XAS spectra is known to contain information on bonding environments and coordination numbers. Neural networks can be used to automatically interpret EXAFS data 83 , permitting the identification of the structure of bimetallic nanoparticles using experimental XAS data, for example 84 . Raman and infrared spectroscopy are also important tools for the mechanistic understanding of electrocatalysis. Together with explainable artificial intelligence (AI), which can relate the results to underlying physics, these analyses could be used to discover descriptors hidden in spectra that could lead to new breakthroughs in electrocatalyst discovery and optimization.

Fuel cell and electrolyser device management

A fuel cell is an electrochemical device that can be used to convert the chemical energy of a fuel (such as hydrogen) into electrical energy. An electrolyser transforms electrical energy into chemical energy (such as in water splitting to generate hydrogen). ML has been used to optimize and manage their performance, predict degradation and device lifetime as well as detect and diagnose faults. Using a hybrid method consisting of an extreme learning machine, genetic algorithms and wavelet analysis, the degradation in proton-exchange membrane fuel cells has been predicted 85 , 86 . Electrochemical impedance measurements used as input for an artificial neural network have enabled fault detection and isolation in a high-temperature stack of proton-exchange membrane fuel cells 87 , 88 .

ML approaches can also be employed to diagnose faults, such as fuel and air leakage issues, in solid oxide fuel cell stacks. Artificial neural networks can predict the performance of solid oxide fuel cells under different operating conditions 89 . In addition, ML has been applied to optimize the performance of solid oxide electrolysers, for CO 2 /H 2 O reduction 90 , and chloralkali electrolysers 91 .

In the future, the use of ML for fuel cells could be combined with multiscale modelling to improve their design, for example to minimize Ohmic losses and optimize catalyst loading. For practical applications, fuel cells may be subject to fluctuations in energy output requirements (for example, when used in vehicles). ML models could be used to determine the effects of such fluctuations on the long-term durability and performance of fuel cells, similar to what has been done for predicting the state of health and lifetime for batteries. Furthermore, it remains to be seen whether the ML techniques for fuel cells can be easily generalized to electrolysers and vice versa, using transfer learning for example, given that they are essentially reactions in reverse.

Smart power grids

A power grid is responsible for delivering electrical energy from producers (such as power plants and solar farms) to consumers (such as homes and offices). However, energy fluctuations from intermittent renewable energy generators can render the grid vulnerable 92 . ML algorithms can be used to optimize the automatic generation control of power grids, which controls the power output of multiple generators in an energy system. For example, when a relaxed deep learning model was used as a unified timescale controller for the automatic generation control unit, the total operational cost was reduced by up to 80% compared with traditional heuristic control strategies 93 (Fig.  2d ). A smart generation control strategy based on multi-agent reinforcement learning was found to improve the control performance by around 10% compared with other ML algorithms 94 .

Accurate demand and load prediction can support decision-making operations in energy systems for proper load scheduling and power allocation. Multiple ML methods have been proposed to precisely predict the demand load: for example, long short-term memory was used to successfully and accurately predict hourly building load 95 . Short-term load forecasting of diverse customers (such as retail businesses) using a deep neural network and cross-building energy demand forecasting using a deep belief network have also been demonstrated effectively 96 , 97 .

Demand-side management consists of a set of mechanisms that shape consumer electricity consumption by dynamically adjusting the price of electricity. These include reducing (peak shaving), increasing (load growth) and rescheduling (load shifting) the energy demand, which allows for flexible balancing of renewable electricity generation and load 98 . A reinforcement-learning-based algorithm resulted in substantial cost reduction for both the service provider and customer 99 . A decentralized learning-based residential demand scheduling technique successfully shifted up to 35% of the energy demand to periods of high wind availability, substantially saving power costs compared with the unscheduled energy demand scenario 100 . Load forecasting using a multi-agent approach integrates load prediction with reinforcement learning algorithms to shift energy usage (for example, to different electrical devices in a household) for its optimization 101 . This approach reduced peak usage by more than 30% and increased off-peak usage by 50%, reducing the cost and energy losses associated with energy storage.

Opportunities for ML in renewable energy

ML provides the opportunity to enable substantial further advances in different areas of the energy materials field, which share similar materials-related challenges (Fig.  3 ). There are also grand challenges for ML application in smart grid and policy optimization.

a | Energy materials present additional modelling challenges. Machine learning (ML) could help in the representation of structurally complex structures, which can include disordering, dislocations and amorphous phases. b | Flexible models that scale efficiently with varied dataset sizes are in demand, and ML could help to develop robust predictive models. The yellow dots stand for the addition of unreliable datasets that could harm the prediction accuracy of the ML model. c | Synthesis route prediction remains to be solved for the design of a novel material. In the ternary phase diagram, the dots stand for the stable compounds in that corresponding phase space and the red dot for the targeted compound. Two possible synthesis pathways are compared for a single compound. The score obtained would reflect the complexity, cost and so on of one synthesis pathway. d | ML-aided phase degradation prediction could boost the development of materials with enhanced cyclability. The shaded region represents the rocksalt phase, which grows inside the layered phase. The arrow marks the growth direction. e | The use of ML models could help in optimizing energy generation and energy consumption. Automating the decision-making processes associated with dynamic power supplies using ML will make the power distribution more efficient. f | Energy policy is the manner in which an entity (for example, a government) addresses its energy issues, including conversion, distribution and utilization, where ML could be used to optimize the corresponding economy.

Materials with novel geometries

A ML representation is effective when it captures the inherent properties of the system (such as its physical symmetries) and can be utilized in downstream ancillary tasks, such as transfer learning to new predictive tasks, building new knowledge using visualization or attribution and generating similar data distributions with generative models 102 .

For materials, the inputs are molecules or crystal structures whose physical properties are modelled by the Schrödinger equation. Designing a general representation of materials that reflects these properties is an ongoing research problem. For molecular systems, several representations have been used successfully, including fingerprints 103 , SMILES 104 , self-referencing embedded strings (SELFIES) 105 and graphs 106 , 107 , 108 . Representing crystalline materials has the added complexity of needing to incorporate periodicity in the representation. Methods like the smooth overlap of atomic positions 109 , Voronoi tessellation 110 , 111 , diffraction images 112 , multi-perspective fingerprints 113 and graph-based algorithms 27 , 114 have been suggested, but typically lack the capability for structure reconstruction.

Complex structural systems found in energy materials present additional modelling challenges (Fig.  3a ): a large number of atoms (such as in reticular frameworks or polymers), specific symmetries (such as in molecules with a particular space group and for reticular frameworks belonging to a certain topology), atomic disordering, partial occupancy, or amorphous phases (leading to an enormous combinatorial space), defects and dislocations (such as interfaces and grain boundaries) and low-dimensionality materials (as in nanoparticles). Reduction approximations alleviate the first issue (using, for example, RFcode for reticular framework representation) 8 , but the remaining several problems warrant intensive future research efforts.

Self- supervised learning , which seeks to lever large amounts of synthetic labels and tasks to continue learning without experimental labels 115 , multi-task learning 116 , in which multiple material properties can be modelled jointly to exploit correlation structure between properties, and meta-learning 117 , which looks at strategies that allow models to perform better in new datasets or in out-of-distribution data, all offer avenues to build better representations. On the modelling front, new advances in attention mechanisms 118 , 119 , graph neural networks 120 and equivariant neural networks 121 expand our range of tools with which to model interactions and expected symmetries.

Robust predictive models

Predictive models are the first step when building a pipeline that seeks materials with desired properties. A key component for building these models is training data; more data will often translate into better-performing models, which in turn will translate into better accuracy in the prediction of new materials. Deep learning models tend to scale more favourably with dataset size than traditional ML approaches (such as random forests). Dataset quality is also essential. However, experiments are usually conducted under diverse conditions with large variation in untracked variables (Fig.  3b ). Additionally, public datasets are more likely to suffer from publication bias, because negative results are less likely to be published even though they are just as important as positive results when training statistical models 122 .

Addressing these issues require transparency and standardization of the experimental data reported in the literature. Text and natural language processing strategies could then be employed to extract data from the literature 77 . Data should be reported with the belief that it will eventually be consolidated in a database, such as the MatD3 database 123 . Autonomous laboratory techniques will help to address this issue 19 , 124 . Structured property databases such as the Materials Project 122 and the Harvard Clean Energy Project 125 can also provide a large amount of data. Additionally, different energy fields — energy storage, harvesting and conversion — should converge upon a standard and uniform way to report data. This standard should be continuously updated; as researchers continue to learn about the systems they are studying, conditions that were previously thought to be unimportant will become relevant.

New modelling approaches that work in low-data regimes, such as data-efficient models, dataset-building strategies (active sampling) 126 and data-augmentation techniques, are also important 127 . Uncertainty quantification , data efficiency, interpretability and regularization are important considerations that improve the robustness of ML models. These considerations relate to the notion of generalizability: predictions should generalize to a new class of materials that is out of the distribution of the original dataset. Researchers can attempt to model how far away new data points are from the training set 128 or the variability in predicted labels with uncertainty quantification 129 . Neural networks are a flexible model class, and often models can be underspecified 130 . Incorporating regularization, inductive biases or priors can boost the credibility of a model. Another way to create trustable models could be to enhance the interpretability of ML algorithms by deriving feature relevance and scoring their importance 131 . This strategy could help to identify potential chemically meaningful features and form a starting point for understanding latent factors that dominate material properties. These techniques can also identify the presence of model bias and overfitting, as well as improving generalization and performance 132 , 133 , 134 .

Stable and synthesizable new materials

The formation energy of a compound is used to estimate its stability and synthesizability 135 , 136 . Although negative values usually correspond to stable or synthesizable compounds, slightly positive formation energies below a limit lead to metastable phases with unclear synthesizability 137 , 138 . This is more apparent when investigating unexplored chemical spaces with undetermined equilibrium ground states; yet often the metastable phases exhibit superior properties, as seen in photovoltaics 136 , 139 and ion conductors 140 , for example. It is thus of interest to develop a method to evaluate the synthesizability of metastable phases (Fig.  3c ). Instead of estimating the probability that a particular phase can be synthesized, one can instead evaluate its synthetic complexity using ML. In organic chemistry, synthesis complexity is evaluated according to the accessibility of the phases’ synthesis route 141 or precedent reaction knowledge 142 . Similar methodologies can be applied to the inorganic field with the ongoing design of automated synthesis-planning algorithms for inorganic materials 143 , 144 .

Synthesis and evaluation of a new material alone does not ensure that material will make it to market; material stability is a crucial property that takes a long time to evaluate. Degradation is a generally complex process that occurs through the loss of active matter or growth of inactive phases (such as the rocksalt phases formed in layered Li-ion battery electrodes 145 (Fig.  3d ) or the Pt particle agglomeration in fuel cells 146 ) and/or propagation of defects (such as cracks in cycled battery electrode 147 ). Microscopies such as electron microscopy 148 and simulations such as continuum mechanics modelling 149 are often used to investigate growth and propagation dynamics (that is, phase boundary and defect surface movements versus time). However, these techniques are usually expensive and do not allow rapid degradation prediction. Deep learning techniques such as convolutional neural networks and recurrent neural networks may be able to predict the phase boundary and/or defect pattern evolution under certain conditions after proper training 150 . Similar models can then be built to understand multiple degradation phenomena and aid the design of materials with improved cycle life.

Optimized smart power grids

A promising prospect of ML in smart grids is automating the decision-making processes that are associated with dynamic power supplies to distribute power most efficiently (Fig.  3e ). Practical deployment of ML technologies into physical systems remains difficult because of data scarcity and the risk-averse mindset of policymakers. The collection of and access to large amounts of diverse data is challenging owing to high cost, long delays and concerns over compliance and security 151 . For instance, to capture the variation of renewable resources owing to peak or off-peak and seasonal attributes, long-term data collections are implemented for periods of 24 hours to several years 152 . Furthermore, although ML algorithms are ideally supposed to account for all uncertainties and unpredictable situations in energy systems, the risk-adverse mindset in the energy management industry means that implementation still relies on human decision-making 153 .

An ML-based framework that involves a digital twin of the physical system can address these problems 154 , 155 . The digital twin represents the digitalized cyber models of the physical system and can be constructed from physical laws and/or ML models trained using data sampled from the physical system. This approach aims to accurately simulate the dynamics of the physical system, enabling relatively fast generation of large amounts of high-quality synthetic data at low cost. Notably, because ML model training and validation is performed on the digital twin, there is no risk to the actual physical system. Based on the prediction results, suitable actions can be suggested and then implemented in the physical system to ensure stability and/or improve system operation.

Policy optimization

Finally, research is generally focused on one narrow aspect of a larger problem; we argue that energy research needs a more integrated approach 156 (Fig.  3f ). Energy policy is the manner in which an entity, such as the government, addresses its energy issues, including conversion, distribution and utilization. ML has been used in the fields of energy economics finance for performance diagnostics (such as for oil wells), energy generation (such as wind power) and consumption (such as power load) forecasts and system lifespan (such as battery cell life) and failure (such as grid outage) prediction 157 . They have also been used for energy policy analysis and evaluation (for example, for estimating energy savings). A natural extension of ML models is to use them for policy optimization 158 , 159 , a concept that has not yet seen widespread use. We posit that the best energy policies — including the deployment of the newly discovered materials — can be improved and augmented with ML and should be discussed in research reporting accelerated energy technology platforms.

Conclusions

To summarize, ML has the potential to enable breakthroughs in the development and deployment of sustainable energy techniques. There have been remarkable achievements in many areas of energy technology, from materials design and device management to system deployment. ML is particularly well suited to discovering new materials, and researchers in the field are expecting ML to bring up new materials that may revolutionize the energy industry. The field is still nascent, but there is conclusive evidence that ML is at least able to expose the same trends that human researchers have noticed over decades of research. The ML field itself is still seeing rapid development, with new methodologies being reported daily. It will take time to develop and adopt these methodologies to solve specific problems in materials science. We believe that for ML to truly accelerate the deployment of sustainable energy, it should be deployed as a tool, similar to a synthesis procedure, characterization equipment or control apparatus. Researchers using ML to accelerate energy technology discovery should judge the success of the method primarily on the advances it enables. To this end, we have proposed the XPIs and some areas in which we hope to see ML deployed.

Davidson, D. J. Exnovating for a renewable energy transition. Nat. Energy 4 , 254–256 (2019).

Article   Google Scholar  

Horowitz, C. A. Paris agreement. Int. Leg. Mater. 55 , 740–755 (2016).

International Energy Agency 2018 World Energy Outlook: Executive Summary https://www.iea.org/reports/world-energy-outlook-2018 (OECD/IEA, 2018).

Chu, S., Cui, Y. & Liu, N. The path towards sustainable energy. Nat. Mater. 16 , 16–22 (2017).

Maine, E. & Garnsey, E. Commercializing generic technology: the case of advanced materials ventures. Res. Policy 35 , 375–393 (2006).

De Luna, P., Wei, J., Bengio, Y., Aspuru-Guzik, A. & Sargent, E. Use machine learning to find energy materials. Nature 552 , 23–27 (2017).

Wang, H., Lei, Z., Zhang, X., Zhou, B. & Peng, J. A review of deep learning for renewable energy forecasting. Energy Convers. Manag. 198 , 111799–111814 (2019).

Yao, Z. et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat. Mach. Intell. 3 , 76–86 (2021).

Rosen, A. S. et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter 4 , 1578–1597 (2021).

Article   CAS   Google Scholar  

Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349 , 255–260 (2015).

Personal, E., Guerrero, J. I., Garcia, A., Peña, M. & Leon, C. Key performance indicators: a useful tool to assess Smart Grid goals. Energy 76 , 976–988 (2014).

Helmus, J. & den Hoed, R. Key performance indicators of charging infrastructure. World Electr. Veh. J. 8 , 733–741 (2016).

Struck, M.-M. Vaccine R&D success rates and development times. Nat. Biotechnol. 14 , 591–593 (1996).

Moore, G. E. Cramming more components onto integrated circuits. Electronics 38 , 114–116 (1965).

Google Scholar  

Wetterstrand, K. A. DNA sequencing costs: data. NHGRI Genome Sequencing Program (GSP) www.genome.gov/sequencingcostsdata (2020).

Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380 , 1347–1358 (2019).

Jeong, J. et al. Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells. Nature 592 , 381–385 (2021).

NREL. Best research-cell efficiency chart. NREL https://www.nrel.gov/pv/cell-efficiency.html (2021).

Burger, B. et al. A mobile robotic chemist. Nature 583 , 237–241 (2020).

Clark, M. A. et al. Design, synthesis and selection of DNA-encoded small-molecule libraries. Nat. Chem. Biol. 5 , 647–654 (2009).

Pilania, G., Gubernatis, J. E. & Lookman, T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput. Mater. Sci. 129 , 156–163 (2017).

Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6 , 19375 (2016).

Lu, S. et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat. Commun. 9 , 3405 (2018).

Askerka, M. et al. Learning-in-templates enables accelerated discovery and synthesis of new stable double perovskites. J. Am. Chem. Soc. 141 , 3682–3690 (2019).

Jain, A. & Bligaard, T. Atomic-position independent descriptor for machine learning of material properties. Phys. Rev. B 98 , 214112 (2018).

Choubisa, H. et al. Crystal site feature embedding enables exploration of large chemical spaces. Matter 3 , 433–448 (2020).

Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120 , 145301–145306 (2018).

Broberg, D. et al. PyCDT: a Python toolkit for modeling point defects in semiconductors and insulators. Comput. Phys. Commun. 226 , 165–179 (2018).

Roch, L. M. et al. ChemOS: an orchestration software to democratize autonomous discovery. PLoS ONE 15 , 1–18 (2020).

Wei, L., Xu, X., Gurudayal, Bullock, J. & Ager, J. W. Machine learning optimization of p-type transparent conducting films. Chem. Mater. 31 , 7340–7350 (2019).

Schubert, M. F. et al. Design of multilayer antireflection coatings made from co-sputtered and low-refractive-index materials by genetic algorithm. Opt. Express 16 , 5290–5298 (2008).

Wang, C., Yu, S., Chen, W. & Sun, C. Highly efficient light-trapping structure design inspired by natural evolution. Sci. Rep. 3 , 1025 (2013).

Ripalda, J. M., Buencuerpo, J. & García, I. Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations. Nat. Commun. 9 , 5126 (2018).

Demant, M., Virtue, P., Kovvali, A., Yu, S. X. & Rein, S. Learning quality rating of As-Cut mc-Si wafers via convolutional regression networks. IEEE J. Photovolt. 9 , 1064–1072 (2019).

Musztyfaga-Staszuk, M. & Honysz, R. Application of artificial neural networks in modeling of manufactured front metallization contact resistance for silicon solar cells. Arch. Metall. Mater. 60 , 1673–1678 (2015).

Sendek, A. D. et al. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci. 10 , 306–320 (2017).

Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J. C. & Viswanathan, V. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent. Sci. 4 , 996–1006 (2018).

Zhang, Y. et al. Unsupervised discovery of solid-state lithium ion conductors. Nat. Commun. 10 , 5260 (2019).

Doan, H. A. et al. Quantum chemistry-informed active learning to accelerate the design and discovery of sustainable energy storage materials. Chem. Mater. 32 , 6338–6346 (2020).

Chemali, E., Kollmeyer, P. J., Preindl, M. & Emadi, A. State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach. J. Power Sources 400 , 242–255 (2018).

Richardson, R. R., Osborne, M. A. & Howey, D. A. Gaussian process regression for forecasting battery state of health. J. Power Sources 357 , 209–219 (2017).

Berecibar, M. et al. Online state of health estimation on NMC cells based on predictive analytics. J. Power Sources 320 , 239–250 (2016).

Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4 , 383–391 (2019).

Attia, P. M. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578 , 397–402 (2020).

Joshi, R. P. et al. Machine learning the voltage of electrode materials in metal-ion batteries. ACS Appl. Mater. Interf. 11 , 18494–18503 (2019).

Cubuk, E. D., Sendek, A. D. & Reed, E. J. Screening billions of candidates for solid lithium-ion conductors: a transfer learning approach for small data. J. Chem. Phys. 150 , 214701 (2019).

Sendek, A. D. et al. Machine learning-assisted discovery of solid Li-ion conducting materials. Chem. Mater. 31 , 342–352 (2019).

Kim, S., Jinich, A. & Aspuru-Guzik, A. MultiDK: a multiple descriptor multiple kernel approach for molecular discovery and its application to organic flow battery electrolytes. J. Chem. Inf. Model. 57 , 657–668 (2017).

Jinich, A., Sanchez-Lengeling, B., Ren, H., Harman, R. & Aspuru-Guzik, A. A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315000 redox reactions. ACS Cent. Sci. 5 , 1199–1210 (2019).

Sarkar, A. et al. High entropy oxides for reversible energy storage. Nat. Commun. 9 , 3400 (2018).

Choudhury, S. et al. Stabilizing polymer electrolytes in high-voltage lithium batteries. Nat. Commun. 10 , 3091 (2019).

Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361 , 360–365 (2018).

Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J. & Seh, Z. W. Predicting the state of charge and health of batteries using data-driven machine learning. Nat. Mach. Intell. 2 , 161–170 (2020).

Steinmann, S. N. & Seh, Z. W. Understanding electrified interfaces. Nat. Rev. Mater. 6 , 289–291 (2021).

Kandasamy, N., Badrinarayanan, R., Kanamarlapudi, V., Tseng, K. & Soong, B.-H. Performance analysis of machine-learning approaches for modeling the charging/discharging profiles of stationary battery systems with non-uniform cell aging. Batteries 3 , 18 (2017).

Wei, Q., Liu, D. & Shi, G. A novel dual iterative Q-learning method for optimal battery management in smart residential environments. IEEE Trans. Ind. Electron. 62 , 2509–2518 (2015).

Murphey, Y. L. et al. Intelligent hybrid vehicle power control — Part II: online intelligent energy management. IEEE Trans. Vehicular Technol. 62 , 69–79 (2013).

Seh, Z. W. et al. Combining theory and experiment in electrocatalysis: insights into materials design. Science 355 , eaad4998 (2017).

Staffell, I. et al. The role of hydrogen and fuel cells in the global energy system. Energy Environ. Sci. 12 , 463–491 (2019).

Montoya, J. H. H. et al. Materials for solar fuels and chemicals. Nat. Mater. 16 , 70–81 (2017).

Pérez-Ramírez, J. & López, N. Strategies to break linear scaling relationships. Nat. Catal. 2 , 971–976 (2019).

Shi, C., Hansen, H. A., Lausche, A. C. & Norskov, J. K. Trends in electrochemical CO 2 reduction activity for open and close-packed metal surfaces. Phys. Chem. Chem. Phys. 16 , 4720–4727 (2014).

Calle-Vallejo, F., Loffreda, D., Koper, M. T. M. & Sautet, P. Introducing structural sensitivity into adsorption-energy scaling relations by means of coordination numbers. Nat. Chem. 7 , 403–410 (2015).

Ulissi, Z. W. et al. Machine-learning methods enable exhaustive searches for active bimetallic facets and reveal active site motifs for CO 2 reduction. ACS Catal. 7 , 6600–6608 (2017).

Nørskov, J. K., Studt, F., Abild-Pedersen, F. & Bligaard, T. Activity and selectivity maps. In Fundamental Concepts in Heterogeneous Catalysis 97–113 (John Wiley, 2014).

Garijo del Río, E., Mortensen, J. J. & Jacobsen, K. W. Local Bayesian optimizer for atomic structures. Phys. Rev. B 100 , 104103 (2019).

Jørgensen, M. S., Larsen, U. F., Jacobsen, K. W. & Hammer, B. Exploration versus exploitation in global atomistic structure optimization. J. Phys. Chem. A 122 , 1504–1509 (2018).

Jacobsen, T. L., Jørgensen, M. S. & Hammer, B. On-the-fly machine learning of atomic potential in density functional theory structure optimization. Phys. Rev. Lett. 120 , 026102 (2018).

Peterson, A. A. Acceleration of saddle-point searches with machine learning. J. Chem. Phys. 145 , 074106 (2016).

Ulissi, Z. W., Medford, A. J., Bligaard, T. & Nørskov, J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8 , 14621 (2017).

Huang, Y., Chen, Y., Cheng, T., Wang, L.-W. & Goddard, W. A. Identification of the selective sites for electrochemical reduction of CO to C 2+ products on copper nanoparticles by combining reactive force fields, density functional theory, and machine learning. ACS Energy Lett. 3 , 2983–2988 (2018).

Chen, Y., Huang, Y., Cheng, T. & Goddard, W. A. Identifying active sites for CO 2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J. Am. Chem. Soc. 141 , 11651–11657 (2019).

Lai, Y., Jones, R. J. R., Wang, Y., Zhou, L. & Gregoire, J. M. Scanning electrochemical flow cell with online mass spectroscopy for accelerated screening of carbon dioxide reduction electrocatalysts. ACS Comb. Sci. 21 , 692–704 (2019).

Lai, Y. et al. The sensitivity of Cu for electrochemical carbon dioxide reduction to hydrocarbons as revealed by high throughput experiments. J. Mater. Chem. A 7 , 26785–26790 (2019).

Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO 2 reduction and H 2 evolution. Nat. Catal. 1 , 696–703 (2018).

Zhong, M. et al. Accelerated discovery of CO 2 electrocatalysts using active machine learning. Nature 581 , 178–183 (2020).

Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571 , 95–98 (2019).

Yao, Y. et al. Carbothermal shock synthesis of high-entropy-alloy nanoparticles. Science 359 , 1489–1494 (2018).

Yao, Y. et al. High-throughput, combinatorial synthesis of multimetallic nanoclusters. Proc. Natl Acad. Sci. USA 117 , 6316–6322 (2020).

Timoshenko, J. & Roldan Cuenya, B. In situ/operando electrocatalyst characterization by X-ray absorption spectroscopy. Chem. Rev. 121 , 882–961 (2021).

Zheng, C., Chen, C., Chen, Y. & Ong, S. P. Random forest models for accurate identification of coordination environments from X-ray absorption near-edge structure. Patterns 1 , 100013–100023 (2020).

Torrisi, S. B. et al. Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships. npj Comput. Mater. 6 , 109 (2020).

Timoshenko, J. et al. Neural network approach for characterizing structural transformations by X-ray absorption fine structure spectroscopy. Phys. Rev. Lett. 120 , 225502 (2018).

Marcella, N. et al. Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy. Phys. Chem. Chem. Phys. 22 , 18902–18910 (2020).

Chen, K., Laghrouche, S. & Djerdir, A. Degradation model of proton exchange membrane fuel cell based on a novel hybrid method. Appl. Energy 252 , 113439–113447 (2019).

Ma, R. et al. Data-driven proton exchange membrane fuel cell degradation predication through deep learning method. Appl. Energy 231 , 102–115 (2018).

Jeppesen, C. et al. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation. J. Power Sources 359 , 37–47 (2017).

Liu, J. et al. Sequence fault diagnosis for PEMFC water management subsystem using deep learning with t-SNE. IEEE Access. 7 , 92009–92019 (2019).

Ansari, M. A., Rizvi, S. M. A. & Khan, S. Optimization of electrochemical performance of a solid oxide fuel cell using artificial neural network. in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) 4230–4234 (IEEE, 2016).

Zhang, C. et al. Modelling of solid oxide electrolyser cell using extreme learning machine. Electrochim. Acta 251 , 137–144 (2017).

Esche, E., Weigert, J., Budiarto, T., Hoffmann, C. & Repke, J.-U. Optimization under uncertainty based on a data-driven model for a chloralkali electrolyzer cell. Computer-aided Chem. Eng. 46 , 577–582 (2019).

Siddaiah, R. & Saini, R. P. A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications. Renew. Sustain. Energy Rev. 58 , 376–396 (2016).

Yin, L., Yu, T., Zhang, X. & Yang, B. Relaxed deep learning for real-time economic generation dispatch and control with unified time scale. Energy 149 , 11–23 (2018).

Yu, T., Wang, H. Z., Zhou, B., Chan, K. W. & Tang, J. Multi-agent correlated equilibrium Q ( λ ) learning for coordinated smart generation control of interconnected power grids. IEEE Trans. Power Syst. 30 , 1669–1679 (2015).

Marino, D. L., Amarasinghe, K. & Manic, M. Building energy load forecasting using deep neural networks. in IECON Proceedings (Industrial Electronics Conference) 7046–7051 (IECON, 2016).

Ryu, S., Noh, J. & Kim, H. Deep neural network based demand side short term load forecasting. in 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm 2016) 308–313 (IEEE, 2016).

Mocanu, E., Nguyen, P. H., Kling, W. L. & Gibescu, M. Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning. Energy Build. 116 , 646–655 (2016).

Lund, P. D., Lindgren, J., Mikkola, J. & Salpakari, J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew. Sustain. Energy Rev. 45 , 785–807 (2015).

Kim, B. G., Zhang, Y., Van Der Schaar, M. & Lee, J. W. Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Trans. Smart Grid 7 , 2187–2198 (2016).

Dusparic, I., Taylor, A., Marinescu, A., Cahill, V. & Clarke, S. Maximizing renewable energy use with decentralized residential demand response. in 2015 IEEE 1st International Smart Cities Conference (ISC2 2015 ) 1–6 (IEEE, 2015).

Dusparic, I., Harris, C., Marinescu, A., Cahill, V. & Clarke, S. Multi-agent residential demand response based on load forecasting. in 2013 1st IEEE Conference on Technologies for Sustainability (SusTech 2013) 90–96 (IEEE, 2013).

Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 , 1798–1828 (2013).

Duvenaud, D. et al. Convolutional networks on graphs for learning molecular fingerprints. in Advances In Neural Information Processing Systems 2224–2232 (NIPS, 2015).

Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4 , 268–276 (2018).

Krenn, M., Hase, F., Nigam, A., Friederich, P. & Aspuru-Guzik, A. Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach. Learn. Sci. Technol. 1 , 045024–045031 (2020).

Jin, W., Barzilay, R. & Jaakkola, T. Junction tree variational autoencoder for molecular graph generation. Mach. Learn. 5 , 3632–3648 (2018).

You, J., Liu, B., Ying, R., Pande, V. & Leskovec, J. Graph convolutional policy network for goal-directed molecular graph generation. Adv. Neural Inf. Process. Syst. 31 , 6412–6422 (2018).

Liu, Q., Allamanis, M., Brockschmidt, M. & Gaunt, A. L. Constrained graph variational autoencoders for molecule design. in Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18) 7806–7815 (Curran Associates Inc., 2018).

Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87 , 184115 (2013).

Ward, L. et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Phys. Rev. B 96 , 024104 (2017).

Isayev, O. et al. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 8 , 15679 (2017).

Ziletti, A., Kumar, D., Scheffler, M. & Ghiringhelli, L. M. Insightful classification of crystal structures using deep learning. Nat. Commun. 9 , 2775 (2018).

Ryan, K., Lengyel, J. & Shatruk, M. Crystal structure prediction via deep learning. J. Am. Chem. Soc. 140 , 10158–10168 (2018).

Park, C. W. & Wolverton, C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys. Rev. Mater. 4 , 063801 (2020).

Liu, X. et al. Self-Supervised Learning: Generative or Contrastive (IEEE, 2020).

Ruder, S. An overview of multi-task learning in deep neural networks. Preprint at https://doi.org/10.48550/arXiv.1706.05098 (2017).

Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: a survey. IEEE Transactions on Pattern Analysis & Machine Intelligence 44 , 5149–5169 (2020).

Vaswani, A. et al. Attention is all you need. in Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17) 6000–6010 (Curran Associates Inc., 2017).

Veličković, P. et al. Graph attention networks. Preprint at https://doi.org/10.48550/arXiv.1710.10903 (2017).

Battaglia, P. W. et al. Relational inductive biases, deep learning, and graph networks. Preprint at https://doi.org/10.48550/arXiv.1806.01261 (2018).

Satorras, V. G., Hoogeboom, E. & Welling, M. E(n) equivariant graph neural networks. Preprint at https://doi.org/10.48550/arXiv.2102.09844 (2021).

Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. Apl. Mater. 1 , 011002–011012 (2013).

Laasner, R. et al. MatD3: a database and online presentation package for research data supporting materials discovery, design, and dissemination. J. Open Source Softw. 5 , 1945–1947 (2020).

Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365 , 557 (2019).

Hachmann, J. et al. The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2 , 2241–2251 (2011).

Bıyık, E., Wang, K., Anari, N. & Sadigh, D. Batch active learning using determinantal point processes. Preprint at https://doi.org/10.48550/arXiv.1906.07975 (2019).

Hoffmann, J. et al. Machine learning in a data-limited regime: augmenting experiments with synthetic data uncovers order in crumpled sheets. Sci. Adv. 5 , eaau6792 (2019).

Liu, J. Z. et al. Simple and principled uncertainty estimation with deterministic deep learning via distance awareness. Adv. Neural Inf. Process Syst. 33 , 7498–7512 (2020).

Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17) 6405–6416 (Curran Associates Inc., 2017).

D’Amour, A. et al. Underspecification presents challenges for credibility in modern machine learning. Preprint at https://doi.org/10.48550/arXiv.2011.03395 (2020).

Barredo Arrieta, A. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion. 58 , 82–115 (2020).

Ribeiro, M. T., Singh, S. & Guestrin, C. “Why should I trust you?” Explaining the predictions of any classifier. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (2016).

Lundberg, S. & Lee, S.-I. An unexpected unity among methods for interpreting model predictions. Preprint at https://arxiv.org/abs/1611.07478 (2016).

Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10 , e0130140 (2015).

Sun, W. et al. The thermodynamic scale of inorganic crystalline metastability. Sci. Adv. 2 , e1600225 (2016).

Aykol, M., Dwaraknath, S. S., Sun, W. & Persson, K. A. Thermodynamic limit for synthesis of metastable inorganic materials. Sci. Adv. 4 , eaaq0148 (2018).

Wei, J. N., Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci. 2 , 725–732 (2016).

Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10 , 370–377 (2019).

Nagabhushana, G. P., Shivaramaiah, R. & Navrotsky, A. Direct calorimetric verification of thermodynamic instability of lead halide hybrid perovskites. Proc. Natl Acad. Sci. USA 113 , 7717–7721 (2016).

Sanna, S. et al. Enhancement of the chemical stability in confined δ-Bi 2 O 3 . Nat. Mater. 14 , 500–504 (2015).

Podolyan, Y., Walters, M. A. & Karypis, G. Assessing synthetic accessibility of chemical compounds using machine learning methods. J. Chem. Inf. Model. 50 , 979–991 (2010).

Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. SCScore: synthetic complexity learned from a reaction corpus. J. Chem. Inf. Model. 58 , 252–261 (2018).

Kim, E. et al. Inorganic materials synthesis planning with literature-trained neural networks. J. Chem. Inf. Model. 60 , 1194–1201 (2020).

Huo, H. et al. Semi-supervised machine-learning classification of materials synthesis procedures. npj Comput. Mater. 5 , 62 (2019).

Tian, C., Lin, F. & Doeff, M. M. Electrochemical characteristics of layered transition metal oxide cathode materials for lithium ion batteries: surface, bulk behavior, and thermal properties. Acc. Chem. Res. 51 , 89–96 (2018).

Guilminot, E., Corcella, A., Charlot, F., Maillard, F. & Chatenet, M. Detection of Pt z + ions and Pt nanoparticles inside the membrane of a used PEMFC. J. Electrochem. Soc. 154 , B96 (2007).

Pender, J. P. et al. Electrode degradation in lithium-ion batteries. ACS Nano 14 , 1243–1295 (2020).

Li, Y. et al. Atomic structure of sensitive battery materials and interfaces revealed by cryo-electron microscopy. Science 358 , 506–510 (2017).

Wang, H. Numerical modeling of non-planar hydraulic fracture propagation in brittle and ductile rocks using XFEM with cohesive zone method. J. Pet. Sci. Eng. 135 , 127–140 (2015).

Hsu, Y.-C., Yu, C.-H. & Buehler, M. J. Using deep learning to predict fracture patterns in crystalline solids. Matter 3 , 197–211 (2020).

Wuest, T., Weimer, D., Irgens, C. & Thoben, K. D. Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4 , 23–45 (2016).

De Jong, P., Sánchez, A. S., Esquerre, K., Kalid, R. A. & Torres, E. A. Solar and wind energy production in relation to the electricity load curve and hydroelectricity in the northeast region of Brazil. Renew. Sustain. Energy Rev. 23 , 526–535 (2013).

Zolfani, S. H. & Saparauskas, J. New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Eng. Econ. 24 , 408–414 (2013).

Tao, F., Zhang, M., Liu, Y. & Nee, A. Y. C. Digital twin driven prognostics and health management for complex equipment. CIRP Ann. 67 , 169–172 (2018).

Yun, S., Park, J. H. & Kim, W. T. Data-centric middleware based digital twin platform for dependable cyber-physical systems. in International Conference on Ubiquitous and Future Networks (ICUFN) 922–926 (2017).

Boretti, A. Integration of solar thermal and photovoltaic, wind, and battery energy storage through AI in NEOM city. Energy AI 3 , 100038–100045 (2021).

Ghoddusi, H., Creamer, G. G. & Rafizadeh, N. Machine learning in energy economics and finance: a review. Energy Econ. 81 , 709–727 (2019).

Asensio, O. I., Mi, X. & Dharur, S. Using machine learning techniques to aid environmental policy analysis: a teaching case regarding big data and electric vehicle charging infrastructure. Case Stud. Environ. 4 , 961302 (2020).

Zheng, S., Trott, A., Srinivasa, S., Parkes, D. C. & Socher, R. The AI economist: taxation policy design via two-level deep multiagent reinforcement learning. Sci. Adv. 8 , eabk2607 (2022).

Sun, S. et al. A data fusion approach to optimize compositional stability of halide perovskites. Matter 4 , 1305–1322 (2021).

Sun, W. et al. Machine learning — assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials. Sci. Adv. 5 , eaay4275 (2019).

Sun, S. et al. Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis. Joule 3 , 1437–1451 (2019).

Kirman, J. et al. Machine-learning-accelerated perovskite crystallization. Matter 2 , 938–947 (2020).

Langner, S. et al. Beyond ternary OPV: high-throughput experimentation and self-driving laboratories optimize multicomponent systems. Adv. Mater. 32 , 1907801 (2020).

Hartono, N. T. P. et al. How machine learning can help select capping layers to suppress perovskite degradation. Nat. Commun. 11 , 4172 (2020).

Odabaşı, Ç. & Yıldırım, R. Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools. Nano Energy 56 , 770–791 (2019).

Fenning, D. P. et al. Darwin at high temperature: advancing solar cell material design using defect kinetics simulations and evolutionary optimization. Adv. Energy Mater. 4 , 1400459 (2014).

Allam, O., Cho, B. W., Kim, K. C. & Jang, S. S. Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries. RSC Adv. 8 , 39414–39420 (2018).

Okamoto, Y. & Kubo, Y. Ab initio calculations of the redox potentials of additives for lithium-ion batteries and their prediction through machine learning. ACS Omega 3 , 7868–7874 (2018).

Takagishi, Y., Yamanaka, T. & Yamaue, T. Machine learning approaches for designing mesoscale structure of Li-ion battery electrodes. Batteries 5 , 54 (2019).

Tan, Y., Liu, W. & Qiu, Q. Adaptive power management using reinforcement learning. in IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers (ICCAD ) 461–467 (IEEE, 2009).

Ermon, S., Xue, Y., Gomes, C. & Selman, B. Learning policies for battery usage optimization in electric vehicles. Mach. Learn. 92 , 177–194 (2013).

Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5 , 83 (2019).

Pilania, G. Machine learning in materials science: from explainable predictions to autonomous design. Comput. Mater. Sci. 193 , 110360 (2021).

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559 , 547–555 (2018).

Kaufmann, K. et al. Crystal symmetry determination in electron diffraction using machine learning. Science 367 , 564–568 (2020).

Chen, C., Zuo, Y., Ye, W., Li, X. & Ong, S. P. Learning properties of ordered and disordered materials from multi-fidelity data. Nat. Comput. Sci. 1 , 46–53 (2021).

Liu, M., Yan, K., Oztekin, B. & Ji, S. GraphEBM: molecular graph generation with energy-based models. Preprint at https://doi.org/10.48550/arXiv.2102.00546 (2021).

Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555 , 604–610 (2018).

Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559 , 377–381 (2018).

Epps, R. W. et al. Artificial chemist: an autonomous quantum dot synthesis bot. Adv. Mater. 32 , 2001626 (2020).

MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6 , eaaz8867 (2020).

Li, Z. et al. Robot-accelerated perovskite investigation and discovery. Chem. Mater. 32 , 5650–5663 (2020).

Aguiar, J. A., Gong, M. L., Unocic, R. R., Tasdizen, T. & Miller, B. D. Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning. Sci. Adv. 5 , eaaw1949 (2019).

Download references

Acknowledgements

Z.Y. and A.A.-G. were supported as part of the Nanoporous Materials Genome Center by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DE-FG02-17ER16362 and the US Department of Energy, Office of Science — Chicago under award number DE-SC0019300. A.J. was financially supported by Huawei Technologies Canada and the Natural Sciences and Engineering Research Council (NSERC). L.M.M.-M. thanks the support of the Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program under cooperative agreement number HR00111920027 dated 1 August 2019. Y.W. acknowledges funding support from the Singapore National Research Foundation under its Green Buildings Innovation Cluster (GBIC award number NRF2015ENC-GBICRD001-012) administered by the Building and Construction Authority, its Green Data Centre Research (GDCR award number NRF2015ENC-GDCR01001-003) administered by the Info-communications Media Development Authority, and its Energy Programme (EP award number NRF2017EWT-EP003-023) administered by the Energy Market Authority of Singapore. A.A.-G. is a Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow. E.H.S. acknowledges funding by the Ontario Ministry of Colleges and Universities (grant ORF-RE08-034), the Natural Sciences and Engineering Research Council (NSERC) of Canada (grant RGPIN-2017-06477), the Canadian Institute for Advanced Research (CIFAR) (grant FS20-154 APPT.2378) and the University of Toronto Connaught Fund (grant GC 2012-13). Z.W.S. acknowledges funding by the Singapore National Research Foundation (NRF-NRFF2017-04).

Author information

These authors contributed equally: Zhenpeng Yao, Yanwei Lum, Andrew Johnston.

Authors and Affiliations

Shanghai Key Laboratory of Hydrogen Science & Center of Hydrogen Science, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Zhenpeng Yao

Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

Zhenpeng Yao, Luis Martin Mejia-Mendoza & Alán Aspuru-Guzik

Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China

State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Innovis, Singapore, Singapore

Yanwei Lum & Zhi Wei Seh

Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada

Yanwei Lum, Andrew Johnston & Edward H. Sargent

School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

Xin Zhou & Yonggang Wen

Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

• Alán Aspuru-Guzik

You can also search for this author in PubMed   Google Scholar

Contributions

Z.Y., Y.L. and A.J. contributed equally to this work. All authors contributed to the writing and editing of the manuscript.

Corresponding authors

Correspondence to Alán Aspuru-Guzik , Edward H. Sargent or Zhi Wei Seh .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Reviews Materials thanks Shijing Sun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note.

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

Related links

MatD3 database: https://github.com/HybriD3-database/MatD3

Materials Project: https://materialsproject.org/

Machine learning techniques that can query a user interactively to modify its current strategy (that is, label an input).

(AI). Theory and development of computer systems that exhibit intelligence.

A system for adjusting the power output of multiple generators at different power plants, in response to changes in the load.

A technology development pipeline that incorporates automation to go from idea to realization of technology. ‘Closed’ refers to the concept that the system improves with experience and iterations.

Process of increasing the amount of data through adding slightly modified copies or newly created synthetic data from existing data.

A generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables, with connections between the layers but not between units within each layer.

(DL). Machine learning subfield that is based on neural networks with representation learning.

The ability to adapt to new, unseen data, drawn from the same distribution as the one used to create the model.

Machine learning techniques that learn to model the data distribution of a dataset and sample new data points.

Degree to which a human can understand a model’s decision. Interpretability can be used to build trust and credibility.

A design method where new materials and compounds are ‘reverse-engineered’ simply by inputting a set of desired properties and characteristics and then using an optimization algorithm to generate a predicted solution.

A special kind of recurrent neural networks that are capable of selectively remembering patterns for a long duration of time.

(ML). Field within artificial intelligence that deals with learning algorithms, which improve automatically through experience (data).

A computerized system composed of multiple interacting intelligent agents.

The combination of ridge regression (a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated) with multiple kernel techniques.

Models that involve the analysis of multiple, simultaneous physical phenomena, which can include heat transfer, fluid flow, deformation, electromagnetics, acoustics and mass transport.

The field of solving problems that have important features at multiple scales of time and/or space.

A neural network is composed of parameterized and optimizable transformations.

A class of artificial neural networks where connections between nodes form a directed or undirected graph along a temporal sequence.

Process of incorporating additional information into the model to constrain its solution space.

Machine learning techniques that make a sequence of decisions to maximize a reward.

Features used in a representation learning model, which transforms inputs into new features for a task.

Technique for solving problems in the planning of chemical synthesis.

A robotic equipment automated chemical synthesis plan.

Design process composed of several stages where materials are iteratively filtered and ranked to arrive to a few top candidates.

Machine learning techniques that involve the usage of labelled data.

Machine learning techniques that adapt a learned representation or strategy from one dataset to another.

Process of evaluating the statistical confidence of model.

Machine learning techniques that learn patterns from unlabelled data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Yao, Z., Lum, Y., Johnston, A. et al. Machine learning for a sustainable energy future. Nat Rev Mater 8 , 202–215 (2023). https://doi.org/10.1038/s41578-022-00490-5

Download citation

Accepted : 14 September 2022

Published : 18 October 2022

Issue Date : March 2023

DOI : https://doi.org/10.1038/s41578-022-00490-5

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Artificial intelligence-based methods for renewable power system operation.

• Yuanzheng Li
• Yizhou Ding
• Zhigang Zeng

Nature Reviews Electrical Engineering (2024)

Nature-inspired interfacial engineering for energy harvesting

• Baoping Zhang
• Wanghuai Xu
• Zuankai Wang

Bringing digital synthesis to Mars

• Mark D. Symes
• Leroy Cronin

Nature Synthesis (2024)

Accelerating discovery in organic redox flow batteries

Nature Computational Science (2024)

Sustainable moisture energy

• Pengfei Wang
• Tingxian Li

Nature Reviews Materials (2024)

Quick links

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

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Why renewables are the cornerstone of the global energy transition

Renewable energy, energy efficiency and electrification are key to energy transition Image:  Dan Meyers on Unsplash

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Dolf Gielen

Francisco boshell.

.chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} Explore and monitor how .chakra .wef-15eoq1r{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;color:#F7DB5E;}@media screen and (min-width:56.5rem){.chakra .wef-15eoq1r{font-size:1.125rem;}} Climate Crisis is affecting economies, industries and global issues

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved with our crowdsourced digital platform to deliver impact at scale

Stay up to date:, decarbonizing energy.

Listen to the article

• It's now clear that renewable energy, energy efficiency and electrification must be the drivers of the deep decarbonization we need.
• New analysis from IRENA finds that renewables are now the cheapest form of energy - and capacity is set to rise significantly over the next few decades.

Addressing climate change requires us to decarbonize both energy supply and demand by 2050. The US, Europe and China have committed to net zero or carbon neutrality by mid-century. Others are following suit. This will have a profound effect on the global energy transition, placing electricity as a key vector in decarbonizing the entire energy sector.

The latest insights from IRENA’s World Energy Transitions Outlook were released on 16 March at the Berlin Energy Transitions Dialogue. It provides in-depth analysis of what these effects will look like, starting from the Paris Climate agreement objective of limiting climate change to well below 2˚C and with an effort for 1.5˚C by the end of this century. While several options are being considered for a deep decarbonization, it is clear that renewable energy, energy efficiency and electrification are at the centre of the global energy transition .

Renewable energy and global energy transition

While climate change mitigation is a powerful driver behind the shift away from fossil fuel-based power generation, this is not the only driver. At the same time, renewable power has become the cheapest form of electricity generation and the costs continue to fall thanks to improvements in technology and economies of scale. The share of renewable power continue to rise from year to year, with nearly 30% renewables in the global power mix at present and renewables dominating yearly capacity additions (see Figure 1, below).

New IRENA analysis indicates a continued swift energy transition to renewable power generation worldwide in the coming three decades, with shares of variable (or intermittent) renewables – solar PV and wind – growing especially rapidly. Variable renewables will dominate the world's total power supply by 2050, a major change from today’s situation. Yet experience from around the world shows it is possible to operate power systems with high shares of variable renewables, as witnessed in Germany, Ireland and the UK, amongst others. During 2020, despite the COVID-19 pandemic, the share of renewables (mainly variable) in total electricity generation was 40% in Europe, a more than 4% increase in the share in comparison to 2019. Most notably, the share of other generation sources fell in Europe over the same period between 6% and 16%, as in the case of coal-based generation.

Increasing flexibility to smoothen energy transition

The operation of power systems with a high share of variable renewables requires much higher flexibility. Today, dispatchable fossil plants (that is, plants that can generate electricity on demand) provide that flexibility, but this will change going forward as their role declines. IRENA has identified 30 options for increasing flexibility across four main pillars : hardware, markets and regulations, and operational practices and business models (see figure 2, below). This toolkit of options must be deployed in the context of each power system’s specific characteristics. Especially the demand side offers interesting possibilities, as the electrification trend results in new loads connected to the system -such as electric vehicles, behind-the-meter batteries and heat pumps- which if operated smartly can support grid balancing. This is helped by rapid digitalization of power systems. Time-of-use pricing, aggregators, Demand Side Management are some of the strategies that benefit from digitalization and smart grids continue to expand worldwide. Still many transmission and distribution grids will require expansion and upgrading in order to deal with the new power system realities.

Also, regulations and grid codes need to be adjusted in order to enable to full deployment of the new flexibility options. This is an area that warrants more attention.

Electrification, including buildings, transport and industry, as well as the production of green hydrogen, will play a key role in a net-zero CO2 emissions future.

IRENA analysis suggests that up to a quarter of all electricity will be used for the production of green hydrogen . At the same time, a massive shift will occur towards electrification of road transportation while synfuels produced from clean hydrogen will play an increasing role in aviation and shipping. Whereas better building efficiency will reduce the need for heating and cooling, this is balanced by a shift to electric heat pumps. The analysis suggests that direct electricity use and indirect electricity use for the production of green hydrogen and derived synfuels may account for 60% of total final energy use by 2050, up from around 21% today. As a consequence, electricity demand will grow 3-4 fold from today’s level. This represents a massive shift; the electricity sector will become the central pillar of global energy supply and demand, a much bigger role than it has played in previous decades. Traditional incumbents in the energy sector, such as oil and gas companies, are already eyeing this trend and developing strategies to become electricity market players . It remains to be seen who will become the dominant player in this market in coming decades.

Moving to clean energy is key to combating climate change, yet in the past five years, the energy transition has stagnated.

Energy consumption and production contribute to two-thirds of global emissions, and 81% of the global energy system is still based on fossil fuels, the same percentage as 30 years ago. Plus, improvements in the energy intensity of the global economy (the amount of energy used per unit of economic activity) are slowing. In 2018 energy intensity improved by 1.2%, the slowest rate since 2010.

Effective policies, private-sector action and public-private cooperation are needed to create a more inclusive, sustainable, affordable and secure global energy system.

Benchmarking progress is essential to a successful transition. The World Economic Forum’s Energy Transition Index , which ranks 115 economies on how well they balance energy security and access with environmental sustainability and affordability, shows that the biggest challenge facing energy transition is the lack of readiness among the world’s largest emitters, including US, China, India and Russia. The 10 countries that score the highest in terms of readiness account for only 2.6% of global annual emissions.

To future-proof the global energy system, the Forum’s Centre for Energy & Materials is working on initiatives including Clean Power and Electrification , Energy and Industry Transition Intelligence, Industrial Ecosystems Transformation , and Transition Enablers to encourage and enable innovative energy investments, technologies and solutions.

Additionally, the Mission Possible Partnership (MPP) is working to assemble public and private partners to further the industry transition to set heavy industry and mobility sectors on the pathway towards net-zero emissions. MPP is an initiative created by the World Economic Forum and the Energy Transitions Commission.

Is your organisation interested in working with the World Economic Forum? Find out more here .

Given the growth in electricity demand and the shift to renewable power a massive expansion of clean power generation will be needed and infrastructure planning must be ramped up accordingly. The investment needs are hefty and it is critical to ensure that the infrastructure rollout speed is commensurate with the needs of the energy transition. This will require further streamlining of planning and approval processes.

IRENA continues to work with its 164 member countries to devise and implement renewable energy transition strategies for power sector transformation based on its Innovation Toolbox , Flextool , power systems planning and grid studies.

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Related topics:

The agenda .chakra .wef-n7bacu{margin-top:16px;margin-bottom:16px;line-height:1.388;font-weight:400;} weekly.

A weekly update of the most important issues driving the global agenda

.chakra .wef-1dtnjt5{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;} More on Climate Action .chakra .wef-nr1rr4{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;white-space:normal;vertical-align:middle;text-transform:uppercase;font-size:0.75rem;border-radius:0.25rem;font-weight:700;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;line-height:1.2;-webkit-letter-spacing:1.25px;-moz-letter-spacing:1.25px;-ms-letter-spacing:1.25px;letter-spacing:1.25px;background:none;padding:0px;color:#B3B3B3;-webkit-box-decoration-break:clone;box-decoration-break:clone;-webkit-box-decoration-break:clone;}@media screen and (min-width:37.5rem){.chakra .wef-nr1rr4{font-size:0.875rem;}}@media screen and (min-width:56.5rem){.chakra .wef-nr1rr4{font-size:1rem;}} See all

How Indigenous expertise is empowering climate action: A case study from Oceania

Amanda Young and Ginelle Greene-Dewasmes

April 23, 2024

What is desertification and why is it important to understand?

Andrea Willige

Global forest restoration goals can be achieved with youth-led ecopreneurship

Agustin Rosello, Anali Bustos, Fernando Morales de Rueda, Jennifer Hong and Paula Sarigumba

The planet’s outlook is in our hands. Which future will we incentivize?

Carlos Correa

April 22, 2024

This Earth Day we consider the impact of climate change on human health

Shyam Bishen and Annika Green

Earth Day: We are almost certainly all eating plastics, says report, and other nature and climate stories you need to read this week

Johnny Wood

• Reference Manager
• Simple TEXT file

People also looked at

Systematic review article, renewable energy consumption and economic growth nexus—a systematic literature review.

• 1 School of Economics, Guangdong University of Finance and Economics, Guangzhou, China
• 2 School of Technology, Management and Engineering, NMIMS, Indore, India
• 3 Department of Banking and Financial Markets, Financial University Under the Government of the Russian Federation, Moscow, Russia
• 4 University Center for Circular Economy, University of Pannonia, Nagykanizsa, Hungary

An efficient use of energy is the pre-condition for economic development. But excessive use of fossil fuel harms the environment. As renewable energy emits no or low greenhouse gases, more countries are trying to increase the use of energies from renewable sources. At the same time, no matter developed or developing, nations have to maintain economic growth. By collecting SCI/SSCI indexed peer-reviewed journal articles, this article systematically reviews the consumption nexus of renewable energy and economic growth. A total of 46 articles have been reviewed following the PRISMA guidelines from 2010 to 2021. Our review research shows that renewable energy does not hinder economic growth for both developing and developed countries, whereas, there is little significance of consuming renewable energy (threshold level) on economic growth for developed countries.

Introduction

Consuming non-renewable energy may produce output and foster economic development, but undoubtedly it is a significant source of carbon emission and environmental degradation ( Awodumi and Adewuyi 2020 ). Using non-renewable energy sources put countries in a dilemma in policy priority between pollution reduction and economic growth. Thus, whether renewable or non-renewable, the energy should be used carefully and efficiently as its sources are limited. In addition, due to climate change and global warming situation, renewable energy could be the most attractive alternative to fossil fuel, reducing the CO 2 emission process. However, introducing new renewable energy technologies, consuming, and making them available for the citizens, is very time-consuming and costly. On the other side, countries struggle to maintain economic growth and development. Due to the COVID-19 crisis, the situation has been worsening. The governments of both developing and developed nations have to balance spending for climate change mitigation and economic growth.

Moreover, there is still limited information regarding all the perceived critical factors in moving toward fully renewable energy sources. This article shows a comprehensive assessment of how renewable energy systems affect the country’s economic growth. In this article, assessment is carried out based on G7 and Next-11 countries. France, Germany, Italy, Japan, the United States, the United Kingdom, and Canada make up the Group of Seven (G7) intergovernmental organization. Government officials from these nations meet regularly to discuss world economic and monetary matters, with each member alternating through the chairmanship.

Along with the BRICs, the Next-11 (or N-11) are eleven countries identified by Goldman Sachs as having a high potential to become the world’s largest economies in the twenty-first century, namely, Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, Philippines, South Korea, Turkey, and Vietnam. Figure 1 shows the name of G7 and Next-11 countries.

FIGURE 1 . (Group Seven) G7 and (Next-11) N-11 countries.

Energy resource has been the fundamental element for an economy or economic development ( Xiong et al., 2014 ). It is clear that economic growth mainly depends on energy consumption, which is highly responsible for greenhouse gas (GHG) emissions, particularly CO 2 , as stated by Gabr and Mohamed (2020) . CO 2 emissions are a by-product generated by primary consumption sources of non-renewable energy, such as fossil fuels ( Thollander et al., 2007 ). Starting from this general environmental framework due to non-renewable sources, several national economies, after having experienced several disasters, have tried to bring about a structural change in production methods and energy use. Some countries have mainly switched to renewable sources, leaving fossil fuels to no longer be based on non-renewable energy sources ( Irfan et al., 2021 ). According to the EY Company’s Renewable Energy Country Attractiveness Index (RECAI), which integrates new global trends, the countries with the most significant opportunities for investments in renewables are the United States, China, and India, three large economies that have been competing for these positions for several years now ( RECAI, 2020 ). Implementing renewable energy sources (RES) is essential but still faces some challenges in some European countries. Perception and awareness toward RES are the main challenges in countries such as Montenegro ( Djurisic et al., 2020 ).

One of the world’s major power resource user countries, China, has put forward the “double carbon” target to reduce emissions ( Jiang et al., 2022 ). China’s domestic market has shown some resilience despite the end of domestic subsidies in December 2020 and the COVID-19 crisis, which affected 10% of new capacity additions. Chinese solar panel production grew by 15.7% compared to 2019 ( RECAI, 2020 ). Australia represents the third, this country has experienced exponential growth in residential photovoltaics, distributing over 10 GW of solar energy to civilian homes and adopting necessary plans to export hydrogen to Asia ( RECAI, 2020 ). India follows, from 7th to 4th place, and thanks to the growth of photovoltaic capacity to meet the ambitious national green goals for 2030 ( RECAI, 2020 ). In addition to G7 and N-11 countries, Table 1 shows the general information and technology-specific scores of the top 10 countries that invest in renewable energy sources, and Figure 2 shows the data visualization of the dataset in Table 1 .

TABLE 1 . Top 10 countries that invest in renewable sources.

FIGURE 2 . Comparison of technology-specific score of top 10 countries.

Some studies tried to relate the consumption of renewable energy and economic growth. But most of the studies concern EU countries and other factors. For example Tutak and Brodny . (2022 ) have tried to analyze the impact of renewable energy on economics, environmental, and conventional energy sources. In addition, ( Smolović et al., 2020 ), by using the pooled mean group (PMG) estimator in a dynamic panel setting (an ARDL model) has carried out a nexus between renewable energy consumption and economic growth in the traditional and new member states of the EU. Furthermore, the panel vector autoregression (PVAR) model ( Koengkan, Fuinhas, and Marques 2019 ) has examined the relationship between financial openness, renewable and non-renewable energy consumption, CO2 emissions, and economic growth in 12 Latin American countries. Furthermore Lorente et al. ( 2022 ) found that there is an association between economic complexity and CO 2 emissions is inverted-U and further N-shaped relationship for Portugal, Italy, Ireland, Greece, and Spain.

We have noticed a research gap of systematic review analysis regarding economic growth and renewable energy consumption in recent years by analyzing other existing research work. From this point of view, our study tried to fill the research gap and make it a collection of systematic reviews in this field. Moreover, there were no such systematic reviews (including developing, developed, and underdeveloped countries) in this field of study.

Due to the higher cost of implementing and maintaining, cost-benefit analysis, and other external–internal factors, renewable energy is still under consideration to entirely depend on the energy source. Thus, this is a burning question for the researchers, policy makers, and related organizations whether introducing the renewable energy source would hinder or slow down the economic growth. Many researchers are trying to answer for their respective country or region of interest. No such review work tried to find the nexus between RE and EG for G7 and N-11 countries. This study attempted to gather the related research outcomes and give a broader picture of introducing and using the renewable energy and economic growth relationship.

Basic Interpretation With Renewable Energy and Economic Growth

Introducing renewable energy and economic growth is a widespread debate among researchers. From this point of view, by executing the panel data (1970–2017) ( Konuk et al., 2021 , 11), examined the relationship between economic growth and biomass energy consumption for N-11 countries. According to their research work, economic development and biomass energy consumption act together in the long run. In addition, Jenniches (2018 ) tried to assess the regional economic impacts of a transition to renewable energy generation in his review article. He believes clearly that defining technologies and assessment periods is very significant. Doytch and Narayan (2021) estimated the effects of non-renewable and renewable energy consumption on manufacturing and services growth. They have found that renewable energy enhances growth in high-growth sectors, that is, the services sector in high-income economies and the manufacturing sector in middle-income economies. Acheampong et al. (2021) investigated the causal relationship between renewable energy, CO 2 emission, and economic growth for 45 African (sub-Saharan) countries over 57 years (1960–2017). Using the GMM-PVAR method, they have concluded that a bidirectional causal relationship exists between economic growth and renewable energy ( Acheampong, Dzator, and Savage 2021 ). Another old study (comparatively) in 2003 by Ugur and Sari examined the causality relationship between the two series in the top 10 emerging economies and G7 countries. They have discovered bi-directional causality for Argentina, GDP to energy consumption causality for Korea and Italy, energy and consumption to GDP for Turkey, France, Germany, and Japan. Additionally, it was found that countries such as Argentina, Brazil, Paraguay, Uruguay, and Venezuela have low renewable energy participation in their energy mix. An effect between renewable energy consumption and fossil fuels, as a possible response to periods of scarcity in reservoirs, was detected for these countries ( Koengkan et al., 2020b ).

In contrast, economic growth may slow down due to energy conservation in the case of the rest four nations ( Soytas and Sari, 2003 ). Another estimation suggested that non-renewable energy consumption has a significant and positive impact on economic activities and development across a large number of Organization for Economic Co-operation and Development (OECD) countries ( Ivanovski, Hailemariam, and Smyth 2021 ). A review of hybrid renewable energy systems (HRES) in developing countries has been conducted by Zebra et al. (2021) . They believe Asian developing countries perform better than African nations for renewable and non-renewable mini-grids maintenance and productivity. They also believe that, in general, the costs of mini-grids will continue to decline, making renewable sources even more competitive at the utility scale. Some researchers also tried to find the opposite relationship between economic growth (barriers) and renewable energy development. Seetharaman et al. (2019 ) believe technological, social, and regulatory barriers hinder the development of RE development, but economic constraints do not directly impact the outcome of renewable energy.

In some countries, renewable energy and consumption do not hinder economic development, and on the other side, it plays a vital role in hindering economic development. So, according to Islam et al. (2022) , income growth shows positive and negative effects on renewable and non-renewable energy consumption. Consider that domestic and foreign investments positively affect renewable and non-renewable energy consumption. Furthermore, institutional quality has a positive impact on renewable energy consumption. Instead, the urbanization process has a negative impact on the consumption of renewable energy because it has a positive influence on the consumption of non-renewable energy ( Islam et al., 2022 ).

Unfortunately, despite the revolutionary attempt to adopt renewable energy technologies, some industrial countries are still firm on the consumption of fossil fuels energies with the aim of recording faster and more impressive economic growth ( Shrinkhal, 2019 ; Islam et al., 2021 ). Contrary to the positive effects on the environment generated with renewable energy sources, the economic serenity that can be reached using non-renewable enriches the coffers of different economies and the lifestyles of their people, but not those of the environment ( Doytch and Narayan, 2016 ). In some cases, renewable energy consumption (threshold level) does not significantly affect economic growth for developed countries. Renewable energy (RE) and economic development indicators may not correlate in selected EU countries. Despite some debate and unstable economic conditions, the share of RE in total energy consumption in EU countries has been systematically growing and was not much dependent on economic factors ( Ogonowski 2021 ). The economic value of solely replacing renewable energy with nuclear power and fossil energy could be very high and infeasible. They consider that electricity and power generation based on only renewable energy would cost an additional 35 trillion KRW/year for South Korea ( Park et al., 2016 ). This method is infeasible, and customer willingness to pay will be low. Lema et al. (2021) by taking in-depth analysis, tried to measure to what extent direct and indirect economic benefits are created when Chinese investments in RE projects in sub-Saharan Africa. Their research revealed that the FDI and investments on RE projects might have “bounded economic benefits” for the region by creating new job opportunities, production and training activities, linkage with local systems, and so on. In addition, economic awareness, public opinion, and mass participation are essential for the use of RE in the region. Citizens of Kenya (73%) (both urban and rural) strongly approved the development of RE sources technologies and (91%) believe that RE technologies will reduce the cost of electricity and power generation ( Oluoch et al., 2020 ).

Methodology Used in Review Assessment

We have considered Group seven (G7: Canada, France, Germany, Italy, Japan, UK, and the United States), countries (as developed nations and the Next-11 (N- 11: Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, Philippines, South Korea, Turkey, and Vietnam) countries (exclude South Korea) as developing countries.

To maintain the whole process, we have followed the PRISMA flowchart explained in Figure 3 :

FIGURE 3 . PRISMA flow diagram.

The PRISMA method—Preferred Reporting Items for Systematic Reviews and Meta-Analyzes—built a set of minimum elements based on the references highlighted in the systematic reviews and meta-analysis. The primary purpose of PRISMA is to focus primarily on studies that evaluate the effects of certain interventions. However, they can also be used to report systematic reviews that present with different objectives (e.g., from the evaluation of interventions) ( Prisma, 2021 ).

For this purpose, PRISMA was used because it is helpful for the critical evaluation of the published systematic reviews of this study, although it is not a tool for assessing the quality of a systematic review. For the main results of the literature review according to the PRISMA guidelines, we have considered the available online date for the “Year” column. We have followed the MLA style for the author’s name. The applied and references related theories are in the “Theories” column. Authors’ article methodologies are considered in the “Methods” column. The author’s near-future predictions or consequences are listed in the “Predictors” column. The results, conclusions, or outcomes are in the “Outcomes” column, followed by article keywords in the “Keywords” section. We have used google scholar citation for the citation column until the last week of December 2021. The citation number may vary as the citations are increasing every day. The last column is “Journal,” which denotes the respective article published journal name.

We have used Google Scholar, Scopus, Science Direct, and PubMed for research articles. Initially, we searched the articles using the keywords “Renewable energy” and “Economic growth.” We have 553 articles related to good governance and sustainable tourism mentioned in the article’s title. There were 17 duplicate articles that we had to remove. We deducted the articles unrelated to the topic content from this initial screening. After removing the irrelevant articles, we had 97 full-text eligible articles. From these 97 articles, we have selected 46 closely matched full-text articles for review ( Figure 3 ).

Effect of Renewable Energy in Economic Growth G7 Countries

While presenting economic prosperity, the G7 countries can still not guarantee environmental well-being. In fact, using the annual frequency data from 1980 to 2016, the impact on the environment of some variables was ascertained using panel data. The results show that financial globalization and eco-innovation reduce the ecological footprint. On the contrary, urbanization stimulates environmental degradation by increasing the ecological footprint values ( Ahmad et al., 2021 ).

Amri (2017) , using the dynamic simultaneous-equation panel data approach, investigated, over the period 1990–2012, the relationship between three indicators (economic growth, renewable energy, and trade) in different income groups of countries and underlined the interdependence of these variables. Notably, the main findings reveal a bidirectional nexus between renewable energy consumption and GDP in all groups of nations; a persistent bidirectional relationship among foreign trade and renewable energies in all groups of countries; finally, a bidirectional nexus between trade and economic growth in developed, developing, and others developed countries. In addition, a team of researchers investigated the dynamic effect of RE consumption, biocapacity, and economic growth in the United States from 1985 to 2014. Using the ARDL model, the authors claim that a decline in environmental degradation can attribute to an increase in RE consumption through its negative effect on the ecological footprint. Their study revealed that biocapacity and economic growth would exert more pressure on the ecological footprint. Furthermore, a causal relationship was built between ecological footprint and economic growth and economic growth and biocapacity ( Usman, Alola, and Sarkodie 2020 ).

Armeanu et al. (2021) , investigated, using several statistical methods, the interrelationships, over the period 1990–2014, among renewable energy, types of energy, economic growth, CO 2 emissions, and urbanization in different income groups of countries, and highlighted that “In the case of the group of countries with a high level of income, the presence of the co-integration of the renewable energy use with the carbon releases, renewable and nuclear energy, electric power consumption, and the urban population was observed” and the relationship was satisfied, due to the interest of this group of countries to preserve the environment. Furthermore, through the Granger causality test, the authors find a single-bidirectional causal relationship between economic growth and energy intensity in the low-income countries, whereas many bidirectional relations among the variables in high-income countries, particularly between energy intensity and CO 2 emissions.

Another study was conducted by Hao et al. (2021) to investigate the effects of green growth on CO 2 emissions for G7 countries over the past twenty-five years, using second-generation panel data methods, for example, the distributive self-regressive-augmented transversal lag model (CSARDL). The results revealed that both short- and long-term GDP growth impact environmental impoverishment. Thus, the thesis that green growth supports the quality of the environment is confirmed. The authors highlighted that any changes in CO 2 , GDP, green growth (GG), environmental taxes (ET), renewable energy consumption (REC), and human capital (HC) in one of the G7 countries would have consequences in other G7 countries in an interconnected nexus between G7 countries.

However, at the regional level, total energy consumption positively affects growth, while renewable sources negatively affect development in some regions in low- and middle-income countries ( Namahoro et al., 2021a ). Instead of testing the relationships among variables with appropriate and feasible econometrics modeling techniques, using panel data methodologies, Li and Leung (2021) evaluated the relationship between energy prices, economic growth, and renewable energy consumption. The results of Li and Lung’s study (2021) highlighted the importance of economic growth in supporting renewable energy consumption, especially in G7 countries with developed economies. However, factors that are affected through renewable energy systems are listed in Table 2 . By focusing on R&D spending and uniform policies, the G7 countries have transformed their economies from copying countries to a community of dynamic economies. As a result, and in tandem with the economy’s digitalization. This study examines the relationship between energy, financial, environmental sustainability, and social performance of G7 countries using a data envelopment analysis (DEA)-like composite score. The foundation of this study is formed over the reconstruction and modification of regional emissions and examining aspects such as energy, efficiency, and usage, in addition to the prospect of having a regional development outline. Most prior research used certain essential methodologies to examine emission levels and variance depending on actors connected to energy efficiency, energy structure, financial development, production, industry, technological development openness, and population.

TABLE 2 . Factor that effected through the renewable energy system.

Namahoro et al. (2021b) underlined that renewable energy consumption affects economic growth, using an asymmetric analysis with a non-linear autoregressive-distributed lagged model (NARDL) and causality test. In contrast, Wang and Wang (2020) reveal that in the G7 countries, renewable energy consumption positively affects economic growth. The threshold value changes influence in this positive relationship. Thus, the role of growing renewable energy use to stimulate economic growth is non-linear. For example, if the EU countries increase their renewable energy over a threshold value, the position of renewable energy in supporting economic development is more significant. In the same line, in 2020, Chen et al. (2020) studied the causal link between renewable energy consumption and economic growth using a threshold model. The reference period is 1995–2015, and they confirm that renewable energies positively and significantly affect the economic growth in the OECD countries, whereas no significant effect is in the developed countries. The authors underlined that in developing and non-OECD countries, renewable energies significantly affect economic growth over a certain threshold of their consumption. In addition, Yang et al. (2021) found feed-in-tarrif (FIT) have higher expected output and profit, and lower market prices. The risks of production and gain is of relatively more significant. By contrast, the production and profit of renewable portfolio standard (RPS) remain relatively more stable. In the same year, Sharma et al. (2021) examined the interrelationships between sustainability indicators and financial growth performance, using Arellano–Bond dynamic panel data estimation, system dynamic panel data estimation, and the augmented mean group model. The results highlighted that the transition toward renewable energy is economically in the long run, positively impacting economic growth in line with the environment. From this point of view, total investment in RE and descriptive statistics with technological specific scores by G7 countries are listed in Tables 3 , 4 , respectively. Table 3 shows the Renewable Energy Country Attractive Index of different countries, and according to the score it is found out in the USA the growth or electricity generation through the renewable energy in the wonderful way. Overall data also shows the growth rate of the onshore wind energy systems, solar PV, solar CSP, geothermal systems are better in the United States; on the other hand, the offshore wind energy system and biomass systems are popular in the United Kingdom. The Renewable Energy Country Attractiveness Index (RECAI) rates the attractiveness of renewable energy investment and deployment prospects in the world’s top 40 markets. The rankings reflect our evaluations of market attractiveness and worldwide market trends. Table 4 describes the different statistical parameters with central tendency in terms of mean, mode, and median of renewable energy sources. It also finds most of the energy sources are minimum RECAI for Canada and maximum for the United States.

TABLE 3 . G 7 countries that invest in renewable sources.

TABLE 4 . Descriptive statistics with technological specific scores of G7 countries.

In Figure 4 , we have listed the comparative technology-specific scores in various factors among G7 countries.

FIGURE 4 . Comparison of technology-specific score of G7 countries. Data source: author elaboration.

There are also different phenomena in energy sector resources, capacity, and different level scales may have different outcomes. There is a possibility of reducing energy and resource consumption and to advance degrowth-related ideals of energy local production at local and small-scale energy systems in Spain and Greece ( Tsagkari, Roca, and Kallis 2021 ). The authors summarize that despite the degrowth potential of these local energy projects, their prospects are limited to revitalizing local economies and empowering local communities. The summary results of the literature review regarding G7 countries are listed in Table 5 .

TABLE 5 . Main results of literature review according to PRISMA guidelines of G7 countries.

Effect of Renewable Energy in Economic Growth Next-11 Countries

Rural people in impoverished and developing nations lack access to electricity that is dependable, economical, and long-lasting. Even though these countries have limited renewable energy sources, many urban and rural people rely on kerosene, diesel, and other fossil fuels to meet their energy needs. The renewable energy capacity in the Next-11 nations is shown in Table 6 .

TABLE 6 . Renewable energy capacity in 11 countries.

The Bangladesh’s energy sector remains deficient, impeding the country’s smooth economic activity, and progress. For greening growth and meeting sustainable development goals (SDGs), increasing the amount of renewable energy in the energy resources mix and reducing and reducing the material consumption utilized for energy generation is critical ( Baniya, Giurco, and Kelly 2021 ). The government attempts to close the gap between supply and demand for electricity by installing short-term power plants, coal-fired power plants, and importing from neighboring nations. However, the country still has a long way to produce and supply enough power. Furthermore, increased FDI inflows connected to energy limit the country’s extensive usage of renewable energy. At the same time, increased economic growth and CO 2 emissions in the area, particularly in Bangladesh, stimulate the use of renewable energy ( Murshed 2021 ). Another renewable energy source, tidal power, may play an essential part in the nation’s electrical supply by adding to it ( Ahmad and Hasan 2021 , 25). This will very certainly stimulate the industry and commercial activity along the shore. The answer may be alternatives to current energy sources, such as renewable energy resources. More renewable energy sources will be introduced and consumed, reducing energy scarcity, and promoting economic activity and growth ( Bhuiyan, Mamur, and Begum 2021 ). Researchers such as Alam et al. (2017) proposed a one-way causal relationship between economic growth and overall energy demand (renewable and non-renewable). They claim that even a cautious approach to energy sources would not affect the country’s economy, but that because economic success leads to increased energy consumption, Bangladesh must pursue renewable energy and demand-side management ( Alam, Ahmed, and Begum 2017 ). Nigeria, one of the NEXT-11 countries, is one of the Africa’s largest fossil fuel exporters. However, this country has recently experienced a significant energy problem. Biofuel has been identified as renewable energy (bioethanol and biodiesel) in recent years. Waste materials and feedstocks are widely available and accessible, potentially fueling Nigeria’s socio-economic progress ( Adewuyi 2020 ). Islam et al. describe the economic effect of renewable and non-renewable energy systems. The dynamic simulations approach looks at the influence of income growth, foreign direct investment, domestic investment, urbanization, physical infrastructure, and institutional quality on renewable and non-renewable energy consumption in Bangladesh from 1990 to 2019. According to empirical evidence, income growth positively and negatively impact renewable and non-renewable energy usage. Domestic investment has a favorable influence on renewable and non-renewable energy usage. It has been observed that foreign direct investment has a beneficial effect on renewable energy use. Although urbanization has a negative impact on renewable energy consumption, it positively impacts non-renewable energy consumption. Physical infrastructure has a positive and negative influence on renewable and non-renewable energy usage. Factor that effected through the renewable energy system on N-11 countries is listed in Table 7 .

TABLE 7 . Factor that effected through the renewable energy system on N-11 countries.

Ramadan et al. discuss the economic evaluation of new regulatory tariffs for renewables in Egypt. After 25 years of operation, the results show that adding a CAES system will increase the profitability of the Egyptian government’s new tariff for wind installations, with an NPV of $306 million compared to $207 million for a stand-alone wind system. Furthermore, the economic advantages rise if the government incentives for new renewable energy system installations or decreases financing rates. Ghouchani et al. investigate Iran’s renewable energy development potential. Three potential possibilities for the Iran’s renewable energy sector are examined in this report “long-term technology acquisition programs,” “policy stabilization,” and “attraction of international investment.” The findings indicated that renewable energy policy planning and implementation success is determined by selecting the most adaptive policies to national goals, technological capabilities, and economy. To swiftly and successfully develop and implement a comprehensive renewable energy plan, a thorough analysis of limits, impediments, available facilities and technologies, international sanctions, and foreign investment is essential. Sovacool et al. investigated and provided remedies to the likelihood of corruption in the Mexico’s renewable energy sector. The report then examines particular corruption risks in four nations (Mexico, Malaysia, Kenya, and South Africa) before offering five recommendations and solutions to help combat corruption. These approaches include corruption risk mapping, subsidy registries, sunset clauses, transparency initiatives, anti-corruption regulations, and shared ownership models. In the Economic Community of West African States’ renewable energy plan framework, Ozoegwe et al. examined Nigeria’s solar energy policy goals and tactics. This initiative is advised since the national solar energy strategy document lacks policies on encouraging the solar technology company in Nigeria. The proposals emphasized the requirements of the Renewable Energy Policy of the Economic Community of the West African States, which are currently in place. Case studies supported the recommendations for a community-shared business model for home end users and clusters of small companies in physical market places and an energy management contract business model for large organizations.

Ajayi et al. (2022) examined the influence of sustainable energy on national climate change, food security, and job opportunities in implications for Nigeria. It looked at international data on the links between energy and renewable energy adoption, national development, population growth, job creation, rural–urban integration, and the inherent benefits of renewable energy resources in mitigating climate change and global warming incidents. If Nigeria wants to continue economic growth, particularly in agriculture and food security, renewable energy for power generation must be included in the country’s rural development policy. It also shows that renewable energy can minimize its anthropogenic climate change contribution. From this point of view, total investment in RE and descriptive statistics with technological specific scores by N-11 countries are listed in Tables 8 , 9 , respectively. According to Table 8 , RECAI of Egypt is maximum, and the growth rate of renewable energy in Egypt is also maximum. Table 8 also shows that the RECAI score of some of the countries in the offshore wind, such as Vietnam and geothermal in Egypt is minimal. The World Bank is putting out a long-term offshore wind roadmap for Turkey to issue a tender in the next 2 to 3 years. Following the cancellation of a 1.2 GW offshore wind auction in mid-2018, the World Bank is now in charge of disbursing EU money to support the feasibility and environmental studies in preparation for a second sale. Table 9 describes the different statistical parameters with central tendency in terms of mean, mode, and median of renewable energy sources. The 57th edition of our Renewable Energy Country Attractiveness Index (RECAI) demonstrates that there is a room for further renewable energy investment and strong demand for it. Institutional investors, in particular, have the ability and desire to offer massive, long-term capital injections required to support the fast-growing global renewable energy sector.

TABLE 8 . Next-11 countries that invest in renewable sources.

TABLE 9 . Descriptive statistics with technological specific scores of N-11 countries.

In Figure 5 , we have listed the comparative technology-specific scores in various factors among N-11 countries.

FIGURE 5 . Comparison of technology-specific score of N-11 countries.

The impact of renewable energy use on Nigeria’s environmental quality in several sectors was studied by Maji and his colleagues. The influence of renewable energy consumption on sectoral environmental quality is being examined in Nigeria as part of the government’s effectiveness. A regression analysis was used to estimate a dataset from 1989 to 2019. The per capita indicator, environmental quality indicators, and sectoral output from the agricultural, manufacturing, construction sectors, transportation, oil, residential, commercial, and public services sectors, and other sectors were examined. Adelaja et al. discussed the several barriers to national renewable energy adoption in Nigeria. Despite the privatization of Nigeria’s largest power utility company, the Power Holding Company of Nigeria (PHCN), the country’s electrical demand is rarely met. Nigeria’s electricity output has lately been reduced, despite a massive increase in demand.

To fill the hole, polluting electric generators, inefficient energy sources including candles, kerosene lamps, paraffin devices, and entire energy abstention have all been employed. These problems lead to missed commercial and economic prospects, low quality of life, and missed long-term development potential. Lin et al. looked at how Nigeria’s renewable energy program affected the country’s total output. Based on Nigeria’s Renewable Energy Program aims, this research asks three main questions, Is it possible for Nigeria’s economy to be built entirely on renewable energy? Is it feasible to replace non-renewable energy with renewable energy? What is renewable energy’s economic impact? This study focuses on the growth of renewable energy in Nigeria. We calculate, among other things, the economic effect, production elasticity, and substitution possibilities of renewable and non-renewable energy sources. Our findings, based on a dataset from 1980 to 2015 and analyzed using the translog production function, demonstrate that capital and labor are the key drivers of output in Nigeria; however, although being positive, the economic effect of renewable and non-renewable energy sources is negligible. Wang and Wang. (2020) studied the non-linear behavior of aggregated and disaggregated renewable and non-renewable energy consumption on GDP per capita in Pakistan. This research looked at how diverse forms of energy, such as renewables, fossil fuels, oil-based electrical generating, and hydroelectric power, impact Pakistan’s output. While using fossil fuels to boost economic growth may be beneficial in the early stages of production, it is not helpful in the later stages of production. According to the study, using clean energy, while not beneficial in the early stages of production in expanding production activities in Pakistan, is useful in the later stages of production, not only for production but also for the environment.

Mohamed et al. (2021) in Pakistan discussed the role of renewable energy in combating terrorism. This study looks at the relationship between terrorism, renewable energy, and fossil fuel consumption in Pakistan, taking into account several variables such as economic development and income disparity. Using the autoregressive-distributed lag testing technique, this study evaluated the long-term connection between the examined variables throughout the yearly period of 1980–2015. Their variables have long-term relationships, as shown by the Wald test. The summary results of the literature review regarding the Next-11 (N-11) countries are listed in Table 10 .

TABLE 10 . Main results of literature review according to PRISMA guidelines of Next-11 Countries.

Granger causality identifies the long-term bi-directional causal links between all variables. The research demonstrates short-term unidirectional causes between terrorism and fossil energy, GDP and renewable energy, and wealth disparity and fossil energy, even though there are bidirectional causal links between renewable energy and fossil energy in the near run. In reality, long-term statistics demonstrate that fossil fuels decrease terrorism while renewable energy increases it.

Wang and Wang (2020) studied renewable energy use, economic growth, and the human development index in Pakistan. This study examines the link between renewable energy consumption, economic growth, and the human development index in Pakistan from 1990 to 2014 using the two-stage least square approach. According to empirical research, using renewable energy does not improve Pakistan’s human development. Surprisingly, the lesser a country’s degree, the higher its income will be. CO 2 emissions also contribute to the enhancement of the human development index. Furthermore, trade liberalization stifles Pakistan’s progress in terms of human development. Again, the long-term feedback idea between environmental influences and human development is supported by causality analysis.

Islam et al. (2022) demonstrate how renewable energy helps Pakistan prosper economically. The research aims to look at the link between renewable energy consumption and economic growth in Pakistan, taking into account capital and labor as possible production function variables. In this work, the autoregressive-distributed lag (ARDL) model and the rolling window approach (RWA) were used to integrate data in a Pakistani scenario. Quarterly data from 1972Q1 to 2011Q4 were used in the study. Bertheau and his colleagues looked into it. A geospatial and techno–economic study for the Philippines was based on 100% renewable energy micro-grids. As a result, this study recommends a hybrid approach that combines geospatial analysis, cluster analysis, and energy system modeling: To begin, they identify islands that are not connected to the power grid. Second, cluster analysis is used to identify trends. Third, we perform simulations of energy systems employing solar, wind, and battery storage to generate 100% renewable energy systems. Our research will focus on 649 non-electrified islands with 650,000 people. These islands are grouped into four groups based on population and renewable resource availability. They determined that cost-optimized 100 percent renewable energy systems rely on solar and storage capacity for each cluster, with additional wind capacity. According to Doytch and Narayan (2021) , renewable energy boosts economic growth. This study examines the influence of non-renewable and renewable energy consumption on economic development, distinguishing between manufacturing and service growth. Our empirical model is based on an endogenous growth framework with an increasing number of intermediate capital goods that comprise non-renewable and renewable energy inputs. We examine the impacts of non-renewable and renewable energy consumption on manufacturing and service growth, broken down by the type of usage (industrial, residential, and total final energy consumption) while accounting for well-known growth variables. Park and his colleagues looked at the procedures used by South Korean renewable energy cooperatives. This research focuses on citizen participatory RE co-ops as a vital niche in the community-led energy route. This study did a narrative analysis based on the RE co-ops’ present state and in-depth interviews. We examined key changes and inertia in the conventional energy system at the national, regional, and local levels by comparing within and across scales. Each scale was made up of a tangle of sub-regimes such as market, policy, and culture. We believe a niche may play a creative role in changing sub-regimes of various sizes based on resources that can be handled, such as money resources, rules, and connections. Sim J et al. looked at the economic and environmental benefits of R&D investment in the renewable energy sector in South Korea. The South Korean government has announced a strategy to invest in renewable energy to shift the country’s economy away from fossil fuels and toward renewables. This study assesses R&D investment in six types of renewable energy sources: biomass, waste, solar thermal energy, photovoltaic energy, marine energy, and wind power energy while taking into account several uncertainty factors such as the amount of renewable energy produced, R&D investment, unit price, and risk-free interest rate. According to Yurtkuran et al., agriculture, renewable energy generation, and globalization all influence CO 2 emissions in Turkey. This study investigates the impact of agriculture, renewable energy production, and globalization on CO 2 emissions in Turkey between 1970 and 2017. It uses the Gregory–Hansen integration test, bootstrap autoregressive-distributed lag (ARDL) approach, fully modified ordinary least squares, dynamic ordinary least squares, and long run estimators. The KOF indices for politics, society, and economics are explanatory variables. The Gregory–Hansen test and the bootstrap ARDL approach imply co-integration variables. In Turkey, Shan et al. investigated the role of green technology innovation and renewable energy in achieving carbon neutrality. A Granger causality test determines the causal relationship between green technology innovation, energy consumption, renewable energy, population, per capita income, and carbon dioxide emissions. Green technology innovation, renewable energy, energy consumption, population, per capita income, and carbon dioxide emissions are all co-integrated in the long run. Furthermore, while green technology innovation and renewable energy reduce carbon dioxide emissions, energy consumption, population, and per capita carbon emissions increase. Kul et al. evaluated the renewable energy investment risk factors for Turkey’s long-term development. This study uses a three-stage decision framework based on the multi-criteria decision methodology (MCDM) to assess and examine the risk factors of REIs in Turkey. The Delphi approach identifies REI risk factors in the first stage. The analytical hierarchy process is used in the second stage to examine the discovered REI risk factors (AHP). The third stage involves applying fuzzy weighted aggregated sum product assessment to evaluate and prioritize methods for overcoming risk issues in REI projects (FWASPAS). The Delphi technique discovered six primary risk variables and 23 sub-risk factors. Economic and commercial risks emerged as prominent risk factors in AHP research. The energy plan for a new era of economic development in Vietnam was examined by Nong et al. (2020) . The prospective implications of such a new power strategy in Vietnam are examined in this research by extending an economic electricity-detailed model. We found that, under a 2030 target scenario, the policy will lower the prices of both fossil- and renewable-based power by 40–78%, benefiting all sectors of the economy by allowing them to replace fossil fuels. Households benefit the most, as indicated by improvements in the per capita utility of 5.64–19.19%. Overall, the Vietnamese economy benefits greatly from the various scenarios, with real GDP increasing by 5.44–24.83%, significantly greater than the results in other countries. Nguyen et al. describe the economic potential of renewable energy in the Vietnam’s electrical industry. In a baseline scenario without renewables, coal provides 44% of total electricity generation from 2010 to 2030. Renewable energy has the potential to reduce that amount to 39%, as well as the sector’s overall CO 2 emissions by 8%, SO 2 by 3%, and NOx by 4%. Furthermore, renewables have the potential to avoid the construction of 4.4 GW of fossil fuel generating capacity, save local coal, and minimize coal and gas imports, therefore boosting energy independence and security. Omri et al. demonstrate how renewable energy helps offset the adverse effects of environmental issues on socio-economic well-being. The findings of this article demonstrate that 1) CO 2 emissions have unconditionally adverse effects on human development and economic growth; 2) the net impact on human growth of the economy from the interaction among renewable power and carbon intensity are positive, that is, renewable energy reduces the impacts of per capita CO 2 emissions on human development and economic growth; and 3) sustainable energy interacts with CO 2 frequency and carbon intensity from liquid fuels.

Conclusion and Policy Implications

Global warming, environmental pollution, and other related issues are no more country-specific problems now. For power generation and carbon dioxide sequestration, the clean development mechanism involves the massive deployment of renewable energy technologies to promote the concept of sustainable development ( Latake et al., 2015 ). In addition to the (greenhouse gas) GHG mitigating potential of renewable energy resources, the energy security guarantee is swiftly becoming a reality with the exploitation of different renewable energy resources. The clean development mechanism is a fundamental idea of the Kyoto Protocol under the canopy of the United Nations Framework on Convention on Climate Change (UNFCCC). However, it was envisaged that the industrialized nations would finance emission reduction mechanisms whereby the fund will be given to developing countries as sponsorship for renewable energy programs. To mitigate this problem, introducing more green technologies and renewable energy sources can be a solution. But, uncertainty, input–output cost analysis, higher production and maintenance cost, skill workforce, enough financial strengths, awareness etc ., are only a few challenges toward mass sustainable energy development. Thus, in comparison of the effects of feed-in tariff (FIT) with a renewable portfolio standard (RPS) in the developing renewable energy industry uncertainty, FIT has higher expected output and profit and lower market prices. On the other hand, the production and profit of RPS remain relatively more stable. If the cost of renewable energy is high, the incentive effect of the policy under FIT seems better. As the price goes down, the incentive effect under RPS probably continues to rise. According to the aforementioned research, it is found out that the renewable energy sector plays a very vital role in the overall growth of the country. Developing a more renewable energy system is necessary for Pakistan, Bangladesh, and Nigeria.

Renewable energy and natural resources significantly reduce emissions ( Usman and Lorente, 2022 ). Consequently, the environmental impact of CO 2 emissions requires widespread monitoring worldwide to analyze the effects on climate change (eg., floods, landslides, droughts, and increase in global average temperature). All these effects weigh under the economic conditions of each country ( Halldó rsson and Kovács, 2010 ). As Hao et al. (2021) , green growth and eco-innovation revolutionize the industrial structure. The G7 countries must focus on a green growth strategy to achieve the SDGs.

In the renewable energy capacity in Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, Philippines, South Korea, Turkey, and Vietnam, it is found that Indonesia plays a vital role using the renewable energy system in the country’s economic growth. The installed capacity of the renewable energy system in Indonesia is 14,690,000 MW. On the other hand, the Pakistan study looked at how different types of energy, such as renewables, fossil fuels, oil-based electrical generation, and hydroelectric power, can affect the output level in Pakistan. Our study concludes that while using fossil fuels to boost economic growth may be beneficial in the early stages of production, it is not helpful in the later stages of production. Whereas using clean energy may not be beneficial in the early stages of production in expanding production activities in developing countries, it is beneficial in the later stages of production not only for production but also for the environment. Policy makers should speed up the deep reforms regarding renewable energy to mitigate environmental degradation ( Koengkan et al., 2020b ). It has been proven that globalization can stimulate renewable energy sources for Latin American countries ( Koengkan et al., 2020a ). This will be beneficial in the region and at the world stage, developing green energy technologies. Thus, it is suggested that policy makers take advantage of globalization to reduce the costs of RE technologies and develop policies encouraging the access of these technologies by households with low income.

This is to note that the study has some limitations. For example, in this article, we have considered mainly G7 and N-11 countries which reflect primarily developed and developing countries. Meanwhile, many underdeveloped countries were not considered in the study. In addition, we have taken the last 10 years (2010–2021) of published articles for this systematic review. But the world economic conditions have been changing rapidly among nations. If we would consider the recent 5 years, the outcome of the review process may vary.

Furthermore, we have only analyzed English language articles. But there may be other critically related articles published in local languages such as Mandarin Chinese, Russians, and Spanish. Thus, we believe there is scope for more research on this topic area.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

MB: conceptualization, methodology, resources and software, writing—original draft, and supervision. VK: original draft. AM: investigation, methodology, writing—original draft, supervision, and formal analysis. GP: data curation, validation, writing—original draft, and writing—review and editing. QZ: Revise, Proofread. XH: Proofread.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We thank the financial support of Széchenyi 2020 under the “EFOP-3.6.1-16-2016-00015.”

Acheampong, A. O., Dzator, J., and Savage, D. A. (2021). Renewable Energy, CO 2 Emissions and Economic Growth in Sub-saharan Africa: Does Institutional Quality Matter? J. Pol. Model. 43 (5), 1070–1093. doi:10.1016/j.jpolmod.2021.03.011

CrossRef Full Text | Google Scholar

Adewuyi, A. (2020). Challenges and Prospects of Renewable Energy in Nigeria: A Case of Bioethanol and Biodiesel Production. Energ. Rep. 6 (February), 77–88. doi:10.1016/j.egyr.2019.12.002

Ajayi, O. O., Mokryani, G., and Edun, B. M. (2022). Sustainable Energy for National Climate Change, Food Security and Employment Opportunities: Implications for Nigeria. Fuel Communications 10, 100045. doi:10.1016/j.jfueco.2021.100045

Ahmad, M., and Hasan, G. M. J. (2021). “Chapter 25 - Renewable Energy in Bangladesh: Status and Potential,” in Design, Analysis, and Applications of Renewable Energy Systems . Editors A T Azar, and N A Kamal (Cambridge: Academic Press ), 607–625. Advances in Nonlinear Dynamics and Chaos (ANDC). doi:10.1016/B978-0-12-824555-2.00023-X

Ahmad, M., Jiang, P., Murshed, M., Shehzad, K., Akram, R., Cui, L., et al. (2021). Modelling the Dynamic Linkages between Eco-Innovation, Urbanization, Economic Growth and Ecological Footprints for G7 Countries: Does Financial Globalization Matter? Sustain. Cities Soc. 70, 102881. doi:10.1016/j.scs.2021.102881

Alam, M. J., Ahmed, M., and Begum, I. A. (2017). Nexus between Non-renewable Energy Demand and Economic Growth in Bangladesh: Application of Maximum Entropy Bootstrap Approach. Renew. Sustain. Energ. Rev. 72, 399–406. doi:10.1016/j.rser.2017.01.007

Amri, F. (2017). Intercourse across Economic Growth, Trade and Renewable Energy Consumption in Developing and Developed Countries. Renew. Sustain. Energ. Rev. 69, 527–534. doi:10.1016/j.rser.2016.11.230

Armeanu, D. S., Joldes, C. C., Gherghina, S. C., and Andrei, J. V. (2021). Understanding the Multidimensional Linkages Among Renewable Energy, Pollution, Economic Growth and Urbanization in Contemporary Economies: Quantitative Assessments across Different Income Countries' Groups. Renew. Sustain. Energ. Rev. 142, 110818. doi:10.1016/j.rser.2021.110818

Awodumi, O. B., and Adewuyi, A. O. (2020). The Role of Non-renewable Energy Consumption in Economic Growth and Carbon Emission: Evidence from Oil Producing Economies in Africa. Energ. Strategy Rev. 27 (January), 100434. doi:10.1016/j.esr.2019.100434

Balsalobre-Lorente, D., Ibáñez-Luzón, L., Usman, M., and Shahbaz, M. (2022). The Environmental Kuznets Curve, Based on the Economic Complexity, and the Pollution haven Hypothesis in PIIGS Countries. Renew. Energ. 185, 1441–1455. doi:10.1016/j.renene.2021.10.059

Baniya, B., Giurco, D., and Kelly, S. (2021). Green Growth in Nepal and Bangladesh: Empirical Analysis and Future Prospects. Energy Policy 149 (July 2020), 112049. doi:10.1016/j.enpol.2020.112049

Bhuiyan, M. R. A., Mamur, H., and Begum, J. (2021). A Brief Review on Renewable and Sustainable Energy Resources in Bangladesh. Clean. Eng. Techn. 4, 100208. doi:10.1016/j.clet.2021.100208

Chen, C., Pinar, M., and Stengos, T. (2020). Renewable Energy Consumption and Economic Growth Nexus: Evidence from a Threshold Model. Energy policy 139, 111295. doi:10.1016/j.enpol.2020.111295

Come Zebra, E. I., van der Windt, H. J., Nhumaio, G., and Faaij, A. P. C. (2021). A Review of Hybrid Renewable Energy Systems in Mini-Grids for Off-Grid Electrification in Developing Countries. Renew. Sustain. Energ. Rev. 144 (July), 111036. doi:10.1016/j.rser.2021.111036

Djurisic, V., Smolovic, J. C., Misnic, N., and Rogic, S. (2020). Analysis of Public Attitudes and Perceptions towards Renewable Energy Sources in Montenegro. Energ. Rep. 6 (November), 395–403. doi:10.1016/j.egyr.2020.08.059

Doytch, N., and Narayan, S. (2016). Does FDI Influence Renewable Energy Consumption? an Analysis of Sectoral FDI Impact on Renewable and Non-renewable Industrial Energy Consumption. Energ. Econ. 54, 291–301. doi:10.1016/j.eneco.2015.12.010

Doytch, N., and Narayan, S. (2021). Does Transitioning towards Renewable Energy Accelerate Economic Growth? an Analysis of Sectoral Growth for a Dynamic Panel of Countries. Energy 235, 121290. doi:10.1016/j.energy.2021.121290

Gabr, E. M., and Mohamed, S. M. (2020). Energy Management Model to Minimize Fuel Consumption and Control Harmful Gas Emissions. Int. J. Energ Water Res. 4 (4), 453–463. doi:10.1007/s42108-020-00085-2

Halldórsson, Á., and Kovács, G. (2010). The Sustainable Agenda and Energy Efficiency. Int. J. Phys. Distribution Logistics Manage. 40 (1/2), 5–13. doi:10.1108/09600031011018019

Hao, L.-N., Umar, M., Khan, Z., and Ali, W. (2021). Green Growth and Low Carbon Emission in G7 Countries: How Critical the Network of Environmental Taxes, Renewable Energy and Human Capital Is? Sci. Total Environ. 752, 141853. doi:10.1016/j.scitotenv.2020.141853

PubMed Abstract | CrossRef Full Text | Google Scholar

Irfan, M., Zhao, Z.-Y., Rehman, A., Ozturk, I., and Li, H. (2021). Consumers' Intention-Based Influence Factors of Renewable Energy Adoption in Pakistan: a Structural Equation Modeling Approach. Environ. Sci. Pollut. Res. 28 (1), 432–445. doi:10.1007/s11356-020-10504-w

Islam, M. M., Irfan, M., Shahbaz, M., and Vo, X. V. (2022). Renewable and Non-renewable Energy Consumption in Bangladesh: The Relative Influencing Profiles of Economic Factors, Urbanization, Physical Infrastructure and Institutional Quality. Renew. Energ. 184, 1130–1149. doi:10.1016/j.renene.2021.12.020

Islam, M. M., Khan, M. K., Tareque, M., Jehan, N., and Dagar, V. (2021). Impact of Globalization, Foreign Direct Investment, and Energy Consumption on CO2 Emissions in Bangladesh: Does Institutional Quality Matter? Environ. Sci. Pollut. Res. 28, 48851–48871. doi:10.1007/s11356-021-13441-4

Ivanovski, K., Hailemariam, A., and Smyth, R. (2021). The Effect of Renewable and Non-renewable Energy Consumption on Economic Growth: Non-parametric Evidence. J. Clean. Prod. 286, 124956. doi:10.1016/j.jclepro.2020.124956

Jenniches, S. (2018). Assessing the Regional Economic Impacts of Renewable Energy Sources - A Literature Review. Renew. Sustain. Energ. Rev. 93 (October), 35–51. doi:10.1016/j.rser.2018.05.008

Jiang, T., Yu, Y., Jahanger, A., and Balsalobre-Lorente, D. (2022). Structural Emissions Reduction of China's Power and Heating Industry under the Goal of "double Carbon": A Perspective from Input-Output Analysis. Sustainable Prod. Consumption 31, 346–356. doi:10.1016/j.spc.2022.03.003

Jordan, S., and Philips, A. Q. (2018). DYNARDL: Stata Module to Dynamically Simulate Autoregressive Distributed Lag (ARDL) Models . Available at: https://econpapers.repec.org/RePEc:boc:bocode:s458572 .

Google Scholar

Koengkan, M., Fuinhas, J. A., and Marques, A. C. (2019). “The Relationship between Financial Openness, Renewable and Nonrenewable Energy Consumption, CO2 Emissions, and Economic Growth in the Latin American Countries: an Approach with a Panel Vector Auto Regression Model,” in The Extended Energy-Growth Nexus . Editors J A Fuinhas, and A Marques (Cambridge: Academic Press ), 199–229. doi:10.1016/B978-0-12-815719-0.00007-3

Koengkan, M., Fuinhas, J. A., and Santiago, R. (2020b). The Relationship between CO2 Emissions, Renewable and Non-renewable Energy Consumption, Economic Growth, and Urbanisation in the Southern Common Market. J. Environ. Econ. Pol. 9 (4), 383–401. doi:10.1080/21606544.2019.1702902

Koengkan, M., Poveda, Y. E., and Fuinhas, J. A. (2020a). Globalisation as a Motor of Renewable Energy Development in Latin America Countries. GeoJournal 85 (6), 1591–1602. doi:10.1007/s10708-019-10042-0

Konuk, F., Zeren, F., Akpınar, S., and Yıldız, Ş. (2021). Biomass Energy Consumption and Economic Growth: Further Evidence from NEXT-11 Countries. Energ. Rep. 7 (November), 4825–4832. doi:10.1016/j.egyr.2021.07.070

Latake, P. T., Pawar, P., and Ranveer, A. C. (2015). The Greenhouse Effect and its Impacts on Environment. Int. J. Innov. Res. Creat. Technol. 1 (3), 333–337.

Lema, R., Bhamidipati, P. L., Gregersen, C., Hansen, U. E., and Kirchherr, J. (2021). China's Investments in Renewable Energy in Africa: Creating Co-benefits or Just Cashing-in?World Development. World Develop. 141 (May), 105365. doi:10.1016/j.worlddev.2020.105365

Li, R., and Leung, G. C. K. (2021). The Relationship between Energy Prices, Economic Growth and Renewable Energy Consumption: Evidence from Europe. Energ. Rep. 7, 1712–1719. doi:10.1016/j.egyr.2021.03.030

Mohamed, H., Alimi, M., and Youssef, S. B. (2021). The Role of Renewable Energy in Reducing Terrorism: Evidence From Pakistan. Renewable Energy 175, 1088–1100. doi:10.1016/j.renene.2021.05.024

Murshed, M. (2021). Can Regional Trade Integration Facilitate Renewable Energy Transition to Ensure Energy Sustainability in South Asia? Energ. Rep. 7, 808–821. doi:10.1016/j.egyr.2021.01.038

Namahoro, J. P., Nzabanita, J., and Wu, Q. (2021a). The Impact of Total and Renewable Energy Consumption on Economic Growth in Lower and Middle- and Upper-Middle-Income Groups: Evidence from CS-DL and CCEMG Analysis. Energy 237, 121536. doi:10.1016/j.energy.2021.121536

Namahoro, J. P., Wu, Q., Xiao, H., and Zhou, N. (2021b). The Asymmetric Nexus of Renewable Energy Consumption and Economic Growth: New Evidence from Rwanda. Renew. Energ. 174, 336–346. doi:10.1016/j.renene.2021.04.017

Nong, D., Wang, C., and Al-Amin, A. Q. (2020). A Critical Review of Energy Resources, Policies and Scientific Studies towards a Cleaner and More Sustainable Economy in Vietnam. Renew. Sustain. Energ. Rev. 134, 110117. doi:10.1016/j.rser.2020.110117

Ogonowski, P. (2021). Application of VMCM, to Assess of Renewable Energy Impact in European Union Countries. Proced. Comput. Sci. 192, 4762–4769. doi:10.1016/j.procs.2021.09.254

Oluoch, S., Lal, P., Susaeta, A., and Vedwan, N. (2020). Assessment of Public Awareness, Acceptance and Attitudes towards Renewable Energy in Kenya. Scientific Afr. 9 (September), e00512. doi:10.1016/j.sciaf.2020.e00512

Park, S.-H., Jung, W.-J., Kim, T.-H., and Lee, S.-Y. T. (2016). Can Renewable Energy Replace Nuclear Power in Korea? an Economic Valuation Analysis. Nucl. Eng. Techn. 48 (2), 559–571. doi:10.1016/j.net.2015.12.012

Pesaran, M. H., and Yamagata, T. (2008). Testing Slope Homogeneity in Large Panels. Journal of Econometrics 142 (1), 50–93. doi:10.1016/j.jeconom.2007.05.010

Prisma (2021). Transparent Reporting of Systematic Reviews and Meta-Analyses. available at http://www.prisma-statement.org/ (Last accessed date December 18, 2021).

RECAI (2020). Renewable Energy Country Attractiveness Index. Available at https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/power-and-utilities/ey-recai-56-country-index.pdf (Last accessed date December 19, 2021).

Seetharaman, K. M., Moorthy, K., Patwa, N., Saravanan, Yash., and Gupta, Y. (2019). Breaking Barriers in Deployment of Renewable Energy. Heliyon 5 (1), e01166. doi:10.1016/j.heliyon.2019.e01166

Sharma, G. D., Tiwari, A. K., Erkut, B., and Mundi, H. S. (2021). Exploring the Nexus between Non-renewable and Renewable Energy Consumptions and Economic Development: Evidence from Panel Estimations. Renew. Sustain. Energ. Rev. 146, 111152. doi:10.1016/j.rser.2021.111152

Shrinkhal, R. (2019). “Economics, Technology, and Environmental Protection,” in Phytomanagement of Polluted Sites (Amsterdam: Elsevier ), 569–580. doi:10.1016/B978-0-12-813912-7.00022-3

Smolović, J. C., Muhadinović, M., Radonjić, M., and Đurašković, J. (2020). How Does Renewable Energy Consumption Affect Economic Growth in the Traditional and New Member States of the European Union? Energ. Rep. 6 (November), 505–513. doi:10.1016/j.egyr.2020.09.028

Soytas, U., and Sari, R. (2003). Energy Consumption and GDP: Causality Relationship in G-7 Countries and Emerging Markets. Energ. Econ. 25 (1), 33–37. doi:10.1016/S0140-9883(02)00009-9

Thollander, P., Danestig, M., and Rohdin, P. (2007). Energy Policies for Increased Industrial Energy Efficiency: Evaluation of a Local Energy Programme for Manufacturing SMEs. Energy Policy 35 (11), 5774–5783. doi:10.1016/j.enpol.2007.06.013

Tsagkari, M., Roca, J., and Kallis, G. (2021). "From Local Island Energy to Degrowth? Exploring Democracy, Self-Sufficiency, and Renewable Energy Production in Greece and Spain". Energ. Res. Soc. Sci. 81 (November), 102288. doi:10.1016/j.erss.2021.102288

Tutak, M., and Brodny, J. (2022). Renewable Energy Consumption in Economic Sectors in the EU-27. The Impact on Economics, Environment and Conventional Energy Sources. A 20-Year Perspective. J. Clean. Prod. 345 (April), 131076. doi:10.1016/j.jclepro.2022.131076

Usman, M., and Balsalobre-Lorente, D. (2022). Environmental Concern in the Era of Industrialization: Can Financial Development, Renewable Energy and Natural Resources Alleviate Some Load? Energy Policy 162, 112780. doi:10.1016/j.enpol.2022.112780

Usman, O., Alola, A. A., and Sarkodie, S. A. (2020). Assessment of the Role of Renewable Energy Consumption and Trade Policy on Environmental Degradation Using Innovation Accounting: Evidence from the US. Renew. Energ. 150 (May), 266–277. doi:10.1016/j.renene.2019.12.151

Wang, Q., and Wang, L. (2020). Renewable Energy Consumption and Economic Growth in OECD Countries: A Nonlinear Panel Data Analysis. Energy 207, 118200. doi:10.1016/j.energy.2020.118200

Westerlund, J. (2007). Testing for Error Correction in Panel Data. Oxford Bulletin of Economics and statistics 69 (6), 709–748. doi:10.1111/j.1468-0084.2007.00477.x

Xiong, P.-p., Dang, Y.-g., Yao, T.-x., and Wang, Z.-x. (2014). Optimal Modeling and Forecasting of the Energy Consumption and Production in China. Energy 77, 623–634. doi:10.1016/j.energy.2014.09.056

Yang, D.-x., Jing, Y.-q., Wang, C., Nie, P.-y., and Sun, P. (2021). Analysis of Renewable Energy Subsidy in China under Uncertainty: Feed-In Tariff vs. Renewable Portfolio Standard. Energ. Strategy Rev. 34 (March), 100628. doi:10.1016/j.esr.2021.100628

AHP analytical hierarchy process

ARDL autoregressive-distributed lag

Brics Brazil, Russia, India, China, South Africa

CAES computer-assisted execution system

CO 2 carbon dioxide

COVID-19 coronavirus disease variant

CSARDL cross sectionally augmented autoregressive distributed lag

CSP concentrated solar power

DEA data envelopment analysis

EDI economic development indicators

ET environmental taxes

FDI foreign direct investment

FIT feed-in tariff

FWASPAS fuzzy weighted aggregated sum product assessment

G7 Group of Seven

GDP gross domestic product

GG green growth

GHG greenhouse gas

GMM generalized method of moments

HC human capital

HRES hybrid renewable energy systems renewable energy

KOF Konjunkturforschungsstelle

MCDM multi-criteria decision methodology

MLA Modern Language Association

N-11 Next-11

NARDL non-linear autoregressive-distributed lagged model

NOX nitric oxide

NPV net present value

OECD Organization for Economic Co-Operation and Development

PHCN Power Holding Company of Nigeria

PMG Pooled Mean Group

PRISMA preferred reporting items for systematic reviews and meta-analyses

PV photovoltaic

PVAR panel vector autoregression

R&D research and development

RE renewable energy

REC renewble energy consumption

RECAI Company’s Renewable Energy Country Attractiveness Index

REI renewble energy investment

RES renewable energy sources

RPS renewable portfolio standard

RWA rolling window approach

SCI/SSCI science citation index/social sciences citation index

SDGs sustainable development goals

SO 2 sulfur dioxide

UNFCCC United Nations Framework Convention on Climate Change

Keywords: renewable energy, economic growth, consumption, Next-11 countries, Group 7

Citation: Bhuiyan MA, Zhang Q, Khare V, Mikhaylov A, Pinter G and Huang X (2022) Renewable Energy Consumption and Economic Growth Nexus—A Systematic Literature Review. Front. Environ. Sci. 10:878394. doi: 10.3389/fenvs.2022.878394

Received: 21 February 2022; Accepted: 28 March 2022; Published: 29 April 2022.

Reviewed by:

Copyright © 2022 Bhuiyan, Zhang, Khare, Mikhaylov, Pinter and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gabor Pinter, [email protected]

Background Reference: Algeria

Algeria is a major crude oil and natural gas producer in Africa and has been a member of the Organization of the Petroleum Exporting Countries (OPEC) since 1969, about ten years after it first began producing crude oil.

Algeria relies on its own oil and natural gas production for domestic consumption, which is heavily subsidized. Natural gas and oil account for almost all of Algeria’s total primary energy consumption. Prices for petroleum products and natural gas in Algeria are among the lowest in Africa and subsidies account for a significant proportion of GDP.[1] The 2016 budget law increased prices for gasoline, diesel, natural gas, and electricity for the first time in more than a decade, but the increase in prices has been marginal and failed to make a meaningful impact on consumption patterns and excess usage. Whether or not the government will follow through with more significant subsidy reductions is unclear.[2]

Sector organization

Sonatrach owns more than 75% of total hydrocarbon production in Algeria, and IOCs account for the remaining 20%. The Hydrocarbon Act of 2005 governs Algeria's oil and natural gas industries. In 2013, Algeria revised parts of the hydrocarbon law in an attempt to attract foreign investors to new projects. Algeria has experienced difficulties attracting foreign investors, particularly at licensing rounds.

The Hydrocarbon Act of 2005 governs Algeria’s oil and natural gas industries. The Hydrocarbon Act of 2005 established terms that guided the involvement of international oil companies (IOCs) in upstream exploration and production, midstream transportation, and the downstream sector. The original 2005 legislation was more favorable to foreign involvement than its predecessor, which was passed in 1986. However, amendments to the bill were made in 2006, and some of the favorable terms were reversed. In the 2006 amendments, Algeria’s national oil company, Entreprise Nationale Sonatrach (Sonatrach), was granted a minimum equity stake of 51% in any hydrocarbon project, and a windfall profits tax was introduced for IOCs.

In 2013, Algeria revised parts of the hydrocarbon law in an attempt to attract foreign investors to new projects. The 2013 amendments introduced a profit–based taxation, as opposed to revenue–based taxation and lowered tax rates for unconventional resources. The amendments also allow for a longer exploration phase for unconventional resources (eleven years compared with seven years for conventional resources) and a longer operating/production period of thirty years and forty years for unconventional liquid and gas hydrocarbons, respectively (compared with twenty–five years and thirty years for conventional liquid and gas hydrocarbons, respectively). The amendments, however, do not change Sonatrach’s mandated role as a majority stakeholder in all upstream oil and natural gas projects.[3]

Sonatrach owns more than 75% of total hydrocarbon production in Algeria, and IOCs account for the remaining 20%, according to BMI Research.[4] IOCs with notable stakes in oil and natural gas fields are Cepsa (Spain), BP ( United Kingdom ), Eni (Italy), Repsol (Spain), Total (France), Equinor ( Norway ), and Anadarko (United States). Sonatrach’s substantial assets in Algeria make it the largest oil and natural gas company, not only in the country, but also in Africa. The company operates in several parts of the world, including Africa (Mali, Niger, Libya , and Egypt ), Europe (Spain, Italy, Portugal, and the United Kingdom), Latin America (Peru), and the United States.

Algeria has experienced difficulties attracting foreign investors, particularly at licensing rounds. In the licensing round in 2014, only 4 of 31 blocks were awarded. Some analysts believe that the lack of fiscal incentives to attract foreign investors to new projects and past Sonatrach corruption allegations were to blame. Algeria’s precarious security environment has also been a concern for investors.

Petroleum and other liquids

Algeria first began producing crude oil in 1958. Algeria is believed to have extensive shale oil and natural gas resources, but little progress has been made toward developing these resources. Without additional upstream investment, decline rates are likely to grow, resulting in lower production.

Exploration and production

Algeria first began producing crude oil in 1958. According to Sonatrach, about two–thirds of Algerian territory remains underexplored or unexplored. Algeria is believed to have extensive shale oil and natural gas resources, but little progress has been made toward developing these resources. Oil production in areas that have already been exploited can potentially expand as well, particularly in the Hassi Messaoud, Illizi, and Berkin Basins. According to Sonatrach, the Hassi Messaoud–Dahar province contains about 71% of the country’s combined proved, probable, and possible oil reserves, while the Illizi Basin, the second–largest area, contains about 15%.[5]

Algerian oil fields produce high quality, light crude oil with very low sulfur content. Sonatrach operates the largest oil field in Algeria. Other large producing areas in Algeria include the Ourhoud and the Hassi Berkine complex.

Algeria’s largest oil fields are mature. Field expansions and enhanced oil recovery techniques have kept the country’s oldest fields at a steady rate of production; however, without additional upstream investment, decline rates are likely to grow, resulting in lower production.

Refining and refined oil products

The country’s largest refinery, Skikda, is located along Algeria’s northern coastline, and it is the largest refinery in Africa. Skikda processes the Saharan blend, which derives from the Hassi Messaoud oil fields. The Algiers and Arzew refineries are the other two refineries located on the coast. The country’s inland refineries, Hassi Messaoud and Adrar, are connected to local oil fields and supply oil products to nearby areas. Sonatrach considered building a 300,000 b/d refinery in Tiaret. However, the project was scaled down to 100,000 b/d and then later delayed indefinitely in October 2017. Upgrades or expansions to facilities at Hassi Massaoud and Biskra have been proposed, but whether or not they will be completed on time is unclear. The upgrade and expansion of the Algiers refinery is expected to be completed within the target completion date.[6]

Petroleum and other liquids exports

Algeria exports mostly light crude oil. The country’s main crude oil grade is the Sahara blend (API–45.3; sulfur content–0.1%; and total acid number (TAN)–0.06 KOH/g), which is a blend of crude oils produced at fields in the Hassi Messaoud region.[7]

Algeria uses multiple coastal terminals to export crude oil, refined products, liquefied petroleum gases, and natural gas plant liquids. These facilities are located at Arzew, Skikda, Algiers, Annaba, Oran, and Bejaia in Algeria and La Skhirra in Tunisia. Algeria’s domestic pipeline network facilitates the transfer of oil from interior production fields to coastal infrastructure. The most important pipelines carry crude oil from the Hassi Messaoud field to refineries and export terminals. Algeria does not have any transcontinental export oil pipelines.

Natural gas

Algeria’s largest natural gas field, Hassi R’Mel, was discovered in 1956. The remainder of Algeria’s natural gas reserves is located in associated and nonassociated fields in the southern and southeastern regions of the country. Algeria also holds vast untapped shale gas resources, but faces many obstacles to developing these resources.

Algeria’s largest natural gas field, Hassi R’Mel, was discovered in 1956. Located in the center of the country to the northwest of Hassi Messaoud, it holds proved reserves of about 85 trillion cubic feet (Tcf), more than half of Algeria’s total proved natural gas reserves. The remainder of Algeria’s natural gas reserves is located in associated and nonassociated fields in the southern and southeastern regions of the country. Overproduction and underinvestment in maintaining the Hassi M’Rel field has resulted in long–term damage to the reservoir, leading to accelerated decline rates. Sonatrach has embarked on a USD $2 billion investment program to be finished by 2020 that would reduce the decline rates at Hassi R’Mel.[8]

The Southwest Gas Project is very important for Algeria’s ability to meet contracted exports and its expected growth in domestic demand. Gross natural gas production in the country will most likely continue to steadily decline in the short term, but it may recover in the medium term if planned projects come online and offset natural declines. However, these projects are contingent on attracting investors and building new infrastructure or upgrading older infrastructure.[9]

The Southwest Gas Project includes the construction of natural gas–gathering facilities, a natural gas treatment plant, and a pipeline to the Hassi R’Mel gas hub, called the GR5 pipeline.[10] The planned infrastructure will connect the remote southwest natural gas fields to the Hassi R’Mel region and allow other fields in the south to be commercialized as well. The development and commercialization of the Ahnet natural gas project in the south will also depend on the new infrastructure.

Algeria also holds vast untapped shale gas resources. According to an EIA–sponsored study released in June 2013, Algeria contains 707 Tcf of technically recoverable shale gas resources, the third–largest amount in the world after China and Argentina. Some industry analysts are cautious about the prospects of Algeria becoming a notable shale producer. To develop these resources, Algeria will face many obstacles including the remote location of the shale acreage, the lack of infrastructure and accessibility to sites, water availability, the lack of roads and pipelines to move materials, and the need for more rigs because shale wells deplete more quickly.

Natural gas exports

Algeria’s natural gas exports have gradually declined during the past decade as gross production decreased and domestic consumption increased, but 2016 saw a reversal of this trend. Algeria is facing pressure to boost natural gas output with new projects to meet growing domestic demand and to fulfill long–term contractual obligations to export natural gas to Europe.

Algeria became the world’s first LNG producer in 1964 when the Arzew LNG facility came online.[11] Algeria has four liquefaction units for liquefied natural gas (LNG) located along the Mediterranean Sea at Arzew and Skikda, although a number of the LNG facilities have been decommissioned, lowering actual production capacity (Table 2).[12]

Algeria plans to develop two additional transcontinental export pipelines, although both projects have suffered delays, and whether or not either pipeline will be built is highly uncertain. The Gasdotto Algeria Sardegna Italia (GALSI) pipeline will transport natural gas to Italy via a pipeline with a subsea section. Initially, its capacity is expected to be 282 Bcf/y. The pipeline project has gone through feasibility studies, and logistics, costs, pricing formulas, and long–term contractual commitments are concerns. The Trans–Saharan Gas Pipeline (TSGP) is proposed to run slightly more than 2,600 miles to deliver natural gas from Warri, Nigeria, to Algeria (via Niger), which will then link to the MEDGAZ route to Spain, although this link may be changed in the future. However, security concerns about militant groups across remote areas in the Sahel, in addition to growth constraints to Nigerian natural gas production, have presented considerable downside risks to investors interested in financing the project.

Electricity

The national electricity system consists of an interconnected network that distributes power to northern and southern parts of the country. The electricity market is unbundled, but competition is restricted to generation, and the market is regulated by the Commission for Regulation of Electricity and Gas.

According to the Electricity and Gas Regulation Commission (CREG), the country’s electricity and natural gas market regulator, the national electricity system consists of an interconnected network that distributes power to northern and southern parts of the country.

The electricity market is unbundled, but competition is restricted to generation, and the market is regulated by the Commission for Regulation of Electricity and Gas. State–owned Société Algérienne de Gestion du Réseau de Transport de l’Electricité is responsible for authorizing private generation and is the sole offtaker for power generation.

Algeria’s transmission network is composed of the Réseau Interconnecté National (RIN) distribution network that connects Salah, Adrar, and Timomoun in the northern area, and a number of isolated, low–voltage systems in the southern region.

Sonelgaz brought additional capacity online to keep up with demand needs. In the past, Sonelgaz imposed rationing to balance electricity supply and demand. In 2012, the government enforced power cuts that provoked public protest in the summer months. Algeria’s power demand peaks during the summer months.[13]

One of Sonelgaz’s main challenges is the ability to finance new generation projects amid fixed electricity prices, which have an effect on the company’s finances. In addition, energy subsidies in Algeria have resulted in budget deficits. Another challenge is natural gas supply. Most of Algeria’s planned capacity additions are natural gas–fired units; meanwhile, Algeria’s gross natural gas production has been declining as new projects slated to boost output have repeatedly been delayed.

Algeria has marginal hydroelectric generation capacity and does not have any nuclear generation capabilities; however, the government is looking to develop and expand its non-hydroelectric renewable generation capacity.

The Algerian Ministry of Energy and Mines set ambitious goals for electricity generation, aiming to generate 40% from renewable sources by 2030. The government of Algeria has since revised its 2015 goal to add 22 gigawatts (GW) by 2030, setting a new target goal for approximately 18.5 GW from renewable sources (13.6 GW of solar PV, and 5 GW of onshore wind). The government also devised a regulatory framework that requires distribution system operators to take all electricity produced by renewable energy plants that have signed a power purchase agreement, thus guaranteeing the sale of power at the agreed feed-in tariff rate. Renewable energy is still dominated by Sonelgaz and its subsidiary SKTM, and regulatory and administrative obstacles still limit the participation of international companies.

• In response to stakeholder feedback, the U.S. Energy Information Administration has revised the format of the Country Analysis Briefs . As of January 2019, updated briefs are available in two complementary formats: the Country Analysis Executive Summary provides an overview of recent developments in a country’s energy sector and the Background Reference provides historical context. Archived versions will remain available in the original format.
• Data presented in the text are the most recent available as of March 25, 2019.
• Data are EIA estimates unless otherwise noted.
• "Algeria Oil & Gas Report Q1 2018," BMI Research Service , November 2017, pg. 27–34.
• "Algeria Ratifies 2016 Budget Despite Opposition," Middle East Economic Survey , Vol. 58, Issue 49, December 4, 2015. "Algeria Oil & Gas Report Q1 2018," BMI Research Service , November 2017, pg. 30.
• Middle East Economic Survey, "Algeria Fleshes Out New Oil Law" (January 11, 2013), volume 56, issue 2.
• "Algeria Oil & Gas Report Q1 2018," BMI Research Service , November 2017, pg. 68.
• "Refining and Marketing Africa: Algeria," IHS Markit , January 2018.
• "Crude by Key Characteristics," Energy Intelligence Group, accessed 7/19/2018.
• "Algeria Oil & Gas Report Q1 2018," BMI Research Service, November 2017, pg. 20–22. "Global Project: Hassi R’Mel," IHS Markit , accessed 8/22/2018.
• "Algeria Oil & Gas Report Q1 2018," BMI Research Service, November 2017, pg. 22.
• "Algeria Gas Set for Record 2018 With Start–Up of Key Southwest Fields," Middle East Economic Survey , Vol. 71, Issue 09, March 2, 2018.
• "Liquefaction Project Profile – Algeria: Arzew LNG and Skikda LNG," IHS Markit, July 2, 2018.
• Middle East Economic Survey, "Algeria’s Peak Power Demand Soars, New Capacity Starting Up" (November 6, 2015), volume 58, issue 45.
• Algerian Ministry of Energy and Mines, Renewable Energy and Energy Efficiency Program, page 4 .

• Renewable Energy

What Is Renewable Energy?

Renewable energy comes from unlimited, naturally replenished resources, such as the sun, tides, and wind. Renewable energy can be used for electricity generation, space and water heating and cooling, and transportation.

Non-renewable energy, in contrast, comes from finite sources, such as coal, natural gas, and oil.

How Does Renewable Energy Work?

Renewable energy sources, such as biomass, the heat in the earth’s crust, sunlight, water, and wind, are natural resources that can be converted into several types of clean, usable energy:

Bioenergy Geothermal Energy Hydrogen and Other Renewable Fuels Hydropower Marine Energy Solar Energy Wind Energy

Learn the truth about clean energy.

Benefits of Renewable Energy

Renewable energy offers numerous economic, environmental, and social advantages. These include:

• Reduced carbon emissions and air pollution from energy production
• Enhanced reliability , security, and resilience of the power grid
• Job creation through the increased production and manufacturing of renewable energy technologies
• Increased U.S. energy independence
• Lower energy costs
• Expanded energy access for remote, coastal, or isolated communities.

Learn more about the advantages of wind energy , solar energy , bioenergy , geothermal energy , hydropower , and marine energy , and how the U.S. Department of Energy is working to modernize the power grid and increase renewable energy production.

Renewable Energy in the United States

Renewable energy generates over 20% of all U.S. electricity , and that percentage continues to grow. The following graphic breaks down the shares of total electricity production in 2022 among the types of renewable power: 

In 2022, annual U.S. renewable energy generation surpassed coal for the first time in history. By 2025, domestic solar energy generation is expected to increase by 75%, and wind by 11%. 

The United States is a resource-rich country with enough renewable energy resources to generate more than 100 times the amount of electricity Americans use each year.  Learn more about renewable energy potential in the United States.

Subscribe to stay up to date on the latest clean energy news from EERE.

Office of Energy Efficiency and Renewable Energy

The U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) has three core divisions: Renewable Energy, Sustainable Transportation and Fuels, and Buildings and Industry. The Renewable Energy pillar comprises four technology offices:

Every American can advocate for renewable energy by becoming a Clean Energy Champion. Both small and large actions make a difference. Join the movement .

Advancing Renewable Energy in the United States

EERE offers funding for renewable energy research and development, as well as programs that support the siting of renewable energy , connection of renewable energy to the grid , and community-led energy projects . Find open funding opportunities and learn how to apply for funding .

The U.S. Department of Energy's 17 national laboratories conduct research and help bring renewable energy technologies to market. 

Renewable Energy at Home

Homeowners and renters can use clean energy at home by buying green power, installing renewable energy systems to generate electricity, or using renewable resources for water and space heating and cooling.

Before installing a renewable energy system, it's important to reduce your energy consumption and improve your home’s energy efficiency .

Visit Energy Saver to learn more about the use of renewable energy at home.

You may be eligible for federal and state tax credits if you install a renewable energy system in your home. Visit ENERGY STAR to learn about federal renewable energy tax credits for homeowners. For information on state incentives, visit the Database of State Incentives for Renewables and Efficiency .

Other Ways EERE Champions Clean Energy

Find clean energy jobs.

EERE is dedicated to building a clean energy economy, which means millions of new jobs in construction, manufacturing, and many other industries. Learn more about job opportunities in renewable energy:

Research & Development

Home > Research > Research Development

• Registration
• Past Events
• Research Fund & Grant
• Final Year Project

Introduction

The Department of Research and Innovation is integral to the overall mission of UniKL BMI. It works with the academic and research communities in UniKL BMI to promote growth and innovation across the campus. Top priorities for the Department of Research and Innovation include increasing UniKL BMI’s participation in funding initiatives, improving the competitiveness of our researchers in their efforts to secure funding, and fostering entrepreneurial exploitation of new products, processes and services.

The research in UniKl BMI is organised broadly into three research clusters:

Renewable Energy

High Performance Computing

Advanced Telecommuniation Technology

UNIVERSITI KUALA LUMPUR

British Malaysian Institute Bt. 8, Jalan Sungai Pusu 53100 Gombak Selangor Darul Ehsan

+603-6184 1000

+603-6186 4040

[email protected]

CONNECT WITH #UniKL

• Job Employability ( Talent Inside, ILD)
• Entrepreneurialship (TeknoPutra)
• UniKL Portal
• Commercialisation (URSB)

UniKL website developed & managed by Corporate Branding & Strategic Communication Department (CBSCD)

An official website of the United States government

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

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

• Publications
• Account settings

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

• Advanced Search
• Journal List
• Int J Environ Res Public Health

Relationships between Renewable Energy and the Prevalence of Morbidity in the Countries of the European Union: A Panel Regression Approach

Robert stefko.

1 Faculty of Management, University of Prešov in Prešov, Konštantínova 16, 080 01 Prešov, Slovakia; [email protected] (R.S.); [email protected] (M.R.); [email protected] (V.I.)

Beata Gavurova

2 Center for Applied Economic Research, Faculty of Management and Economics, Tomas Bata University in Zlín, Mostní 5139, 760 00 Zlín, Czech Republic

Miroslav Kelemen

3 Faculty of Aeronautics, Technical University of Kosice, 041 21 Kosice, Slovakia; [email protected]

Martin Rigelsky

Viera ivankova, associated data.

The analytical procedures included data from the Eurostat database, namely the share of energy from renewable sources as an environmental indicator, and data from the Global Burden of Disease Study, specifically, health indicators of disease prevalence. The data were collected for the period 2010–2019. Thus, each of the countries of the European Union reported annual data for the observed period, that is, 10 years for individual variables.

The main objective of the presented study was to examine the associations between the use of renewable energy sources in selected sectors (transport, electricity, heating, and cooling) and the prevalence of selected groups of diseases in the European Union, with an emphasis on the application of statistical methods considering the structure of data. The analyses included data on 27 countries of the European Union from 2010 to 2019 published in the Eurostat database and the Global Burden of Disease Study. Panel regression models (pooling model, fixed (within) effects model, random effects model) were primarily used in analytical procedures, in which a panel variable was represented by countries. In most cases, positive and significant associations between the use of renewable energy sources and the prevalence of diseases were confirmed. The results of panel regression models could be generally interpreted as meaning that renewable energy sources are associated with the prevalence of diseases such as cardiovascular diseases, diabetes and kidney diseases, digestive diseases, musculoskeletal disorders, neoplasms, sense organ diseases, and skin and subcutaneous diseases at a significance level (α) of 0.05 and lower. These findings could be explained by the awareness of the health problem and the response in the form of preference for renewable energy sources. Regarding statistical methods used for country data or for data with a specific structure, it is recommended to use the methods that take this structure into account. The absence of these methods could lead to misleading conclusions.

1. Introduction

Energy is a fundamental necessity of modern life, but its dark side is the fact that the energy sector is responsible for more than 75% of greenhouse gas emissions in the European Union [ 1 ]. Today, there is a need to focus on renewable energy technologies that have the potential to improve the environment in terms of reducing greenhouse gases and global warming [ 2 ], which can also affect human health [ 3 , 4 , 5 ]. Climate change and global warming may lead to a significant increase in heat-related mortality and morbidity in the future [ 5 ]. In this context, renewable energy appears to be a key aspect to improve the environment and health, but renewable energy also has a positive effect on economic growth and human development [ 6 , 7 ]. Based on these benefits, renewable energy plays an important role in a modern and responsible world consisting of healthy and prosperous countries. Increasing the use of energy from renewable sources in various sectors of the economy is therefore a key element of an integrated energy system aimed to achieve climate neutrality as the main environmental ambition of the countries of the European Union [ 1 , 8 ]. In this sense, many innovative ideas offer positive prospects for improving the situation [ 9 ], while the basic pillar of the solution is the use of solar power, wind power, ocean and hydropower, biomass, and others [ 10 ].

The growing emphasis on proactive action on the part of developed countries cannot be overlooked. In particular, renewable energy policies in the European Union, but also research and scientific dissemination, are key to achieving the goals, and should take into account all forms of incentives that are offered to them [ 11 ]. In 2018, the 27 countries of the European Union achieved 18.9% of energy from renewable sources in total energy consumption, while their goal is to increase this share in further years [ 12 ]. In this sense, the share of energy from renewable sources in the electricity, heating, and cooling sectors was systematically above the level of the expected increasing trajectory, but in the transport sector it was slightly below the share planned by the National Renewable Energy Action Plans for the Member States of the European Union [ 13 ].

The evidence revealed by a newly developed technique, dynamic panel analysis under cross-sectional dependence, clearly shows that renewable energy causes a reduction in environmental degradation [ 14 ]. At the same time, it is well-known that environmental degradation and pollution have a negative effect on public health [ 15 ]. Therefore, it seems to be clear that there is a link between renewable energy and health.

Findings for a panel of 42 African countries between 1995 and 2011 revealed a long-run unidirectional causality running from renewable energy to health expenditure as an indicator of health [ 16 ], suggesting the importance of this issue for public health as such. The authors dealing with this topic, Apergis et al. [ 16 ], used a number of methodologies relevant to panel data to verify interactions, namely second-generation panel unit root tests, panel cointegration approaches, panel long-run estimates, and panel causality tests. Their results can be explained by the fact that if countries used their renewable sources efficiently, the benefits would come from reduced fossil energy bills and air pollution levels, and this would enable countries to save money for health care and, subsequently, to improve the health status of the population. The authors also emphasized the implementation of modern renewable energy projects in the health care sector [ 16 ]. Very similar findings were provided in a study conducted by Mujtaba and Shahzad [ 17 ], who addressed this issue in 28 countries of the Organisation for Economic Co-operation and Development (OECD) between 2002 and 2018. In their study, a panel fully modified ordinary least squares (OLS) regression model and cointegration tests were applied, while the authors confirmed long-run causality from renewable energy and carbon dioxide emissions to health care expenditure, as well as a significant and positive association between renewable energy and health care expenditure [ 17 ]. With a focus on another health indicator, Ben Jebli [ 18 ] analysed the relationship between the consumption of combustible renewables and waste and health status expressed by a number of doctors. Using the autoregressive distributed lag approach, the author [ 18 ] found that combustible renewables and waste consumption have a positive and significant effect on health, where simultaneously, the estimated lagged error correction terms showed a bidirectional long-run causality between health and combustible renewable waste consumption.

The findings in the previous paragraph indicate a possible relationship between renewable energy and health as such, that is, health expressed by morbidity or mortality. As it has been shown that the use of renewable energy can translate into higher health care expenditure and a higher number of doctors, an improvement in the health of the population can be expected.

Another study was conducted by Taghizadeh-Hesary et al. [ 19 ], who examined the relationship between non-renewable energy sources and health, and they used a generalized method of moments (GMM) estimation technique as a panel data analysis for 18 low- and middle-income countries between 1991 and 2018. Their results provided evidence that fossil fuel energy consumption increases the risk of lung and respiratory diseases, and there was a significant effect of carbon dioxide emissions and fossil fuel consumption on undernourishment and mortality rates. Similar results were revealed by Khan et al. [ 20 ], who used the techniques of fully generalized least squares (FGLS) and GMM estimation in a sample of 10 Central European countries over the period 1991–2018. As the consumption of non-renewable energy sources may lead to greater environmental degradation and pollution with a negative effect on public health, it is recommended to focus more intensively on renewable energy sources, which could improve the situation of energy insecurity, reduce greenhouse gas emissions, and decrease negative health effects [ 19 , 20 , 21 ]. In other words, energy efficiency and renewable energy can be beneficial for the environment and public health [ 22 , 23 ]. However, there are still many opportunities to examine the issue from different perspectives and using different statistical methods.

There are also many statistical methods that can be used in the examined issue. Regression analysis is a basic tool, but it is still used in different variations in contemporary studies with a transnational effect. Regression models are commonly applied to cross-sectional or time series data, while a panel regression model is some kind of compromise. The advantage of the panel regression models is evident especially in its ability to identify and take into account effects that cannot be found by cross-sectional models, where it means that the panel regression models take the data structure into account. Another equally important advantage is the ability to control individual heterogeneity, to increase variability for more efficient estimation or to increase the accuracy of estimates, as these models work with microdata and not aggregated data [ 24 ]. Currently, the panel models represent a relatively large part of the statistical investigation. There are not only classical one-way and two-ways variations of fixed effects models, random effects models, and nested models, but also well-known strong dynamic models, models capable of solving endogeneity problems, count data models, or spatial models [ 25 ]. The use of these models has found wide application possibilities especially in econometrics or public health, as when using high-quality statistical methods, it is possible to achieve very high-quality and relevant results. An important part of proper use is deciding on the choice of a specific model, taking into account several assumptions. Among the most commonly used tests for this purpose are the Breusch-Pagan test [ 26 ] that helps to assess the variability of residues, Wooldridge’s test for unobserved individual effects [ 27 ] that assesses the significance of unobservable effects through residue distribution, the Baltagi and Li one-sided LM test [ 28 ] that assesses the significance of the internal data structure and thus the suitability of the use of panel models, and likewise, the F test for individual and/or time effects. In addition, there is the Hausman test and its robust variant, which help to decide on a model with fixed (within) effects or a model as a Generalized Least Squares (GLS) alternative in the form of random effects. It is also possible to mention Angrist and Newey’s test [ 29 ], which identifies the limitations of models with fixed effects.

The presented research studies examining the connection of the renewable energy dimension with the health dimension are quite heterogeneous, but they provide valuable information about the applied methods, their application potential, and limitations. Thus, they enable the creation of an area for subsequent research and to formulate new research trajectories. The development of a methodological platform in each research area is quite demanding as it is a dynamic process, while it is extremely important to create international research networks to share knowledge from the application of the methodological processes. Additionally, the development of social systems, the processes of globalisation, and demographic development are important determinants influencing the methodological processes that are also related to the difficult nature of the data [ 30 , 31 ]. For this reason, an issue of examination of the applicability of the methods and the methodological processes linking different research areas, such as renewable resources and health, possesses great importance [ 32 , 33 , 34 ]. The creation of the national and international policies is determined by the availability of the quality research reports that have to not only aggregate the current situation, but also reveal the causes of this situation and the possibilities of its solution to quantify the effect of the alternatives, and thus, to help to create stabilisation and the regulatory mechanisms [ 35 , 36 , 37 ]. Without the appropriate analyses, it is not possible to create the relevant policies, but these analyses also require access to the deeper structured data enabling the emergence of the new methodological procedures, as well as the development of the current ones [ 38 , 39 , 40 , 41 ]. These consistent facts created motivation for us to carry out our research aimed at examination of the selected dimensions of the use of renewable energy resources and the health parameters—the prevalence of the selected diseases.

2. Materials and Methods

The main objective of the presented study was to examine the associations between the use of renewable energy sources in selected sectors (transport, electricity, and heating and cooling) and the prevalence of selected groups of diseases in the European Union, with an emphasis on the application of statistical methods considering the structure of data. The classification of sectors, namely transport, electricity, and heating and cooling, represents one of the structures of renewable energy consumption to total energy consumption in the Eurostat database. This classification was chosen on the basis of the most appropriate logical connection with the application aspect. To achieve our objectives, several analytical procedures were performed, the most important of which was regression analysis. This analysis was implemented in three variants (pooling model, fixed effects model, random effects model) and the selection of one of them was conditioned by individual tests of assumptions. When using the analyses, the emphasis was placed on the need to take the structure of data into account (in this case, the structure of countries).

In the context of achieving the objective of the study, three research questions were formulated, which also determine the methodological framework of the study within three research areas:

RQ1: Is there an association between the share of energy from renewable sources in total energy consumption in the transport sector and the prevalence of diseases classified into selected diagnosis groups?

RQ2: Is there an association between the share of energy from renewable sources in total energy consumption in the electricity sector and the prevalence of diseases classified into selected diagnosis groups?

RQ3: Is there an association between the share of energy from renewable sources in total energy consumption in the heating and cooling sector and the prevalence of diseases classified into selected diagnosis groups?

The analytical procedures included data from the Eurostat database [ 42 ], namely the share of energy from renewable sources as an environmental indicator, and data from the Global Burden of Disease Study [ 43 ], specifically, health indicators of disease prevalence. The data were collected for the period 2010–2019. Thus, each of the countries of the European Union reported annual data for the observed period, that is, 10 years for individual variables. It should also be noted that no missing data were found.

The share of energy from renewable sources in total energy consumption (RNWe) appeared in the classification of three sectors: (i) transport (RNWe TSP), (ii) electricity (RNWe ELC), and (iii) heating and cooling (RNWe H&C). This environmental indicator was presented as a percentage of renewable energy sources from total consumption, while the higher the value, the higher the consumption of renewable sources.

The diagnosis groups covered 11 areas of diseases: cardiovascular diseases (CRD), diabetes and kidney diseases (DIA), digestive diseases (DGS), chronic respiratory diseases (RSP), mental disorders (MNT), musculoskeletal disorders (MLT), neoplasms (NPL), neurological disorders (NRL), sense organ diseases (SNS), skin and subcutaneous diseases (SKN), and substance use disorders (SBC). The values of these health indicators represented the prevalence of diseases calculated per 100,000 inhabitants of individual countries.

In general, this study focused on the analysis of the associations between the change in the share of energy from renewable sources in total energy consumption (in%) and the change in the prevalence of diseases classified into selected diagnosis groups (prevalence per 100,000 population in a country).

Several statistical procedures were selected for analytical processing. First, a statistical description and additional visualizations showing trends in selected variables were provided for a more detailed look at the indicators. Non-parametric tests of differences (Kruskal Wallis test) were also used, which were preferred based on the results of the Shapiro–Wilk test of normality. Second, the assumptions were assessed in order to choose a suitable panel regression model. The Baltagi and Li one-sided LM test was chosen to identify the possible occurrence of a serial correlation [ 28 ]. The F test for the presence of individual effects (or time effects) was used to assess the significance of effects in the internal data structure in terms of individual countries, but also individual years. The robust regression-based Hausman test (vcov: vcovHC) was used in order to appropriately choose a fixed (within) effects model or a random effects model. Third, the effects were presented in three variants of regression models, including a robust version of the OLS pooling model (vcovHC), a model with fixed effects, specifically the one-way (individual) effects within model (Arellano estimator), and a model with random effects, specifically the one-way (individual) random effect model: Swamy–Arora’s transformation (White 2 estimator).

The essence of the above-mentioned tests lies in the optimal selection of a particular regression (panel) model. If the Baltagi and Li one-sided LM test showed a significant result, a robust estimator was preferred. If the F test showed a significant result, such as for countries, the internal data structure from the point of view of countries was taken into account, and a one-way model was preferred. If the F test showed a significant result both for countries and for time, it would be appropriate to prefer a two-way model. Commonly used panel models were applied, namely the fixed (within) effects model and the random effects model. The choice of a suitable alternative between the models was supported by the Hausman test, in which a significant result suggests the use of the fixed (within) effects model.

The main analytical calculations were performed using the programming language R v 4.0.3 (RStudio, Inc., Boston, MA, USA), while Tableau v 2020.2 (Tableau Soft-ware, LLC, Seattle, WA, USA) was used secondarily.

This section presents the results of analytical procedures, which were divided into two parts according to the used analyses: (i) descriptive analysis and (ii) regression analysis. The Appendix A contains a table showing the average values of the processed variables.

Table 1 provides the basic output of descriptive statistics, and attention should be paid to the measures of central tendency (average, median). In terms of the share of energy from renewable sources, the lowest share was found in the transport sector (RNWe TSP mean = 6.00; median = 5.67) and the other two sectors were roughly balanced (mean: RNWe ELC = 27.18; RNWe HaC = 26.35). With a focus on the health variables, neurological disorders (mean NRL = 42,982.91) could be considered the diagnosis group with the highest prevalence, while the lowest mean prevalence was shown in substance use disorders (mean SBC = 3262.79). At this point, it should be noted that the chosen metric (prevalence per 100,000 population) does not take into account the severity of the disease, and focuses only on the occurrence of specific diagnoses of the group. When assessing the descriptive measures, it should be borne in mind that these outcomes have been obtained by the countries of the European Union over a period of time. The skewness and kurtosis measures showed possible deviations, and the highest deviation from the normal distribution can be observed in the variable RNWe TSP (Skew = 2.48, Kurt = 8.99).

Descriptive analysis of renewable energy (%) and disease prevalence (per population of 100,000).

Note: Std. Dev.—standard deviation, Skew—skewness, Kurt—kurtosis, Min—minimum, Max—maximum, Perc. 25—25th percentile, Perc. 75—75th percentile, RNWe TSP—share of energy from renewable sources in transport, RNWe ELC—share of energy from renewable sources in electricity, RNWe H&C—share of energy from renewable sources in heating and cooling, CRD—cardiovascular diseases, DIA—diabetes and kidney diseases, DGS—digestive diseases, RSP—chronic respiratory diseases, MNT—mental disorders, MLT—musculoskeletal disorders, NPL—neoplasms, NRL—neurological disorders, SNS—sense organ diseases, SKN—skin and subcutaneous diseases, SBC—substance use disorders.

Table 2 shows the results of the univariate Shapiro-Wilk normality test (U SW), as well as the non-parametric Kruskal–Wallis tests to identify statistically significant differences in selected indicators between countries (KW C) and between years (KW Y). Based on the above-mentioned results, it could be stated that the assumption of normality was not confirmed for the vast majority of variables. The preference for non-parametric tests was therefore more acceptable. The results of the tests of differences between countries showed significant values in all cases. Accordingly, it was possible to confirm significant differences in selected environmental and health indicators between the analysed countries. However, on the basis of this test, it was not possible to identify the countries between which the differences were found and between which no differences were found. Thus, Appendix A shows the average values in the classification of countries, which may provide a closer look at the results. Differences within the classification of years were significant only in two cases (RNWe TRP = 53.52 †; DIA = 23.30 ***). These results indicated that when examining the relationships between the indicators, it was appropriate to use methods that are able to take the structure of countries into account.

Normality and differences tests—countries and years.

Significance: * p -value < 0.1; *** p -value < 0.01; † p -value < 0.001. Note: Diff.—differences, U SW—univariate Shapiro-Wilk normality test, KW C—Kruskal Wallis test (countries), KW Y—Kruskal Wallis test (years).

Figure 1 shows the development of the share of energy from renewable sources, and an upward trend is evident. The year-on-year changes were slightly unstable in the transport sector compared to the electricity and heating and cooling sectors, where the growth rate was relatively stable. Figure 2 presents the development of the prevalence of diseases classified into selected diagnosis groups, and an upward trend could be observed in most cases. This can be explained by population growth, increasing life expectancy, improving diagnostic methods in health care, but also by a deteriorating environment, when the use of renewable energy sources needs to be considered. On the other hand, a declining trend was observed for mental disorders (MNT) and substance use disorders (SBC). An interesting case was the group of neoplasms (NPL), in which it was possible to observe a break in the growing trend in 2017 and then a relatively strong decline.

Development of the share of energy from renewable sources in total energy consumption (%) in the observed period (2010–2019).

Development of the prevalence of diseases classified into selected diagnosis groups (per population of 100,000) in the observed period (2010–2019).

The following parts of this section focus on evaluating the associations between the share of energy from renewable sources and the health of the population, that is, morbidity of the population in the European Union.

Table 3 presents the test outputs for the assessment of selected assumptions of regression models. As can be seen, the table consists of three sections according to the sectors (transport, electricity, heating and cooling) with four statistical characteristics in each of these sections. Serial correlation was tested using the Baltagi and Li one-sided LM test (BLT), which revealed significant results in all cases, suggesting a more appropriate use of robust estimation methods. Based on the results of the F test for individual effects within countries (F C), it was possible to confirm significant effects in all cases, while the results of the test within years (F Y) indicated a significant effect only in one case (DIA). Hence, it seemed appropriate to use the regression models that take into account the structure of countries, namely a fixed (within) effects model or a random effects model. Given the fact that the effect of years was significant only in one case, a one-way variant of models was preferred in all of the analysed cases, including this individual one. Subsequently, if the robust Hausman test for panel models (RHT) showed a significance at an α level lower than 0.05, the fixed (within) effects model was chosen, otherwise (RHT p -value > 0.05) the use of the random effects model was preferred.

Assumptions for the selection and application of panel regression models.

Significance: * p -value < 0.1; ** p -value < 0.05; *** p -value < 0.01; † p -value < 0.001. Note: RNWe—share of energy from renewable sources in total energy consumption, BLT—Baltagi and Li one-sided LM test, F C—F test for individual effects within countries, F Y—F test for individual effects within years, RHT—robust Hausman test.

Based on the research experience, the authors of this study consider the above-mentioned tests of assumptions to be the best practices and, simultaneously, the minimum requirements necessary for the responsible selection of a suitable model. To ensure relevant results, a very important decision is to choose a model that takes the structure into account or not. Another issue when choosing a model is deciding on model preferences with fixed or random effects. In order to choose an adequate method, it seems reasonable to assess the suitability of using the classical (OLS) model or its robust alternative. However, the robust alternative has the least risk of skewing and disrupting the results, as in the vast majority of cases it does not appear to be detrimental to the results. The previously used tests can responsibly assess all necessary assumptions and support an appropriate and relevant decision.

The outputs in Table 4 present the examined associations between the share of energy from renewable sources in the transport sector and the prevalence of diseases classified into selected diagnosis groups. A significant association could be confirmed in almost all cases, while an exception was observed in models involving chronic respiratory diseases (RSP) and neurological disorders (NRL). Regarding the significant results, the positive β coefficient indicated that in countries where the share of energy from renewable sources in the transport sector was higher, the prevalence of certain types of diseases was also higher. A positive trajectory was observed in most of the analysed diagnosis groups. In contrast, a significant and negative association between the indicators was found in models involving mental disorders (MNT) and substance use disorders (SBC). Thus, in countries with a higher share of energy from renewable sources in the transport sector, a lower prevalence of the mentioned diseases and disorders was observed.

Outputs of PLM models: RNWe transport (%) → prevalence of selected diseases (per population of 100,000).

Significance: * p -value < 0.1, ** p -value < 0.05, *** p -value < 0.01, † p -value < 0.001. Note: SE—standard error. The preferred model is underlined.

The outputs in Table 5 present the examined associations between the share of energy from renewable sources in the electricity sector and the prevalence of diseases classified into selected diagnosis groups. The results were similar to the previous table; thus, a significant association was not found only in models involving chronic respiratory diseases (RSP) and neurological disorders (NRL). The most significant associations showed positive β coefficients, while the negative ones were identified only in models involving mental disorders (MNT) and substance use disorders (SBC). It was possible to conclude that the outputs were very similar to those in the transport sector and could be interpreted in the same way.

Outputs of PLM models: RNWe electricity (%) → prevalence of selected diseases (per population of 100,000).

The outputs in Table 6 present the examined associations between the share of energy from renewable sources in the heating and cooling sector and the prevalence of diseases classified into selected diagnosis groups. Again, similar results were as in the previously analysed cases. A different finding was that no significant association was observed only in the model involving chronic respiratory diseases (RSP). Focusing on significant results, the β coefficient indicated positive trajectories in most cases, while negative trajectories were observed in three models, which included mental disorders (MNT), neurological disorders (NRL), and substance use disorders (SBC).

Outputs of PLM models: RNWe heating and cooling (%) → prevalence of selected diseases (per population of 100,000).

Significance: * p -value < 0.1, *** p -value < 0.01, † p -value < 0.001. Note: SE—standard error. The preferred model is underlined.

In all three sectoral specifications, the positive trajectories were found for the diagnosis groups such as cardiovascular diseases (CRD), diabetes and kidney diseases (DIA), digestive diseases (DGS), musculoskeletal disorders (MLT), neoplasms (NPL), sense organ diseases (SNS), and skin and subcutaneous diseases (SKN). These associations could be interpreted as meaning that in countries where the share of renewable energy is higher, the prevalence of these diseases is also higher. This finding can be explained by the idea that countries and their main actors are aware of environmental pollution and its negative effects, which are also reflected in the high prevalence of diseases. With more pollution, they take more steps to mitigate these negatives, or do not exacerbate them. Renewable sources provide such a path. When focusing on the coefficients of determination, higher coefficients were observed in the diagnosis groups, such as diabetes and kidney diseases (DIA), musculoskeletal disorders (MLT), sense organ diseases (SNS), and skin and subcutaneous diseases (SKN). The average values (for the observed period 2010–2019) are given in Appendix A . Based on these values, it is possible to assess the examined indicators in comparison between individual countries of the European Union.

Regarding the outputs from a statistical point of view, it was possible to observe the largest deviations when comparing the pooling model with the other models. The outputs of this model, on the one hand, acquired low values of the coefficient of determination and, on the other hand, did not show significant β coefficients compared to the models with fixed and random effects in several cases. On this basis, the fixed or random effects models are more appropriate for estimating the relationships of a data set with a specific structure. There were no pronounced differences between the fixed and random models.

4. Discussion

The study as a whole provided some interesting insights into the dimension of health and the dimension of the environment in the European Union. The results of descriptive statistics showed that the average share of renewable energy sources in the electricity sector was 27%, 26% in the heating and cooling sector, and 6% in the transport sector. These results are in line with the information provided in the report on the progress of renewable energy in the European Union, which also indicated that the transport sector is the sector with the lowest share of renewable energy use under the planned trajectory [ 13 ]. With a focus on the health indicators, the highest average prevalence of morbidity was found in the diagnosis group of neurological disorders, followed by the group of skin and subcutaneous diseases and digestive diseases. The Member States of the European Union were characterized by the lowest average prevalence of morbidity in the group of substance use disorders.

During the observed period 2010–2019, the share of energy from renewable sources in total energy consumption showed an upward trend. The upward trend was also evident in most diagnosis groups, while the prevalence of mental disorders and substance use disorders showed a declining trend. In recent years, a declining trend in neoplasms has also been identified. Additionally, it was possible to confirm the significant differences in health and environmental indicators between countries. Thus, there was a difference between countries, both in the intensity of the use of renewable energy sources and in the prevalence of diseases.

The results of panel regression models revealed that in countries with a higher share of energy from renewable sources in total energy consumption, the prevalence of diseases such as cardiovascular diseases, diabetes and kidney diseases, digestive diseases, musculoskeletal disorders, neoplasms, sense organ diseases, skin and subcutaneous diseases was also significantly higher. The results of regression analyses mathematically indicated the fact that with a higher share of renewable energy sources, a higher prevalence of the above-mentioned diseases can be expected. However, this interpretation could be misleading, and a higher share of renewable energy sources can be seen as a response to the increasing morbidity, to which environmental pollution has also contributed. The second possible explanation is that some diseases with a high coefficient of determination (diabetes and kidney diseases, musculoskeletal disorders, sense organ diseases, skin and subcutaneous diseases) were frequent in more developed countries, and it can be stated that more developed countries were more inclined to prefer renewable energy than less developed ones in order to reduce poor health. At the same time, changes in health under the influence of the environment manifest themselves later.

These ideas have been supported by other studies. It is well-known that poor health can be the result of environmental degradation [ 15 ]. Additionally, the use of non-renewable energy sources can lead to greater environmental degradation and pollution, which have a negative effect on human health [ 3 , 4 , 5 ]. Therefore, there is a need to focus more on the use of renewable energy, which can have environmental and health benefits [ 19 , 20 , 21 ]. With high levels of pollution and high levels of poor health, the use of renewable energy sources needs to be considered. At the same time, the savings in pollution-related costs make it possible to increase the level of health care provision, which can be linked to increased health care expenditure and improved public health [ 16 ]. This benefit resulting from the use of renewable energy sources can be translated into better diagnostics; therefore, hiding health complications can be detected and treated in time. It is beneficial for the entire population, although increased diagnostics can also lead to increased morbidity rates for a particular period of time due to the higher number of detected diseases. On the other hand, these diseases can be treated in time, and improvements in the health of the whole population could be expected in future. This idea is consistent with the findings of Apergis et al. [ 16 ], Mujtaba and Shahzad [ 17 ], or Ben Jebli [ 18 ], who suggested that higher renewable energy may lead to higher health expenditure and the higher number of doctors. These authors used several statistical methods for panel data in their studies to help reveal the findings, while the methods used in the presented study also expand the knowledge about the problem.

The study also provided a statistical view, and three regression models in a robust variant were used and compared for this purpose. Specifically, it was the OLS pooling model (vcovHC), the fixed effects model (one-way (individual) effect within model), and the random effects model (one-way (individual) Random effect model: Swamy-Arora’s transformation). The most suitable model was selected using tests such as (i) the Baltagi and Li one-sided LM test, (ii) the F test for the presence of individual effects (or time effects), or (iii) the robust regression-based Hausman test. In general, each of the used regression models has its own specifics when considering the data structure and links between individual clusters (in this study, these were countries). From the above-mentioned outputs, it was clear that the pooling model showed different results compared to the fixed and random effects models. These deviations can be explained by the level of acceptance of the data structure. The pooling model acquired low levels of determination coefficients, and simultaneously, the results of this model indicated insignificant results, although the fixed or random effect models supported different results. It should be noted that the applied tests of assumptions strongly recommended a method that takes into account the structure of countries. The structure of the years did not appear in most cases to be a characteristic that should be taken into account. The analytical processes applied in the presented study can be understood as best practice, taking into account computational complexity and added value. The presented analytical processes can relatively reliably capture the relationships in various dimensions, including health and the environment, which have been investigated in similar studies using a much more complex statistical technique [ 16 , 17 , 18 ]. The panel models have a very wide application for structured data [ 25 ] and it can be concluded that very interesting and valuable results can be revealed using the panel models.

Within similar approaches when performing analyses, it is appropriate to use models capable of taking the data structure into account at a sufficient level (e.g., countries). In the absence of this approach, significant bias in results can be expected. This recommendation is especially important for data with shorter time series (e.g., 10 years for annual observations). A potential extension could be the use of models taking into account endogeneity, such as panel models with instrumental variables. In the cases where the structure of countries appears to be significant, the assessment of spatial dependence and the subsequent use of spatial panel models should be considered.

Limitations could also be identified in this research. The prevalence of diseases does not take into account the severity of the disease, and only provides a number. A potential limitation from a statistical point of view may be the fact that data from the last 10 available years have been included in the analyses. For a longer time period, it may be necessary to take into account the time aspect, not just the country aspect. Another limitation that needs to be noted is that all results can only be seen in terms of associations, while a consideration of causal relationships can be misleading.

5. Conclusions

The present study examined the associations between the use of renewable energy sources and the prevalence of selected groups of diseases in the European Union. An increased emphasis was placed on the application of analytical methods. It has been revealed that most of the associations showed a significant and positive trajectory. This can be interpreted in several ways suggested in the study. The preference for green energy and the promotion of a sustainable way of life is a trend that is established across the European Union. It should be underlined that green energy is associated with health in several respects, while the results of this study present one of them. At the same time, this issue should be examined in more detail.

Several studies have presented the relationships in given dimensions using a variety of complex analytical techniques, but already using the techniques presented in this study, it is possible to confirm demonstrable results. The application of panel models is the best practice to examine the relationships, when the data are formed by the internal structure of countries (or other spatial clusters) and, simultaneously, a time factor enters the data. Cross-sectional models (e.g., OLS) may not be sufficiently effective in estimations.

The presented study provided a basis for future research, which should focus on a more detailed explanation of the revealed relationships. An interesting view of the issue could be provided by a research that would include individual groups of disease prevalence according to age categories, but also other social, health, and environmental aspects. From a statistical point of view, it would be interesting to evaluate a longer time series, or to assess the appropriateness and relevance of including into the panel models additional instrumental variables, and thus to minimize the problem of endogeneity.

Acknowledgments

The authors thank the journal editor and anonymous reviewers for their guidance and constructive suggestions.

Mean values of selected variables (2010–2019).

Note: The number in parentheses represents the order—the lowest value is marked (1) and the highest (27).

Author Contributions

Conceptualization, R.S. and B.G.; methodology, M.K., M.R. and B.G.; software, M.R.; validation, V.I., R.S. and B.G.; formal analysis, M.R. and M.K.; investigation, R.S.; resources, B.G. and V.I.; data curation, M.R.; writing—original draft preparation, V.I. and B.G.; writing—review and editing, M.K. and B.G.; visualization, M.R.; supervision, B.G.; project administration, R.S.; funding acquisition, R.S. All authors have read and agreed to the published version of the manuscript.

This research was funded by the Slovak Research and Development Agency under the contract APVV-17-0166: “Economic and psychological factors of tourists’ expenditures: microeconometric modeling”. This research was supported by the Internal Grant Agency of FaME Tomas Bata University in Zlin: RO/2020/05: “Economic quantification of marketing processes that focus on value increase for a patient in a process of system creation to measure and control efficiency in health facilities in the Czech Republic”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.