Amid intensifying energy challenges and ecological constraints, promoting green economic transformation has become vital for achieving sustainable growth and reducing reliance on resource-intensive development models. This study explores the heterogeneity of green economic efficiency (GEE) between the Group of Seven (G7) and Emerging Seven (E7) economies using a three-stage super-efficiency SBM-DEA that integrates non-radial measurement, effective unit ranking, and stochastic noise correction. A Tobit regression model is further employed to assess the nonlinear effects of the technological innovation and natural resource rents on GEE. The results indicate that: (1) G7 economies consistently outperform E7 economies in GEE, although the efficiency gap has gradually narrowed since 2016. Within E7 economies, marked efficiency disparities persist, reflecting limited technological diffusion and weaker innovation capacity. (2) G7 economies exhibit synergistic improvements across management, technological, and scale efficiencies, whereas E7 economies display higher volatility and structural constraints. (3) Technological innovation exhibits a pronounced U-shaped effect on green economic efficiency (GEE), whereas natural resource rents display an inverted U-shaped relationship with GEE. These effects are more pronounced and rapidly transmitted in G7 economies, while in E7 economies they are weaker and the turning points are delayed. Further analysis indicates that industrial structure significantly constrains the impact of innovation, whereas government effectiveness does not constitute a stable moderating mechanism in either group of economies. These findings highlight the need for differentiated policy strategies to promote green economic efficiency through innovation-driven growth, efficient resource use, and institutional reform.

The world is stepping forward to a carbon-neutral economic system in response to the rising issues caused by climate change. Fossil fuel combustion is the primary source of increased carbon emissions, energy mix adjustment is critical to climate change mitigation and the carbon neutrality goal. This study investigates the different responses to energy sources’ economic growth and environmental sustainability using balanced panel data from 34 Organization for Economic Cooperation and Development (OECD) countries during 1995–2019. This study uses oil, natural gas, and renewable energy to represent traditional, emerging alternative fossil, and green energy sources, respectively. Results show that renewable energy, oil, and natural gas all impose impacts on economic growth, however, renewable energy contributes more than oil and natural gas. Furthermore, there is a significant inverse relationship between the amount of renewable energy produced and carbon dioxide (CO2) emissions. While both natural gas and oil have a positive effect on CO2 emissions, the effect of natural gas is much smaller than that of oil. Furthermore, the causality investigation reveals that renewable energy, oil, and natural gas all show impacts on carbon emissions but do not contribute to economic growth. These findings suggest that increasing investment in renewable energy, with natural gas playing the role of a transitional replacement for oil, will contribute to the “carbon neutrality” process of these countries.

Environmental degradation has profoundly impacted both human society and ecosystems. The environmental Kuznets curve (EKC) illuminates the intricate relationship between economic growth and environmental decline. However, the recent surge in trade protectionism has heightened global economic uncertainties, posing a severe threat to global environmental sustainability. This research aims to investigate the intricate pathways through which trade protection, assessed by available trade openness data, influences the nexus between economic growth and environmental degradation. Leveraging comprehensive global panel data spanning 147 countries from 1995 to 2018, this study meticulously examines the non-linear dynamics among trade, economy, and the environment, with a particular emphasis on validating the EKC hypothesis. This study encompasses exhaustive global and panel data regressions categorized across four income groups. The research substantiates the validity of the EKC hypothesis within the confines of this investigation. As income levels rise, the impact of economic growth on environmental degradation initially intensifies before displaying a diminishing trend. Additionally, trade protection manifests as a detriment to improving global environmental quality. The ramifications of trade protectionism display nuanced variations across income strata. In high-income nations, trade protection appears to contribute to mitigating environmental degradation. Conversely, within other income brackets, the stimulating effect of trade protection on environmental pressure is more conspicuous. In other words, trade protectionism exacerbates environmental degradation, particularly affecting lower-income countries, aligning with the concept of pollution havens. The study’s results illuminate nuanced thresholds in the relationship between trade, economic growth, and environmental degradation across income groups, emphasizing the heterogeneous impact and underlying mechanisms. These findings provide valuable insights for policymakers, urging collaborative efforts among nations to achieve a harmonious balance between economic advancement and environmental preservation on a global scale.

The 26th United Nations Climate Change Conference (COP26) has promulgated pivotal climate change mitigation goals for our planet. Decarbonization efforts that align the COP26 goals are underway, especially in high emission sectors. Decarbonization plays a role in reducing air pollution from power plants because of co-benefits. This study proposes a new method to quantify the co-benefits of air pollution reduction by integrating two models, where the effects of reduced air pollution from end-of-pipe measures and climate strategies are disentangled. Results show that in a more rapid future decarbonization scenario in the Chinese electric power sector (International Energy Agency's Sustainable Development Scenario), climate strategies bring about 10–87 times more co-benefits of reduced air pollution than in a slower decarbonization scenario (International Energy Agency's New Policies Scenario). The fossil fuel use factor contributes the most to the additional co-benefits of air pollution reduction from decarbonizing. Substitution of non-fossil energy sources for fossil fuels plays a pivotal role in facilitating these co-benefits. Future decarbonization policies, such as increased end-use efficiency and generation efficiency in power plants can also reduce additional air pollution from power plants as they accelerate the decarbonization process.

China's commitment to promoting free trade, even as facing with surging protectionism, inspires the exploration of the trades' impacts on it. This study has proposed a novel research framework based on Multi-Regional Input-Output analysis and Scenario Analysis model to quantify the impacts of trade. Three dimensions (Environmental, Economic, Environmental-Economic) are designed to discuss the absolute quantified impact and relative quantified impact from total amount to sectors. As the world's major trader, it gives the support by quantitative data, for that trade had indeed made China a pollution heaven. But trade can benefit China by increasing its economy and decreasing its carbon intensity. And the decomposition analysis for quantified impacts is adopted to explain driving factors with positive and negative effects mainly from total amounts and structure. More than that, quantified effects on various sectors affected are different. Mostly sectors got positive quantified impacts by trade with carbon emission and value added, expect mining sector. And only transport sector had an obvious positive impact on carbon intensity by trade. For China's sustainable development, policies making should take into account of achieving more environmental-economic gains and avoiding environmental losses in trade, by effectively adjustment of total amounts and product structure effects.

Nuclear power has received renewed attention during the energy transition in recent years. This study is aimed to explore whether nuclear energy can promote economic growth without increasing carbon emissions. In order to have a more comprehensive understanding of the relationship between nuclear energy, economic growth, and carbon emissions, this study also discusses the impact of coal, oil, natural gas, and renewable energy on economic growth and carbon emissions. The second-generation panel unit root test, panel cointegration test, panel fully modified ordinary least squares, and Heterogeneous Dumitrescu and Hurlin causality test were used to estimate the long-term elasticity and causality among variables. Results based on panel data from 24 countries with nuclear energy from 2001 to 2020 show that both nuclear energy and renewable energy can curb carbon emissions. Especially in Canada, Finland, Russia, Slovenia, South Korea, and The United Kingdom, nuclear energy reduces carbon emissions more significantly than renewable energy. Meanwhile, there is a positive relationship between increased nuclear energy, increased renewable energy, and economic growth, which means that nuclear energy and renewable energy could increase economic growth as well. There is a positive relationship between increased oil, increased natural gas, and economic growth, while there is a negative relationship between the increase in coal and economic growth. Meanwhile, there is a positive relationship between increased oil, increased coal, and increased carbon emissions, while the positive relationship between increased natural gas and increased carbon emissions is not significant. Thus, in the 22 countries with nuclear power, increased coal consumption does not drive economic growth but increases carbon emissions. Increased oil consumption increases economic growth, but it increases carbon emissions. Increased natural gas consumption boosts economic growth but adds little to carbon emissions. In the authors' view, nuclear power and renewable energy are all options for these nuclear-power countries to pursue economic growth without increasing carbon emissions. Moreover, nuclear power has a better effect on curbing carbon emissions in some countries than renewable energy. Therefore, under the premise of safety, nuclear power should be seriously considered and re-developed.

Previous research shows that, in the USA, the elasticity of carbon emissions with respect to GDP is greater when GDP declines than when GDP increases. Using monthly US data, we examine each individual recession since 1973. We find asymmetric changes in carbon emissions in the 1973–1975, 1980, 1990–1991, and 2020 recessions but not in the 1981–1982, 2001, or 2008–2009 recessions. The former four recessions are associated with negative oil market shocks. In the first three, there was a supply shock and in 2020, a demand shock. Changes in oil consumption that are not explained by changes in GDP explain these asymmetries. Furthermore, the asymmetries are due to emissions in the transport and industrial sectors, which are the main consumers of oil. We conclude that emissions behaved similarly in 2020 to the way they did in recessions associated with oil supply shocks, but, actually, this pattern is not inherent to the business cycle itself.

The COVID-19 pandemic has seriously impacted scientific research activities, especially international cooperation in scientific research. Using bibliometric methods and scientific knowledge graph software, and by calculating collaboration indicators such as international collaboration rates, this work conducts a comprehensive review of carbon neutrality publications in the Web of Science database before and during the COVID-19 pandemic, aiming to explore whether the COVID-19 pandemic derail China-U.S. collaboration on carbon neutrality research. The results show that (i) During the COVID-19 pandemic, more extensive research on carbon neutrality was carried out around the world, with China and the United States leading the way in carbon neutrality scientific output. (ii) Following the outbreak of the COVID-19, the global center of global carbon neutrality shifted from the United States to China. (iii) During the COVID-19 pandemic, research ties between China and the United States strengthened. The number of joint publications on carbon neutrality between China and the United States has greatly increased during the COVID-19 pandemic compared to those before. (iv) The proportion of China-U.S. cooperation in China's international cooperation has decreased, while it is the opposite for the United States. At the end of the article, we put forward relevant suggestions for realizing the sustainable development goals of climate change in the post-epidemic era for policymakers' reference. This paper provides important insights into the theoretical research of scholars in the carbon neutrality field.

According to the United Nations Environment Programme, the COVID-19 pandemic has created challenges for the economy and the energy sector, as well as uncertainty for the renewable energy industry. However, the impact on renewable energy during the pandemic has not been consistently determined. Instead of relying on data from year-to-year comparisons, this study redesigned the analytical framework for assessing the impact of a pandemic on renewable energy. First, this research designed an "initial prediction-parameter training-error correction-assignment combination" forecasting approach to simulate renewable energy consumption in a "no pandemic" scenario. Second, this study calculates the difference between the "pandemic" and "no pandemic" scenarios for renewable energy consumption. This difference represents the change in renewable energy due to the COVID-19 pandemic. Various techniques such as nonlinear grey, artificial neural network and IOWGA operator were incorporated. The MAPEs were controlled to within 5% in 80% of the country samples. The conclusions indicated that renewable energy in China and India declined by 8.57 mtoe and 3.19 mtoe during COVID-19 period. In contrast, the rise in renewable energy in the US is overestimated by 8.01 mtoe. Overall, previous statistics based on year-to-year comparisons have led to optimistic estimates of renewable energy development during the pandemic. This study sheds light on the need for proactive policy measures in the future to counter the global low tide of renewable energy amid COVID-19.

The outbreak of the COVID-19 pandemic has brought enormous challenges to the global marine environment. Various responses to the COVID-19 pandemic have led to increased marine pollution. Has the COVID-19 pandemic affected marine pollution research? This work comprehensively reviewed marine pollution publications in the Web of Science database before and during the COVID-19 pandemic. Results show that the COVID-19 outbreak has influenced the marine pollution research by: (i) increasing the number of publications; (ii) reshaping different countries' roles in marine pollution research; (iii) altering the hotspots of marine pollution research. The ranking of countries with high productivity in the marine pollution research field changed, and developed economies are the dominant players both before and after the outbreak of the COVID-19 pandemic in this field. Other high-productivity countries, with the exception of China, have higher international cooperation rates in marine pollution research than those before the pandemic. Microplastic pollution has been the biggest challenge of marine pollution and has been aexplored in greater depth during the COVID-19 pandemic. Furthermore, the mining results of marine pollution publications show the mitigation of plastic pollution in the marine environment remains the main content requires future research. Finally, this paper puts forward corresponding suggestions for the reference of researchers and practitioners to improve the global ability to respond to the challenges posed by the pandemic to the marine environment.

This research investigates the drivers of the paradox that air pollutant emissions have fallen in the Chinese electric power sector as coal combustion continues to play a dominant role. With directly measured unit-level air pollution emissions data from Chinese power plants during 2014–17, this study quantifies the contributions of eight factors to reducing three key air pollutants (sulfur dioxide, nitrogen oxides, and particulate matter) using the Logarithmic Mean Divisia Index model. The main results are presented for aggregates of the 10 wealthiest and 20 less wealthy provinces. The dominant driver of the fall in air pollution is the emission intensity of fossil fuel use, cutting air pollution by 69%–86% in the wealthier region and 65%–72% in the poorer region. Electricity consumption per unit of gross domestic product, the second largest contributor to air pollution reduction, has reduced the three key air pollutants emissions from power plants by 7%–9% and 11%–12% in the wealthier and poorer regions. Results suggest some policy implications; implementing end-of-pipe techniques cuts air pollution by lowering the emission intensity of fossil fuel use. Meanwhile, measures such as renewable energy generation subsidies reduce the fossil fuel intensity of electricity use and indirectly cut air pollution from power plants.

Industrial agglomeration not only brings economic prosperity, but also raises concerns about its environmental impact. How to balance industrial agglomeration and inclusive economic growth is a major challenge for today's social development. On the foundation of the balanced panel data of 164 cities in China from 2003 to 2013, our study employs the dynamic spatial Dubin model to empirically analyze the possible spatial effects between industrial agglomeration and air pollution at China's urban level, and detects the potential transmission mechanism through the lens of environmental regulation, technological progress and industrial structure upgrading. The findings denote that: (1) An inverted “N" type relationship exists between industrial agglomeration and air pollution, namely, with the deepening of industrial agglomeration, the air pollution of the city will first decline, then rise, and finally recover the trend of decline. (2) The influence of local industrial agglomeration on neighbor air pollution also presents an inverted “N" shape, denoting that local industrial agglomeration also has an alleviating influence on air pollution in adjacent regions in the long run. (3) Industrial agglomeration can reduce the air pollution level in urban agglomeration by affecting environmental regulation, technological progress and industrial structure upgrading. These results provide a detailed reference for promoting industrial development and air pollution control.

The carbon emission rebound of the post-2008 financial crisis teaches us a lesson that avoiding a rebound in carbon intensity is key to prevent the carbon emission increase afterward. Although how carbon emission will change the world after the COVID-19 pandemic is unknown, it is urgent to learn from the past and avert or slow down the potential rebound effect. Therefore, this study aims to identify key drivers of carbon intensity changes of 55 sectors, applying the decomposition techniques and the world input-output data. Our results demonstrate that global carbon intensity fluctuates drastically when shocked by the global financial crisis, presenting an inversed-V shape for the period 2008–2011. Industrial carbon emission and gross output vary among different industries, the growth rate of industrial carbon intensity varies from -55.55% to 23.77%. The energy intensity effect and economic structure effect have opposite impacts on carbon intensity decrease, accelerating and hindering the decreasing carbon intensity, respectively. However, the energy mix effect has a minor impact on carbon intensity decrease. The industrial carbon intensity decomposition results show the impact of technological and structural factors are significantly different among industries. Moreover, the impact of energy intensity is slightly stronger than the energy mix. More measures targeting avoiding the rebound in carbon intensity should be developed.

The 2020 COVID-19 driven recession saw a sharp drop in carbon dioxide emissions as transportation and some other energy uses were curtailed. This was an unusual recession as it was driven by a pandemic. Previous research shows that when GDP declines carbon emissions fall faster relative to GDP than they rise in economic booms. Using monthly US data, we examine each individual recession in the US since 1973 finding that there is an asymmetric response in the 1973-5, 1980, 1990, and 2020 recessions but not in the 1981-2, 2001, or 2008-9 recessions. The former four recessions are associated with negative oil market shocks. In the first three there was a supply shock and in 2020 a demand shock. Changes in oil consumption that are not explained by changes in GDP explain these asymmetries. Furthermore, the asymmetries are due to emissions in the transport and industrial sectors, which are the main consumers of oil.

Energy-related carbon dioxide (CO2) emissions dropped 12% between 2007 and 2016 in the United States (U.S.), while the gross domestic product (GDP) increased by 19%. empirical data related to the decoupling of carbon emissions from economic growth in the U.S. provides a useful study pilot opportunity and serves as a good example for other countries to learn about CO(2 )emission mitigation. This study identified the relationship between CO2 emissions and economic growth in the U.S. The goal was to determine if the rigid link between the two can be changed, and identify the potential drivers of this trend. We combined the Cobb-Douglas (C-D) production function and the extended Kaya equation to develop decomposition and decoupling techniques to quantify six potential effects. The results show that the investment effect and economy structure effect played the most important roles in increasing carbon dioxide emissions. In contrast, the energy intensity effect cut carbon emissions in most of the years studied. Strong decoupling and weak decoupling are the main states; the energy intensity effect accelerated the decoupling process. In contrast, the investment effect and labor effect decelerated the decoupling process in recent decades. The study concludes that emission mitigation and decoupling policies should emphasize energy efficiency, investment patterns, and improvements in labor force quality.

With the limited amount of resources, developing effective strategies to make full use of them and decrease the energy consumption without too much sacrifice of economic output requires identifying key drivers of energy consumption growth rate as a prerequisite. Meanwhile, as top three consumers of primary energy of the world, China, the United States of America, and India burn over 45% of global fuels in 2016. Conducting an empirically comparative analysis of them can also set up pilot scheme for other economies to develop more efficient strategies for energy consumption. The paper modified the original Geographical Detector model with a different sampling method to detect the key driver of energy consumption growth rate, which filling the gap that there are possible interactions of potential factors. The results show that coal intensity is the biggest driver to change overall energy consumption growth rate in China and India. In comparison, for the United States, the leading drivers of energy use are the factors of individual incomes and oil intensity. In addition, all factors have interactions and enhance each other when influencing total energy consumption growth rate. India has the strongest factor interactions when influencing the energy consumption growth rate among the three economies, all interactions between factors in US is not significant as those in China and India. Besides providing outcomes that can contribute towards developing new strategies to use energy more efficiently, this research offers a pilot example of analyzing energy issues from the perspective of stratified heterogeneity in consideration the characteristic differences of each factor.

With the boom of vehicles, especially the dramatic rise of private car ownership, in China, transport CO2 emission in China has surged. However, China has been taking the responsibility to cut down carbon emissions and to make positive efforts towards technology innovations in the transport sector. Breaking the link between transport carbon emissions and transport turnover capacity for the past decades should be analyzed. The paper tested the decoupling degree and ranked its potential determinants for every transport mode in consideration of specific transport mode characteristics. We extended the original Kaya identity to make the factor analysis more pertinent to the analysis of transport-related CO2 emissions. Besides, we combined the decomposition technique with decoupling analysis, decomposing the transport decoupling index into five distinct aspects to detect the key drivers of the decoupling of transport-related CO2 emissions from transport turnover volume. Moreover, we analyzed the relationship between transport-related CO2 emission and transport output, which also offers a novel perspective on transport and corresponding environmental research. The results uncovered that a weak decoupling state appeared between 1990–1995 and 2000–2010 in China’s transport sector. Transport energy efficiency exerted the most significant impact in accelerating the decoupling of transport-related CO2 emissions from turnover volume for all transport modes while the energy mix effect impeded the decoupling evolution in most observed periods. Railway transport turnover and rail locomotives shared rises boosted by decoupling evolution, while vehicular transport showed adverse effects. The rise of the transport facilities’ shares of railways, waterways, and airways also advanced the decoupling evolution. Hence, policies of switching travel modes and establishing a “smart growth” pattern for private vehicles should be considered.

As world's top two carbon emitters, driver analysis of China and the USA helped the governments to develop policies to cut or slow down carbon emission. Many studies identified the factors affecting carbon emission in China and the USA (emitting more than 40% of the global CO2 emission), however, few studies considered stratified heterogeneity or the interactions of factors. Here, we adopted the modified Geographical Detector tool to investigate the main drivers of carbon emission from the perspective of stratified heterogeneity. The results of this analysis showed that human economic activities in China were the dominant effect of carbon emission changes, while energy intensity contributed toward controlling the carbon emission in China. Furthermore, population growth was the most significant driving force followed by energy intensity toward controlling the carbon emission of the USA. All these factors are mutually enhancing in changing carbon emissions, while oil share with energy intensity and coal share were more significantly enhanced in China's carbon emission than other interactions. The factors of human activities and energy mix posed a more powerful effect when they mutually enhanced each other to change carbon emission compared to other enhancing interactions. This work represents a pilot scheme for a carbon dioxide emission analysis from the categorical stratified heterogeneity based on statistical methods.

Carbon emissions from China’s electricity sector account for about one-seventh of the global carbon dioxide emissions, or half of China’s carbon dioxide emissions. A better understanding of the relationship between CO2 emissions and electric output would help develop and adjust carbon emission mitigation strategies for China’s electricity sector. Thus, we applied the electricity elasticity of carbon emissions to a decoupling index that we combined with advanced multilevel Logarithmic Mean Divisia Index tools in order to test the carbon emission response to the electric output and the main drivers. Then, we proposed a comparative decoupling stability analysis method. The results show that the electric output effect played the most significant role in increasing CO2 emissions from China’s electric sector. Also, “relative decoupling” was the main state during the study period (1991–2012). Moreover, the electricity elasticity of CO2 emissions had a better performance regarding stability in the analysis of China’s electricity output.