Revisiting the environmental kuznets curve hypothesis in 208 counties: The roles of trade openness, human capital, renewable energy and natural resource rent

A study on the relationship between high-quality development of the logistics industry and rural revitalization across different levels of industrial structure

Due to regional differences in industrial structure levels, the development of the logistics industry in rural areas faces various challenges and opportunities. The objective is to explore how the high-quality development of the logistics industry influences rural revitalization at various tiers of industrial structure. It employs a benchmark regression model to dissect the influence that the logistics industry's high-quality development exerts on fostering rural revitalization. Moreover, a panel threshold model is employed to examines this impact across different levels of industrial structure. It demonstrates that enhancing the logistics industry significantly supports rural revitalization, with its impact varying across different levels of industrial structure. Hence, it is imperative to develop customized strategies for the logistics industry that account for the varying industrial structure in rural areas. It emphasizes the logistics industry's role in facilitating rural growth and recommends adopting tailored development strategies corresponding to the industrial structure's evolution to boost rural revitalization. Despite valuable insights, this study has limitations in reflecting regional trends and exploring other factors beyond the direct impact of logistics on rural revitalization, pointing to potential avenues for future research.

World economies' progress in decoupling from CO2 emissions

The relationship between economic growth and CO2 emissions has been analyzed testing the environmental Kuznets curve hypothesis, but traditional econometric methods may be flawed. An alternative method is proposed using segmented-sample regressions and implemented in 164 countries (98.34% of world population) over different periods from 1822 to 2018. Results suggest that while the association between GDP per capita and CO2 emissions per capita is weakening over time, it remains positive globally, with only some high-income countries showing a reversed association in recent years. While 49 countries have decoupled emissions from economic growth, 115 have not. Most African, American, and Asian countries have not decoupled, whereas most European and Oceanians have. These findings highlight the urgency for effective climate policies because decoupling remains unachieved on a global scale, and we are moving away from, rather than approaching, the Paris Agreement goal of limiting temperature increase to 1.5 °C above preindustrial levels.

Intelligent manufacturing and green innovation: Quasi-natural evidence from China

Intelligent manufacturing is an important driving force for improving quality and efficiency and promoting green innovation. Based on the data of Chinese listed companies and taking the Chinese intelligent manufacturing pilot demonstration projects as a quasi-natural experiment, this paper constructs a difference-in-differences (DID) model to explore the effect and mechanism of intelligent manufacturing on enterprise green innovation. The results show that intelligent manufacturing has significantly promoted green innovation in China, and this effect is still valid after considering various robustness tests. Heterogeneity analysis shows that in areas with a good green development foundation and poor information infrastructure, the impact is more obvious. In non-state-owned enterprises and mature enterprises, the impact is more obvious. Mechanism analysis indicates that intelligent manufacturing enhances green innovation through cost management effects, efficiency improvement effects, and employment structure optimization effects. The conclusions provide clear policy implications for developing countries to promote intelligent manufacturing practices and green high-quality development.

Research on financing and technological innovation efficiency of China's energy-saving and environmental protection enterprises

Green, low-carbon circular economy and the development model of energy-saving and environmental protection are emerging worldwide, and the world's economies have taken the energy-saving and environmental protection industry as a strategic emerging industry to cultivate and develop. Coordinating finance and technological innovation is crucial to promoting the healthy development of Energy-Saving and Environmental Protection Enterprises (ESEPEs). According to industry classification, this paper divides 121 ESEPEs into 12 categories as research objects. Firstly, the DEA-SBM model is used to calculate financing efficiency (FE) and technological innovation efficiency (TIE). Subsequently, the changes in the two efficiencies are analyzed from the perspectives of time series, industry, and annual mean value, and the changes of the two efficiencies are analyzed from the perspectives of time series, industry, and annual average values. Secondly, through the coupling coordination degree (CCD) model, the development status of the coupling coordination between the two efficiencies is innovated expounded. Finally, the Tobit regression model is used to discuss the factors influencing CCD from the whole and industry perspectives. The results show that: (1) TIE is generally lower than FE, and FE shows a downward trend while TIE shows an upward trend. (2) CCD between the two efficiencies is Near dysfunction, and TIE restricts the development of CCD between the two efficiencies to a certain extent. (3) R&D intensity, enterprise size, and government subsidy intensity have a significant positive impact on CCD between FE and TIE of ESEPEs, while the impact of other factors is not significant.

Does marine ecological compensation policy have improved marine carbon emission efficiency? Evidence from coastal areas in China

The marine ecological compensation (MEC) policy is a scheme for protecting and restoring marine ecology. The previous studies have verified that the policy has improved the marine environment, ignoring the comprehensive performance of the economy and environment. Is the MEC policy actually effective for reducing marine carbon emission considering resource input and economy output? To address the question, this paper evaluates the impact of MEC policy on marine carbon emission efficiency by introducing the multi-stage difference-in-difference with propensity score matching. The results show that the MEC policy significantly improves marine carbon emission efficiency, and the positive effects dynamically enhance over time. Low-carbon technology innovation is confirmed to play a mediating role by which the MEC policy improves marine carbon emission efficiency. Therefore, gradually popularizing the MEC policy and accelerating low-carbon technology innovation are crucial measures to improve marine carbon emission efficiency and achieve sustainable marine development.

Can environmental policies improve marine ecological efficiency? Examining China's Ecological Civilization Pilot Zones

Terrestrial ecosystems can benefit from environmental protection policies; however, their impact on marine ecological efficiency deserves further exploration. This study uses China's Ecological Civilization Pilot Zone (ECZ) policy as an example of a quasi-natural experimental study, with data from 11 coastal provinces in China from 2006 to 2019 as the initial sample. First, a Super-SBM model considers undesired outputs to measure marine eco-efficiency, while a synthetic control method (SCM) investigates the effect of environmental regulations on marine eco-efficiency. The results show that ECZ policies can promote marine eco-efficiency and the effect mechanisms of these policies are discussed from national and regional perspectives. This study contributes to the current literature by theoretically evaluating the impact of ECZ policies on the marine environment in coastal areas, enriching the mechanism of integrated environmental policies on marine ecological protection, and providing references for formulating and implementing environmental policies.

Assessing the impact of industrial intelligence on urban carbon emission performance: Evidence from China

With the growing emphasis on sustainable development, there has been increasing attention given to measures aimed at promoting environmental improvements and reducing carbon emissions, including the adoption of intelligent industry. Recent studies have analyzed the influence of industrial intelligence on urban carbon emission performance while ignore the spatial spillover effects and lack in-depth discussion of the mechanisms, which reduces the reliability of the assessment of industrial intelligence's impact on carbon emission performance. To address this issue, the paper examines direct effect, spatial spillover effects, and mechanisms, utilizing a balanced panel data from 2008 to 2019 for 238 Chinese cities. The findings reveal that a 1 % improvement in industrial intelligence results in a 2.747 % enhancement of local carbon emission performance. Moreover, through the spatial spillover analysis, we determined that industrial intelligence has a notable negative impact on the carbon emission performance of surrounding areas. The mechanism analysis demonstrated that industrial intelligence affects the carbon emission performance of local and neighboring areas by influencing the agglomeration of productive services. Furthermore, our study illustrates that the industrial intelligence's enhancement effect on carbon emission performance shows more significantly in eastern, resource-dependent, and ordinary prefecture-level cities. Finally, endogeneity and robustness tests conducted yielded consistent conclusions. Our study provides a new perspective on industrial intelligence's carbon reduction effect and contributes to the development of policies related to industrial upgrading and green development.

Trade openness, human capital, natural resource, and carbon emission nexus: a CS-ARDL assessment for Central Asian economies

There is a call for global efforts to preserve the ecological systems that can sustain economies and people's lives. However, carbon emission (CEM) threatens the sustainability of humanity and ecological systems. This analysis looked into the influence of energy use (ERU), human capital (HCI), trade openness (TOP), natural resource (NRR), population, and economic growth (ENG) on CEM. The paper gathered panel data from the Central Asia region from 1990 to 2020. The CS-ARDL was applied to establish the long-term interaction among the indicators. The paper's findings indicated the presence of the environmental Kuznets curve (EKC) in the Central Asia regions. Also, the empirical evidence highlighted that energy use, natural resources, and trade openness cause higher levels of CEM. However, the research verified that CEM can be improved through human capital and urban population growth. The study also found that HCI moderates the interaction between NRR and CEM. The causality assessment indicated a one-way interplay between ENG, ERU, NRR, and CEM. The study proposes that to support ecological stability in these regions, policy-makers should concentrate on developing human capital, investing in renewable energy sources, and utilizing contemporary technologies to harness natural resources in the economies of Central Asia.

Assessing the impact of geopolitical, economic, and institutional factors on China's environmental management in the Russian-Ukraine conflicting era

In contemporary times, geopolitical risk, and natural resources prices are susceptible due to the Russian-Ukraine conflict. In the meantime, emerging economies are struggling to explore the factors that could reduce ecological challenges and enhance environmental management. This research aims to analyze several economic, environmental, political, and institutional variables to ascertain their influence on greenhouse gas emissions in China. Covering the latest period from 1990 to 2022, various time series tests, including normality, stationarity, and cointegration tests. The results confirm that the variables studied have a stable pattern over time and are connected in the long run. The non-normal distribution of variables leads to opt novel moment quantile regression, where the results are tested for robustness via parametric approaches. The empirical results asserted that economic growth, natural resource prices, and trade significantly enhance ecological challenges (emissions). However, globalization, geopolitical risk, and institutional quality significantly reduce such environmental challenges. The results are robust, and both unidirectional and bidirectional causal associations confirm the importance of these variables in environmental management. Based on the results, this study recommends engagement in environmentally-friendly trading, investment in clean and green energy, and strengthening institutional quality for the region's environmental recovery.

Renewable energy, inequality and environmental degradation

The connection between income inequality and environmental degradation remains a topic of persistent debate, marked by inconsistencies in both theoretical and empirical studies. This study offers a novel contribution to this discourse by investigating the simultaneous influences of renewable energy and income inequality on environmental degradation. Utilizing data from 158 nations from 2000 to 2017, our research reveals a crucial moderating role of renewable energy in the nexus between income inequality and environmental degradation. The study's key finding is that the impact of income inequality on environmental degradation is contingent on the level of renewable energy development. In scenarios with limited renewable energy, income equality leads to increased environmental degradation. However, when renewable energy is more developed, income equality contributes to reducing environmental degradation. This novel insight suggests that renewable energy development can mitigate the trade-off between pursuing income equality and environmental sustainability, thereby enabling their simultaneous achievement. The research also highlights that a more equitable income distribution enhances the environmental benefits of renewable energy. Further analysis demonstrates the significant role played by household consumption behavior and social norms in shaping this phenomenon. By adding these new dimensions to the existing literature, the study significantly enriches the understanding of the complex interplay among economic factors, renewable energy, and environmental sustainability.

Environmental implications and levelized cost analysis of E-fuel production under photovoltaic energy, direct air capture, and hydrogen

The ecological transition in the transport sector is a major challenge to tackle environmental pollution, and European legislation will mandate zero-emission new cars from 2035. To reduce the impact of petrol and diesel vehicles, much emphasis is being placed on the potential use of synthetic fuels, including electrofuels (e-fuels). This research aims to examine a levelised cost (LCO) analysis of e-fuel production where the energy source is renewable. The energy used in the process is expected to come from a photovoltaic plant and the other steps required to produce e-fuel: direct air capture, electrolysis and Fischer-Tropsch process. The results showed that the LCOe-fuel in the baseline scenario is around 3.1 €/l, and this value is mainly influenced by the energy production component followed by the hydrogen one. Sensitivity, scenario and risk analyses are also conducted to evaluate alternative scenarios, and it emerges that in 84% of the cases, LCOe-fuel ranges between 2.8 €/l and 3.4 €/l. The findings show that the current cost is not competitive with fossil fuels, yet the development of e-fuels supports environmental protection. The concept of pragmatic sustainability, incentive policies, technology development, industrial symbiosis, economies of scale and learning economies can reduce this cost by supporting the decarbonization of the transport sector.

The impact and mechanism of vertical fiscal imbalance on green development efficiency: An empirical analysis based on city-level samples in China

Green development has become a prevalent theme due to the tightening of resource constraints. This article explores the institutional factors that may contribute to the slow pace of green modernization in prefecture-level cities during the new era through the examination of the central-local fiscal relationship that local governments in China must navigate. A two-way fixed-effects model is used to theoretically analyze the impact of the increase in vertical fiscal imbalance (VFI) on green development efficiency (GDE) based statistical data from 270 cities between 2007 and 2020. The research shows that the increase in VFI has an N-shaped nonlinear effect on GDE, which is supported by various robustness and endogeneity tests. The greening process is significantly affected by the fluctuating dynamics of China's central-local fiscal relations. The VFI values of 0.2801 and 0.8892 are important transition points along the GDE curve, representing its peak and valley, respectively. At the end of the study period, only 12.13% of the studied cities experienced a higher quality facilitation effect. Streamlining the relationship between central and local finance is urgently needed for the widespread implementation of greening. The stock and supply of scientific and technological personnel play crucial roles in shaping the impact of the central-local fiscal relationship on green modernization. Specifically, VFI has an inverted U-shaped nonlinear impact on the level of scientific and technological human resources (S&TL). The inflection point occurs at VFI = 0.2710, which is close to the point of GDE. Furthermore, heterogeneity tests indicate that the institutional dividend of VFI is more pronounced in economically developed regions, eastern coastal areas, and regions with a more developed industrial structure. The study provides valuable insights for the government to promote green development. However, the lack of indicators and specific samples, as well as the reliance on limited assumptions, constrains the ability of this study to draw meaningful research conclusions. These limitations highlight the necessity for further related research in the future.

The race between global economic growth and carbon emissions: based on a comparative study of developed and developing countries

In recent years, there has been a persistent intensification of the global greenhouse effect. Balancing carbon emission reduction with economic growth poses an unprecedented global challenge. To better comprehend the relationship between economic growth and carbon emissions, this study first utilized the Tapio decoupling index to compare the decoupling relationship (the USA, Japan, and Germany) and three developing countries (China, India, and Russia) from 2000-2020. Additionally, the logarithmic mean Divisia index (LMDI) method was employed to investigate the factors influencing changes in carbon emissions. Our findings indicate that (1) the USA and Germany basically achieved strong decoupling; China, India, and Russia mainly showed weak decoupling; and Japan showed recessive decoupling. (2) Economic growth predominantly contributed to increased carbon emissions, with a lesser impact from population growth. A significant reduction in energy intensity restrained carbon emissions growth, as did energy structure replacement in most countries, excluding Japan. Based on this, a decoupling effort index was formulated. It has shown that the decoupling efforts made by developing countries are weaker than those of developed countries, primarily attributed to a lesser degree of decoupling between energy intensity and structure. This paper offers valuable insights for developing countries undergoing a low-carbon economic transformation. They should counterbalance carbon emission escalation resulting from economic growth through technological and energy structure improvements.

An interregional environmental assessment framework: revisiting environmental Kuznets curve in China

Achieving the national emission reduction targets requires joint efforts of all jurisdictions, whose sustainable development is affected by complex economic and environmental interactions among regions. An interregional environmental assessment (IREA) framework is constructed for China using multiregional input-output techniques to unravel the carbon emission connections behind interregional economic activities. Then, consumption-based emission accounting is applied in sustainability assessment, in comparison with production-based environmental Kuznets curve (EKC) tests to examine the role of regional connections in shaping EKC. Empirical results expose significant asymmetric CO2 transfer among regions in China, where the Central and Western regions have become CO2 haven for the Eastern region. EKC is valid at the national level and manifests marked regional differences between production- and consumption-based curves. The pollution haven effect alters the EKC curve by expediting the emission peak in the developed Eastern region while delaying it in the developing Western region. Thus, revisiting EKCs in the IREA framework reveals that ignoring interregional connections would lead to misleading results. Only when both production- and consumption-based EKCs transcend their turning points can we claim that environmental governance has ushered in a new era of sustainable development.

Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990-2019)

Reducing environmental pollution is a critical goal in global environmental economics and economic development. The European Union (EU) faces environmental challenges due to its development activities. Here we present a comprehensive approach to assess the impact of carbon dioxide (CO2) emissions, energy consumption (EC), population structure (POP), economy (GDP), and policies on the environment within the EU using the environmental Kuznets curve (EKC). Our research reveals that between 1990 and 2019, the EU-27 experienced an increase of +1.18 million tonnes of oil equivalent (Mtoe) per year in energy consumption (p < 0.05), while CO2 emissions decreased by 24.25 million tonnes (Mt) per year (p < 0.05). The highest reduction in CO2 emissions occurred in Germany (-7.52 Mt CO2 annually), and the lowest in Latvia (-0.087 Mt CO2 annually). The empirical EKC analysis shows an inverted-U shaped relationship between GDP and CO2 emissions in the EU-27. Specifically, a 1% increase in GDP results in a 0.705% increase in carbon emission, while a 1% increase in GDP2 leads to a 0.062% reduction in environmental pollution in the long run (p < 0.01). These findings indicate that economic development within the EU has reached a stage where economic growth positively impacts the environment. Overall, this study provides insights into the effectiveness of environmental policies in mitigating degradation and promoting green growth in the EU 27 countries.

Energy, technology, and economic growth in Saudi Arabia: An ARDL and VECM analysis approach

This paper investigates the effects in short and long run of renewable and non-renewable energy, technological advancement, population, foreign direct investment, energy export, energy price, and carbon dioxide emissions on economic growth in Saudi Arabia as one of the largest oil producing and richest countries in the world and as a leading country in investing in modern technology, during 1990-2022 by using the Autoregressive Distributed Lag(ARDL) approach and the Vector Error Correction Model (VECM) Granger causality technique. In first step, the ADF and DF-GSL tests are used to identify the order of integration of variables. In the second step, the Bounds test and the Wald test are used respectively to verify the existence of long run cointegration relationships and the long run relationships between variables. In the third step, we have applied the ARDL approach to capture the effect of each variable on Saudi economic growth in long term. Finally, the VECM technique was used to detect the direction of causality running from variable to another. It is appearing that all variables are stationary in first difference, and there are a long run cointegration and relationships among variables. The results of ARDL estimation show that non-renewable energy, renewable energy, population, foreign direct investment, energy export, and energy price positively affect the Saudi economic growth. While technological advancement and carbon dioxide emissions have negative effects on the economic increase of Saudi Arabia. These two results appear important and useful because of their consequences. In effect, it could damage its worldwide standing and dishearten foreign investment, stopping economic diversification efforts and increasing the income inequality. Though, the results of VECM technique show four bidirectional causal relationships between economic growth and non-renewable energy, foreign direct investment, energy export, and energy price. The findings of this study have several policy implications for Saudi Arabia. First, Saudi government should continue investing in the energy sector. Second, to attract more FDI, Saudi government should continue its efforts to reduce bureaucracy, simplify regulations, and provide a business-friendly environment. This strategy can help transfer technology and knowledge. Third, the government should monitor and control energy prices, as these can significantly impact economic growth. The government should invest in technological advancement, as this can help reduce carbon dioxide emissions and improve energy efficiency; also, investing in human capital is essential for long-term economic growth. Policies that promote the health, education, and general well-being of the population can lead to a more productive and innovative workforce. However, the article reveals that technological advancements have a negative impact on economic growth in Saudi Arabia. This could be due to a number of factors, such as a lack of skilled workers to implement new technologies or a mismatch between the skills of the workforce and the needs of the economy. As solutions, Saudi government must invest in education and training can help address these challenges by developing a workforce capable of adapting to the changing needs of the economy and effectively using new technologies. Also, it's important to create science and technology parks to foster innovation and collaboration between businesses and universities. By taking these steps, the Saudi government can help create more diverse and knowledge-based economy, making it less dependent on oil and gas exports and more resilient to economic shocks.

The impact of carbon trading on the "quantity" and "quality" of green technology innovation: A dynamic QCA analysis based on carbon trading pilot areas

To study the multi-factor linkage effect of carbon trading on green technology innovation, this paper employs the dynamic QCA analysis method and uses panel data from China's carbon trading pilot areas. The aim is to explore the causal path considering the time effect. Additionally, the Kruskal-Wallis rank sum test is applied to investigate the provincial coverage difference of the configuration and reveal the variation in configuration preferences between regions from a spatial dimension. The results indicate that a single factor alone does not constitute the necessary conditions for the "quantity" and "quality" of high-green technology innovation. However, the necessity of carbon trading price exhibits a declining trend over the years, demonstrating the presence of a time effect. Regarding the sufficiency analysis of conditional configuration, it mainly includes a "price-market scale" dual effect model and a single market scale effect model, with three configuration paths for each model. Among them, the "price-market scale" dual effect model can drive the increase in the quantity of green technology innovation through carbon trading price, market scale, government intervention degree, and other factors. The single market scale effect model can promote the high-quality development of green technology innovation, but the impact of carbon trading price on the quality of green technology innovation is relatively insignificant. In terms of the time dimension, the three configurations still maintain good applicability to green technology innovation under normal conditions. However, when considering the spatial dimension, the coverage distribution of the three configurations exhibits evident regional differences. This study introduces the dynamic panel QCA method into the research field for the first time. It addresses the limitations of the traditional QCA method, which is constrained by cross-section data and lacks the ability to explore the linkage effect between factors over time. Additionally, the study analyzes the effects of carbon trading price and market size on the "quantity" and "quality" of green technology innovation, considering both time and space dimensions, from a configuration perspective.

The validity of the environmental Kuznets curve in the presence of long-run civil wars: A case of Afghanistan

The war in Afghanistan left significantly negative consequences in all spheres of its society, leading the country to the highest levels of poverty, hunger, and environmental damage. This study explores the long-run impact of civil wars on environmental degradation in Afghanistan using the conceptual framework of the Environmental Kuznets Curve and models augmented with pollutants, civil wars, comprehensive financial development index, and macroeconomic predictors on a set of data from the first quarter of 2002 to the first quarter of 2020. However, while the results confirm long-run relationships amid indicators by the autoregressive distributed lags bound test, the results of the vector error-correcting model to Granger causality reveal bidirectional causality links between CO2 emissions, per capita real GDP, civil wars, the financial development index, energy consumption, trade openness, and the inflation rate in the long-run, while the findings extend to confirm multidimensionality and interdependencies among predictors in the short-run. Moreover, the results indicate dual findings. First, it confirms that civil wars, the financial development index, per capita real gross domestic product, population growth, and the inflation rate significantly increase CO2 emissions, while the squared per capita real gross domestic product, energy consumption, and trade openness reduce CO2 emissions both in the short and long runs. Second, the results confirm an inverted U-shaped relationship, supporting the validity of the Environmental Kuznets Curve hypothesis in Afghanistan. Based on the findings, appropriate policy measures are recommended.

Revisiting the environmental Kuznets curve: assessing the impact of climate policy uncertainty in the Belt and Road Initiative

Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this research is to develop strategies to grow the economy without harming the environment. Therefore, we revisit the environmental Kuznets curve (EKC) hypothesis, considering the impact of climate policy uncertainty along with other control variables. We investigated yearly panel data from 47 Belt and Road Initiative (BRI) nations from 1998 to 2021. Pooled regression, fixed effect, and the generalized method of moment (GMM) findings all confirmed the presence of inverted U-shaped EKC in BRI counties. Findings from this paper provide policymakers with actionable ideas, outlining a framework for bringing trade and climate agendas into harmony in BRI countries. The best way to promote economic growth and reduce carbon dioxide emissions is to push for trade and climate policies to be coordinated. Moreover, improving institutional quality is essential for strong environmental governance, as it facilitates the adoption of environmentally friendly industrialization techniques and the efficient administration of climate policy uncertainties.

Revisiting the Environmental Kuznets Curve (EKC) Hypothesis of Carbon Emissions: Exploring the Impact of Geopolitical Risks, Natural Resource Rents, Corrupt Governance, and Energy Intensity

The global imperative to mitigate carbon emissions for sustainable development has spurred extensive research into economic, social, and energy-related factors. However, prior studies present a complex landscape, yielding mixed conclusions regarding the influence of geopolitical risk, natural resource rents, corrupt governance, and energy intensity. To untangle this ambiguity, we construct a research model grounded in the Environmental Kuznets Curve, employing panel data from 38 countries spanning 2002 to 2020. Employing panel quantile regression models, we directly assess the impact of identified factors. Our findings affirm the alignment between economic growth and carbon emissions, supporting the Environmental Kuznets Curve hypothesis. Notably, increased geopolitical risk and energy intensity correlate with heightened carbon emissions over time, while corruption governance and natural resource rents exhibit a mitigating effect. Additionally, our study explores the indirect impact of these factors using a panel threshold regression model. Results indicate a diminishing influence of economic growth on carbon emissions. Intriguingly, natural resource rents initially curtail, then amplify the connection between economic growth and carbon emissions. Conversely, rising energy intensity magnifies the relationship between economic expansion and carbon emissions.

The existence of environmental Kuznets curve: Critical look and future implications for environmental management

Against the backdrop of the great challenge of climate change and growing global environmental concerns, this study deals a systematic literature review of research related to Environmental Kuznets Curve (EKC) from 1991 to 2023, details the background, definition, significance, critiques, theoretical foundations and model specifications of EKC, and summarizes the data, variables, econometric methods and findings used in over 100 EKC studies. This study focuses on EKC studies that examine the relationship between energy consumption, economic growth and environmental degradation, with most of the studies reviewed using global pollutants (carbon emissions) to measure the level of environmental degradation. This study found that EKC still has great research potential, and with the development of energy diversification, energy consumption in EKC studies have been further subdivided into renewable or non-renewable energy consumption; innovative EKC studies in the last few years have favoured the use of novel environmental and economic indicators and econometric method, and have validated the existence of EKC at the sectoral level rather than the national level. Finally, the present study summarizes the development and innovations of EKC and provides suggestions for future research aimed at advancing the development of EKC and environmental management.

How does China's big data policy affect the digital economy of cities? Evidence from national big data comprehensive pilot zones

Based on the panel data of 280 cities in China from 2011 to 2019, this paper examines the effects of big data policies on the digital economy development by using the national big data comprehensive pilot zones as a quasi-natural experiment. The findings of this paper are as follows. First, the big data policy significantly promotes China's digital economy development. Second, cities at a higher administrative level receive higher policy benefits than cities at a lower administrative level. Third, technological innovation and human capital are important ways for the big data policy to improve the development of the digital economy. The conclusions of this paper not only help to assess the effectiveness of big data policies and ensure the efficacy of policy implementation, but also provide policy support for local governments to fully explore the value of data elements and grasp the new opportunities for the digital economy development with the help of the pilot zones.

Pathways for regions to achieve carbon emission peak: New insights from the four economic growth poles in China

Regional synergy is critical to achieving High Quality Development (HQD) and reducing emissions in China. Economic growth poles (EGPS), namely Beijing-Tianjin-Hebei, the Yangtze River Delta, Guangdong-Hong Kong-Macao, and Cheng-Yu, are typical examples of regional synergy in China. It is critical to explore whether the pulling power of the EGPS to other regions can accelerate China's carbon peaking. First, this study applies the Miller-Round model to measure the spillover effects of the EGPS and selects the radiation-driven areas. Second, based on the environmental Kuznets curve hypothesis, a panel smoothing transformation model is applied to explore the relationship between regional HQD and carbon emissions. Finally, under different scenarios, the inter-regional spillover effect is used to explore the path to achieving the carbon emissions peak. The results show an inverted U-shaped relationship between carbon emissions and HQD. Additionally, with the spillover pull of the EGPS, the peak carbon emission time of all provinces is earlier by 1-6 years in different scenarios, and it can promote Ningxia, Qinghai, Gansu, Guizhou to achieve a carbon peak by 2030. However, the pulling effects of Shanxi, Shaanxi, Jilin, and Guangxi require further improvement. Therefore, the policy implications of increasing inter-regional production efficiency, improving innovation levels, and using renewable energy are proposed to improve the level of HQD, thus achieving a carbon peak. Moreover, improving the industrial linkage between the EGPS and other regions would also be effective. The industrial structure promotes the cultivation of the EGPS in Cheng-Yu and strengthens regional integration in the western region.

Study on the spatial and temporal correlation and allometric growth mechanism between population aging and carbon emissions in China

Population aging and carbon emissions are critical issues for China's development. As an enormous complex system, the population and the carbon emission development process have non-negligible differences in time, space, and speed. Therefore, this paper first demonstrates the spatial and temporal correlation between population aging and carbon emissions from 1995 to 2020, then uses the allometric growth analysis model to make a cross-sectional temporal comparison and a vertical spatial comparison of the relationship and development rate of the two, and finally uses the ridge regression model to determine the forces and interaction mechanisms of the factors influencing the relationship between population aging and carbon emissions at allometric rates. The results show that (1) China has a long-term positive temporal correlation effect relationship between population aging and carbon emissions from 1995 to 2020, and the overall correlation is high. The spatial correlation intensity between population aging and carbon emissions varies significantly across Chinese provinces, with a general spatial distribution trend of high in the south, low in the north, and prominent in the center. (2) China's population aging and carbon emissions mainly show a negative allometric growth type of relationship, i.e., a strong trend of population aging expansion and a strengthening trend of carbon emission system shrinking. The number of provinces with negative allometric growth is gradually increasing, mainly in North, East, Central, and Southwest China. (3) From 1995-2010 period to the 2011-2020 period, the influence of the factors of the population, production, and economic dimensions on the population aging index and the carbon emission allometric scalar index gradually weakened, and the influence of the consumption and technology dimensions increased significantly. The factors on the population and consumption side of the dimension mainly contribute to the expansion of carbon emissions and drive positive allometric growth. The production side, the economic structure, and technology dimension factors drive negative allometric growth. The paper fully explores the bidirectional correlation, differential development trend, and interaction mechanism between the two systems of population and carbon emissions and effectively compensates for the lack of research content in terms of elemental correlation, spatial and temporal connection, and speed synergy.

Impact of income inequality on carbon emissions: a matter of corruption governance

Corruption is often linked with income inequality and its impact on carbon emissions. This study investigates the moderating effect of corruption governance on the relationship between income inequality and carbon emissions. Panel data for 62 countries from 2012 to 2020 were used. We employed a threshold panel regression approach, considering income inequality as the explanatory variable and carbon dioxide emissions as the dependent variable, with corruption governance as the threshold variable. Our findings suggest that enhancing the level of corruption governance can mitigate the CO2 emissions driven by income inequality. Specifically, we found a shift in the impact on CO2 emissions when corruption governance crosses a certain threshold. This study provides insights into how improving corruption governance can help in managing the environmental effects of income inequality.

Global value chains participation and trade-embodied net carbon exports in group of seven and emerging seven countries

A vast literature has examined the empirical link between gross exports and total carbon emissions for different country groups. However, countries' increasing participation in global value chains (GVCs) challenges this traditional approach since the gross measures neglect trade-embodied carbon emissions and intermediates-driven value-added trade. Therefore, this study scrutinizes how backward participation (foreign contents in domestic exports) and forward participation (domestic contents in foreign exports) in GVCs affect per capita net exports of trade-embodied carbon dioxide emissions. The study adopts input-output accounting and value-added decomposition framework for Group of Seven (G7) and Emerging Seven (E7) countries over the 1995-2018 period. (i) Pre-estimation analyses reveal that the net carbon importer G7 group had a comparative advantage in high-tech exports and a lower export product concentration level, while the net carbon exporter E7 group had a comparative advantage in resource-intensive exports and a higher export product concentration level, albeit significant within-group heterogeneities. (ii) The augmented mean group estimates reveal that increasing backward participation raises net carbon exports for both G7 and E7. The forward participation-net carbon exports nexus is negative for G7 but positive for E7. (iii) While economic growth reduces net carbon exports in both groups, the effects of comparative advantages in resource-intensive and high-tech exports differ. Practitioners should be aware of the GVCs-driven carbon circle when assessing decarbonization performances and obligations of countries.

Does artificial intelligence (AI) reduce ecological footprint? The role of globalization

This article explores the impact of artificial intelligence (AI) on global ecological footprints, which has important implications for global sustainability in the digital age. Using the comprehensive evaluation index of AI constructed by the entropy method and the dataset at the global national level, we find that from 2010 to 2019, the overall level of global AI shows an upward trend, in which the growth rate of AI in developed countries is more pronounced and exhibits a stable growth trend, while the growth rate of AI in developing countries displays a trend of instability. The research results show that AI has a significant inhibitory effect on ecological footprints. This conclusion holds even after endogeneity and robustness tests. In addition, under the effect of globalization, the impact of AI on ecological footprints shows nonlinear characteristics. As globalization deepens, the marginal effect of AI in reducing the ecological footprint shows an increasing trend. These findings emphasize the important role of AI in environmental governance and provide a new and comprehensive perspective for policymakers. Therefore, the government should continue to support the research and application of AI, promote the cross-industry integration of AI, and play a positive role in the process of globalization to promote global sustainable development.

Multiscale levels CO2 decouple reinforcement in China

Decoupling economic growth from CO2 emissions is imperative for China. Meanwhile, establishing a consistent and comprehensive decoupling inventory that includes national (N), regional and provincial (RP), and city and county (CC) levels is essential for further policy formulation. This research aims to investigate the decoupling status using the "N-RP-CC" approach while considering changes in decoupling trends at the different levels. A combination of the Tapio decoupling model and cluster analysis is employed to study the decoupling's spatiotemporal characteristics and trends. The study first calculates the decoupling value for "national, 7; regions, 30; provinces, 1501 CCs" in China, 2006-2017. The results show that there continues to be an improvement in the decoupling trend at the national level. Conversely, the regional scale exhibits a more vulnerable decoupling trend compared to the national level, with weak and extended negative decoupling observed in northeastern and northern China. Moreover, provincial heterogeneities are increasingly evident, with poor decoupling statuses appearing in Jilin, Heilongjiang, Liaoning, and Xinjiang, as well as many central provinces. Additionally, although more than half of CCs exhibit weak decoupling during most years, seven different states of decoupling were also identified during the time frame. These findings further indicate that spatiotemporal heterogeneities extend beyond RP scales within CCs. Taking the Yangtze River as a boundary line reveals a severe situation in northern areas along with rapid development trends observed in southern regions. Finally, we clustered 1414 CCs based on their industrial proportions for 2017 which further highlights increasingly prominent heterogeneities that should be carefully considered. Based on these findings, policy recommendations such as spatial organization and optimization and technique investment are proposed to achieve CO2 emission decoupling under the N-RP-CC levels.

Prolonged war reverses carbon emissions from an early decline to a late increase - Evidence from Syria

Given that war can have a serious impact on the climate, this article is aimed to discuss the impact of warfare on carbon emissions by examining changes in CO2 before and during the war in Syria based on the kaya constant equation and the LMDI decomposition method. In the decade before the war, population was the largest contributor, making up 32.64% of the total 51.02% increase in carbon emissions. The only factor that offsetting carbon emissions was energy intensity, making a 22.30% curbing effect. In the early stage of the war, carbon emissions decreased by 56.38%, in which per capita GDP contributed 37.55% of the total CO2 decline. Carbon intensive of energy was the only factor promoting the carbon increase with a 4.67% contribution. In the late war, carbon emissions start to resume slow increase with energy intensity and economy turning negative to positive. It can be speculated that the impact of the war on CO2 emissions: (i) in the first years of the war, CO2 would drop significantly at the cost of significant population decline and economic recession, the least desirable and the worst way to reduce carbon emissions. (ii) if evolves into a prolonged war, it would reverse carbon emissions from decline to increase, although the population and the economy are both falling. This research, therefore contends that once war is triggered, there is no other solution to prevent this worst-case scenario of Population Decline - Economic Recession - Increased Carbon Emissions from happening, unless the war is stopped immediately.

A clean road to international trade: Environmental regulations and the cleanliness of export enterprises

This study explores the impact of environmental regulations on the clean product exports of firms and the mechanism of this impact from the dual perspectives of the extensive and intensive margins. It uses matched micro data and aims to explore the clean road to international trade. According to the benchmark regression, environmental regulations improve the export intensity of clean products. However, their impact on the export probability of clean products is not significant. That is, environmental regulations promote the intensive margin of enterprise clean product exports but do not promote the extensive margin of enterprise clean product exports. The mechanism analysis shows that the cost effect is an important way for environmental regulations to promote the cleanliness of export products. However, the channel of technological innovation is not verified. In addition, compared with command-and-control and voluntary public policy tools, market-inspired environmental regulations have a stronger promoting effect on the cleanliness of export enterprises. This study provides microevidence and a policy basis for improving the environmental policy system and for the sustainable development of international trade.

The nexus between labour force participation and environmental sustainability: Global comparative evidence

The pursuit of environmental sustainability and decent employment are among the fundamental macroeconomic priorities of the 21st century. Extant studies reveal that labour market dynamics have a bearing on global greenhouse gas (GHG) emissions. Thus, this study empirically examines the effect of labour force participation on environmental sustainability from a global perspective. Employing the Driscoll-Kraay fixed effects (DKFE) and system Generalised Method of Moments (GMM) estimators for a panel of 173 countries from 1996 to 2020, we find that labour force participation (LFP) enhances environmental quality. When controlled for income differences, the study reveals that while LFP significantly reduces environmental pollution in Low-income and High-income countries, it is environment-degrading in Upper-middle-income countries. Furthermore, with regard to level of development and geographical region, rising LFP significantly reduces GHG emissions in developing countries, whereas the effect is insignificant in developed economies. Likewise, the effect of LFP is divergent across geographical regions. However, when LFP is disaggregated into the male and female components, the results show that male-LFP is environment degrading while female-LFP is environmental augmenting. Contingent on these findings, practical policy implications are discussed.

Decoupling analysis between economic growth and aluminum cycle: From the perspective of aluminum use and carbon emissions

Increasing aluminum demand under the clean energy and low-carbon transformation background increases the fuzziness of relationships between economic growth and aluminum use or aluminum related carbon emissions. To figure this out, this paper established an aluminum use and carbon emissions integrated decoupling model within the framework of anthropogenic aluminum cycle. A material flow analysis (MFA) during 2000-2020 for China's aluminum cycle was firstly conducted to quantify both aluminum flow and carbon emissions in each aluminum life-cycle process. Then, this paper evaluated and decomposed the decoupling index of aluminum use-economy and carbon emission-economy via the LMDI decomposition model. Results show that: (1) secondary aluminum has not become effective supplement for primary aluminum in China; (2) the expansive negative decoupling state was the most prevalent state. The decoupling effects of carbon emission were better than that of aluminum use; (3) technology improvement was an important impactor to decoupling process but didn't offset the growth in aluminum consumption or carbon emissions at most of the time. The government and industry organizers should implement active countermeasures to stimulate aluminum companies developing technology to improve aluminum use efficiency and reduce carbon emissions.

Analyzing the carbon emission effect and systematic emission reduction mechanism of the Sino-USA manufacturing trade

This study analyzed the theoretical mechanism of the carbon emission effect embodied in the Sino-USA manufacturing trade. We constructed a trade and carbon emission input-output model for 16 manufacturing sub-sectors in China and the USA from 2000 to 2018. A comprehensive empirical test of the systematic emission reduction mechanism was conducted. There were four main findings: (1) indirect and direct carbon emissions indicators can comprehensively analyze the link between production and demand across sectors; (2) indirect carbon emissions are higher than direct carbon emissions in half of the sectors in both China and the USA, and other sectors bear part of the carbon emissions for these sectors; (3) compared to 2000, the change in net exports of both countries is the main reason for the change in indirect carbon emissions, while the change in net exports of intermediate goods is the main reason for the change in direct carbon emissions; and (4) the Sino-USA trade surplus comes at the expense of China's environmental losses, while the USA obtains environmental benefits. Overall, the theoretical analytical framework not only comprehensively considers the interlinkages between production and demand across sectors but also provides a more reasonable evaluation of the environmental effects of Sino-USA trade. Additionally, this study provides a solid theoretical and empirical basis for China to achieve its dual-cycle and dual-carbon goals, thus promoting the rapid transformation of China's economy toward green and high-quality development.

Is fiscal deficit 'curse' or 'haven' for environmental quality in India? Empirical investigation employing battery of distinct ARDL approaches

Undoubtedly, throughout the past half-century, environmental quality has emerged as a significant obstacle to both economic and social endeavors. Recent local and international policy debates have focused on environmental deterioration and global warming, but how governments balance economic growth and environmental sustainability is still enigmatic. For this reason, we have examined the determinants of environmental quality in India from 1972 to 2021. More specifically, we have investigated whether the fiscal deficit is 'curse' or 'haven' for environmental quality (CO2) in India. Moreover, this study deliberated four other predictors, comprising technological development (TIN), fossil fuel consumption (FFC), urbanization (Ub), and human capital index (HCI). In order to attain this objective, a range of econometric estimation techniques are employed to ensure the validity and reliability of the outcomes. For instance, we have employed a battery of ARDL approaches, such as standard ARDL, nonlinear ARDL, and multiple threshold NARDL approaches. In light of our research findings, we will be focusing directly on the examination of the NARDL and MTNARDL outcomes. This is due to the empirical evidence indicating the existence of asymmetric effects resulting from FD on CO2 emissions in India. The NARDL approach reveals that the consequence of fiscal deterioration is more pronounced, and the influence of fiscal progress is mild in terms of CO2 emission growth. Further, the outcomes of the MTNARDL approach revealed that the size of the extremely low changes in FD is much higher than the extremely high changes in FD in both models. This implies that as the FD rises, CO2 ascends more significantly, and when the FD lowers, CO2 declines progressively. In a nutshell, FD has a long-run positive and asymmetric impact on CO2 in India; thus, we may conclude that FD is considered the 'curse' for CO2 in India. Furthermore, TIN, HCI, and Ub have detrimental effects on CO2, whereas FFC stimulates CO2 in India. This research work provides some important policy implications for environmentalists, economists and macroeconomic policymakers to promote a green and healthy environment.

Evaluation of carbon emission efficiency based on urban scaling law: take 308 cities in China as an example

The development of urban low carbonization is an important foundation for achieving the goal of "dual carbon." The differences in spatial differences based on scientific methods revealing the characteristics of urban carbon emissions and its efficiency are of great significance for shaping the green low-carbon land space pattern. This article uses urban standard models and scale adjustments to the urban index (SAMIs) identification in 2006-2017 308 Chinese urban carbon discharge standards and efficiency time and space evolution pattern and their characteristics. The results of the study show that there is a stable secondary relationship between carbon emissions and urban population scale, which means that the growth rate of urban carbon emissions is significantly lagging behind the growth of urban systems. The analysis of urban carbon emission efficiency based on SAMI values shows significant spatial heterogeneity: the carbon emission efficiency of cities in North China, East China, and Northeast China is lower than that of other regions. In terms of temporal changes, there was a phenomenon of SAMI high-value cities moving north and low-value cities moving south between 2006 and 2017, and △SAMIs showed a pattern of hot in the west and cold in the east. This study broadens the applicability of urban scaling law models in urban environmental research, providing a new perspective for evaluating urban carbon emission efficiency and China's energy conservation and emission reduction work.

Does environmental quality react asymmetrically to unemployment and inflation rates? African OPEC countries' perspective

Environmental degradation, inflation, and unemployment are unquestionably among the current global issues. However, there has not been an in-depth investigation of how unemployment and inflation rates affect environmental quality, particularly when considering the asymmetric scenario in oil-producing countries. This gap in the literature motivated this study to investigate how the environment (proxied by CO2 emissions) reacts to asymmetric shocks in inflation and unemployment rates using the panel NARDL model methodology. This study also examines whether the environmental Phillips curve (EPC) hypothesis holds true in the context of African OPEC countries over the period 1990 to 2019. The study presents three interesting findings. First, CO2 emissions are adversely associated with unemployment and inflation rates, meaning that protecting a healthy environment would have to come at the expense of two undesirable outcomes: losing employment and a decline in purchasing power. Second, the asymmetry analysis demonstrates that both negative unemployment and positive inflation shocks have a larger effect on CO2 emissions than the opposite scenario. Finally, long-term evidence exists to support the presence of the EPC in these countries.

Research on the spatial spillover effect of China's carbon emission trading on total-factor carbon emission efficiency of the power industry

Improving the carbon emission efficiency in the power industry is a crucial step to achieve China's "double carbon" goal. Carbon emission trading (CET) is an important tool for carbon emission reduction in the power industry. This paper collects data on the power industry of 30 provinces in China from 2005 to 2020 and applies an undesirable super-efficiency epsilon-based measure (EBM) model to measure the total-factor carbon emission efficiency of the power industry (CEEP). A spatial difference-in-differences (SDID) model is constructed to analyze the spatial spillover effects of China's CET on CEEP. Then, a spatial mediating effect model is employed to explore the influence mechanism of CET. The results show that (1) during the sample period, CEEP shows a trend of fluctuating growth, and the overall level of CEEP is still relatively low; (2) CET has a significant promotion effect on CEEP, resulting in an average increase of 6.02% in the efficiency value of the pilot areas; (3) the spatial spillover effect test proves that CET not only improves the CEEP in the pilot areas, but also promotes the improvement of CEEP in the adjacent areas; and (4) the influence mechanism test shows that CET can improve CEEP by reducing energy intensity, promoting technological progress, and upgrading industrial structure. This study provides a new perspective for the measurement of CEEP and expands the research on the emission reduction effect of CET in the power industry. Finally, based on the research results, this study proposes targeted suggestions to provide reference for the government to formulate emission reduction policies.

Digital development and the improvement of urban economic resilience: Evidence from China

Digital technologies are empowering economic and social development, which attracts scholars' attention to the relationship between digitalization and economic resilience, However, the empirical analysis for different countries and stages of development are inconsistent, and the influencing mechanism need to be further explored. Using panel data for 284 prefecture-level cities in China from 2007 to 2020, this study examines the impact of urban digital development on economic resilience. The findings are as follows: (1) The increased digitalization significantly enhances the urban economic resilience, and this effect was more pronounced in eastern regions and large-scale cities. (2) The relationship between digitalization and economic resilience follows an inverted U-shape as population density increases. (3) The spatial effects show that increased digitalization has a significant positive effect on local economic resilience, but weakens the resilience of the surrounding areas. (4) The analysis of mechanism reveals that the positive impact of digitalization on the urban economic resilience is mainly achieved by improving the quality of the regional labor force and total factor productivity. The study provides theoretical and empirical evidence for accelerating the digital construction, fully releasing the digital dividend in order to strengthen economic resilience.

Financial inclusion and environmental pollution in Türkiye: Fresh evidence from load capacity curve using AARDL method

Sustainability is an important concept for the whole world. Generally, in order to measure the sustainability of the environment, carbon dioxide emissions and ecological footprint indicators are used. However, these variables do not reflect the supply side of natural resources. Therefore, load capacity factor is an important environmental indicator for a sustainability. Environmental assessment based on the load capacity factor is more meaningful. Besides, improved access to financial services can contribute to environmental sustainability. The effect of financial inclusion on the load capacity factor in Türkiye has not been examined in the current literature. In this context, this study analyzes the impact of financial inclusion, hydropower energy consumption, and life expectancy at birth on environmental sustainability from a different perspective by focusing on load capacity factor. To this end, this study used the newly developed Augmented ARDL method to determine the cointegration relationship between the series and measure the values of the long-term coefficients. Based on the Augmented ARDL method, there is a cointegration relationship between the series. In the long run, hydropower energy consumption reduces pollution, while financial inclusion decreases load capacity factor. The effect of life expectancy at birth on pollution is not significant. Moreover, the results reveal that the load capacity curve hypothesis is valid in Türkiye. As a result, the Turkish government should promote renewable energy sources, especially hydropower energy consumption, align financial services with pollution reduction measures, and contribute to the creation of an environmentally conscious society.

Exploring the potential of the belt and road initiative as a gateway for renewable energy in diverse economies

The Belt and Road Initiative (BRI), spearheaded by China, is anticipated to bolster trade, investment, and economic growth among participating countries to advance the United Nations' Sustainable Development Goal 7 through international trade. Within this context, renewable energy has emerged as a promising avenue to address environmental degradation and foster sustainable development. However, the impact of BRI's trade volume on renewable energy development and adoption in these nations remains unresolved. To address this, our study examines the influence of trade openness, foreign direct investment, economic growth, and oil prices on renewable energy consumption in 94 BRI countries with varying income levels from 2000 to 2019. Employing panel data analysis, including fixed effects (FE), random effects (RE), and the system generalised method of moments (GMM), we present findings across income groups: i) Trade openness exhibits a positive effect on renewable energy consumption in high-income and upper-middle-income countries; ii) In contrast, it diminishes renewable energy consumption in lower-middle-income countries; iii) Trade openness demonstrates insignificant effects on renewable energy consumption in low-income countries; iv) On the panel level, trade openness significantly and positively impacts renewable energy consumption. Our research underscores the significance of trade openness as a crucial instrument for advancing renewable energy development in high-income BRI countries, thereby fostering environmental sustainability. Policy interventions targeting renewable energy hold promise for enhancing environmental quality in low-income countries.

The impact of environmentally related taxes and productive capacities on climate change: Insights from european economic area countries

In a world increasingly threatened by climate change and its associated risks, there's an urgent need to actively seek solutions for environmental protection and sustainable economic development. Central to this effort is understanding the role of environmental taxes and productive capacities in shaping environmental outcomes. Focusing on countries within the European Economic Area (EEA), this research uses advanced second-generation econometric techniques to examine this relationship. The use of cross-sectional autoregressive distributive lag (CS-ARDL) and dynamic common correlated effects (DCCE) models allows for a robust examination of panel data and provides reliable results. The results reveal an inverted U-shaped relationship, or Environmental Kuznets Curve (EKC), between GDP growth and environmental degradation in the EEA economies. Furthermore, while our data reveal a significant negative correlation between environmental taxes and CO2 emissions, we find that productive capacities have a more significant impact on reducing these emissions. These findings call for further research into the effectiveness of policies to support productive capacities in achieving environmental protection goals in the EEA.

Investigating the fishing grounds load capacity curve in G7 nations: Evaluating the influence of human capital and renewable energy use

This paper introduces a novel approach to explore marine ecosystem by examining the Fishing Grounds Load Capacity Curve (FGLCC) hypothesis. The study investigates the impact of income on the marine condition in G7 nations from 1970 to 2019 by exploring the U-shaped relationship between income and the Fishing Grounds Load Capacity Factor (FGLCF). This research diverges from previous studies focused solely on the demand side through fishing footprints, as it considers the neglected aspect of the marine supply side. Furthermore, it explores the influence of renewable energies and human capital as indicators inversely related to non-renewable energy use and population on the marine condition. The findings reveal the U-shaped FGLCC hypothesis. Additionally, the results demonstrate that renewable energies and human capital have a positive impact on the marine ecosystem. These outcomes provide valuable insights for decision-makers, enabling them to identify key variables that contribute to the preservation of marine diversity.

Spatial differences, dynamic evolution, and convergence of carbon productivity in China

China is currently developing a green economy, and improving carbon productivity (CAP) is an important part of this process. The current study applied a minimum distance to strong efficient frontier (MinDS) model to measure China's CAP. The Dagum Gini coefficient and kernel density estimation methods were further used to reveal its spatial differences and dynamic evolution, while the coefficient of variation and spatial convergence models were employed to examine its convergence characteristics. The results showed significant spatial differences in China's CAP, with primarily high and low spatial distribution characteristics in the east and west, respectively. Between-regional differences were the main sources of the overall differences. Moreover, the differences between overall, eastern, central, and western regions of China all exhibited a widening trend. Although none showed σ convergence, all had significant absolute β spatial convergence and conditional β spatial convergence characteristics. Collectively, the findings of this study objectively reflect the real level, distribution characteristics, and spatial convergence characteristics of CAP in China as a whole and in each region, while also providing a reference basis for achieving peak carbon neutrality.

Evolution and driving factors of ocean carbon emission efficiency: A novel perspective on regional differences

Existing studies on carbon emission efficiency seldom discuss ocean carbon emission efficiency, and few studies on ocean carbon emission efficiency hardly discuss its regional differences. To fill this research gap, this paper innovatively measures and evaluates the ocean carbon emission efficiency of 11 Chinese coastal provinces from 2001 to 2019 using the super-efficiency SBM-GML model, and empirically analyzes the dynamic link between ocean carbon emission efficiency, trade openness and financial development by constructing a PVAR model based on an endogeneity perspective. Meanwhile, another major innovation of this study is to divide China's 11 coastal provinces into two coastal areas, north and south, with the Huaihe River as the boundary, in order to investigate the regional heterogeneity of ocean carbon emission efficiency and its influencing factors. The results show that (i) China's average ocean carbon emission efficiency has improved significantly, which is mainly due to the driving effect of technological progress. (ii) China's ocean carbon emission efficiency generally presents a spatial pattern that is higher in the south and lower in the north. Technological progress is the main source of the improvement in ocean carbon emission efficiency in the two regions. (iii) Significant regional heterogeneity exists in the impact of trade openness and financial development on ocean carbon emission efficiency, that is, trade openness and financial development both promote and hinder ocean carbon emission efficiency in the southern region than in the northern region. Finally, targeted policy recommendations are proposed.

Does green investment reduce carbon emissions? New evidence from partially linear functional-coefficient models

Green investment (GI) has great potential to reach China's 'double carbon' target. However, it is still unknown what factors will govern the impact of GI on carbon emissions. This research relaxes the homogeneity and linearity assumed in traditional empirical models and adopts a newly developed partially linear functional-coefficient model to estimate the specific response function of GI on carbon emissions under regional heterogeneity. The results indicate that the role of GI plays a relatively greater role in the western and central regions than in the eastern regions. This highlights the latecomer advantage of the central and western regions under the 'double carbon' target. The beneficial effect of GI on carbon emission intensity is only apparent once the province's economic development level exceeds a certain threshold. For provinces with low GDP per capita, it is recommended to prioritize economic development. When the logarithm of the province's GDP per capita is higher than 9.70, we encourage strong GI. As the industrial structure continues to upgrade, the marginal effect of GI on carbon emissions will continue to increase after a key inflection point.

Exploring the nexus of green finance and renewable energy consumption: unraveling synergistic effects and spatial spillovers

As China transitions towards a green and low-carbon energy system, it is crucial to have the support of green finance. In this study, we explore the effects of synergy and spatial spillovers in the development of green finance and the consumption of renewable energy. By taking a synergistic perspective, we aim to provide new insights for energy structure reform. We use a spatial simultaneous equations model in combination with a three-stage generalized spatial least squares approach, our findings are the following: firstly, there is a positive synergy between the development of green finance and the consumption of renewable energy. Secondly, there are positive spatial spillovers in the development of green finance and the consumption of renewable energy, but the regional interaction effects of green finance development on renewable energy consumption are negative. Furthermore, we observe that the impact of renewable energy consumption on green finance development has been increasing since 2013. However, the reverse relationship is not true, indicating that the renewable energy industry has stabilized and is gaining appeal in financial markets. Our study highlights that the development of green finance can promote an increase in renewable energy consumption through the facilitation of economic growth, green technology innovation, and the upgrading of the industrial structure. We emphasize the importance of regional and industrial coordination to create synergy between green finance development and renewable energy consumption.

Interaction patterns between economic growth and atmospheric environment in China under the "carbon neutrality" target

Clarifying the interaction patterns between economic growth and atmospheric environment (EG-AE) in China is important to achieve the "carbon neutrality" target. A conceptual framework of air pollutant emission in urban economic growth (APEUEG) was proposed to explore the interaction patterns in China from 2007 to 2017. The empirical analysis revealed that a N-shaped EKC exists between aerosol optical depth (AOD) and gross domestic product (GDP), with inflection points of $5000 and $27,000, respectively. Therefore, we speculated that when GDP per capita of a city exceeded $5000, the AOD gradually decreased. However, when GDP per capita of a city gained over $27,000, the economic growth and the atmospheric environment would be coordinated steadily. The interaction of EG-AE experienced three stages-pollution, improvement, and coordination-in China. Spatially, the interaction patterns of EG-AE presented five clusters, which were associated with the spatial distribution of city levels. China's prefecture-level cities have undergone the cluster of low AOD-low GDP (LL), the cluster of high AOD-high GDP (HH), and the cluster of low AOD-high GDP (LH), as urban level improves. By 2017, about 44% of Chinese cities had not completed the coordinated development yet. We found that policymakers should formulate differentiated urban greener economic development policies to reduce APEUEG.

In-depth analysis of the effect of decomposed growth on the environment: A global perspective

The traditional Environmental Kuznets Curve (EKC) theory, which establishes a relationship between economic growth and a select number of pollutants, does not fully capture the broad and nuanced impacts on environmental qualityThis research examines the implications of decomposed economic growth by considering the separate contributions of scale, composition, and technique effects on environmental health and ecosystem vitality. The research spans 121 countries from 2001-2019, using robust statistical methods, including Driscoll-Kraay standard error, fully modified ordinary least squares, and panel quantile estimation techniques. The study reveals complex relationships that depend on countries' income levels. A predominantly positive and non-linear relationship between the scale effect and environmental health is observed for the full sample of countries and for low-income countries. The scale effect also shows a non-linear and predominantly positive relationship with ecosystem vitality in lower-middle-income, upper-middle-income, and high-income countries. The association between the composition effect and environmental health is inverted U-shaped in lower-middle-income countries, while it is mostly negative and non-linear in low-income and high-income countries. For ecosystem vitality, the composition effect shows a negative, non-linear relationship in all sampled countries, but a positive, non-linear relationship in higher-income countries. The relationship between the technology effect and environmental health is largely positive and non-linear in all sampled countries, lower-middle-income countries, upper-middle-income countries, and higher-income countries. However, the relationship is negative in lower-middle-income countries. These results have important policy implications. Governments are encouraged to adopt renewable, sustainable, and low-carbon technologies to address the scale effect. In addition, the formulation and enforcement of stringent environmental regulations for polluting industries is crucial, given the significant impact of the composition effect.

Analysis of spatial correlation networks of carbon emissions in emerging economies

Studies have shown that energy consumption from economic development leads to an increase in carbon emissions. Emerging economies, as important sources of carbon emissions with high growth potential, play a crucial role in global decarbonisation efforts. However, the spatial pattern and evolution trend of carbon emissions in emerging economies have not been studied in depth. Therefore, this paper uses the improved gravitational model and carbon emission data from 2000 to 2018 to construct a spatial correlation network of carbon emissions in 30 emerging economies around the world, aiming to reveal the spatial characteristics and influencing factors of carbon emissions at the national level. The results show that the spatial network structure of carbon emissions in emerging economies is closely linked, forming a "big network" of interconnection. Amongst them, Argentina, Brazil, Russia, Estonia, etc. are at the centre of the network and play a leading role. Geographical distance, economic development level, population density, and scientific and technological level have a significant impact on the formation of spatial correlation between carbon emissions. Further use of GeoDetector shows that the explanatory power of two-factor interaction on centrality is greater than that of a single factor, indicating that a single economic development cannot well enhance the influence of countries in the carbon emission network, and needs to be combined with factors such as industrial structure and scientific and technological level. These results are helpful to understand the correlation between carbon emissions between countries from the perspective of the whole and part and provide a reference for optimizing the carbon emission network structure in the future.

Decomposition and decoupling analysis of multi-sector CO2 emissions based on LMDI and Tapio models: Case study of Henan Province, China

The peak carbon dioxide emissions at the provincial level is the foundation for achieving the national target of carbon emission peak, thus it is important to analyze the characteristics of provincial CO2 emissions. However, there is a lack of comprehensive analysis and research on quantifying the contributions of the driving factors to decoupling at the provincial level. Therefore, taking Henan Province as the research object, this study establishes the decoupling effort model by combining the traditional LMDI model and Tapio model based on compiling the CO2 emission inventories from 2006 to 2019. The results showed that total CO2 emissions increased from 2006 to 2011, and decreased after 2011 in Henan Province. Raw coal was the primary fuel source of Henan's CO2 emissions, and the sector of "power and heat production" was the major industrial source, accounting for above 45% of the total emissions. Economic output and energy intensity were the major factors promoting and restraining the increase in Henan's CO2 emissions, respectively. In terms of the decoupling state, Henan achieved the transformation from weak decoupling to strong decoupling from 2006 to 2019. Industry presented a strong decoupling condition, while weak decoupling was detected in the agriculture sector during the study period. The changing trend of energy intensity decoupling effort was consistent with that of total decoupling effort, indicating that energy intensity is a crucial factor in achieving decoupling. This study is helpful to grasp the CO2 emission characteristics of Henan Province and provide the theoretical basis for formulating emission mitigation measures of peak carbon dioxide emissions in Henan and other provinces.