Spatiotemporal differentiation of carbon emission efficiency and influencing factors: From the perspective of 136 countries

Towards sustainable development goals: An analysis of environmental efficiency and the impacts of self-purification capacity across diverse income levels

The increasing urgency of global environmental degradation, particularly across diverse economic development stages, underscores a critical need for nuanced understanding and targeted strategies to achieve Sustainable Development Goals. Our study examines environmental efficiency trends over 27 years in 163 countries, utilizing greenhouse gases and particulate matter 2.5 as indicators. We address the challenge by developing and applying a two-stage method that combines a hyperbolic distance function with a stochastic meta frontier approach to assess environmental meta-efficiency. The average meta efficiency of these countries is 0.464, which remains at a relatively low level. Our model indicates that the high-income country group needs to reduce greenhouse gas and pollutant emissions by 25% and increase non-fossil energy usage by 33% to improve environmental efficiency. This suggests these countries must transition towards more sustainable energy sources and practices. Moreover, recognizing that existing income grouping inadequately characterizes each country, we use k-means cluster analysis for regrouping, more accurately reflecting individual differences. The regrouping results show that some high-income countries are classified into inactive groups, implying serious environmental problems. Our findings advocate for collaborative and tailored strategies to address these disparities. We conclude that income levels cannot solely drive environmental efficiency but must also consider geographical and climatic factors, which are pivotal in shaping a country's environmental policies and efforts. This approach offers a clearer understanding of current inefficiencies and sets the stage for more informed policy-making that can better address the specific needs and capabilities of different countries.

Assessing and gauging the carbon emission efficiency in China in accordance with the sustainable development goals

In light of the growing urgency of climate change, carbon emissions reduction has emerged as a pivotal concern within global governance. In this paper, we take carbon emission efficiency (CEE) as the research object to characterize the relationship between economic, social, and environmental development in the context of the Sustainable Development Goals (SDGs). According to the regional division standard of eight comprehensive economic zones in China, this paper analyzed the spatial differences, evolutionary characteristics, and influencing factors of CEE in 257 Chinese cities over the period 2003-2019. The analysis conducted the Dagum Gini Coefficient, Markov Transition Probability Matrix, and geographically and temporally weighted regression model (GTWR). The results demonstrate that: (1) The CEE of Chinese cities exhibits an upward trajectory. (2) The inter-differences among the eight comprehensive economic zones represent the primary spatial source of CEE divergence. (3) The CEE of Chinese cities is a staged process of gradual enhancement with spatial spillover effects. (4) Environmental regulation, energy consumption intensity, and green finances are significant factors affecting CEE, and the direction and intensity of their influence have distinct spatial heterogeneity. Ultimately, this paper proposes measures to narrow the development gap between regions and enhance the CEE across the region. Meanwhile, implementing regional refinement management and formulating differentiated regional sustainable development planning.

A machine learning algorithm to explore the drivers of carbon emissions in Chinese cities

As the world's largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China's carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.

Greenhouse gas emissions and reduction strategies for the world's largest greenhouse gas emitters

In the context of climate change, it is crucial to examine the contributions of leading countries in greenhouse gas (GHG) emissions. This research provides an overview of global GHG emissions from 1970 to 2022 for the world's most polluting countries: the United States, China, India, Russia, Brazil, Indonesia, Japan, Iran, Mexico, and Saudi Arabia. These countries collectively account for approximately 64% of GHG emissions. The aim is to understand the impact of various economic sectors, such as industry, energy, agriculture, and transportation, on overall emissions. The analysis highlights the disparity in per capita emissions, with smaller but major oil-producing countries in the Persian Gulf, such as Qatar and the United Arab Emirates, exhibiting high per capita emission levels, while more populated countries like the United States and South Korea show lower per capita values but significant total emission volumes. The study suggests that transitioning to renewable energy, improving energy efficiency in industry, promoting sustainable agriculture, reforestation, and electrifying transportation are key methods to achieve United Nations Sustainable Development Goals (UN SDG). Recommendations include encouraging technological innovations, implementing stringent government regulations and standards, and garnering active support for GHG reduction programs from governments, financial institutions, and the business community. The urgency is emphasized for global efforts to combat climate change for ensuring a sustainable future.

Spatial characteristics and dynamic differences of power industry's low carbon transition efficiency

The power industry's low carbon transition is pivotal for achieving carbon reduction and sustainable development. This study uses the super epsilon-based measurement (Super-EBM) model and the Malmquist index to evaluate the power industry's low carbon transition efficiency using data from 30 provinces in China from 2010 to 2020, and utilizes the Tobit model to comprehensively analyze the factors affecting the low carbon transition of power industry. In addition, this paper examines the spatial differences in the power industry's low carbon transition efficiency as well as its distributional characteristics and dynamic evolutionary patterns. Conclusion is drawn as follows this paper analyzes the regional differences, spatial distribution characteristics and dynamic evolutionary trends of the power industry's low carbon transition. The main conclusions are as follows: (1) The power industry's low carbon transition efficiency in China shows an uptrend, with the western China region having the highest overall level of efficiency, greater fluctuations in the central China region, and more stability in the eastern China region, technological progress is a central factor in increasing total factor productivity, the efficiency of the power industry's low carbon transition is positively influenced by the electricity prices, and negatively influenced by the energy structure, environmental regulations and economic structure; (2) the Intraregional differences and hypervariable density are the main reasons sources of the overall differences in the efficiency of the power industry's low carbon transition; Intraregional differences in the eastern, central, and western China regions are decreasing year by year, but the efficiency of the power industry's low carbon transition in the western China region is still distributed in a multipolar way; (3) The dynamic evolutionary trends of the efficiency distribution of the low carbon transition in power industry is influenced by the type of spatial lag in the neighboring area. Where areas with low efficiency makes it difficult to achieve short-term leapfrog development, and areas with a cluster of high-efficiency provinces are prone to "Siphon Effect". The findings provide a theoretical basis for promoting the efficiency of the power industry's low carbon transition and coordinating the strategic adjustment of economic and environmental green development.

Chinese FDI outflows and host country environment

This study provides first evidence on the effects of Chinese FDI (Foreign Direct Investment) outflows on host country environments. The study draws upon a comprehensive dataset covering aggregate Chinese FDI outflows and sector specific data into 65 host nations over the 2007-2019 period. Employing a STIRPAT (Stochastic Regression on Population, Affluence and Technology) model and several different techniques including DID (Difference-in-Difference), pooled OLS (Ordinary Least Squares), quantile regression, IV (Instrumental Variable) estimation, threshold and Tobit regression, the findings suggest that Chinese FDI leads to an increase in host country CO2 (Carbon Dioxide) emissions, aligning with the pollution haven hypothesis at the aggregate level. A closer investigation at the development regime and sectoral levels indicates that in the low development regime, FDI inflows into the financial and real estate sector increase emissions. Conversely in the high-income regime, Chinese FDI into the entertainment sector is associated with an increase in carbon emissions. Chinese FDI is further found to lead to an increase in emissions in countries with a per capita GDP (Gross Domestic Product) of below USD72041.7. However, as per capita income rises above USD72041.7, FDI leads to a fall in carbon emissions.

Will structural adjustment and financial support affect low-carbon agricultural production in the Yellow River Basin?

Based on the panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this manuscript measures the agricultural low-carbon production efficiency scientifically through the Super-SBM model. In addition, the deviation degree of agricultural industry is used as the index of structural adjustment. Finally, the spatial Durbin model is used to analyze the effect direction and degree of structural adjustment, financial support, and their synergistic effect on agricultural low-carbon production efficiency. The results show that ① the agricultural low-carbon production efficiency in the Yellow River Basin shows a trend of fluctuating downward and a spatial distribution pattern of "high in the east and low in the west". ② Structural adjustment in local region and adjacent areas has a significantly negative impact on agricultural low-carbon production, and the inhibitory effect in adjacent areas is more obvious, and the negative spatial spillover effect is strong. Financial support has a significantly positive impact on agricultural low-carbon production, but the spatial spillover effect of adjacent areas is not obvious. ③ By region, structural adjustment has a significantly negative impact on low-carbon agricultural production in the midstream and downstream regions, while financial support has a significantly positive impact on low-carbon agricultural production in the upstream region. The impact of control variables on agricultural low-carbon production varies from region to region. ④ The synergistic effect of structural adjustment and financial support in the whole and midstream region shows a significantly positive impact on agricultural low-carbon production, indicating that financial support has a certain correction effect on structural adjustment. The coefficient between the upstream and downstream regions is positive but not significant. The conclusions have important reference significance for promoting the ecological protection and high-quality development and agricultural low-carbon development in the Yellow River Basin.

Synergizing economic growth and carbon emission reduction in China: A path to coupling the MFLP and PLUS models for optimizing the territorial spatial functional pattern

Carbon emissions caused by economic growth are the main cause of global warming, but controlling economic growth to reduce carbon emissions does not meet China's conditions. Therefore, how to synergize economic growth and carbon emission reduction is not only a sustainable development issue for China, but also significant for mitigating global warming. The territorial spatial functional pattern (TSFP) is the spatial carrier for coordinating economic development and carbon emissions, but how to establish the TSFP of synergizing economic growth and carbon emission reduction remains unresolved. We propose a decision framework for optimizing TSFP coupled with the multi-objective fuzzy linear programming and the patch-generating land use simulation model, to provide a new path to synergize economic growth and carbon emission reduction in China. To confirm the reliability, we took Qionglai City as the demonstration. The results found a significant spatiotemporal coupling between TSFP and the synergistic states between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through the path of TSFP optimization. The urban space of Qionglai City in 2025 and 2030 obtained by the decision framework was 6497.57 hm2 and 6628.72 hm2 respectively, distributed in the central and eastern regions; the rural space was 60,132.92 hm2 and 56,084.97 hm2, concentrated in the east, with a few located in the west; and the ecological space was 71,072.52 hm2 and 74,998.31 hm2, mainly located in the western and southeastern areas. Compared with the TSFP in 2020, the carbon emission intensity of the TSFP obtained by the decision framework was reduced by 0.7 and 4.7 tons/million yuan, respectively, and realized the synergy between economic growth and carbon emission reduction (decoupling index was 0.25 and 0.21). Further confirming that TSFP optimization is an effective way to synergize economic growth and carbon emission reduction, which can provide policy implications for coordinating economic growth and carbon emissions for China and even similar developing countries.

A significance of smart city pilot policies in China for enhancing carbon emission efficiency in construction

The Chinese government seeks to promote economic growth and sustainable development while achieving carbon neutrality by establishing phased smart city pilots. Therefore, it is important to study whether smart city pilots can promote carbon emission efficiency (CEE). This paper constructs a multi-period difference-in-difference (DID) model based on panel data from 241 prefecture-level cities in China from 2007 to 2019, aiming to investigate the mechanism of the impact of smart city pilot policies (SCPP) on CEE and whether there is a rebound effect. The study found that smart city construction (SCC) significantly improves carbon efficiency, with pilot cities increasing their CEE by 1.4% compared to non-pilot cities. The conclusions remain robust under a variety of scenarios including the introduction of placebo tests, counterfactual tests, sample data screening, and omitted variable tests. The results of the mechanism test show that although the rebound effect can inhibit the improvement of CEE, the environment can be improved and the CEE can be enhanced through green technology innovation, industrial structure upgrading, energy structure optimization, environmental regulation effect, information technology support, and resource allocation effect. The heterogeneity results indicate that the SCPP is more effective in promoting CEE in cities in the eastern region, southern cities, environmentally friendly cities, large cities, and medium-sized cities. This study contributes to the existing literature in clarifying the environmental benefits of SCPP and provides valuable policy insights for cities to address climate change and sustainable development.

Research on the spatiotemporal evolution and influencing factors of urbanization and carbon emission efficiency coupling coordination: From the perspective of global countries

Strategic coordination between urbanization and carbon emission efficiency (CEE) is vital for promoting low-carbon urbanization and sustainable urban planning. In order to assess the coupled coordination degree (CCD) of urbanization and CEE and investigate the factors influencing the CCD, this research employs the Super slacks-based measure (SBM) model, the coupled coordination degree model (CCDM), and the Tobit model. Four key findings emerge from the analysis of the temporal and spatial evolution traits of the CCD based on data from 106 nations worldwide between 2005 and 2020. (1) The global CEE shows a significant downward trend, and the spatial disparity is unambiguous. high CEE countries hang in the north and west of Europe, while those in Asia, Africa and the east of Europe have lower CEE. (2) The combined urbanization level and demographic, economic and social urbanization are all on an upward trend. Singapore has the greatest degree of urbanization overall globally. (3) The CCD of urbanization and CEE shows a fluctuating upward trend, with particularly strong changes in 2018-2020. 2017 and 2018 are the years with better global coupling coordination status. During the study period, the CCD results of countries are mostly uncoordinated and low coordination, and the CCD of the United States, China, India and Japan is in the front. (4) The effect of urban electrification rate on the CCD is positive; the effect of foreign trade and net inflow of foreign direct investment is negative; while energy structure and industrial structure have no significant effect. A number of policy proposals are put forth in light of the outcomes of the research to enhance the coordination.

Decomposition of the site-level energy consumption and carbon dioxide emissions of the iron and steel industry

Identifying the key factors influencing energy consumption and CO2 emissions is necessary for developing effective energy conservation and emission mitigation policies. Previous studies have focused mainly on decomposing changes in energy consumption and CO2 emissions at the national, regional, or sectoral levels, while the perspective of site-level decomposition has been neglected. To narrow this gap in research, a site-level decomposition of energy- and carbon-intensive iron and steel sites is discussed. In this work, the logarithmic mean Divisia index (LMDI) method is used to decompose the changes in the energy consumption and CO2 emissions of iron and steel sites. The results show that the production scale significantly contributes to the increase in both energy consumption and CO2 emissions, with cumulative contributions of 229.63 and 255.36%, respectively. Energy recovery and credit emissions are two key factors decreasing site-level energy consumption and CO2 emissions, with cumulative contributions to the changes in energy consumption and CO2 emissions of -158.30 and -160.45%, respectively. A decrease in energy, flux, and carbon-containing material consumption per ton of steel promotes direct emission reduction, and purchased electricity savings greatly contribute to indirect emission reduction. In addition, site products and byproducts promote an increase in credit emissions and ultimately inhibit an increase in the total CO2 emissions of iron and steel sites.

Impact of digitization on carbon productivity: an empirical analysis of 136 countries

Enhancing carbon productivity (CP) is key to achieving carbon reduction goals while maintaining economic growth. Digital technology plays a significant role in improving CP. Based on panel data from 136 countries worldwide from 2000 to 2020, this study empirically examines the impact of digitalization on CP and its mechanisms using fixed-effects and mediation models. The conclusions are as follows: (1) Overall, digitalization significantly enhances CP. (2) In terms of the mechanism, digitalization primarily improves CP through technological innovation and mitigating income inequality. (3) In terms of the quantile regression results, as the quantile level of CP increases, the promoting effect of digitalization on CP gradually strengthens. (4) From the perspective of heterogeneity among regions, income levels and human capital levels, digitalization has the greatest promotion effect on carbon productivity in European countries, high-income countries and high human capital countries. This study provides a reference for policymakers worldwide to use digital technology in achieving carbon emission reduction targets.

Environmental regulations and agricultural carbon emissions efficiency: Evidence from rural China

Reducing carbon emissions while maintaining simultaneous economic growth has been the focus of agricultural and environmental management research in recent times. To examine the influence of agricultural environmental regulations and related factors on agricultural carbon emissions efficiency, the entropy method was utilized to weigh each index and develop an index system for evaluating agricultural environmental regulations. This study utilizes the Super Slacked-Based Measure model that takes into account undesirable outputs. The research data used spans the years 2010-2019 and covers 31 provinces in China to calculate the efficiency of agricultural carbon emissions. A spatial Durbin model was employed to investigate the influence of environmental regulations and other influential factors on the efficiency of agricultural carbon emissions. The efficiency levels in the eastern region of China have consistently exceeded the national average, whereas the central region has demonstrated the lowest efficiency levels across the nation. Both the efficiency of agricultural carbon emissions and the intensity of agri-environmental regulations measured in this paper are strongly spatially autocorrelated between provinces. The environmental regulations index on local agricultural carbon emissions efficiency is significantly positive, while the effect on the agricultural carbon emissions efficiency in adjacent areas is not significant. Overall, agricultural environmental regulations effectively enhance agricultural carbon emissions efficiency, which in turn promotes technological innovation and economic growth. At the same time, local governments should actively adopt targeted strategies based on the actual situation of different regions in terms of their resource endowments and differences in the production characteristics of different crops.

Evaluation and spatial convergence of carbon emission reduction efficiency in China's power industry: Based on a three-stage DEA model with game cross-efficiency

Reducing carbon emissions is essential for achieving sustainable development in China. In this study, we developed a framework for measuring carbon emission reduction considering both thermal power and clean energy generation perspectives. Subsequently, we constructed a three-stage data envelopment analysis (DEA) model with game cross-efficiency to eliminate the influence of external environmental factors, random factors, and regional competition. Thereafter, we calculated the carbon emission reduction efficiencies (CEREs) of the power industries of 30 provinces in China from 2010 to 2020. Based on this, we conducted temporal and spatial analyses of the carbon emission reduction amount (CERA), evaluated CERE, compared different DEA models, and assessed spatial convergence effects. The evaluation results of CERE across 30 provinces, cities, and autonomous regions in China showed that: (1) although China's CERA increased, substantial regional differences exist in the carbon emission reduction structure. (2) The overall average CERE ranged from 0.485 to 0.737. Since 2016, the highest CERE values have been observed in southwest China, followed by the eastern coastal and central regions, while the northwestern region experienced notable fluctuations. (3) The three-stage DEA model with game cross-efficiency, which eliminates the influence of competition and external environmental factors, can be used to accurately measure CERE. (4) CERE has a spatial convergence effect on China's power industry and is promoted by energy structure, upgrading of industrial structure and government interventions. These findings provide important insights for optimizing the carbon emissions reduction structure and improving CERE.

Impact of artificial intelligence on carbon emission efficiency: evidence from China

Artificial intelligence (AI) has been extensively used as a revolutionary and versatile technology in various fields. However, scholars have not given substantial consideration to the impact of AI on the environment, particularly carbon emission efficiency (CEE). This study adopts the global super-efficiency slacks-based model to evaluate CEE of 30 provinces in China from 2006 to 2019. Thereafter, the current study investigates the impact mechanism of AI on CEE using the stochastic impact of population, affluence, and technology (STIRPAT) model. The empirical analysis provides the following valuable research findings. First, AI, represented by industrial robots, can significantly improve CEE. Second, AI can enhance CEE by promoting technological innovation and upgrading industrial structures. Lastly, the relationship between AI and CEE is influenced by marketization and government intervention.

Spatiotemporal characteristics and influencing factors of carbon emissions from land-use change in Shaanxi Province, China

Due to global warming, there evolves a global consensus and urgent need on carbon emission mitigations, especially in developing countries. We investigated the spatiotemporal characteristics of carbon emissions induced by land use change in Shaanxi at the city level, from 2000 to 2020, by combining direct and indirect emission calculation methods with correction coefficients. In addition, we evaluated the impact of 10 different factors through the geodetector model and their spatial heterogeneity with the geographic weighted regression (GWR) model. Our results showed that the carbon emissions and carbon intensity of Shaanxi had increased overall in the study period but with a decreased growth rate during each 5-year period: 2000-2005, 2005-2010, 2010-2015, and 2015-2020. In terms of carbon emissions, the conversion of croplands into built-up land contributed the most. The spatial distribution of carbon emissions in Shaanxi was ranked as follows: Central Shaanxi > Northern Shaanxi > Southern Shaanxi. Local spatial agglomeration was reflected in the cold spots around Xi'an, and hot spots around Yulin. With respect to the principal driving factors, the gross domestic product (GDP) was the dominant factor affecting most of the carbon emissions induced by land cover and land use change in Shaanxi, and socioeconomic factors generally had a greater influence than natural factors. Socioeconomic variables also showed evident spatial heterogeneity in carbon emissions. The results of this study may aid in the formulation of land use policy that is based on reducing carbon emissions in developing areas of China, as well as contribute to transitioning into a "low-carbon" economy.

Spatiotemporal differences in and influencing effects of per-capita carbon emissions in China based on population-related factors

Intensive human activities and resource consumption in China have led to increasing carbon emissions, placing enormous pressure on achieving sustainable development goals. Nonetheless, the effects of population-related factors and carbon emissions remain controversial. This study focuses on the spatiotemporal differences in and influencing effects of per-capita carbon emissions using 2010-2019 panel data covering 30 regions in China. Differing from previous studies, population-related factors are employed to classify the 30 regions into 4 classes, and kernel density estimation, σ convergence and spatial econometric models are used to analyse the spatiotemporal differences in and influencing effects of per-capita carbon emissions. The results demonstrate that overall per-capita carbon emissions rose, but there was heterogeneity in the change in per-capita carbon emissions in the 4 classes of regions. The difference in regional per-capita carbon emissions has been widening, but the change rate of the difference stabilized. Overall, per-capita carbon emissions are heavily affected by household size; however, the driving forces behind per-capita carbon emissions in the 4 classes of regions vary. These results suggest that precise and coordinated governance of carbon emissions and reverting to the traditional household structure should be considered to meet the dual carbon goal.

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.