Influence mechanism of industrial agglomeration on carbon emission intensity-a perspective on borrowing performance

Under the background of urban connectivity, whether there are similarities and differences in the impacts of local industrial agglomeration and inter-city industrial agglomeration borrowing performance on carbon emission intensity(CI), and how cities can fully utilize the external force-borrowing performance to reduce local CI, these issues are of great significance for the cost saving and efficiency enhancement in the process of carbon emission (CE) reduction. Based on panel data of 280 prefecture-level cities in China from 2003 to 2020, the panel dual fixed-effect model, instrumental variable method, and adjustment effect model were used to analyze the impacts of the manufacturing agglomeration (MA), producer service agglomeration (PA), and the collaborative agglomeration (CA) on the CI from the perspective of individual cities and the urban system. The results showed that the influence of MA on CI presents a significant inverted U-shaped relationship, PA significantly reduces CI, and the CA of the two industries increases CI. Further analysis showed that the borrowing MA performance improves CI, especially in newer industrial-based cities, non-resource-based cities, and medium and big cities; the borrowing PA performance reduces CI, especially in old industrial-based cities, non-resource-based cities, and large cities; and the borrowing CA performance has no significant effect on CI. In addition, the development of the Internet strengthens the influence of borrowing performance in MA and PA on CI.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Can carbon emission trading improve carbon emission performance? Evidence from a quasi-natural experiment in China

Carbon emission trading policies play a key role in reducing carbon emissions through market-based mechanisms. In the context of China's carbon neutrality goals and carbon peaking targets, it is important to predict and evaluate the effectiveness of such policies. The combined impact of carbon trading policies on carbon emission reduction and economic output has not been well investigated in previous studies. In this study, the impact of carbon emission trading policies on regional carbon emission performance was assessed through mechanism analysis and empirical tests. The mechanism analysis showed that carbon emission intensity reduction relied on three mediating effects: technological innovation incentives, industrial structure optimization, and energy substitution. For the empirical test, the multi-time difference-in-differences (DID) method was adopted to study the impact using panel data from 30 provinces in China between 2005 and 2019. Moreover, the specific impact mechanism was further tested using mediating effects. The results showed that China's carbon trading policy has significantly affected the carbon emission performance of the pilot regions, and factors such as GDP per capita, urbanization level, and capital-labor ratio have notably contributed to the reduction of carbon emission intensity. The proportions of the three mediating effects in the total effect were estimated to be 60.98%, 23.17%, and 10.14%, respectively. This study provides an empirical approach to the study of the impact of carbon trading policy on carbon emission reduction and economic output and can serve as a reference for addressing climate change and alleviating conflicts between the environment and economic growth in similar regions.

Does digital economy curb carbon intensity? New insights from China

China is in the period of vigorously developing the "digital economy" and "low-carbon economy," facing the double pressure of realizing the "dual-carbon" target and maintaining stable economic growth. This paper tests the role of the digital economy (DIEC) in the process of carbon emission reduction for the advancement of low carbon economy based on this issue from the perspective of carbon intensity (CI) by constructing a fixed effects and mediation effects model using data from 30 areas from 2011 to 2021. The study results show that at the national level, the advancement of DIEC significantly inhibits CI, and the conclusion still holds after various robustness tests. From the geographic region level, the suppression of CI by digital economic advancement has the most substantial impact in the central region. Although the eastern and western areas have similar results, the significance level is higher in the east region. When considering the economic development level, the impact of DIEC on CI is more significant in areas with lower economic development than those with higher economic growth. In analyzing the path of the DIEC affecting CI, it is found that the DIEC mainly inhibits CI by promoting technological advancement and reducing energy consumption intensity.

Dynamic evolutionary characteristics and influence mechanisms of carbon emission intensity in counties of the Yangtze River Delta, China

Clarifying the intrinsic mechanism of county carbon emission intensity (CEI) is essential for guiding the realization of a low-carbon economy as well as for the strategic goals of carbon peaking and carbon neutrality. However, at present, scholars mostly focus on provincial and city scales, with the identification of influencing factors and spatial effect mechanisms of CEI rarely included in the analysis framework. Herein, with the help of three spatial weight matrices, the spatial autocorrelation, the "F + S" influence factor identification method, and the spatial panel econometric model were used to analyze the evolutionary paths and influencing factors of CEI for 209 counties in the Yangtze River Delta (YRD) from 2007 to 2020. The results show that (1) the CEI of the YRD decreased from 1.998t/104 RMB to 0.858t/104 RMB. Furthermore, the spatial pattern was low in the southeast and high in the northwest, with high-value areas concentrated in municipal districts and resource-based counties. (2) Moran's I spatial autocorrelation index indicated significant spatial clustering of county CEI. (3) Financial science and technology expenditure, industrial structure, share of urban built-up land, and the urban-rural income gap affected the change in CEI and its spatial effect, whereas total imports and exports had a significant negative effect on local CEI. Therefore, to achieve China's "double carbon" goal, it is necessary to consider the five development concepts as the core, strengthen inter-county exchanges and collaboration, as well as promote collaborative management of the ecological environment.

How does government efficiency affect carbon emission intensity? A comprehensive empirical study

This study utilized panel data from 132 countries spanning from 1996 to 2019 to examine the effect of government efficiency on carbon emission intensity. Using a fixed effect model, the study found that stronger government efficiency is associated with a significant decrease in carbon emission intensity. Robustness tests were performed, the results of which consistently supported the main findings. Additionally, the study investigated the mechanisms underlying the linkage between government efficiency and carbon emission intensity, revealing that improved government efficiency can inhibit carbon emission intensity by fostering environmental innovation and promoting renewable energy consumption. Finally, the study examined the moderating effects of national income level, economic freedom, democracy, and ruling party ideology on the nexus of government efficiency and carbon emission intensity, and found empirical evidence supporting these moderating effects. These results provide new insights for governments seeking to reduce carbon emission intensity.

Regional differences, dynamic evolution, and spatial spillover effects of carbon emission intensity in urban agglomerations

Taking three major urban agglomerations in China as examples, this paper uses the Dagum Gini coefficient and its decomposition method, a Kernel density estimation method, and Markov chain and spatial Markov chain to study the regional differences, dynamic evolution characteristics, and spatial spillover effects of carbon emission intensity (CEI) of urban agglomerations, and accordingly, it proposes differentiated emission reduction and carbon reduction policies. The following results were obtained: (1) The overall CEI of the three major urban agglomerations and each individual urban agglomeration were found to have declined significantly over time, with an overall spatial pattern of "high in the north and low in the south," with inter-group differences being the main source of the overall differences. (2) The imbalance in CEI between cities was more obvious within the Beijing-Tianjin-Hebei (BTH) urban agglomeration, while the synergistic emission reduction effect of the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations increased over the study period. (3) The probability of a city maintaining a stable level of CEI was much higher than the probability of a state shift, and there was a spatial spillover effect of carbon emissions between neighboring cities. This study can provide theoretical support for the global response to greenhouse gas emissions, promoting green development and carbon reduction in various countries and urban agglomerations and providing a quantitative basis for the formulation of relevant policies.

All roads lead to Rome? The impact of heterogeneous green finance on carbon reduction of Chinese manufacturing enterprises

Based on the system theory and Pareto efficiency theory, this paper, based on the data of listed companies in China's A-share manufacturing industry in 2011-2022, explores the impact of market-driven green finance and government-guided green finance on the carbon emission intensity of manufacturing enterprises, and analyzes the intermediary role of debt financing cost. A negative "U" relationship exists in market-driven green finance/government-guided green finance and the carbon emission intensity of manufacturing enterprises. Further research shows that under the higher debt financing cost, market-driven green finance played a weaker carbon reduction effect. The heterogeneity analysis found that market-driven green finance can have a significant non-linear impact of "promoting growth first and weakening later" on the carbon emissions of energy-saving and environmental protection enterprises, large enterprises, and enterprises with high human capital levels. Government-guided green finance has a significant non-linear impact on non-energy-saving and environmental protection enterprises and small enterprises. This paper provides the theoretical basis and practical inspiration for the government to formulate relevant low-carbon development policies and promote the innovation of green financial tools in the financial market.

What impacts do green bonds have on carbon emissions and how? A dynamic spatial perspective in China

Green bonds are adopted to exclusively finance or refinance green projects and thus is an important policy instrument for sustainable development. The development of these eco-friendly projects benefits the reduction of carbon emission. What impacts do green bonds have on carbon emission intensity and how? This issue needs to be revealed, including the dynamic spatial interactive rules and regional heterogeneity. This is especially true for China, with its vast territory and a short history of green bonds. Different with existing literature, this paper collects the data of the amount of green bond issued in each province in China rather than the policy dummy variable of green bond. The spatio-temporal interactions of both the impact of green bonds and the related mechanisms are studied. A dynamic spatial Durbin model (DSDM) combined with a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model reveals the negative impacts of green bonds on carbon emissions and the spatio-temporal interactions. On this basis, the mediation model is introduced to clarify the three impact mechanisms of green bonds and find the predominant role of technology mechanism. In addition, different characteristics in spatial interactive rules and impact mechanisms of green bonds are found in various regions of China. Finally, the study proposes some policy recommendations on how to effectively reduce carbon emissions with green bonds.

The complex impacts of economic growth pressure on carbon emission intensity: an empirical evidence from city data in China

Excessive carbon emissions are the major challenge to global sustainable development. In the context of the coronavirus pandemic, pressure on global economic growth is gradually rising, threatening established carbon reduction targets. However, the relationship between economic growth pressures and carbon emission intensity has yet to be clearly discussed. Thus, this study quantitatively discusses the impacts of economic growth pressures from central (EGPN) and provincial (EGPP) governments on city carbon intensity. The study is based on data from China's city panels from 2005 to 2019. This study finds that (1) there is a U-shaped correlation between economic growth pressure and a city's carbon emission intensity, whether the economic growth pressure comes from the central government or the provincial government; (2) carbon emission intensity is more sensitive to economic growth pressure from the provincial government than it is to economic growth pressure from the central government. The findings of this study will help enhance the understanding of the relationship between economic growth pressure and carbon emission intensity, and can also provide a reference for global sustainable development that balances economic growth and environmental protection.

Spatiotemporal evolution of carbon emission intensity and the driving effect of green technology innovation: Evidence from China

The "double carbon" goal has proposed new "green" requirements for China's low-carbon economic development, and green technology innovation (GTI) has become an important way to coordinate economic and sustainable development. The study explores the spatial-temporal evolution of carbon emission intensity (CEI) of Chinese prefecture-level cities, analyses the nonlinear impact of GTI on the CEI by constructing a panel quantile model, and draws the following conclusions. First, CEI shows a decreasing trend from 2006 to 2019 and a spatial distribution pattern of "high in the north and low in the south, high in the west and low in the east". Second, GTI significantly reduces CEI, and as the quantile point increases, the carbon reduction effect of GTI is characterized by a U-shaped change, decreasing first and then increasing. Overall, GTI has a significantly more profound inhibiting effect on high CEI regions than on low CEI regions. Third, there is spatial heterogeneity in the impact of GTI on CEI across the four major regions and diverse policy contexts. The study proposes countermeasures for low-carbon development in terms of tapping the potential of GTI, strengthening its regional synergy, and applying locally appropriate measures, to gain the great practical significance for achieving the double carbon target.

Do industrial robots reduce carbon intensity? The role of natural resource rents and corruption control

Although the research on the impact of robotics on carbon emissions is increasing, there are still relatively few studies on the impact of robots on carbon intensity from the perspective of natural resources and corruption. In order to fill in the research gaps, panel data from 66 countries between 1993 and 2018 are collected, and linear and nonlinear panel regression approaches are developed. Natural resource rent and corruption control are used as threshold variables, robot penetration is used as explanatory variables, and carbon emission intensity is the explained variable. The results of the linear model show that robot penetration is negatively correlated with carbon emission intensity, which means that robot penetration reduces carbon emission intensity. The results of the nonlinear model show that when natural resource rents and corruption control are used as thresholds, the relationship between robot penetration and carbon emission intensity presents a U shape and an inverted U shape, respectively. Specifically, the threshold for natural resource rents is 4.7%. When the natural resource rent is lower than this threshold, the robot penetration rate reduces the carbon emission intensity, but when the natural resource rent is higher than this threshold, the robot penetration rate increases the carbon emission intensity. The threshold value of corruption control is -0.4349. When the corruption control is lower than this threshold, the robot penetration rate increases the carbon emission intensity. If the corruption control is higher than this threshold, the robot reduces the carbon emission intensity. Finally, policy recommendations for better use of robotics to reduce carbon emission intensity are put forward from the perspective of natural resource rent and corruption control.

Exploring necessary and sufficient conditions for carbon emission intensity: a comparative analysis

This research investigates the factors influencing carbon emission intensity in 94 countries during 2018 using two qualitative methods: necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA). The study covers variables related to economics, human geography, energy, and institutions, showing significant variations among them. The NCA model identifies economic complexity and fossil energy consumption as necessary conditions for high-carbon emission intensity. On the other hand, the fsQCA model reveals sufficient conditions for both high- and low-carbon emission intensity, presenting different causal combinations of variables. For high-carbon emission intensity, nine causal solutions are identified, emphasizing the roles of economic growth, urbanization, fossil energy consumption, and institutional quality. Reducing carbon emission intensity requires addressing economic complexity and reducing reliance on fossil energy consumption. Policymakers should focus on sustainable economic development, environmentally friendly urbanization, and transitioning to renewable energy sources. This research's originality lies in its qualitative approach, going beyond traditional regression methods to explore necessary and sufficient conditions for carbon emission intensity. It offers valuable insights into the complex interplay of variables, providing multiple causal configurations for both high- and low-carbon emission intensity.

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.

How does financial development influence carbon emission intensity in the OECD countries: Some insights from the information and communication technology perspective

Based on an extended STIRPAT framework, this paper investigates the effects of financial development on carbon emission intensity in OECD countries from linear and non-linear perspectives, where financial development is proxied by three dimensions: financial deepening, financial deepening, and financial size, and financial efficiency. Fortunately, three types of financial development significantly alleviate carbon emission intensity. An extended moderation effect model is built to estimate the effect of financial development via information and communication technology on carbon emission intensity. The results reveal that internet-based information and communication technology and service-based information and communication technology are positively correlated with carbon emission intensity. To effectively handle the endogeneity issue triggered by causal relationships between variables and allow potential non-linear nexus, an advanced dynamic panel threshold model incorporating the generalised method of moments is employed to investigate how financial development affects carbon emission intensity under different types of information and communication technology. Empirical evidence demonstrates the significance of the non-linear nexus between financial development and carbon emission intensity. Lastly, heterogeneity analysis demonstrates the existence of heterogeneity associated with institutional quality, degree of economic development, and resource endowment concerning the effect of financial development on carbon emission intensity among the OECD countries.

Regional differences and driving factors of carbon emission intensity in China's electricity generation sector

As an industry with immense decarbonization potential, the low-carbon transformation of the power sector is crucial to China's carbon emission (CE) reduction commitment. Based on panel data of 30 provinces in China from 2000 to 2019, this research calculates and analyzes the provincial CE intensity in electricity generation (CEIE) and its spatial distribution characteristics. Additionally, the GTWR model based on the construction explains the regional heterogeneity and dynamic development trend of each driving factor's influence on CEIE from time and space. The main results are as follows: CEIE showed a gradual downward trend in time and a spatial distribution pattern of high in the northeast and low in the southwest. The contribution of driving factors to CEIE has regional differences, and the power structure contributes most to the CEIE of the power sector, which promotes regional CE. Concurrently, most provinces with similar economic development, technological level, geographic location, or resource endowment characteristics show similar spatial and temporal trends. These detections will furnish broader insights into implementing CE reduction policies for the regional power sector.

The impact of vertical fiscal asymmetry on carbon emissions in China

Facing the double pressure of promoting economic growth and achieving the goal of "emission peak" by 2030, China must cut down the carbon emission intensity. Focusing on the typical characteristics of China's financial system arrangement, we theoretically analyze the mechanism of vertical fiscal asymmetry affecting carbon emission intensity and use a panel data from 30 Chinese provinces to conduct an empirical examination. The results show that (1) vertical fiscal asymmetry significantly increases the local carbon emission intensity. After a series of robust tests, such as replacement variables and sample data, the conclusion is still valid. (2) The analysis of regional heterogeneity shows that the influence of vertical fiscal asymmetry in carbon emission intensity is the largest in the central area of China, followed by the eastern provinces, and not evident in the western area. The rise in carbon emission intensity brought on by vertical fiscal asymmetry can be successfully reduced by the central transfer payment. The impact of vertical fiscal asymmetry on carbon emission intensity will be greatly lessened when the central transfer payment surpasses the threshold. (3) The mechanism test shows that vertical fiscal asymmetry increases the carbon emissions intensity by three paths: reducing the intensity of environmental regulation, strengthening local governments' dependence on land finance, and local government competition. The above analysis further enriches the relevant research on how China's vertical fiscal asymmetry system affects carbon emission intensity through land finance and local government competition while pointing out the role of transfer payment, and it can help to provide new ideas and empirical evidence for further improving the financial system and promoting the green development of the economy.

Nexus amongst environmental regulations, carbon emission intensity and technological innovation in China's construction industry

China's construction industry confronts with the dilemma of carbon emissions in adjusting the environmental regulations. Many studies are neglected on discovering the potential nexus amongst environmental regulations (ERs), technological innovation (TI) and CEI (CEI) and ignores the relationships amongst TI for reducing CEI. To mitigate this gap, this study bridges institutional theory to integrate the practices in the construction industry. This study applies a panel dataset on the construction industry from 30 provinces during 2004-2018 and uses it with a two-step system-generalised method of moments for analysis. The proposed method enables the prevention of the interference of the heteroscedasticity problem and improves certain analytical efficiency. The results are as a guideline for policymakers in rechecking the policies and regulations adequacy. The findings indicate that (1) the forced emission reduction effect is proven by command-and-control and market-based ERs, which can inhibit CEI; (2) voluntary ERs have an inverted U-shaped nexus with CEI; in other words, the green paradox effect shifts to the forced emission reduction effect once the intensity of voluntary ERs increases; and (3) market-based and voluntary ERs reduce CEI effectively by using TI as the mediator in construction industry.

Synergistic assessment of air pollution and carbon emissions from the economic perspective in China

The synergistic evaluation integrating air quality, human health, climate impact, and socioeconomic development is significant for green and low-carbon transition. Here, we quantified the contribution of pollutant emissions in 30 provinces (source) to PM_2.5 concentration and related premature mortality in each 20 km grid (receptor) of China in 2020 by an integrated model for the first time. Further, we established a cross-province contribution matrix of health impact intensity (HII, PM_2.5-related deaths per GDP). According to HII and CEI (carbon emission intensity, defined as CO₂ emission per GDP) levels, 30 provinces were divided into 4 regions including LL, HL, LH and HH. In order to assess the synergy in air pollution and carbon emission, we established an index system consisting of ISEC-AC (index for synergistic assessment) and its two sub index: IHI (index for HII assessment), and ICE (index for CEI assessment). Results showed that the ISEC-AC was more easily influenced by IHI as the variance of IHI was much higher than that of ICE. Influenced by various factors, e.g., economic structure, population density, pollution transport, ISEC-AC exhibited substantial spatial heterogeneity. In general, the ISEC-AC of southeast provinces was higher than that of central and western, indicating the environmental and climate impact per GDP was relatively lower in southeast China. For provinces, ISEC-AC of SH and GD were ~ 16 times higher than NX. For regions, due to low carbon emission intensity and health impact intensity, ISEC-AC of LL was the highest with 176; followed by HL (128), LH (126) and HH (77). Further, we figured out the main control problems and then put forward targeted synergetic control suggestions for air pollution and carbon emission from the perspective of energy structure, industry structure and industry layout, which can provide insights into future green and low-carbon policy making in China and other countries.

Influence of green finance on carbon emission intensity: empirical evidence from China based on spatial metrology

Based on the panel data of 30 provinces in China from 2011 to 2020, this paper empirically examines the direct effect, spatial spillover effect, and total effect of green finance on carbon emission intensity through the spatial econometric model, considering both spatial and temporal patterns. The results show the following: (1) The carbon emission intensity of each province in China shows a noticeable spatial spillover effect and a positive spatial correlation distribution of "high-high" and "low-low" agglomeration. (2) The development of green finance in China is interrelated but uneven in space, which presents a gradient strengthening trend from the west to the east. (3) Green finance development will curb the intensity of carbon emissions, and this effect has gradually been increasing over time and differs by region. Specifically, green finance will increase the carbon emission intensity of adjacent areas in the short term but will significantly reduce the local province's carbon emission intensity to a larger extent. Finally, it puts forward policy recommendations to promote the coordinated development of green finance and a low-carbon economy.

Urban land use carbon emission intensity in China under the "double carbon" targets: spatiotemporal patterns and evolution trend

In-depth research on the spatiotemporal patterns and evolution trend of urban land use carbon emission intensity (ULUCEI) can reveal the internal relationship between urban land use and carbon emissions, which is crucial for achieving carbon emission reduction and "double carbon" targets. This paper proposed a conceptual framework of ULUCEI; the methods of kernel density estimation (KDE), exploratory spatial data analysis (ESDA), and spatial Markov chains were adopted for exploring the spatiotemporal patterns and evolution trend of China's ULUCEI from 2000 to 2017. The following conclusions are drawn through research. (1) There was an increasing trend in ULUCEI in China from 0.102 in 2000 to 0.283 in 2017. From the regional perspective, the ULUCEI in the eastern region is markedly higher than that in the central and western regions. Moreover, the results of nuclear density estimation indicate that China's ULUCEI shows an obvious upward and polarized trend. (2) China's ULUCEI shows a positive spatial autocorrelation. The types of spatial agglomeration include "high-high" agglomeration, "high-low" polarization, "low-high" collapse, and "low-low" homogeneity, and there are obvious disparities in the distribution rules of cities with different spatial agglomeration forms. (3) China's ULUCEI presents strong stability and "club convergence" trend. Moreover, spatial factors significantly affect the dynamic transition of China's ULUCEI, and its effect on the shifting upwards gradually enhances with increasing lag type. This paper therefore suggests that policymakers should formulate differentiated urban land low-carbon use models and carbon emission reduction policies to reduce ULUCEI.

Multi-scale variations and impact factors of carbon emission intensity in China

China's carbon emissions have developed swiftly in recent decades, which will not only affect the nation's own sustainable development, but have a potentially negative impact on global climate stability. Given that socioeconomic development is susceptible to regional heterogeneity and geographic scales, a systematic exploration of spatiotemporal variations of carbon emission intensity (CEI) and their drivers across different levels is conducive to enacting more reasonable and efficient measures for emission reduction. However, there is still a lack of comprehensive analysis of these issues. In this paper, we attempted to quantify and compare the spatiotemporal evolution and spatial spillover effects of impact factors on CEI from nighttime light imagery and socioeconomic data at two China's administrative levels by utilizing the variation coefficient, spatial autocorrelation model and spatial econometric methods. The results showed that the spatiotemporal variations of CEI were greater at the prefecture level compared to the provincial level during 2000-2017. There were significant positive spatial autocorrelation of CEI at two administrative levels, and self-reinforcing agglomeration was more substantial at the prefectural level than that provincial level. While the local spatial clustering of CEI of each administrative level altered with scale dependence, the binary spatial structure (High-High and Low-Low) of CEI remained relatively steady in China. Various driver factors not only had direct effects on local CEI, but had spatial spillover effects on neighboring areas. Our findings illustrate that China's CEI is sensitive to the space-time hierarchy of multi-mechanisms, and suggest that "proceed in the light of local conditions" strategies can assist the Chinese government for CEI mitigation.

Impact of digital economy development on carbon emission intensity in the Beijing-Tianjin-Hebei region: a mechanism analysis based on industrial structure optimization and green innovation

Under the "Digital China" strategy and "Carbon Peaking and Carbon Neutrality" goal, it is significant to explore the carbon reduction effect from the digital economy development in a multi-dimensional way. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study uses mechanism test model, threshold effect model, and spatial Durbin model which empirically test the influence mechanism and spatial spillover effect of digital economy development on regional CEI. The research found that (1) the digital economy development in the BTH region can reduce regional CEI, and it passes the endogenous test; (2) the digital economy indexes of 13 cities in the BTH region have significantly increased with time evolution, but there is obvious spatial unevenness; the CEI of each city except Tianjin decreases significantly with time evolution, and Tianjin shows a trend of decreasing and then increasing; (3) digital economy has a positive spatial correlation, showing the characteristics of "H-H" and "L-L" clustering. Furthermore, the digital economy has a spatial spillover effect on the CEI of neighboring cities; (4) the digital economy development can promote the industrial structure rationalization and upgrade, improves the urban green innovation quantity and quality, then reduces the regional CEI through them; and (5) the impact strength of digital economy on CEI varies at different threshold intervals of the mechanism variable.

Can green finance reduce carbon emission intensity? Mechanism and threshold effect

Against the background of carbon emission reduction, green finance (GF) has become a crucial financial instrument that promotes industrial transformation and low-carbon development. Although some scholars have explored the driving factors affecting the carbon emission intensity (CEI), there is a dearth of literature on the mediation and threshold effects of GF on CEI. Based on the panel data of 30 provinces in China during the period of 2004~2019, this study examined the direct, indirect, and threshold effects of GF on CEI by adopting the panel ordinary least squares, mediation effect, and threshold regression models, respectively. This study draws the following conclusions: GF can directly reduce the CEI. In addition, the scale economics effect and green technology innovation caused by GF have an inhibiting effect on the CEI. However, GF can promote the CEI through structural transformation. What's more, this study interestingly found that the effect of GF reducing CEI is dynamic and nonlinear. These findings can provide references for policy-makers who hope to accelerate carbon emission reduction and achieve low-carbon development.

Allocating China's 2025 CO2 emission burden shares to 340 prefecture cities: methods and findings

Peak emission is an important policy/scheme for all the countries to respond greenhouse gas mitigation. The key is how to distribute the emission burden shares to its sub-regions. This study aims to develop a prefecture city leveled CO₂ emission allocation model by integrating multi-indicators method and benchmark method so that China's 2025 (end year of 14th Five-Year Plan, FYP) CO₂ emission burdens can be allocated to its prefecture cities and provinces. Results show that China's total CO₂ emission will reach 12 billion tons in 2025. The majority of such emission will occur in the east China due to its more developed economy and dense population. Cities with high emissions are usually allocated more emission quotas, such as Shanghai, Tianjin, Chongqing, Tangshan, Yulin, Suzhou, and Ningbo. The top five provinces with higher CO₂ emission quotas are traditionally high-emission and energy-intensive provinces, including Shandong, Jiangsu, Inner Mongolia, Henan, and Hebei. The national CO₂ emission intensity will decrease by 69.35% in 2025 compared to the 2005 level. The CO₂ emission intensity reduction rates among the 340 Chinese cities is found to be fluctuating significantly from 16 to 74% during the 14th FYP. Finally, policy recommendations are raised for mitigating city level CO₂ emissions by considering the local realities.

Does green innovation suppress carbon emission intensity? New evidence from China

The sustainable development of human beings is facing severe challenges in the context of a new era. The effective reduction of carbon emission intensity is essential to achieve the goal of sustainable development. Obviously, green innovation is an important factor in mitigating carbon emission intensity. It is important to measure the effect of green innovation on carbon emission intensity for accelerating industrial transformation and building a circular economy system. Therefore, this paper uses the data of 30 provinces in China from 2000 to 2019, obtained by the State Intellectual Property Office, using fixed effects model quantile regression model and Spatial Durbin Model to empirically verify the theoretical hypothesis. The conclusions are as follows: (1) Green innovation inhibits carbon emission intensity. Instrumental variable model and robustness test support this conclusion. (2) For carbon emission intensity under different quantiles, green innovation has a more significant effect on provinces with high carbon emission intensity through "target accountability system" and "reverse coercive system." (3) There is a significant spatial correlation between green innovation in China's provinces. The reduction of carbon emission intensity in the region will benefit from the improvement of green innovation in surrounding cities.

The nonlinear effect of land freight structure on carbon emission intensity: new evidence from road and rail freight in China

The extensive literature has debated the varying effects of factors on carbon dioxide (CO₂) emissions. However, it has paid little attention to land freight structure (FS), including road and rail freight share, which may have different effects on CO₂ emissions. Based on the data from 6 eastern provinces in China during 2005-2019, the panel threshold model is used to explore the dynamic influence mechanism of road and rail freight share on transport carbon emission intensity (CE), respectively. The results show different nonlinear relationships between the share of road and rail freight and transport carbon emission intensity. First, the effect of road freight share on carbon emission intensity is all positive across different stages of trade openness, while such effect goes through a process of increasing and then decreasing with the level of trade openness improving. Second, the driving effect of rail freight share on carbon emission intensity exhibits a "negative-positive-negative" feature as the level of trade openness increases. Third, trade openness generates a double-threshold effect on carbon emission intensity. The differentiated nonlinear effects provide significant evidence of the modal shift from road to rail freight, which would be effective to alleviate transport CO₂ emissions.

Decomposition and prediction of China's carbon emission intensity towards carbon neutrality: From perspectives of national, regional and sectoral level

China has actively participated in global climate governance and put forward its ambitious carbon neutrality target. The formulation of scientific plans has become the primary concern of the policy makers, especially for the 14th and 15th Five-Year Plans (FYP) which are important periods to secure the neutrality pledge and transform the whole economy. Since the carbon emission intensity play a key role in achieving carbon neutrality, it is necessary to summarize and explore the evolution trend of carbon emission intensity as well as its driving factors. Therefore, an integrated decomposition framework is developed to study the carbon emission intensity in the past three FYPs from the national, regional and industrial levels. Furthermore, towards the carbon neutrality target, moderate scenario and advanced scenario are designed to predict the future evolution trend of the carbon emission intensity and driving factors in the 14th and 15th FYPs (2021-2030). The main results are as follows: (1) During the three FYPs, factor substitution is the main force contributing to the decreased carbon emission intensity, but this effect gradually decreased. This indicates that it is an inevitable trend to further promote internal optimization and reform of energy system. (2) The change of energy structure exerts a positive effect on the carbon emission intensity decline, but it is not significant, especially in the industrial sector. (3) With the rich factor endowment, central and eastern regions can reduce carbon emission intensity through factor substitution and industrial structure transformation, while the western region is not. (4) In the future, the role of industrial structure optimization and technology progress will be gradually significant. Finally, our findings provide practical guidance on achieving carbon emission intensity reduction and enlightenments on policymaking.

The influence mechanism and spatial effect of carbon emission intensity in the agricultural sustainable supply: evidence from china's grain production

Agricultural carbon mitigation is critical for China to encourage the sustainable development of agriculture and achieve the carbon peak by 2030 and carbon neutrality by 2060. By exploring the impact mechanism of the carbon emission intensity (CEI) of grain production, we can effectively promote the low-carbon transformation of agricultural production and ensure the sustainable development of the food supply. This article analyzes the temporal and spatial evolution of the total carbon emission (TCE) and CEI of staple crops and adopts a dynamic spatial model to explore the influence mechanism and spatial spillover effects of the CEI of grain production based on evidence from China's major grain-producing provinces from 2002 to 2018. The results indicate that the TCEs of rice, wheat, and maize fluctuate upward and that the CEI in most producing areas decreases with low-low agglomeration (or high-high agglomeration). Among the influencing factors, technology is the main factor reducing CEI. Technical efficiency, urbanization, industrial structure, agricultural agglomeration, and agricultural trade openness can be transmitted to neighboring areas through spatial spillover mechanisms. The spatial spillover mechanisms are resource flow, technology spillover, and policy learning, producing the demonstration effect and siphon effect. Based on our findings, agricultural technology innovation and popularization, urbanization, optimization of the agricultural structure, financial payments, and factor flow among regions should be improved to encourage the low carbon transformation of grain production.

Is the "pollution haven hypothesis" valid for China's carbon trading system? A re-examination based on inter-provincial carbon emission transfer

In recent years, China had released various environmental regulations in order to respond climate change and corresponding environmental issues. However, due to imbalanced economic development and industrial structure, different Chinese regions had different enforcement levels on environmental regulations, which led to the regional transfer of pollution-intensive industries. To study the regional disparities on carbon emission transfer, this paper used the propensity score matching-difference in differences method (hereinafter abbreviated as "PSM-DID") to evaluate the mechanism between carbon trading pilot policies and the transfer of pollution-intensive industries. Panel data on 30 Chinese provinces were used to test the validity of the "pollution haven hypothesis," covering the period of 2010-2018. The empirical results showed that under the constraints of established environmental regulation, the pilot policy promoted the transfer of pollution-intensive industries to a certain extent and verified the "pollution haven hypothesis"; the proportion of the secondary sector and energy industry in the pilot areas had been reduced after the pilot policy; on the contrary, the technical level and the economic development level of the pilot provinces and cities had been further improved.

Carbon emission intensity and biased technical change in China's different regions: a novel multidimensional decomposition approach

The decomposition analysis has been employed to discover the driving factors of carbon emission intensity, but the current studies assume that production functions are under the condition of the neutral technical change. Grounded on biased technical change production theory, this paper proposes a novel multidimensional decomposition approach which combines production-theory decomposition analysis (PDA) and index decomposition analysis (IDA). This novel approach can illustrate how energy structure effect, element substitution effect, efficiency change effect, input biased technical change, output biased technical change and magnitude of technical change affect carbon emission intensity of China's 30 provinces. The results indicate that during the 11th FYP and 13th FYP, output biased technical change and the magnitude of technical change are the critical factors in China's carbon emission intensity, while other four drivers increase carbon emissions. But, during the 12^th FYP, the role of six drivers has been reversed contrasting 11th FYP and 13th FYP. In addition, we also explore the impact of each driver from the perspective of regional heterogeneity.

Study on the spatial interaction between carbon emission intensity and shadow economy in China

Both high carbon emission intensity (CEI) and large scale of shadow economy in China are the undesirable products of economic development with too fast growth rate. For the rapid and healthy development of the economy in China, the research on the relationship between the two should attract more attention, while the relevant literatures are very few at present. According to the panel data of 30 provinces in China from 2004 to 2016, this paper firstly examines the spatial correlation between CEI and the scale of shadow economic. Then verifies the interaction relationship between them with SPDM (spatial panel Dubin Model). Moreover, the robustness test is conducted with three different spatial weight matrices. As the interaction between CEI and shadow economy has been proved, providing new ideas for carbon emission reduction, environmental protection, and healthy economic development with rapid rate in the future. The specific conclusions are as follows: first, CEI and shadow economy both have significant positive spatial autocorrelation. Second, there is a spatial interaction between CEI and shadow economy, indicating provincial cooperation plays a very important role in both economic growth and environment protection. Third, the impacts from economic development on both CEI and shadow economy satisfy the EKC hypothesis. Also, the development of the tertiary industry plays a positive role in promoting the growth of CEI, while promotes and inhibits the expansion of shadow economic scale at the same time.

Inter-provincial responsibility allocation of carbon emission in China to coordinate regional development

To establish the carbon emission trading scheme and achieve the carbon emission reduction goals in China, it is critical to allocate the carbon emission allowance (CEA). Using the entropy method and the modified fixed cost allocation model (MFCAM), we calculated the CEA and the carbon emission intensity (CEI) reduction targets of 30 Chinese provinces in 2030, from four principles (equity-efficiency-feasibility-sustainability) and three dimensions (economy-society-environment). The results are shown as follows. First, China's total carbon emissions in 2030 calculated in this paper are 17567.9 Mt. Second, on the whole, CEA in China's southeast half of the Hu line is higher than that in the northwest half. Eastern China has a larger final CEA than western China and central China. Third, Henan, Guangdong, Shandong, and Jiangsu are the four provinces with the most CEA, while Gansu, Qinghai, Ningxia, and Hainan are the four regions with the least carbon allowances. Fourth, the regions of Shanxi, Shaanxi, Xinjiang, Ningxia, Inner Mongolia, Guizhou, and Anhui will take on greater responsibility for carbon reduction in the future. On the contrary, the zones of Tianjin, Qinghai, Guangxi, Jilin, Yunnan, and Beijing will be able to sell CEA in the future. Fifth, provinces are divided into three categories from the perspective of CEI reduction. Finally, we put forward relevant policy recommendations based on the conclusions.

China's carbon emissions from the electricity sector: Spatial characteristics and interregional transfer

As a major carbon emitter, the electricity sector is crucial to the realization of China's emission reduction objectives. Existing studies focus mostly on the influencing factors, emission efficiency and low carbon development of carbon emissions in the electricity sector. Missing from the literature is an analysis of spatial characteristics of carbon emissions and the embodied carbon emission transfer caused by the separation of electricity production and consumption, which is the basis for assigning the responsibility for emission reduction. Thirty provinces in China were taken as research objects, and Moran's I index was adopted to analyze the spatial characteristics of the electricity sector's carbon emissions and carbon emission intensity. Based on multiregional input-output tables, we compared the transfer situation of China's provincial electricity carbon emissions in 2010 and 2015. The results demonstrate that, from 2010 to 2015, the electricity carbon emissions in 20 provinces increased, whereas the carbon emission intensity in 21 provinces decreased. Carbon emissions and carbon emission intensity of electricity in most provinces demonstrate positive spatial clustering characteristics. The total amount of carbon emission transfer in the electricity sector increased from 421.22 million tons in 2010 to 581.369 million tons in 2015, the number of net transfers out of areas increased from 13 to 15, and the number of net transfers into areas decreased from 16 to 15. The active degree of carbon emission transfer reveals the eastern region > the central region > the western region. Different emission reduction policies should be formulated based on the difference in resource endowment between the north and south. Provinces that transferred out large amounts of electricity carbon emissions should take greater responsibility for emission reduction. Integr Environ Assess Manag 2022;18:258-273. © 2021 SETAC.

Multilevel-growth modelling for the study of sustainability transitions

Sustainability Transitions (ST) is a complex phenomenon, encompassing environmental, societal and economic aspects. Its study requires a proper investigation, with the identification of a robust indicator and the definition of a suitable method of analysis. To identify the most informative geographical boundaries for analysing ST pathways, we consider the Carbon Emission Intensity (CEI) and estimate a four-level growth model to study its pattern over time for all the EU regions. We apply this model to a novel longitudinal dataset that covers CEI data of European regions at four different geographical scales (state, areas, regions, and provinces) over a nine-year timespan. This approach aims at supporting the decision-makers in developing more effective sustainability transitions policies across Europe, especially focusing on regions and overcoming the well-known “one-size fits all” approach.•

The unconditional growth model has been applied to a multi-level structure considering four levels, defined by three geographical scales and time.

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The ideal structure of the model would have required five levels, but the sample size of the dataset made the application computationally unfeasible;

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The application of the model allowed to identify patterns of stability and change over time of the variable amongst different geographical units.

Regional difference decomposition and its spatiotemporal dynamic evolution of Chinese agricultural carbon emission: considering carbon sink effect

The current study aims to analyze the regional differences and spatiotemporal dynamic evolution of carbon emission intensity (CEI) and carbon emission per capita (CEPC) of planting industry with consideration of carbon sink effect. The results indicate that: (i) The CEI and CEPC of China's planting industry present significant non-equilibrium distribution characteristic during the investigate period, provinces with high CEI are mainly distributed in major agricultural provinces, while high CEPC provinces are mainly located in northeast and individual central provinces with large planting industry. (ii) Inter-regional difference is the principal course of the total differences, the CEI Theil index demonstrates gradient decreasing pattern of "western > central > eastern > northeast," the contribution rate of CEI Theil index shows "northeast > eastern > central > western," the CEPC Theil index shows the spatial pattern of "northeast > central > western > eastern," and the contribution rate of CEPC Theil index presents the spatial pattern of "eastern > central > western > northeast." (iii) The dynamic evolution of CEI and CEPC curve presents polarization or multipolar differential phenomenon accompanies with distinct gradient characteristics, the regional difference of agglomeration level in CEI is gradually narrowing, while the CEPC gradually expanding and the dispersion level is increasing, which implies the "intra-regional convergence and inter-regional divergence." Consequently, differential carbon reduction policies have been put forward according to the study findings.

Industrial carbon emission intensity: A comprehensive dataset of European regions

The dataset has been developed within the framework of the EU EIT-Climate Kic Flagship Project “Re-Industrialise” and it includes data of Carbon Emission Intensity (CEI) from industrial sources for the European Regions. CEI is considered as a proxy for analysing the Industrial Sustainability Transition pathways and is calculated as the ratio between CO2 equivalent emissions (CO2_e) and Gross Domestic Product (GDP) of the industrial sector over a nine-year timespan, i.e. from 2008 to 2016. CO2_e data at plant level have been retrieved from EU Emission Trading System (EU ETS) register and aggregated at different geographical scales, corresponding to the nested structure of NUTS (Nomenclature of Territorial Units for Statistics), proposed by EUROSTAT. Industrial GDP data have been selected from EUROSTAT database to match the industrial sectors covered by EU ETS.

Spatial effects of carbon emission intensity and regional development in China

Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China's provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China's 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China's provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China's carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity.

Does bank credit to the private sector promote low-carbon development in Brazil? An extended STIRPAT analysis using dynamic ARDL simulations

In support of the global efforts to tackle climate change, policy makers in the past decades have been actively involved, exploring possible options for ensuring low-carbon pattern of development. This study contributes to this important stream of policy discussion by using a newly developed econometric technique, dynamic ARDL simulations, to estimate and simulate the impact of bank credit to the private sector on aggregate carbon emissions and carbon emission intensity in Brazil over the period 1971-2014. The examined empirical model is based on a framework that incorporates the impact of population, economic growth, fossil energy intensity of consumption, and economic globalization. The analysis produced interesting results. First, the estimates show that economic growth and fossil energy intensity of consumption have significant long-run increasing impact on CO2 emissions in Brazil. Second, bank credit to the private sector has significant short-run and long-run reducing effects on aggregate CO2 emission and carbon emission intensity in the economy. Overall, the results reflect the tendency of the economy to become less carbon-intensive as bank credit supply to the private sector increases.

The heterogeneous effect of driving factors on carbon emission intensity in the Chinese transport sector: Evidence from dynamic panel quantile regression

The transport sector is becoming a key sector for China to accomplish its targets for reducing carbon emission intensity (CEI). Identifying the dominant factors driving CEI of the transport sector is important for CEI mitigation. This paper applied dynamic panel quantile regression to explore the effect of driving factors on CEI in the Chinese transport sector at the provincial level during 2000-2016. The empirical findings indicate that economic growth has a positive influence on CEI at low quantiles, whereas this effect is the opposite at high quantiles. Further, the findings show an inverted U-shaped pattern between economic growth and CEI at low quantiles, which validates the Environmental Kuznets Curve hypothesis in low-CEI provinces. Energy intensity positively influences CEI, with the greatest impact occurring at higher quantiles. Among the lowest CEI provinces, private vehicles and cargo turnover appear to contribute to CEI, and a positive impact of urbanization exists, except at the 5th and 30th quantiles. In conclusion, policy implications for effectively promoting the CEI abatement in the transport sector are discussed.

How does urbanization affect carbon emission intensity under a hierarchical nesting structure? Empirical research on the China Yangtze River Delta urban agglomeration

Urbanization is an important direction for China's future social development and an important focus of its carbon emission reduction path. China's current administrative management is a vertical nested structure, and the characteristics of high-scale regions have a non-negligible impact on low-scale areas. Taking the county scale of the basic unit of economic and social development as the research scale, according to the panel data of the Yangtze River Delta from 2008 to 2016, a two-level hierarchical linear model (HLM) for carbon emission intensity is constructed, especially considering the characteristics of high-scale regions (i.e., low-carbon pilot cities) at the second level, and is combined with the mediating effect test method to analyze the impact path of urban development on carbon emissions intensity. The results show that (1) there is a spatial nesting relationship between regions of different scales, and the city scale can explain 85.21% of the carbon emissions intensity, which is much higher than the county scale. (2) There is an N-shaped curve relationship between urban development and carbon emissions intensity. After considering the high-scale factor (low-carbon pilot cities) at the city scale (the second level of the HLM), if a high-scale city is a low-carbon pilot city, then improvement in the level of urbanization in the county can promote a reduction in carbon intensity. (3) The impact path of urban development ⇄ per capita gross domestic product (the proportion of secondary industry, patent application volume) → carbon emissions intensity and urban development → the proportion of tertiary industry → carbon emissions intensity is significant. However, the path of the proportion of tertiary industry → urban development → carbon emissions intensity is not significant.

Spatial econometric analysis of carbon emission intensity in Chinese provinces from the perspective of innovation-driven

This study estimates the carbon emission intensity of China's provinces during the period from 2000 to 2015. First, the temporal and spatial pattern evolution of China's carbon emission intensity was analyzed using spatial statistics. Then, from an innovation-driven perspective, combining the data of innovative technologies and scale factors to construct a spatial panel model to explore the main influencing factors of carbon emission intensity and its spatial spillover effect. The results show that: China's provincial carbon emission intensity has obvious spatial agglomeration characteristics, and regional differences are improving, and the spatial spillover effect of some influencing factors is obvious; innovation indicators such as the number of patent authorizations, technical market turnover, and foreign direct investment, and GDP have a significant negative impact on carbon intensity, and the effects of general scale variables such as urbanization rate, energy consumption, and population density on carbon intensity are significantly positive.

Carbon emission intensity of electricity generation in Belt and Road Initiative countries: a benchmarking analysis

The scope of this study is to analyze the carbon emissions intensity of electricity generation in "Belt and Road Initiative" (BRI) countries. The total CO₂ emissions from electricity generation in BRI nations increases from 4232.34 Mt in 2013 to 4402.38 Mt in 2015, accounting for 34.45% of global CO₂ emissions from electricity generation. Logarithmic mean Divisia index methodology is applied to analyze the drivers of carbon emissions intensity in BRI nations. The decomposition results revealed that the regional carbon emissions intensity in BRI nations increases during 2013-2015 and the power generation efficiency is the essential factor to improve carbon emissions performance in BRI developing countries. For BRI developing countries, promoting clean and efficient thermal power is a pragmatic priority for green power development.