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In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

To address global climate change, achieving carbon peak and carbon neutrality has become a global consensus. However, the means to simultaneously achieve carbon reduction and promote green economic development, particularly in developing countries, require further investigation. This study evaluates the impact of e-commerce on CO 2 emissions. Through an examination of the effects of the National E-Commerce Demonstration City (NEDC) policy from 2006 to 2017, this paper reveals that e-commerce growth facilitated by the NEDC policy resulted in a 7.89% reduction in total CO 2 emissions and a per capita reduction of 1.1146 tons in the pilot cities. Mechanism analysis demonstrates that the upgrading of industrial structure, development of digital finance, and the growth of innovation and entrepreneurship serve as primary pathways for this impact. The robustness of the findings is supported by parallel trend tests, placebo tests, and additional sensitivity analyses. Furthermore, the research reveals that the NEDC policy exhibits a more significant reduction in CO 2 emissions in cities with higher levels of economic development and non-resource-based cities. Welfare analyses show that the NEDC policy has significant socio-economic effects. These findings provide new evidence on the environmental effects of the digital economy and offer insights into achieving carbon neutrality. • The National E-commerce Demonstration Cities (NEDC) policy reduced CO 2 emissions. • Optimizing industrial structure, enhancing digital finance, and promoting innovation and entrepreneurship are mechanisms. • The effect is more pronounced in cities with high economic levels and high resource dependence. • The NEDC policy substantially enhanced social welfare by reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

Based on the tightening regulation of carbon emissions, China has launched the pilot carbon emission trading scheme (CETS) since 2013. There is growing empirical evidence of the actual effect of CETS to promote enterprises' productivity which is characterized by total factor productivity (TFP). However, most studies ignored the further analysis of influence mechanisms. This paper aims to explore the impact of CETS on the TFP of enterprises and discuss the mediating role of government participation and carbon trading market efficiency. Using data from A-share listed enterprises from CETS-covered enterprises, this paper employed a combination of the propensity score matching (PSM) and difference-in-differences (DID) strategies and found that the CETS has a statistically significant positive impact on the TFP of enterprises, and the positive effect has been maintained for six years since its inception. The moderation analysis indicated that: (1) two dimensions of government participation in terms of the market incentive and government supervision significantly moderate the positive impact of CETS on TFP of enterprises; (2) two dimensions of carbon trading market efficiency in terms of the market scale and liquidity significantly moderates the positive impact of CETS on TFP of enterprises. In light of Chinese pilots CETS policy, the study highlights the important moderating roles of government participation and high carbon trading market efficiency on enterprise's TFP. [Display omitted] • Chinese pilots CETS significantly improved the TFP of enterprises by 9.48%. • The government participation moderates the positive impact of CETS on enterprises' TFP. • The higher carbon trading market efficiency is, the greater the moderating effect of CETS on TFP is. [ABSTRACT FROM AUTHOR]

Green credit policy (GCP) can achieve economic growth and environmental conservation, notably by lowering air pollutants (PM2.5). Green credit is a significant component of China's green financing for environmental regulation and achieving carbon neutrality. In this paper, to understand the causal relationship between GCP and PM2.5, we apply a bootstrap full-sample Granger causality test, parameter stability test, and quantile-on-quantile test for the period between 2003:M01 to 2019:M12. The result shows a bidirectional relationship and reveals that GCP has varied environmental implications in its early and mature stages because of a low percentage of green credit and a lack of motivation for financial institutions. In the long run, GCP and PM2.5 interaction confirm the favorable effects of GCP on PM2.5 as the green credit system improves. For robustness, we used quantile-based granger causality to evaluate the causative link between GCP and PM2.5. In light of the findings, this research advises legislative reforms and stresses the relevance of green credit in improving air quality. This study adds additional evidence that air pollution affects green credit policies. Air quality being a leading indicator helps firms anticipate changes in bank credit preferences and alter financing techniques. • The study explores the causal relationship between GCP and PM2.5 • The study used advanced econometrics methods for the period 2003:M01 to 2019:M12. • The result shows a bidirectional relationship between GCP and PM2.5. • GCP has a positive influence on PM2.5 as the green credit system improves. • This study adds further evidence that air pollution affects green credit policies. [ABSTRACT FROM AUTHOR]

In this paper, we identify the causal effects of the industrial transfer policy (ITP) on carbon emissions. Specifically, we treat the demonstration zones of undertaking industrial transfer in China as a quasi-natural experiment and evaluation its carbon mitigation effects using the difference-in-differences (DID) and matching DID strategies. Our findings suggest that the ITP exerts a significant negative impact on carbon emissions. In particular, both the DID and matching DID estimates indicate that the ITP leads to mitigate carbon emissions by approximately 4.9 and 4.7 million tons, respectively. We also investigate the heterogeneous carbon mitigation effects of the ITP. Moreover, various checks and falsification tests are also presented to confirm the causal effects, including the placebo test, concurrent events test, alternative measures of the ITP and carbon emissions, alternative estimation method and matching technique. The results indicate that our key conclusion, the carbon mitigation effect of the ITP is robust. These results indicate that policymakers should seize the carbon mitigation dividend of the ITP to achieve the goal of carbon neutrality. Policymakers should also reduce the regional development differences, improve the level of opening to the outside world, strengthen the construction of green demonstration zones for industrial transfer to mitigate carbon emissions. • The carbon mitigation effects of the demonstration zones of undertaking industrial transfer in China are investigated. • The industrial transfer policy leads to mitigate carbon emissions by approximately 4.7 million tons. • The heterogeneous carbon mitigation effects of the industrial transfer policy are examined. • The results are supported by robustness and falsification checks. [ABSTRACT FROM AUTHOR]

As an important new tool for ecological poverty alleviation, carbon trading compensation is combining ecological protection with rural poverty alleviation. This paper evaluates the poverty alleviation impact of China's pilot carbon emissions trading schemes (ETS) at the provincial level before and after the program, which was implemented from 2007 to 2017. Specifically, we measure poverty alleviation in terms of rural residential income growth and rural job creation. By exploiting the quasi-experimental variation in whether provinces were affected by this pilot ETS policy, our analysis finds that the ETS policy resulted in increases in rural residential income and employment. We find the estimated effects correspond to an increase of approximately 752.6 yuan in annual rural residential income and 2.35% in the ratio of rural employed population to the total employed population, accounting for 9.5% of the rural residents' income and 7.11% of the rural employment, specifically. The results imply that the implementation of ETS is beneficial to income growth and job creation for rural areas of China, which mean the ETS policy may be conducive to poverty alleviation in affected provinces. • The effect of China's pilot carbon emissions trading schemes on poverty alleviation is tested. • Difference-in-Differences model is used. • The effect is an increase of approximately 752.6 yuan. [ABSTRACT FROM AUTHOR]