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In the context of China's rapidly growing digital economy, exploring the potential to reduce carbon emissions from the perspective of enterprise digitization is of vital practical significance for achieving ambitious "carbon peak" and "carbon neutrality" goals. This paper uses data from Ashare listed companies as a sample pool spanning the years 2014–2020 and finds that a nonlinear, inverted U-shaped relationship exists between enterprise digital transformation and carbon emissions. We further construct several robustness tests and show that the effect remains significant after accounting for the endogeneity issue using IV regressions. Heterogeneity analysis indicates that the effect of digital transformation on carbon emissions is influenced by the regional economic development level and the ownership structure of enterprises. Notably, in non-stateowned enterprises and economically advanced regions, the influence of digital transformation on carbon emissions becomes more pronounced. This paper provides explicit policy implications. It is suggested that enterprises accelerate their digital transformation by integrating smart device development into their production and operations and government should give support to digital transformation to facilitate industrial upgrading, and further promote China's pursuit of highquality and sustainable economic development. [ABSTRACT FROM AUTHOR]

As a noteworthy initiative of financial supply-side reform to precisely support the green development system, can green finance (GF) help achieve the dual goals of "carbon peaking" and "carbon neutrality"? Using data from China's provincial panel between 2007 and 2019, this paper measured the green finance index by the entropy method and the carbon emission efficiency (CEE) with carbon emission as the non-desired output by the Super-SBM model. Then, the influence of GF on CEE was empirically investigated by the dynamic panel model and the spatial Durbin model. The findings show that GF can significantly improve CEE and has a positive spillover impact on CEE in provinces with close economic ties; the upgrading of the industrial structure is a key mediator in the transmission of GF to CEE; and regional heterogeneity analysis finds that GF notably improves CEE in eastern, high development levels of economic and GF regions. The research can offer some theoretical and empirical references for green finance to contribute to low-carbon economic growth. [ABSTRACT FROM AUTHOR]

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Copyright of China Population Resources & Environment is the property of Shandong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

On the path to carbon neutrality, the journey to net‐zero emissions in the industry is achievable, and the green development of industrial parks is crucial for a smooth transition. We examined a sample of 171 green industrial parks certified by China's Ministry of Industry and Information Technology (MIIT). Using the Tapio decoupling model, we conducted decoupling analyses at both the national and regional scales of their economic growth and environmental conservation for three consecutive years. Spatial heterogeneity is found in carbon‐economy decoupling states and carbon‐pollutant synergy effects. Industrial parks in the east of China present better carbon‐economy decoupling states and carbon‐pollutant synergy effects than the other regions. The CO2 emissions determinants were explored by using the STIRPAT model. The variable industrial value‐added per capita, which is representative of economic growth, presents a positive and significant effect on emissions; the energy output and the proportion of high‐tech industries present a negative effect. However, after dividing the parks into groups by their leading industries, the influence of the variables started to show a power discrepancy. The heterogeneity analysis of this study can provide a basis for developing diversified strategies for Chinese industrial parks under the net‐zero emission goal. Plain Language Summary: This paper evaluated the economic development and environmental impacts of the 171 green industrial parks certified by the Ministry of Industry and Information Technology (MIIT) of China, using data and materials that were inventoried and summarized for the first time. The relationship of economic growth and CO2 emissions and other major pollutants was examined using the Tapio decoupling model, and the results suggest strong decoupling states for most of the cases, and a significant carbon‐pollution synergy effect. Further regression analysis indicated that industrial output and green and high‐tech industries are significant drivers of decoupling. The discoveries will guide future actions and policies toward sustainable development for the industrial parks of China. Key Points: Economic growth and environmental impacts are strongly decoupled for most of the studied green industrial parksA significant carbon‐pollution synergy effect is found in the green industrial parksIndustrial output and green and high‐tech industries are significant drivers of CO2 emissions but show discrepancy in different industries [ABSTRACT FROM AUTHOR]

Income inequality affects not only social well-being, health, and carbon emissions but also the strategy for green development in China. Based on the panel data of 205 cities in China from 2010 to 2020, a panel model with partial linear functional coefficient is used to analyze and test the relationship between income inequality, carbon emissions, and green development efficiency under different regional economic development levels. The empirical results show that the impact of income inequality on carbon emissions and green development efficiency is significant. The worsening of income inequality could aggravate carbon emissions, but the effect of income inequality on carbon emissions shows an increase–decrease-flattening with the continuous improvement of regional economic development. In terms of affecting the green development efficiency, the effect of income inequality on the efficiency of green development presents an inverted U shape. However, the number of cities where income inequality has an inhibitory effect on carbon emissions and green development efficiency has increased over time, and the impact of income inequality in a few cities on green development efficiency is not significant. These findings provide new insights into the understanding of shared prosperity and the strategy of carbon peaking and carbon neutralization in China. [ABSTRACT FROM AUTHOR]

Background: China's high-quality economic development depends on achieving sustainable economic development, reaching peak carbon emissions, achieving carbon neutrality, and intensifying the development of an industrial and energy structure that saves resources and protects the environment. This study used the data envelopment analysis (DEA) model and the Malmquist productivity index to measure the economic development performance of mainland China under carbon emission constraints. Then, it described the spatiotemporal evolution of economic development performance and analyzed its influencing factors using the Tobit model. Results: The results revealed that there were obvious differences in the trends of the static and dynamic performance of economic development. On the one hand, the static performance of economic development exhibited an upward trend from 2008 to 2020. Its distribution characteristics were dominant in the higher and high-level areas. On the other hand, the dynamic performance had a downward trend from 2008 to 2016 and then an upward trend from 2016 to 2020. In most provinces, the dynamic performance was no longer constrained by technological progress but rather by scale efficiency. It was found that the main factors influencing economic development performance were urbanization level, energy efficiency, vegetation coverage, and foreign investment, while other factors had no significant influence. Conclusions: This study suggests that China should improve its economic development performance by increasing the use of clean energy, promoting human-centered urbanization, increasing carbon absorption capacity, and absorbing more foreign capital in the future. [ABSTRACT FROM AUTHOR]

For a healthy industrial structure (IS) and stable economic development in China, this study proposes an improved link prediction algorithm (LP) based on complex networks. The algorithm calculates the similarity by constructing a mixed similarity index. A regional IS network model is built in the study, and the direction of IS adjustment is calculated with the mixed similarity indicators. In this study, the prediction accuracy of the proposed improved LP algorithm in the real network dataset is up to 0.944, which is significantly higher than that of the other algorithms. In the reality of IS optimization, industries of high similarity could be obtained through similarity algorithms, and reasonable coordinated development strategies are proposed. In addition, the simulated IS adjustment strategy in this study shows that it is highly sustainable in development, which is reflected in its lower carbon emissions. The optimization of IS adjustment could be achieved through IS network model and the improved LP algorithm. This study provides valuable suggestions for China's regional industrial structure adjustment. [ABSTRACT FROM AUTHOR]

As a financial activity with the main purpose of ecological protection and environmental governance, green investment has important practical significance for promoting sustainable economic development. Previous studies have not addressed the relationship between green investment, clean energy consumption, carbon emissions, and economic growth. We use panel data from 30 provinces and cities in China from 2003 to 2017 to build a simultaneous equation model, which can evaluate the nonlinear relationship and avoid the endogeneity of the model. The research results show that, firstly, green investment has a significant positive impact on clean energy consumption and economic growth. However, it has no significant effect on carbon dioxide emissions. Second, the curve of clean energy consumption and per capita GDP conforms to the positive U-shaped characteristic, while the curve of carbon emissions and per capita GDP conforms to the EKC curve, meeting the inverted U-shaped characteristic. And the inflection point of clean energy use occurred earlier than the inflection point of CO2 emissions. When per capita GDP is greater than 105,735.93 (RMB), the use of clean energy will increase, and carbon dioxide emissions will decrease, thereby achieving a win-win situation for the environment and the economy. Finally, according to the survey results, it is suggested that green investment is an effective means to encourage clean energy consumption and economic growth. [ABSTRACT FROM AUTHOR]