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Digital development is critical for achieving coordinated regional economic growth, energy conservation, and emissions reduction, which makes it a viable method for achieving carbon neutrality and peaks. This study investigates the impact of the digital economy on urban total factor productivity and carbon emissions, as well as its mechanism, from 2011 to 2019 by developing a digital economy evaluation index system. We find that the growth of the digital economy can increase urban total factor productivity while lowering carbon emissions and also has a significant spatial spillover effect on regional economic growth, energy conservation, and emissions reduction. The main influencing mechanism is that the digital economy accelerates marketization by encouraging industrial structure upgrades and technological progress, which results in higher total factor productivity and lower energy consumption. In terms of the spatial distribution, the digital economy promotes total factor productivity more in the central and western regions and large cities and promotes carbon emission reduction less in the eastern regions and smaller cities. The findings extend the benefits of the digital economy beyond economic and social welfare benefits and serve as a model for achieving a win–win situation in terms of both economic growth and carbon emissions reduction. [ABSTRACT FROM AUTHOR]

Ensuring the coordinated development of carbon emissions and green economy has important implications for China's sustainable development. For examining the coordinated development between carbon emissions and green economy in China and its influencing factors, this study assessed the coupling coordination degree (CCD) between carbon emission intensity (CEI) and green total factor productivity (GTFP) using the coupling coordination degree model (CCDM), revealed the spatiotemporal evolution law of CCD using a spatial correlation model and empirically examined their influencing factors using the spatial Durbin model (SDM) to test influencing factors. The following findings were made: (1) The green economy in China synchronously developed with carbon emission reductions from 2000 to 2020. (2) CCD has global spatial concentration effects, and the objects are divided into three categories: cold spots, hot spots, and random distributions. (3) Government scale expansion, transportation volume increase, industrial agglomeration, and urbanization are all not conducive to CCD, but population density and industrial structure upgrading have opposite effects. To promote the coordinated development of carbon emissions and green economy, we can improve the efficiency of energy utilization and reduce carbon emissions, by using clean energy, adopting clean production technology, increasing non-productive public investment in environmental protection, industrial agglomeration in reasonable space, and improving the moderate population agglomeration. Our findings offer valuable insight for understanding the relationship between carbon emission reduction and green economic development. They will allow authorities to take specific actions to ensure the coordinated development of CEI and GTFP. [ABSTRACT FROM AUTHOR]

This study aims to examine how carbon trading policies impact total factor carbon productivity and subsequently provide recommendations for sustainable regional development. Through analyzing the trends in total factor carbon productivity and its mechanisms, this paper suggests that carbon trading policies can significantly enhance total factor carbon productivity. Additional mechanistic tests reveal that carbon trading can promote technological progress, factor accumulation, scale allocation and energy substitution effects; all contributing towards a low-carbon economic transition. These four effects serve to both promote economic growth and contribute towards carbon reduction targets. Therefore, to encourage low-carbon and high-quality development of the region's economy, policymakers must focus not only on constructing and expanding carbon markets, but also emphasize the synergies between these different effects. [ABSTRACT FROM AUTHOR]

Achieving the win-win goal of sustainable economic growth and carbon neutrality is a sustainable development challenge facing all countries worldwide. This study examines the impact of carbon emission trading pilot policy on green development performance, aiming to provide experience for developing countries to reduce carbon emissions and achieve carbon neutrality goals. Based on the panel data of 30 provinces from 2010 to 2017 in China, green development performance (GD) is deconstructed into green total factor productivity (GTFP) and carbon emissions intensity (CEI), DEA-DDF-Malmquist index model and PSM-DID (Propensity Score Matching-Differences-in-Differences) model are adopted to explore the policy assessment and impact of the carbon emission trading pilot, which verifies the influence of the pilot policy on GTFP and CEI between experimental group and the control group. The empirical results showed that the carbon emission trading pilot policy could not effectively improve the green total factor productivity and carbon emissions reduction in the pilot areas, R&D intensity improvement, technology introduction improvement, and industrial structure optimization have positive and significant promoting effects on GTFP, the optimization of energy structure and industrial structure contributes to the carbon emissions reduction. This study provides information and decision-making references to address the challenge of climate change, energy conservation, and emission reduction, as well as a scientific basis for expanding pilot policies for China. • The carbon emission trading pilot has limited impact on GD in China. • R&D, FI and IS have significant positive promoting effects on GTFP. • ES and IS have significant inhibitory effect on GICE. • Providing reference for developing countries in energy conservation & emission reduction. [ABSTRACT FROM AUTHOR]

The present trend of rapid expansion in the textile industry is highly polluting and energy intensive, and the government intervention is necessitated. This study develops a Data Envelopment Analysis (DEA) model, examines the green technology innovation of listed textile enterprises, and explores its determining factors in order to address the issue of carbon emission reduction in the textile sector. The research sample for this study is obtained from 42 listed textile enterprises from 2017 to 2020. The research demonstrates: (1) the green technology innovation efficiency of domestic listed textile enterprises remains marginal; (2) from 2017 to 2020, the mean value of total factor productivity (TFP) and its decomposition index of listed enterprises in China's textile industry exhibit a downward trend, primarily as a consequence of the slowdown of technological progress; (3) the green technology of the entire industry is in recession from 2017 to 2020; (4) green technology innovation efficiency is inversely correlated with government subsidies, but positively correlated with net profit and asset size of enterprises. In light of the above information, enterprises should boost their ability to innovate with green technologies and implement a research and development (R&D) management development strategy that gives equal consideration to both economic and environmental benefits. [ABSTRACT FROM AUTHOR]

The implementation of the carbon trading system has a great impact on all aspects of the economy and society. Agriculture, which accounts for 34% of global carbon emissions, has also been hit hard as a high carbon emitting industry (Crippa M, 2021). This study uses agricultural green total factor productivity as an important indicator of agricultural green sustainable development, and analyzes the impact of China's carbon trading system on agricultural green total factor productivity, which can provide a reference for other countries that want to improve agricultural sustainable development and implement carbon trading. In this paper, the producer theory is used to build the basic model, and the differences-in-differences (DID) method is used to analyze the provincial data samples in China from 2000 to 2020. It is found that: the implementation of carbon trading pilot policy has a significant promotion effect on agricultural green total factor productivity; and after the implementation of carbon trading policy, with the advancement of time, under the constraint of reducing carbon quota, the promotion effect of agricultural green total factor productivity becomes stronger and stronger. Even in the regions without carbon trading pilot program, the policy spillover effect will also be obvious and promote the development of green total factor productivity of agriculture in this area. [ABSTRACT FROM AUTHOR]

Inter-regional capital flow is one of the critical factors affecting economic growth and regional inequality. This paper examines the impact of inter-regional investment from publicly listed companies on green total factor productivity (GTFP) in 215 cities and their green innovation mechanism between 2008 and 2018 in China. To address the endogeneity of inter-regional investment and GTFP, we adopted the opening of high-speed rail as the instrumental variable in the form of regression discontinuity design (RDD) and two-stage least squares (2SLS) estimates. Validated by five robustness and validity tests, our empirical results show that inter-regional investment significantly enhanced the local GTFP in the host cities. Moreover, the mechanism analysis shows that green patents had a significant mediating effect between inter-regional investment and local GTFP in the host cities. Specifically, green invention patents have had full mediation effects while the green utility model was not part of the transmission mechanism. Further investigation revealed that the effects of inter-regional investment on local GTFP were completely channeled through three categories of green invention patent: alternative energy production, transportation, and energy conservation, while another three categories of patent (waste management, agriculture/forestry, administrative, regulatory or design aspects) had partial mediating effects between inter-regional investment and GTFP. The decarbonization of polluting transportation and energy sectors is therefore a cost effective way to facilitate China's goal of carbon neutrality by 2060. • Inter-regional capital flow based on investment data of Chinese listed companies. • Analysis of the impact of inter-regional investment on GTFP based on RDD and 2SLS estimation. • Analysis of the transmission mechanism of seven types of green patents based on WIPO in inter-regional investments on GTFP. [ABSTRACT FROM AUTHOR]

Green development is a common goal pursued globally for carbon neutrality, and whether environmental regulatory system drives it in pollution-intensive industries (PIIs) is puzzled. Using provincial panel data of Chinese PIIs during 2005–2017, this paper applied entropy method to integrate existing environmental regulatory system into three types of environmental regulations, and then explored non-linear effects of them on green total factor productivity (GTFP). Several conclusions are generated. The annual growth rate of GTFP is 10.61% and it presents industrial differences. Technical change is the driving factor, while technical efficiency shows inhibiting effect. The effect of command-and-control environmental regulation on GTFP is a significant inverted U-shape curve, which is currently the primary driving role in green development, but it is too strict and increasingly less effective. Both market incentive and voluntary environmental regulations appear as positive U-shape curves, and their proper enhancement is breakthrough for future green development. Besides, market incentive environmental regulation is more effective in PIIs with relatively low pollution, while voluntary environmental regulation is sensitive to PIIs with relatively serious pollution. The Chinese government should allow environmental regulatory system more marketability and voluntariness, and production characteristics and pollution density of targeted industries should be considered. • The green development grows rapidly but shows technical inefficiency. • Command-and-control environmental regulation drives green development effectively. • Market incentive and voluntary environmental regulations can be enhanced properly. • Market incentive environmental regulation is sensitive to slight-polluted industries. • Voluntary environmental regulation works in heavy-polluted industries. [ABSTRACT FROM AUTHOR]