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The power sector is one of the major CO2-emitting industries in China. It is also the first key emissions control industry included in China's national carbon trading market established in 2021. Therefore, based on the data of 30 provinces and cities in China from 2015 to 2022, this study analyzes the impact of the national carbon market policy on the carbon emissions of the power industry based on the generalized Difference-in-Difference (DID) model. Based on the method of text analysis, this paper constructs the carbon market policy intensity index of each region and incorporates it into the DID model. Empirical analysis finds that the national carbon market policy can significantly inhibit the carbon emissions of the power industry. However, the heterogeneity analysis of 30 provinces and cities shows that the national carbon market policy has a significant impact on the regions that already have a carbon pilot, but not on the non-pilot regions. Therefore, this paper demonstrates the effectiveness of the national carbon market policy in the power industry emissions control, and also exposes the heterogeneity between regions, providing an important empirical basis for the inclusion of other energy-intensive industries. [ABSTRACT FROM AUTHOR]

Climate anomalies are affecting the world. How to reduce carbon emissions has become an important issue for governments and academics. Although previous researchers have discussed the factors of carbon emission reduction from environmental regulation, economic development, and industrial structure, limited studies have explored the carbon emission reduction effect of a city's spatial structure. Based on 108 Chinese cities from the Yangtze River Economic Belt between 2003 and 2017, this paper examines the impact of the city cluster policy on city carbon emissions using the difference-in-differences (DID) method. We find that: (1) The city cluster policy has significantly reduced the cities' carbon emissions by 7.4%. Furthermore, after a series of robust and endogenous tests, such as parallel trend and PSM-DID, the core conclusion still remains. (2) We further identify possible economic channels through this effect, and find that city cluster policy would increase city productivity, city technological innovation, and industrial structure optimization. The conclusions of this paper have important practical significance for China to achieve carbon neutrality and facilitate future deep decarbonization. [ABSTRACT FROM AUTHOR]

China is one of the world's largest energy consumers and carbon emitters, and the situation of carbon emission reduction is serious. This paper forecasts the future trend of China's carbon emissions by constructing a system dynamics model of China's carbon emissions. The results show that China cannot fulfill its commitment to peak its carbon emissions in 2030 as scheduled. Secondly, the Logarithmic Mean Divisia Index model (LMDI) was used to analyze the influencing factors of China's carbon emissions. The contribution rates of the five factors to China's carbon emissions are as follows: economic development (226.30%), technological innovation (−105.92%), industrial structure (−26.55%), population scale (11.44%) and energy structure (−5.28%). Finally, this paper formulates five carbon emission reduction paths according to the size and direction of various factors that affect China's carbon emissions. The paths of carbon emission reduction were simulated by using the system dynamics model of China's carbon emissions. It is found that technological innovation is the key pathway for China to realize its commitment to carbon emission reduction. Slowing economic growth will delay the arrival time of peak carbon emissions and increase the intensity of carbon emissions. Optimizing the industrial structure, reducing the population scale and adjusting the energy structure can reduce the peak and carbon emissions in China, but the effect is small. [ABSTRACT FROM AUTHOR]

The launch of the national carbon emissions trading (CET) market has resulted in a closer relationship between China's CET market and its electricity market, making it easy for risks to transfer between markets. This paper utilizes data from China's CET market and electric power companies between 2017 and 2023 to construct the spillover index model of Diebold and Yilmaz, the frequency-domain spillover approach developed by Barun'ik and Křehl'ik, and a minimum spanning tree model. The comparison is made before and after the launch of the national CET market. Subsequently, this paper examines the market spillover effects, as well as the static and dynamic properties of network structures, considering both the time domain and frequency-domain perspectives. The research findings suggest the following: (1) There is a strong risk spillover effect between China's CET market and the stock prices of electric power companies; (2) There is asymmetry in the paired spillover effects between carbon trading pilot markets and the national CET market, and differences exist in the impact of risk spillovers from power companies between the two; (3) The results of the MST model indicate that the risk contagion efficiency is higher in the regional CET pilot stage compared to the national CET market launch stage, with significant changes occurring in key nodes before and after the launch of the national CET market; (4) Both the dynamic spillover index and the standardized tree length results demonstrate that crisis events can worsen the risk contagion between markets. Besides offering a theoretical foundation and empirical evidence for the development of China's CET and electricity markets, the findings of this paper can provide recommendations for financial market participants as well. [ABSTRACT FROM AUTHOR]

With the proposal of China's "carbon peak, carbon neutral" strategy, the increasing awareness of low carbon production among consumers, and the government's introduction of carbon trading mechanism and low carbon consumption subsidy policies, enterprises are facing good opportunities for development. However, how the government can reasonably formulate low carbon policies and how enterprises can implement optimal low-carbon production decisions are still key issues in China's low-carbon transition development. In this context, this paper is based on the carbon trading mechanism and carbon consumption subsidies. In this context, based on the carbon trading mechanism, this paper focuses on green production and green consumption, considers the impact of low-carbon consumer preferences and government subsidies on enterprises' low-carbon production decisions, and uses the optimal theory to study the optimal pricing strategy and the optimal carbon reduction strategy. The study shows that the increase in carbon price has a positive effect on the increase in enterprise profit; the increase of carbon emission has a negative effect on the increase of enterprise profit and the high carbon price will intensify this effect. In addition, changing the intensity of government subsidies to consumers will lead to the change of enterprise carbon emission strategy. The study of this paper provides a certain reference for the government to reasonably formulate carbon trading prices and consumer low-carbon subsidies. In addition, considering consumer low-carbon preferences is also conducive to promoting green production practices of enterprises, thus promoting the realization of the carbon neutral strategy. [ABSTRACT FROM AUTHOR]

China's economic development is characterized by openness, and trade and investment are important engines for promoting economic development. China's economy is now in a transitional period, during which excessive carbon emission reduction would inevitably hinder economic development. In this context, improving carbon emission efficiency is an effective way to achieve sustainable development. This paper deals with the relationships among foreign direct investment, technological innovation and carbon emission efficiency. Our research findings include the following. First, carbon efficiency shows regional differences. East China has the highest mean value of carbon emission efficiency, followed by central China and west China over the sample period. Second, FDI exerts both direct and indirect impacts on carbon emission efficiency through technological innovation, which confirms the intermediate effect of technological innovation. Finally, sub-sample analysis indicates that the impact of FDI and technological innovation on carbon emission efficiency show regional heterogeneity. According to these findings, we offer policy recommendations as follows. The government should stimulate independent innovation, promote technological progress in renewable energy and green energy, and attract environmentally friendly foreign investment to improve carbon emission efficiency and boost green development. [ABSTRACT FROM AUTHOR]

A growing number of countries worldwide have committed to achieving net zero emissions targets by around mid-century since the Paris Agreement. As the world's greatest carbon emitter and the largest developing economy, China has also set clear targets for carbon peaking by 2030 and carbon neutrality by 2060. Carbon-reduction AI applications promote the green economy. However, there is no comprehensive explanation of how AI affects carbon emissions. Based on panel data for 270 Chinese cities from 2011 to 2017, this study uses the Bartik method to quantify data on manufacturing firms and robots in China and demonstrates the effect of AI on carbon emissions. The results of the study indicate that (1) artificial intelligence has a significant inhibitory effect on carbon emission intensity; (2) the carbon emission reduction effect of AI is more significant in super- and megacities, large cities, and cities with better infrastructure and advanced technology, whereas it is not significant in small and medium cities, and cities with poor infrastructure and low technology level; (3) artificial intelligence reduces carbon emissions through optimizing industrial structure, enhancing information infrastructure, and improving green technology innovation. In order to achieve carbon peaking and carbon neutrality as quickly as possible during economic development, China should make greater efforts to apply AI in production and life, infrastructure construction, energy conservation, and emission reduction, particularly in developed cities. [ABSTRACT FROM AUTHOR]

To cope with huge carbon emission pressure, China has implemented a carbon emissions trading pilot policy that aims to provide reasonable suggestions for the smooth operation of the national carbon market. This paper selects the provincial panel data in China from 2005 to 2019 and uses the propensity score matching-difference in difference (PSM-DID) method to evaluate the carbon emission policy's reduction effect. Based on carbon emissions (CE) and carbon emission intensity (CI), provinces and cities are divided into four regions, and each region is verified by spatial difference analysis. Furthermore, the mediating effects of carbon emission reduction through the dual aspects of technological progress and industry structure are also discussed. Results verified that, (1) under the carbon emission trading policy, regional carbon emissions and carbon emission intensity are both significantly reduced. (2) Technological progress helps to reduce carbon emissions, while industrial structure shows no obvious contribution. (3) The four regions all show ideal emission reduction effects, of which the High CE-High CI region shows the best, but is greatly restricted by techniques. The industrial structure of the High CE-Low CI region needs to be further optimized for carbon reduction. In the Low CE-High CI region, the carbon emissions brought by economic development fail to effectively improve per capita GDP. The Low CE-Low CI region contributes greatly to carbon emission reduction with technical advantages. [ABSTRACT FROM AUTHOR]

China's industrial sector generally relies on electricity as its main source of energy, and industrial production can be affected if there are problems with the electricity supply. In order to deal with the uncertain electricity supply and achieve the "dual carbon" target, the industrial sector needs to take effective measures to enhance carbon total factor productivity (CTFP). We use the biennial Luenberger productivity index (BLPI) to try to provide strategies for low-carbon industrial development in China. The results indicate that the overall CTFP of China's industrial sector showed an increasing trend from 2006 to 2019. Technology change was the main contributor to the change in CTFP, but fluctuations in efficiency change remained a challenge. Differences were observed between the light industry sector (LIS) and the heavy industry sector (HIS) in terms of changes in CTFP, with LIS showing more stable changes and HIS experiencing larger fluctuations. Most sub-sectors showed increased CTFP during the sample period. R&D investment and R&D personnel have a positive impact on CTFP, while energy structure is found to hinder CTFP. According to the research results of this study, we offer the corresponding policy implications. This study is the first to explore the balance between low-carbon goals and industrial production from the perspective of improving CTFP, providing a new viewpoint on the contributions of technological innovation to solving socio-economic issues. [ABSTRACT FROM AUTHOR]

The manufacturing industry is the pillar industry of China's economy and a major carbon emitter, and its carbon emission reduction efforts directly determine whether the country's carbon emission reduction target can be successfully met. In the context of the goals of the carbon peak and carbon neutrality policy, we examine the impact of manufacturing structure optimization on carbon emissions from 2003 to 2020 through a spatial econometric model, taking the old industrial centers in Northeast China as an example. We then apply a machine learning model to simulate manufacturing carbon emissions during the carbon peak stage and identify the optimal path for carbon emission reduction, which is important for promoting manufacturing carbon emission reduction in Northeast China. Since the goal of low-carbon economic development has gradually replaced the goal of maximizing economic efficiency in recent years, manufacturing structure optimization has come to focus on energy saving and emission reduction. Therefore, we define manufacturing structure optimization from the dual perspective of technology and energy consumption to broaden the existing research perspective. The results show the following: (1) The overall trend in manufacturing structure optimization in Northeast China is steadily improving, and the level of manufacturing structure optimization from the technology perspective is higher than that from the energy consumption perspective. (2) Manufacturing structure optimization and manufacturing carbon emissions in Northeast China both show a positive spatial correlation. Manufacturing structure optimization in Northeast China can effectively promote carbon emission reduction, and it also has a spatial spillover effect. (3) The carbon emission reduction effect of manufacturing structure optimization from the energy consumption perspective is better than that from the technology perspective, and the carbon emission reduction effect under the institutional innovation scenario is better than that under the baseline scenario and the technological innovation scenario. Focusing on manufacturing structure optimization from both technology and energy consumption perspectives, as well as continuously improving technological innovation and institutional innovation, can help to achieve manufacturing carbon emission reduction in Northeast China. [ABSTRACT FROM AUTHOR]

Based on China's goal of achieving carbon neutrality by 2060, this study focused on its coal gasification in 2010–2019. Carbon emissions were calculated from industrial data, and an LMDt model was established to analyze the influencing factors of carbon emissions. Through scenario analysis, the paths of carbon emission reductions in the chemical industry were analyzed, and their emission reduction potential was estimated. The results showed that the carbon emissions in the chemical industry increased rapidly in 2010–2019, reaching 196 million tons in 2019. The emission structure was the most important factor in mitigating carbon emissions, and the emission intensity, industrial structure, economic development level, and labor force scale had different degrees of promotion effects, of which emission intensity was the strongest. The chemical industry can reach a carbon peak before 2030 under the three analyzed scenarios, and the emission reduction potential is the largest under the landing policy scenario. The results showed that carbon capture, usage, and storage (CCUS) technology is key for carbon emission reductions and that it is necessary to adjust the industrial structure, reduce emission intensity, and increase forest carbon sink to achieve carbon neutrality in the chemical industry. [ABSTRACT FROM AUTHOR]