A cooperative game analysis for the allocation of carbon emissions reduction responsibility in China's power industry

Electric power industry, as one of the main industries leading to the increase of China's carbon emissions, accounts for about 40% of the total carbon emissions. It is of great practical significance to study the influencing factors of carbon emission decoupling index in power industry and put forward relevant policy suggestions. Based on the decoupling index of China's electric power industry from 1995 to 2018, this paper explores the influence of each index on the decoupling index through the autoregressive- distributed lag model. It turns out that the policy will significantly change the rate of change of carbon emissions and the rate of economic growth, but the impact of the policy is extremely short-lived; power generation structure, environmental regulations, and total lighting value at night play a positive role in promoting the decoupling index, while thermal power fuel efficiency and power generation conversion ratio play a negative role in inhibiting the decoupling index. In addition, the influence of power generation structure, environmental regulations, and the total value of night light on decoupling index also has a lagging and cumulative effect. Therefore, we propose targeted policy recommendations for policy formulation, green development, and low carbon construction in China's power industry from different perspectives based on the findings of the study.

In this paper, we focus on China's power sector to investigate ways to improve emissions reductions in the context of global warming and rising carbon emissions. This paper reviews the current state of adapted carbon emission reduction policies in China's power industry and assesses the potential effectiveness of two mechanisms, carbon taxation, and carbon trading, in achieving substantial emissions reductions. China's power sector is a major contributor to global carbon emissions, and the paper explores the balance between short-term carbon tax policies and long-term carbon trading strategies aimed at promoting an early peak in carbon emissions as well as carbon neutrality within the country. This research finds that the carbon tax can have positive impacts in the short term, whereas carbon trading exhibits higher efficiency in the long term. The paper addresses this by proposing a combination of the two mechanisms to achieve effective emissions reductions in the power industry, supporting China's goal of achieving carbon neutrality by the time of 2060. Finally, we explore the feasibility and benefits of implementing a carbon tax, highlighting its suitability for near-term implementation in the power industry. We conclude that a combination of carbon taxes and carbon trading can make a significant contribution to China's carbon peak and carbon neutrality targets.

At the 75th session of the United Nations General Assembly, China clearly put forward the goals of "carbon peak" in 2030 and "carbon neutrality" in 2060. Achievement of carbon targets. Therefore, the goal of this paper is to analyze the low-carbon development level of China's power industry and study the impact of carbon market policies on the low-carbon development level of the power industry. Based on this, this paper first constructs the low-carbon development evaluation index system of the power industry around the connotation of low-carbon development in the power industry and uses the global principal component analysis model to measure the low-carbon development level of China's power industry. Then, the dynamic change trend and spatial distribution characteristics of the low-carbon development level of China's power industry are analyzed using kernel density estimation and the K-means clustering method. Finally, propensity score matching and difference-in-difference methods are used to analyze the impact of carbon market policies on the low-carbon development level of China's power industry. The results show that, first, the low-carbon development level of China's power industry generally shows an upward trend and a polarized development trend. Second, the low-carbon development level of China's power industry has regional effects and gradient effects. The low-carbon development level of the power industry from high to low is the eastern region, central region and western region. Third, carbon market policies can help improve the low-carbon development level of China's power industry. The research results provide some reference and guidance for the evaluation of the low-carbon development level of China's power industry and the improvement of carbon market policies.

Improving green total factor productivity (GTFP) in the power industry is crucial for achieving China's clean and low-carbon transition and sustainable economic growth. Green technological innovation (GTI) is a key factor driving carbon emissions reduction and improving economic efficiency in the power industry. Therefore, this study utilizes data from 30 provinces in China covering the period 2005–2021 and employs a multiple period difference-in-difference (DID) model to investigate the impact of the carbon trading pilot policy (CTPP) on GTFP in the power industry from the perspective of GTI. The results indicate the following: First, CTPP significantly enhances GTFP in the pilot regions of the power industry. Second, GTI plays a positive mediating role in the relationship between CTPP and GTFP in the power industry, validating the existence of Porter effect in China's power industry carbon emission trading system. Third, there are heterogeneities in the policy effects across the pilot regions, with Beijing and Guangdong achieving outstanding results in improving GTFP compared to other carbon trading pilots. Finally, relevant policy recommendations are provided for carbon market development and improving GTFP in the power industry.

How to peak carbon emissions in China's power sector: A regional perspective

WIth the introduction of "carbon peak and neutrality" targets, China's power industry is under enormous pressure to reduce carbon dioxide (CO2) emissions, as it produces more than 40% of emissions. In response, China's power industry is actively reducing the investment in thermal energy and gradually shifting toward non-fossil energy sources. However, the CO2 reduction effect of these measures is still unknown. This study aims to analyze CO2 emissions from China's power industry from 2009 to 2018 from an entire lifecycle perspective, considering that CO2 emissions also exist in non-fossil power generation. The logarithmic mean Divisia index (LMDI) method is employed to identify the factors influencing CO2 emissions. Then, the modified STochastic Impacts by Regression on Population, Affluence and Technology model is used for comparative validation. The results show that (1) CO2 emissions from China's power industry increased significantly, from 276.5 million tons of CO2 equivalent (Mtce) in 2009 to 436.44 Mtce in 2018; (2) the investment intensity, investment structure, and emission intensity dampen CO2 emissions, with cumulative contribution rates of - 28.88%, - 11.89%, and - 3.16%, respectively. The investment efficiency, economic development level, and population size contribute to CO2 emissions, with cumulative contribution rates of 29.76, 24.68, and 1.07%, respectively; and (3) Investment into the hydropower contributes the least to CO2 emissions, followed by wind, nuclear, photovoltaic, and thermal power. These research findings suggest that the power industry should improve its investment decision-making capabilities and pay particular attention to the hydropower-led non-fossil energy sector.

Factors influencing CO2 emissions in China's power industry: Co-integration analysis

Evaluation and spatial convergence of carbon emission reduction efficiency in China's power industry: Based on a three-stage DEA model with game cross-efficiency

The electric power sector is the largest contributor of CO2 emissions in China. With an increasing concern about environment problems, it is essential to identify key factors that affect CO2 emissions from China's electric power industry so as to help the fossil fuel-based country reduce carbon emissions. For this purpose, the two-phase Logarithmic Mean Division Index (LMDI) decomposition method is presented in this paper. Covering the whole industry chain including power generation, transmission, and consumption, the two-phase LMDI decomposition model is constructed. Then, the influencing aspects are decomposed into ten driving factors, namely, (1) fossil energy power generation structure, (2) fossil energy consumption coefficient, (3) thermal power proportion, (4) power generation and consumption ratio, (5) transmission and distribution loss, (6) industrial power consumption intensity, (7) industrial structure, (8) per capita gross domestic product (GDP), (9) total population, and (10) resident power consumption intensity. Based on data from China statistical yearbook, China energy statistics yearbook, and China power statistics yearbook (2005–2017 edition), the decomposition calculation results show that the power generation and consumption ratio, industrial structure, resident power consumption intensity, per capita GDP, population size, and transmission and distribution loss factors are positive driving factors with contributions of 1.2%, 2.47%, 1.5%, 94.29%, 5.43%, and 4.64%, respectively. However, the fossil energy power generation structure, fossil energy consumption coefficient, thermal power proportion, and industrial power consumption intensity are negative driving factors with contribution rates of −0.34%, −21.72%, −9.85%, and −8.44%, respectively. According to the main effect factors identified, some corresponding measures are proposed to reduce carbon emissions from China's power industry.

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential for carbon emissions reductions in the power industry to achieve China’s emissions reduction targets. The main contribution of this paper is the use of a Generalized Divisia Index Model for the first time to factorize the change of carbon emissions in China’s power industry from 2000 to 2015, and gives full consideration to the influence of the economy, population, and energy consumption on the carbon emissions. At the same time, the Monte Carlo method is first used to predict the carbon emissions of the power industry from 2017 to 2030 under three different scenarios. The results show that the output scale is the most important factor leading to an increase in carbon emissions in China’s power industry from 2000 to 2015, followed by the energy consumption scale and population size. Energy intensity levels have always promoted carbon emissions reduction in the power industry, where energy intensity and carbon intensity effects of energy consumption have great potential to mitigate carbon levels. By setting the main factors affecting carbon emissions in the future three scenarios, this paper predicts the carbon emissions of China’s power industry from 2017 to 2030. Under the baseline scenario, the maximum probability range of the potential annual growth rate of carbon emissions by the power industry in China from 2017 to 2030 is 1.9–2.2%. Under the low carbon scenario and technological breakthrough scenario, carbon emissions in China’s power industry continue to decline from 2017 to 2030. The maximum probability range of the potential annual drop rate are measured at 1.6–2.1% and 1.9–2.4%, respectively. The results of this study show that China’s power industry still has great potential to reduce carbon emissions. In the future, the development of carbon emissions reduction in the power industry should focus on the innovation and development of energy saving and emissions reduction technology on the premise of further optimizing the energy structure and adhering to the low-carbon road.

The power sector is one of the major contributors to China’s carbon emissions, and its low-carbon transformation is of vital importance to China’s long-term sustainable development. This paper aims to investigate the spillover effect between the carbon emission trading (CET) market and power sector in China from a systematic perspective. We adopted the recently developed method of connectedness network and rolling window approach, and found that: (i) during our sample period, the total static spillover index and the average of total dynamic spillover indexes were 60.5735% and 57.9704%, respectively, and the spillover effect of this carbon-power system was relatively strong; (ii) there is weak bidirectional spillover effect between the CET market and the power sector, and the CET market is a net receiver of the information from the power sector; (iii) the CET market may exert a relatively high degree of impact on the power sector occasionally; (iv) for regulated power companies, their interactions with the carbon-power system may be related to its total holding installed capacity and the proportion of renewable energy installed. This study provides implications for policymakers, company managers, and market participants.

China's power industry, 1980–1990: Price reform and its effect on energy efficiency

Promoting the low carbon development of the power industry is an essential path for China to achieve the “3060” dual carbon goal. This paper uses the SBM model and the Moran index to construct a carbon emission efficiency assessment system that meets the requirements of a high proportion of renewable energy access in China. The spatial and temporal evolution and correlation of the carbon emission efficiency of the power industry in 29 provinces in China were analyzed. The results show that the carbon efficiency of China's power industry has been increasing year by year since the 13th Five-Year Plan, and the overall pattern is that the periphery has led to the gradual development of the center, while the Yangtze River Delta and Northeast China show a clear spatial correlation, with prominent policy and technology spillovers. The results may provide scientific guidance for efficiency improvements in each region.

Carbon emission trading policy (CETP) is an important tool for energy savings and emission reduction. However, the effect of CETP on carbon emission reduction in power industry is still unknown. This paper uses the difference-in-differences (DID) model and the intermediary effect model to test the impact and mechanism of CETP on power industry carbon emissions. In addition, a spatial difference-in-differences (SDID) model is established to analyze the spatial spillover effect. The results show that CETP has a significant inhibitory effect on power industry carbon emissions and the results are still valid after endogenous and robust tests. The improvement of technology level and power conversion efficiency plays an intermediary role for CETP to reduce power industry carbon emissions. The optimization of power generation structure is likely to become another important way for CETP to play its role in the future. The spatial spillover effect test shows that CETP not only has a significant inhibitory effect on power industry carbon emissions in the pilot areas but also has a negative spatial spillover effect on power industry carbon emissions in the surrounding non-pilot areas. The heterogeneity tests show that CETP has the most significant reduction effect in the central region of China and the strongest spatial spillover inhibiting effect in the eastern region. The purpose of this study is to provide decision-making references for government to achieve China's dual-carbon goal.

SummaryThis study employs an undesirable‐output–oriented data envelopment analysis model to measure the carbon emission performance of the power industry throughout China's 30 administrative regions during the period of 2003–2012. Also, it further studies the regional disparity and spatial correlation of the carbon emission performance of China's power industry. The main findings are as follows: (1) The carbon emission performance of China's power industry is at a relatively low level, but shows a rising trend. (2) The regional carbon emission performance of China's power industry is extremely unbalanced: The eastern area ranks first, with the highest performance of 0.851, followed by the central area, whereas the western area falls behind, with the lowest performance of 0.760. Provinces in the eastern area generally perform better than those in other areas. (3) According to spatial analysis, the global Moran's I values of carbon emission performance are significantly positive during the sample period, which indicates that the carbon emission performance is a positive spatial correlation and has an obvious clustering effect. The estimate of the local spatial autocorrelation index confirms the imbalance of spatial distribution of the power sector's carbon emission performance. Based on the above findings, several policy suggestions are presented in this article.