Allocation of carbon emission quotas among provinces in China: efficiency, fairness and balanced allocation.

Investigating interior driving factors and cross-industrial linkages of carbon emission efficiency in China's construction industry: Based on Super-SBM DEA and GVAR model

Under the constraint of total carbon emissions, the allocation of carbon emission quotas of 18 railway bureaus in China is conducted to the realization of carbon emission reduction targets of China’s railway transportation industry. This paper proposes a carbon emission quota model for China’s railway industry from the perspective of equity and efficiency and innovatively undertakes research on the allocation of carbon emission quotas for railway administrations. This paper constructs an econometric model to analyze the impact of various influencing factors on China’s railway operation carbon emission and predicts the total carbon emission of China’s railway operation from 2021 to 2030 by scenario analysis method. From the perspective of equity and efficiency, apply the entropy method to give weight to historical responsibility, egalitarianism, and efficiency principle to obtain the initial allocation value of the carbon emission quota of the operator’s 18 regional railway bureau groups; the ZSG-DEA model is used to obtain the optimal allocation. The results show that railway passenger turnover, freight turnover, vehicle structure, and per capita GDP have a promoting effect on railway carbon emission, and the proportion of clean energy has an inhibitory effect on carbon emission. There is a gap between the distribution results under the single principle and the comprehensive distribution results; the combination of both can more effectively promote the development of the railway industry. From the perspective of equity and efficiency, the carbon emission quota of 18 railway bureau groups in China is high in the east and low in the west. Among them, the Shanghai railway bureau obtains the most carbon emission quota, while the Qinghai–Tibet railway bureau obtains the least carbon emission quota. The research results provide a reference for the railway bureau to coordinate emission reduction and the construction of the railway transport carbon emission market.

Carbon emissions have risen in line with China's economic expansion. The key to sustainable development is finding a way to strike a balance between economic expansion and environmental protection, so improving carbon emission efficiency is vital. This paper uses provincial data from 2010 to 2020 to account for total carbon emissions using the emission factor method and obtains carbon emission efficiency data on this basis. A dynamic spatial Durbin model is then used to empirically test the possible influencing factors. The results show that, firstly, the growth rate of total carbon emissions is generally in line with the growth rate of GDP, indicating that there is no 'decoupling' in the economic system. Second, regional carbon emissions and carbon emission efficiency are not necessarily related. Thirdly, there is a clear spatial effect on carbon emission efficiency. The eastern region has the highest carbon emission efficiency, the western region has the lowest, and the northeastern and central regions have little difference in carbon emission efficiency. Further spatial and temporal migration analysis reveals that five provinces have made the migration between 2010 and 2020. Fourthly, in the short term, the direct and indirect effects of the factors affecting carbon emission efficiency are insignificant, but in the long term, most of the factors have significant direct and indirect effects on carbon emission efficiency. Finally, based on the above research findings, this paper makes policy recommendations.

中国旅游业碳排放效率趋同演变及其趋势预测

The freight transport industry is an important field in which to achieve the goal of carbon emission reduction within the transportation industry. Analyzing the spatial–temporal characteristics and regional differences in the freight transport industry’s carbon emissions efficiency (CEE) is an essential prerequisite for developing a reasonable regional carbon abatement policy. However, few studies have conducted an in-depth analysis of the freight transport industry’s CEE from the perspective of geographic space. This study combines the super-efficiency slack-based measure (SBM) model and the window analysis model to measure the freight transport industry’s CEE in 31 Chinese provinces from 2008 to 2019. We then introduced a spatial autocorrelation analysis and the Theil index to analyze the spatial–temporal evolution characteristics and regional differences in the freight transport industry’s CEE in China. The results show that (1) the overall level of the freight transport industry’s CEE is low, with an average of 0.534, which showed a weak downward trend during the study period. This indicates that the freight industry’s CEE has not improved, and there is a massive requirement for energy conservation and emission reduction. (2) From 2008 to 2019, CEE gradually shows a spatial distribution pattern of being “low in the west and high in the east,” with a significant, positive spatial correlation (all passed the significance level test at P < 0.01). This indicates that the spatial diffusion and inhibition of the freight transport industry’s CEE in adjacent areas cannot be ignored. (3) The overall differences in the freight transport industry’s CEE show a fluctuating upward trend from 2008 to 2019. The inter-regional differences of the three regions (east, central, and west) are the main contributors of the total differences. Therefore, narrowing inter-regional gaps in CEE is one of the main ways to improve the freight transport industry’s CEE.

PurposeChina has proposed two-stage goals of carbon peaking by 2030 and carbon neutralization by 2060. The carbon emission reduction effect of the power industry, especially the thermal power industry, will directly affect the progress of the goal. This paper aims to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency of the thermal power industry and proposes policy suggestions for realizing China’s carbon peak and carbon neutralization goals.Design/methodology/approachThis paper evaluates and compares the carbon emission efficiency of the thermal power industry in 29 provinces and regions in China from 2014 to 2019 based on the three-stage slacks-based measure (SBM) of efficiency in data envelopment analysis (DEA) model of undesired output, excluding the influence of environmental factors and random errors.FindingsEmpirical results show that during the sample period, the carbon emission efficiency of China’s thermal power industry shows a fluctuating upward trend, and the carbon emission efficiency varies greatly among the provincial regions. The carbon emission efficiency of the interregional thermal power industry presents a pattern of “eastern > central > western,” which is consistent with the level of regional economic development. Environmental factors such as economic level and environmental regulation level are conducive to the improvement of carbon emission efficiency of the thermal power industry, but the proportion of thermal power generation and industrial structure is the opposite.Originality/valueThis paper adopts the three-stage SBM–DEA model of undesired output and takes CO2 as the undesired output to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency in China’s thermal power industry. The results provide a more comprehensive perspective for regional comparative evaluation and influencing factors of carbon emission efficiency in China’s thermal power industry.

With the development of China’s economy, China is emitting more and more carbon. At the same time, it has also exposed the problem of carbon emission efficiency differences caused by the unbalanced development of resources and economy among regions. Based on the carbon emission panel data of provinces and cities in China from 2009 to 2018, this paper studies carbon emission efficiency and regional differences by constructing a three-stage data envelopment analysis (DEA) model that eliminates the influence of environmental factors and random factors. The research shows that: (1) Carbon emission efficiency in China is spatially distributed; carbon emission efficiency in the western region is generally lower than that in the eastern region. (2) China’s carbon emission efficiency is not entirely synchronized with economic development; carbon emission efficiency in some underdeveloped western regions has reached the forefront of China, and some developed regions in the east are in the middle position. (3) China’s carbon emission efficiency is restricted by scale efficiency; many regions in China have high pure technical efficiency, but due to low scale efficiency, overall efficiency is low. (4) Overall, China’s carbon emission efficiency is currently on the rise, but the rising rate is relatively slow, and there is still plenty of room for improvement.

The allocation of provincial carbon emission quotas under total amount control is an effective way for China to achieve its carbon peak and neutrality targets. Firstly, in order to study the factors influencing China's carbon emissions, the expanded STIRPAT model was constructed; and combined with the scenario analysis method, the total of national carbon emission quota under the peak scenario was predicted. Then, the index system of regional carbon quota allocation is constructed based on the principles of equity, efficiency, feasibility, and sustainability; and the allocation weight is determined by the grey correlation analysis method. Finally, the total carbon emission quota under the peak scenario is distributed in 30 provinces of China, and the future carbon emission space is also analyzed. The results show that: (1) only under the low-carbon development scenario, can China reach the peak target by 2030, with a peak carbon of about 14,080.31 million tons; (2) under the comprehensive allocation principle, China's provincial carbon quota allocation is characterized by high levels in the west and low in the east. Among them, Shanghai and Jiangsu receive fewer quotas, while Yunnan, Guangxi, and Guizhou receive more; and (3) the future carbon emission space for the entire country is modestly surplus, with regional variations. Whereas Hainan, Yunnan, and Guangxi have surpluses, Shandong, Inner Mongolia, and Liaoning have significant deficits.

Study on total-factor carbon emission efficiency of China’s manufacturing industry when considering technology heterogeneity

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions.

Carbon emission efficiency of China’s industry sectors: From the perspective of embodied carbon emissions

Allocation of CO2 emission quotas for industrial production in Industry 4.0: Efficiency and equity

Based on the full‐sample panel data from 23 provinces and cities in China from 2013 to 2020, this paper studies the influences of environmental regulations on regional carbon emission efficiency in China under the background of opening carbon emission trading after dividing the country into high, medium, and low emission areas with emission quantity as the classification standard and constructing a regression model. The experiment reveals that the environmental regulation strength and carbon dioxide emission efficiency are positively correlated with each other in the first place. Second, there exists a positive correlation between environmental regulation strength and carbon dioxide emission efficiency in different emission regions, while in terms of the impact of environmental regulation on carbon emission efficiency, it influences the medium emission areas most, followed by low emission areas, and the high emission areas to the least. Then, current domestic environmental regulation is effective and plays a positive role in improving carbon emission efficiency and promoting the development of a low‐carbon economy in China.

Purpose - The industrial structure upgrading can play an important role in promoting the carbon emission efficiency. Thus, this paper attempts to study the impact of industrial structure upgrading on carbon emission efficiency in order to reduce carbon emissions. Research design, data, and methodology - This paper selects panel data of 30 provinces and municipalities (autonomous regions) in China from 2001 to 2016, and divides them into three regions. The Moore index is used to measure the industrial structure upgrading, the non-radial SBM model based on undesired output is used to measure the slack variable to calculate the total factor carbon emission efficiency. Finally the impact of industrial structure upgrading on the carbon emission efficiency are analyzed. Results - It is found that the Moore index and the carbon emission efficiency in the eastern region is the highest in the three regions. Conclusions - The influence of various influencing factors on carbon emission efficiency is different between regions. The Moore index has a positive effect on the carbon emission efficiency in the eastern region, and has a negative influence coefficient on the central region. The effect on the western region is not obvious.

Purpose - The industrial structure upgrading can play an important role in promoting the carbon emission efficiency. Thus, this paper attempts to study the impact of industrial structure upgrading on carbon emission efficiency in order to reduce carbon emissions. Research design, data, and methodology - This paper selects panel data of 30 provinces and municipalities (autonomous regions) in China from 2001 to 2016, and divides them into three regions. The Moore index is used to measure the industrial structure upgrading, the non-radial SBM model based on undesired output is used to measure the slack variable to calculate the total factor carbon emission efficiency. Finally the impact of industrial structure upgrading on the carbon emission efficiency are analyzed. Results - It is found that the Moore index and the carbon emission efficiency in the eastern region is the highest in the three regions. Conclusions - The influence of various influencing factors on carbon emission efficiency is different between regions. The Moore index has a positive effect on the carbon emission efficiency in the eastern region, and has a negative influence coefficient on the central region. The effect on the western region is not obvious.