Calculation of carbon emission efficiency in China and analysis of influencing factors.

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.

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

Spatial difference, dynamic evolution and influencing factors of carbon emission efficiency in China at the provincial level

In accordance with the "dual carbon" objective, China is required to effectively pursue economic expansion and environmental preservation while concurrently enhancing carbon emission efficiency (CEE). This study examines the influence of digital finance on CEE and evaluates the moderating effect of government intervention. The analysis uses panel data collected from 282 cities in China at the prefecture level and above, spanning the period from 2011 to 2021. The findings indicate the following: (1) CEE in China is relatively low, and there are notable regional disparities. Specifically, there is a discernible downward trend in CEE throughout the eastern, central, and western areas. (2) In general, the implementation of digital finance has the potential to enhance the efficiency of carbon emissions. The observed effect is significant in the eastern and central regions but not in the western region. (3) Government subsidies have the potential to amplify digital finance's impact on CEE in the eastern region. Conversely, in the central and western regions, its influence can be increased by environmental regulations. Based on these findings, this study presents recommendations for advancing digital finance, enhancing the targeting and assessment of government subsidies, refining environmental regulations, and encouraging the adoption of green technologies.

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.

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.

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.

PDF HTML阅读 XML下载 导出引用 引用提醒 中国旅游业碳排放效率趋同演变及其趋势预测 DOI: 10.5846/stxb202108102207 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家社会科学基金青年基金项目(19CGL030) Convergent evolution and trend prediction of carbon emission efficiency in China's tourism industry Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:低碳旅游是实现旅游业可持续发展的必然之路,准确把握中国旅游业碳排放效率趋同演变及其发展趋势,对中国"双碳"目标的实现具有重要的意义。基于超效率-SBM模型对2009-2019年中国旅游业碳排放效率科学测度,再采用空间自相关分析与时空Markov链,检验其趋同效应并深入探析其时空趋同特征,最后结合Markov链的无限分布矩阵,科学预测中国旅游业碳排放效率的发展趋势。结果表明:(1)时间特征上,2009-2019年中国旅游业碳排放效率呈"过山车式"波动上升态势,东部效率均值大于西部大于中部。研究期内效率均呈显著的俱乐部趋同分布,从2009-2014年偏向于较低效率"单峰"趋同向2015-2019年偏向于低效率和高效率的"双峰"趋同演变,且相邻效率等级俱乐部更容易发生转移,其中后期比前期的俱乐部趋同效应更强。(2)空间特征上,不同空间滞后条件下,中国旅游业碳排放效率均呈显著的俱乐部趋同分布,但趋同程度随滞后水平的提升有所减弱。空间滞后水平越高,效率向上转移可能性越大。(3)省域效率转移上,大多中西部省份效率保持平稳,但部分沿海发达省份和西部省份实现向上转移,仅少数中东部省份向下转移。(4)省域及邻域效率转移上,大多数省域与其邻域保持相同的转移方向,其中中西部省份处于"近墨者黑"的困境,少数东部沿海和西南省市摆脱邻域效率"下滑"陷阱,实现向上转移.(5)发展趋势上,未来中国旅游业碳排放效率将整体有所提升,空间分布由"双峰"向"单峰"趋同演变,但仍聚类于较低效率趋同。随着空间滞后水平的提升,俱乐部趋同程度有所减弱,但趋同质量显著提升。最后本文提出针对性地建议,以期促进中国旅游业的可持续发展,助推"双碳"目标的实现。 Abstract:Low-carbon tourism is an inevitable way to realize the sustainable development of tourism. Accurately grasping the convergence characteristic and development trend of China's tourism carbon emission efficiency are of great significance to realization of China's "dual carbon" goal. This paper is based on the Super-SBM model to scientifically measure the carbon emission efficiency of China's tourism industry from 2009 to 2019. Then, the spatial autocorrelation analysis and spatio-temporal Markov chain were used to test its convergence effect and in-depth analysis of its temporal and spatial convergence characteristics. Finally, combined with the infinite distribution matrix of the Markov chain, we scientifically predicted the development trend of China's tourism carbon emission efficiency. The result shows that: (1) In terms of time characteristics, the carbon emission efficiency of China's tourism industry has fluctuated and increased in a "roller coaster style" from 2009 to 2019. The eastern region has the highest average efficiency, followed by the western and finally the central region. During the study period, the efficiency showed a significant club convergence distribution, from a "single peak" distribution biased towards lower efficiency in 2009-2014 to a "double peak" distribution biased towards low efficiency and high efficiency in 2015-2019, and clubs with adjacent efficiency levels are more likely to transfer, and the later period is stronger than the earlier period of club convergence effect. (2) In terms of spatial characteristics, under different spatial lag conditions, the carbon emission efficiency of China's tourism industry has a significant club convergence distribution, but the degree of convergence has weakened as the lag level increases. The higher the level of spatial lag, the greater the possibility of an upward shift in efficiency. (3) In terms of provincial efficiency transfer, the efficiency of most central and western provinces remained stable, but some developed coastal provinces and western provinces achieved upward transfer, and only a few central and eastern provinces transferred downward. (4) In terms of the efficiency transfer of provinces and neighbors, most provinces and their neighbors maintain the same transfer direction. Among them, the central and western provinces are in the "with the bad, it also goes bad" situation, and a few eastern coastal and southwestern provinces get rid of the trap of decline in their neighborhoods and realized upward shifts. (5) In terms of the development trend, in the future, the overall carbon emission efficiency of China's tourism industry will be improved, and the spatial distribution will evolve from a "double peak" to a "single peak", but it will still be clustered towards lower efficiency. With the increase in the level of spatial lag, the degree of club convergence has weakened, but the quality of convergence has improved significantly. Finally, this article puts forward specific suggestions in order to promote the sustainable development of China's tourism industry and promote the realization of the "dual carbon" goal. 参考文献 相似文献 引证文献

The scale effect of urbanization on improving carbon emission efficiency and achieving low-carbon targets is an important topic in urban research. Using dynamic panel data from 64 prefecture-level cities in four typical urban agglomerations in China from 2006 to 2016, this paper constructed a stochastic frontier analysis model to empirically measure the city-level total-factor carbon emission efficiency index (TCEI) at different stages of urbanization and to identify rules governing its spatiotemporal evolution. We quantitatively analyzed the influences and functional characteristics of TCEI in the four urban agglomerations of Pearl River Delta, Beijing-Tianjin-Hebei, the Yangtze River Delta, and Chengdu-Chongqing. Results show that the TCEI at different stages of urbanization in these urban agglomerations is increasing year by year. The overall city-level TCEI was ranked as follows: Pearl River Delta &gt; Beijing-Tianjin-Hebei &gt; Yangtze River Delta &gt; Chengdu-Chongqing. Improvements in the level of economic development and urbanization will help achieve low-carbon development in a given urban agglomeration. The optimization of industrial structure and improvement of ecological environment will help curb carbon emissions. This paper provides decision-making references for regional carbon emission reduction from optimizing industrial and energy consumption structures and improving energy efficiency.

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.

Previous studies on the carbon emission efficiency (CEE) in the power industry have neglected concerns related to regional heterogeneity and the integer character of certain indicators. In response to these issues, this study proposes a meta-frontier DEA model that integrates integer constraints for evaluating the CEE of China’s provincial power industry from 2011 to 2021. This study also proposes to apply the Theil, technology gap ratio, and inefficiency decomposition indexes to analyze regional disparities, technological gaps, and strategies for enhancing CEE within China’s provincial power industry. The research findings highlight several key points. First, China’s power industry exhibits inefficiencies in CEE. The central region mainly contributes to the overall CEE decline, and approximately 70% of provinces demonstrate an average CEE below 0.70. Second, the technological level of the western region is leading, while that of the central region is the worst. Specifically, Ningxia, Hainan, and Jiangsu have the most advanced production technology levels. Third, substantial disparities in CEE within China’s power industry primarily stem from regional imbalances in development. Fourth, technical inefficiency contributed 68.24% of the CEE in the central region, and management inefficiency contributed 96.91% and 65.42% in the western and eastern regions, respectively. Overall, China’s power industry still has 37% potential for improvement.

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.

Improvements in carbon emission efficiency are crucial to China’s economic growth; carbon emission reduction and urbanization are two of the focuses of research on carbon emission efficiency. This paper selects 2000–2015 panel data from 30 provinces in China, evaluates the carbon emission efficiency of each province using the DEA method and, based on the STIRPAT expansion form, empirically looks at the effect of urbanization on carbon emission efficiency. The results show that, during the chosen time frame, not only did the carbon emission efficiency of China’s provinces show an upward trend but the carbon emission efficiency of the Eastern, Central and Western regions differed markedly, with the highest efficiency in the Eastern region, the second highest in the Central region and the lowest in the Western region. After controlling for population density, economic development level, energy intensity and industrial structure, urbanization we determine that urbanization can indeed improve carbon emission efficiency, although there are regional differences. Urbanization is conducive to improvements in carbon emission efficiency in both the Central and Western regions but the promotion effect of the Western region is stronger. The effect in the Eastern region is not significant. Based on the conclusions above, this paper puts forward policy recommendations that promote both China’s lower carbon efficiency and future environmental protection.

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