Evaluation of carbon emission efficiency in China's airlines

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

Global warming is one of the key issues attracting international concern. The carbon dioxide emission produced by energy combustion is the main cause of the greenhouse effect, and reducing carbon emissions is considered the most effective way to deal with the greenhouse effect. The extensive production mode characterized by high energy consumption, high emission, and low efficiency in China’s construction industry intensifies the contradiction between economic development and resources and the environment, and the growth under this mode is at the expense of consuming a lot of resources and energy. The improvement of carbon emission efficiency is an effective means of achieving the goal of economic growth and carbon emission reduction simultaneously, making it necessary to accurately measure the carbon emission efficiency of the construction industry in each province, determine the influencing factors, and formulate reasonable emission reduction policies for this industry. In this study, an input-output index system of carbon emission efficiency of China’s construction industry was constructed, the carbon emission efficiency of the construction industry in each province was evaluated using the super-efficiency SBM model, and the factors affecting the carbon emission efficiency of this industry were analyzed via the Tobit model. The results showed that the average value of carbon emission efficiency of the construction industry generally showed a rising trend in a fluctuating way during the study period. From 2014 to 2022, the average carbon emission efficiency of the national construction industry presented an upward trend, from 1.122 in 2014 to 1.148 in 2022; the regional economic level (p= 0.020 < 0.05) and human capital level (p= 0.000 < 0.01) exerted obvious promoting effects on the carbon emission efficiency of China’s construction industry, while the urbanization development (p= 0.049 < 0.05) generated evident negative effects on carbon emission efficiency of this industry. The research results have important reference values for making cross-provincial emission reduction plans for the construction industry, promoting its carbon emission efficiency, and driving the research and development of green building materials and clean energy.

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.

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.

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

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

In light of the Chinese government’s dual carbon goals, achieving cleaner production activities has become a central focus, with regional environmental collaborative governance, including the management of agricultural carbon reduction, emerging as a mainstream approach. This study examines 268 prefecture-level cities in China, measuring the carbon emission efficiency of city agriculture from 2001 to 2022. By integrating social network analysis and a modified gravity model, the study reveals the characteristics of the spatial association network of city agricultural carbon emission efficiency in China. Additionally, the quadratic assignment procedure is employed to investigate the driving factors. The findings indicate that: (1) The carbon emission efficiency of cities agriculture in China displays substantial spatiotemporal heterogeneity, characterized by marked regional clustering. Central cities generally exhibit higher efficiency levels, while the surrounding cities tend to have lower efficiency. (2) The carbon emission efficiency of city agriculture in China displays multidimensional, complex, and organic characteristics, with potential for enhanced network stability. (3) Agricultural regions in central and southeastern China dominate the spatial network, while regions with weaker agricultural sectors, like Beijing, Shanghai, and Ningxia, occupy peripheral positions. (4) Within the spatial association network of city agricultural carbon emission efficiency in China, Intra block spatial correlations are low, while interblock spatial correlations are strong, exhibiting significant spillover effects. (5)Variations in agricultural development levels and mechanization significantly enhance the formation of networks related to city agricultural carbon emission efficiency in China. Conversely, differences in industrial structure and fertilizer application levels exert a substantial negative influence on these networks.

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.

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.

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.

With a primary focus of achieving carbon neutrality, the energy-intensive industrial sectors (EIIs) contribute to more than half of China’s carbon dioxide (CO2) emissions. During the process of China’s rapid economic development, distorted energy prices gradually became the main obstacle to energy conservation and emission reductions in the EIIs. Therefore, this study focused on determining the mechanisms affecting the energy price distortions of carbon emission efficiency (CEE) in China’s EIIs. Based on a stochastic frontier analysis, the changing trend of CEE in China’s EIIs was evaluated. The channels impacting the energy price distortions of CEE were further analyzed by a mediating effect and moderated mediating effect model. The main contributions and findings include the following: energy price distortions have a significant negative impact on CEE by suppressing technological innovations and hindering the restructuring of energy consumption; governmental environmental regulatory policies mitigate their suppressing effect on technological innovations and reduce the preventing effect by improving the energy consumption structure. The results suggest that market-based price reforms and moderate environmental regulation by the government could help to improve CEE in China’s EIIs. These findings are of great significance for promoting the sustainable development of the energy-intensive industrial sectors and achieving carbon neutrality.

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.

This paper constructs the ZSG-SBM model, comprehensive fairness allocation model, and balance allocation model from the perspectives of efficiency, fairness, and balance. Making use of the actual input-output data of China's provincial economic system in 2019, and the above three models to study the reasonable scheme of China's provincial carbon emission quota allocation in 2019. The results show that ZSG efficiency allocation can significantly improve the carbon emission efficiency of inefficient provinces, and the carbon emissions of inefficient provinces after allocation reach the efficiency frontier. The carbon emission quota of 16 provinces which located in the central and western regions of China with underdeveloped economy and relatively low carbon emission efficiency need to be decreased, while the eastern coastal provinces with more developed economy and high carbon emission efficiency in China should increase their carbon emission quota. On the one hand, comprehensive fairness allocation reduces the carbon emission reduction target constraints of economically underdeveloped provinces; on the other hand, it strengthens the carbon emission reduction target constraints of low-carbon technology backward provinces. As a result, the carbon emission quota of economically developed provinces and provinces with high carbon intensity per unit GDP is reduced in this method. The result of balancing efficiency and fairness lies between ZSG efficiency allocation and comprehensive fairness allocation. In order to alleviate the huge pressure on the emission reduction of provinces with low actual carbon emission efficiency under the ZSG efficiency allocation mode, the Chinese government can gradually increase the weight of ZSG efficiency allocation results, and finally adopt a complete ZSG efficiency allocation scheme in the carbon peak year to realize the transformation of low-carbon economy.

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

Based on relational data and a network perspective, this study calculates the carbon emission efficiency of China's logistics industry from 2006 to 2021 and analyzes the characteristics of the spatial correlation network structure of this efficiency through social network analysis. The results are as follows: (1) During the study period, the carbon emission efficiency of China's logistics industry exhibited a fluctuating downward trend, with notable regional disparities. Overall, there was an imbalance among regions, with the eastern region showing higher efficiency than the central region, and the central region higher than the western region. (2) From the perspective of the overall network structure, the spatial correlation of carbon emission efficiency in China's logistics industry tended to become closer, with a relatively stable and balanced spatial correlation structure. Additionally, (3) differences in spatial distance, economic development level, energy intensity, urbanization level, and industrial structure had significant impacts on the formation of the spatial correlation network of carbon emission efficiency in China's logistics industry.