Carbon Emission Efficiency Of China Research Articles

Regional differences and improvement potential of carbon emission efficiency in China’s power industry: insights from meta-frontier DEA model considering integer constraints

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.

A DEA game cross-efficiency based improved method for measuring urban carbon emission efficiency in China.

An accurate evaluation of carbon emission efficiency (CEE) at the city level can provide guidelines for understanding low carbon performance, which is crucial to achieving dual carbon targets. Existing CEE studies focused on national, industrial, and provincial scales while neglecting the city level and failing to consider competing relationships among decision-making units in their measurement models. To fill these gaps, this paper introduces the data envelopment analysis game cross-efficiency model (DEA-GCE) to measure urban CEE performance and compares it with the traditional Super-SBM model using the data from 283 Chinese cities between 2006 and 2019. The results show that (1) the DEA-GCE method provided more intensive and stable results. (2) Overall CEE of Chinese cities declined slightly amidst fluctuations during this period. (3) CEE in cities exhibits spatial clustering characteristics. CEE performance in Northeast China has improved, while CEE in Northwest China continues to lag behind. This study introduced an innovative method for calculating urban CEE and conducted an empirical study of 283 Chinese cities, which has implications for formulation of emission reduction policies.

Research on the impact and intermediary effect of carbon emission trading policy on carbon emission efficiency in China

In this paper, the impact of the carbon emission trading policy on carbon emission efficiency is discussed based on the local data of China from 2004 to 2019, based on the natural experiment of carbon emission trading policy. The intermediary model is introduced to analyze the mediating effect of industrial structure and energy consumption structure in emission reduction. The results show that the carbon emission trading policy not only directly improves the carbon emission efficiency, but also indirectly improves the carbon emission efficiency by upgrading the industrial structure and adjusting the energy consumption structure. Finally, this paper verifies that carbon trading policy has a synergistic emission reduction effect on sulfur emission efficiency. The mechanism of the SO2 reduction is similar to that of the CO2 reduction mechanism, with direct and intermediary effects.

Evaluation of Carbon Emission Efficiency of Chinese Oil and Gas Enterprises

This paper constructs a carbon emission efficiency evaluation model using the non-expected output SBM model and the Global-Malmquist-Luenberger model to statically and dynamically evaluate the carbon emission efficiency of 69 oil and gas enterprises from 2016 to 2021. The results found that the overall carbon emission efficiency of China's oil and gas enterprises is at a relatively low level, but with an upward trend. The results also show that there are differences in carbon emission efficiency among oil and gas enterprises, and that the carbon emission efficiency of most oil and gas enterprises improves thanks to the improvement of technical efficiency.

Carbon emission efficiency of China's logistics industry: Measurement, evolution mechanism, and promotion countermeasures

Improving carbon emission efficiency of the logistics industry (LCEE) is important in achieving carbon neutrality. We examine the carbon emissions of 31 provinces and cities in China that aim to reduce emissions and increase efficiency in the logistics industry. We construct the evaluation index system of LCEE, using the Super-EBM-unexpected model to measure it. Moreover, the global Malmquist–Luenberger index and the spatial autocorrelation model are used to analyze its temporal and spatial evolution mechanisms and identify its influencing factors via the spatial Durbin model. The results show a very low average LCEE value and a decreasing gradient distribution in space for China. The average pure technical efficiency is the main driving force for improving LCEE. Environmental regulation and the residents' consumption level positively affect LCEE. However, economic level, industrial structure, government input, and energy intensity have a negative impact. Countermeasures and recommendations are proposed to improve LCEE and provide theoretical and methodological support for its research and management. The study provides implications for policymaking related to ecosystem protection and sustainable development in the logistics industry.

Spatiotemporal differences in and influencing factors of urban carbon emission efficiency in China's Yangtze River Economic Belt.

Improving urban carbon emission efficiency (CEE) is vital to achieving the goal of urban carbon neutrality. However, the synergistic configurational effect of multiple influencing factors on CEE is not clear. Taking the Yangtze River Economic Belt (YREB) as an example, this paper adopts the standard deviation ellipse and Dagum Gini coefficient method to investigate the spatiotemporal differences in urban CEE in the YREB, and using the fuzzy-set qualitative comparative analysis (fsQCA) method, it explores the configurational effect of CEE influencing factors from the system perspective. The main conclusions are as follows: first, the overall level of urban CEE in the YREB is low, with a certain polarization phenomenon. Second, the relative differences in urban CEE in the YREB show a fluctuating upward trend, and the regional differences mainly originate from the overlapping part between regions. Finally, the main CEE influencing factors do not act in isolation, they constitute a complex process of synergistic interaction, with complementary substitution and causal asymmetry.

Strategies for improving the industrial carbon emission efficiency in China: an approach based on trend prediction and regional learning mechanism.

It is an important way for mining the characteristics of regional carbon emission efficiency and exploring regional similarity to learn from intergovernmental learning mechanisms and regional industrial low-carbon development experiences. This study proposes a regional learning mechanism of industrial carbon emission efficiency (ICEE) prediction and regional similarity analysis to explore strategies for carbon emission reduction. We first calculated the industrial carbon emission efficiency of 30 provinces in China from 2000 to 2021 using the super-SBM model. Secondly, the spatiotemporal characteristics of industrial carbon emission efficiency were explored through the space-time cube model, time series clustering method, and local outlier analysis. Finally, the screening of regions with low efficiency levels and the search for learning objects were realized by forest regression prediction and regional similarity calculation. The results of the study were as follows: (1) There were significant differences in industrial carbon emission efficiency among different provinces. (2) Based on the time series clustering results, we found that there were similar change characteristics of industrial carbon emission efficiency in different provinces. (3) The industrial carbon emission efficiency of most provinces had significant correlation in space and time, mainly in high-high clustering. (4) The industrial carbon emission efficiency of most regions will maintain a high efficiency level in the next 10 years, but the six provinces of Xinjiang, Qinghai, Gansu, Ningxia, Liaoning, and Heilongjiang will always be at a low efficiency level. It is possible to set appropriate learning targets for each region and to find lessons to be learned from the regions with high similarity by calculating the similarity between each province and the six provinces.

Digital finance, government intervention, and carbon emission efficiency in China.

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.

An empirical study on the measurement and influencing factors of carbon emission efficiency in China's transportation industry

<p>The severe climate problem has forced the Chinese government to put forward the goal of "carbon peak" and "carbon neutrality", and the transportation industry is a key area of carbon emission reduction. Based on this background, this paper introduces the BCC model and the Malmquist-Luenberger index model to measure the carbon emission efficiency of China's provincial transportation industry from the static and dynamic perspectives from 2014 to 2020. At the same time, the Tobit model is used to estimate the influencing factors of carbon emission efficiency. The results show that: (1) The overall transportation carbon emission efficiency level is low. The comprehensive efficiency of 30 provinces is at [0.6634,0.7154], which needs to be improved urgently. (2) The carbon emission efficiency of transportation is in a growing trend, and the ML index is 1.142, which is significantly higher than 1.0. In addition, the mean value of technical efficiency is 1.015, and the mean value of technological progress efficiency is 1.125. The two factors jointly affect comprehensive efficiency. (3) The carbon emission efficiency of different regions is heterogeneous. The carbon emission efficiency of East China and North China is better than that of Northeast China and South China, while the carbon emission efficiency of Northwest China and Southwest China is at the middle level in China. (4) External environmental factors, such as regional GDP, resident population and consumption level, have a significant impact on the carbon emission efficiency of transportation.</p>

Coupling coordination evaluation and driving path of digital economy and carbon emission efficiency in China: A fuzzy-set qualitative comparative analysis based on 30 provinces.

Enhancing the level of coupling coordination between the digital economy (DIE) and carbon emission efficiency (CEE) is not only an inevitable choice for achieving the goals of energy conservation and emission reduction and promoting green development in China, but also a key path to implementing China's "Double Carbon" strategy. Based on the relevant statistical data of 30 provincial-level regions in China from the period covering 2011 to 2019, this paper empirically analyzed the coupling coordination between the DIE and CEE and its influencing factors. In this study, an improved coupling coordination degree (CCD) model was used to evaluate the degree of the coupling and coordinated development of the DIE and CEE in provincial regions of China. Finally, based on the Technology-Organization-Environment (TOE) framework, a fuzzy-set qualitative comparative analysis (fsQCA) method was employed to identify the realization path of the coupling and coordinated development of the DIE and CEE from the perspective of configuration. The results demonstrated that the coupling coordination between the DIE and CCE in China demonstrated a gradual upward trend, and exhibited regional differences, showing a decreasing trend of east > middle > west. Regarding the influencing factors, no single influencing factor could act as a necessary condition for the high CCD, the coupling and coordinated development of the DIE and CEE is a multifactorial synergy. There were five paths for the high degree of coupling coordination between the DIE and CEE, which were divided into three types: organization-environment-led type, environment-led type, and technology-organization-led type. Furthermore, technological innovation level and industrial structure could substitute for one another in some conditions, and environmental regulation and economic development level were synchronized. These conclusions provide a theoretical basis for countries to formulate policies to promote the coupling and coordinated development of their DIE and CEE.

Spatial and temporal evolution of urban carbon emission efficiency in China.

Improving carbon emission efficiency is crucial for achieving carbon peak and carbon neutrality. This paper analyzes the carbon emission efficiency of Chinese cities using panel data from 275 prefecture-level cities between 2006 and 2020. The super-efficiency SBM (undesirable slacks-based measurement) model of undesired output, combined with kernel density estimation and exploratory data analysis, is employed. This study finds, firstly, from 2006 to 2020, the average efficiency of carbon emissions in Chinese cities declined from 0.53 to 0.48, indicating a downward trend. Secondly, from a temporal perspective, the level of dispersion in urban carbon emission efficiency has become increasingly distinct over time, coupled with a decrease in polarization. Thirdly, in terms of spatial analysis, urban carbon emission efficiency in China shows a positive correlation in a global context. In terms of local spatial correlation, it primarily exhibits high-high and low-low clusters, with a few instances of low-high and high-low clusters. Finally, based on the research findings, this paper proposes suggestions to improve urban carbon emission efficiency.

Impact of aging-related consumption trend on carbon emission efficiency in China: mediation effect model based on industrial structure adjustment

With the deepening of China’s aging process, changes in the age structure of the population affect the industrial structure and consumption structure in different ways and have a knock-on effect on the whole economic system. Therefore, aging is one of the objective factors affecting future carbon emissions in China. This study analyzes the impact mechanism of aging-related consumption trend on carbon emission efficiency (CEE) based on panel data of 30 Chinese provinces from 2000 to 2019. The results show that the aging-related consumption trend is conducive to the improvement of regional CEE, and the mediation transmission mechanism of industrial structure adjustment is obvious, with a coefficient of 0.1496. The core industry closely linked to the demand for aging-related consumption is consumer services. The promotion of the consumption demand of the aging in the eastern region on the CEE and the transmission stimulation of the industrial structure adjustment are the most obvious. The mediation effect in the central and western regions is relatively weak, and the aging-related consumption demand has not formed a positive interaction with the aging industry. Therefore, improving the market construction of products and services for the aging is beneficial to achieve a virtuous cycle of aging-related consumption upgrading and carbon emission efficiency. This research can provide insights for China to promote industrial structure transformation within the aging trend and also help China meet its carbon neutrality target on schedule.

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

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.

Exploring the role of green finance and natural resource policies in carbon emission efficiency of China's manufacturing industry in the context of post-COVID-19 period

China's manufacturing industry accounts for a significant portion of its total carbon emissions. While the activities of manufacturing have been found to be inevitably influenced by the pandemic, how COVID-19 influences the utilization of manufacturing industry has rarely been discussed in previous studies. Understanding how COVID-19 has reshaped manufacturing activities may also be helpful for providing updated policy implications in the post-pandemic era. The objective of this research is to investigate the extent to which the impact of the built environment on the utilization of manufacturing industry has changed post pandemic, as compared to pre-pandemic period. This study empirically investigates the spatial effects of green finance on manufacturing industry's carbon emission efficiency (CEE) in China, as well as the role that natural resource policies play in this mechanism. Based on panel data from 30 provinces and municipalities in China during the period from 2013 to 2022, a Spatial Durbin Model applied to analyze the nexus between green finance, natural resource policies and CEE. Results show green finance significantly improves CEE and this effect is strengthened in the post-pandemic period. This research also verifies the synergy effect of green finance and natural resource policies on CEE of manufacturing industry in post-pandemic. The results of this study provide policymakers with a timely understanding of the changes in CEE during the pandemic, which can help redesign manufacturing industry in the post-pandemic era.

The impact of industrial collaborative agglomeration on total factor carbon emission efficiency in China

Improving total factor carbon emission efficiency (TCE) is the key to achieving carbon emission reduction targets while ensuring economic growth. In this paper, the global Malmquist index based on the SBM model is used to measure TCE of 283 cities in China from 2011 to 2019. On this basis, this paper uses the spatial econometric model and intermediary effect model to empirically analyze the impact of industrial co-agglomeration on TCE and its transmission mechanism. Furthermore, considering the differences in geographical location and resource endowment among regions, this paper analyzes the heterogeneous effect of industrial collaboration agglomeration on TCE in different regions and cities. The results show that: (1) Industrial co-agglomeration can improve TCE, and its main transmission channel is technological innovation. (2) Industrial co-agglomeration has a positive spatial spillover effect. Industrial co-agglomeration in one region can improve the TCE in the surrounding regions. (3) Industrial co-agglomeration of cities with different geographic locations and resource endowments has a heterogeneous effect on TCE. Regarding geographical heterogeneity, the industrial co-agglomeration in the eastern region has the greatest promoting effect on TCE, followed by the central region. However, the impact of industrial co-agglomeration in the western region on TCE is not significant. Regarding resource endowment heterogeneity, the industrial co-agglomeration in non-resource-based cities has a greater promoting effect on TCE than that in resource-based cities.

Impact of labor and energy allocation imbalance on carbon emission efficiency in China's industrial sectors

Greenhouse gas emission is the focus of global climate change concerns. The change in industrial structure can impact carbon emission efficiency (CEE) by affecting labor and energy input. However, there is an obvious imbalance of labor and energy allocation within China's industrial sectors. Here, we use the super-slacks-based model data envelopment analysis (Super-SBM-DEA) to calculate the CEE of 32 industrial sectors and adopt the Tobit model to analyze the impact of industrial allocation imbalance on CEE. The results show that the overall industry and manufacturing CEE is still at a low level, with an average CEE of 0.53. The industrial sectors with higher CEE are these sectors with advanced innovative technology and low energy consumption. The results of the Tobit model show that the imbalance of labor and energy allocation is the key factor limiting carbon emission efficiency improvement. Furthermore, the imbalance of labor allocation hurts the CEE of labor-intensive sectors. The coefficient of labor allocation imbalance (distL) is −2.483, and the inflow of labor can improve the CEE of non-labor-intensive sectors. The CEE of energy-intensive sectors is sensitive to the imbalance of energy allocation, the marginal impact of energy allocation imbalance (distE) is −2.296. Improving energy efficiency is a key task to reduce carbon emissions in sectors relying on energy input. But for non-energy-intensive sectors, optimizing energy allocation has a limited effect on reducing carbon emissions. This research can provide insights for emerging economies to coordinate carbon reduction and industrial transformation.

Carbon emission efficiency evaluation of wastewater treatment plants: evidence from China.

A scientific evaluation of the carbon emission efficiency is crucial for ensuring the sustainable development of wastewater treatment plants (WWTPs). In this paper, we applied a non-radial data envelopment analysis (DEA) model to calculate the carbon emission efficiency of 225 WWTPs located in China.The results showed that the average carbon emission efficiency of China's WWTPs was 0.59, indicating that the efficiencies of most samples still require improvement. The carbon emission efficiency of WWTPs from 2015 to 2017 decreased because of the decrease in technology efficiency. Among the influencing factors, different treating scales had positive impact on carbon emission efficiency improvement. WWTPs with anaerobic oxic process and the first-class A standard were likely to have higher carbon emission efficiency in the 225 WWTPs. By incorporating direct and indirect carbon emissions into WWTP efficiency evaluation, this study helped decision-makers and related water authorities to better understand the contribution of WWTPs to the aquatic and atmospheric environments.

A study on the spatial and temporal evolution of carbon emission efficiency in China's provincial power industry

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.

Do technical differences lead to a widening gap in China's regional carbon emissions efficiency? Evidence from a combination of LMDI and PDA approach

The growing disparity in China's regional carbon emission efficiency is an easily overlooked but crucial issue in China's transition to carbon neutrality. To better understand drivers of widening gap of regional carbon emission efficiency in China from technological progress, industrial inequality, regional inequality, and management inequality, a novel approach is developed by combining a logarithmic mean divisia index and production theoretical decomposition analysis based on multi-layer frontier. The results indicate (i) Except for Ningxia and Xinjiang, the energy utilization efficiency of the remaining provinces continued to improve during the study period. The fossil energy consumption structure in most provinces gets gradually improved, favoring fossil energy with less carbon emissions. The industrial structure of most provinces has not improved significantly. (ii) Improving overall energy efficiency and technological progress is extremely important for carbon emission reduction. Potential energy intensity effect and energy technology efficiency gap effect are the key factors to restrain the increase of carbon emission intensity. (iii) For economically developed regions (such as Beijing and Guangdong), technological factors, potential energy efficiency and scale have a greater impact, while industries, regions, and management efficiency have a smaller impact, indicating that developed regions are at the frontier of efficiency and are improving production and energy structure. Moreover, there is an efficiency gap between underdeveloped regions (such as Xinjiang and Qinghai) and economic regions. Hence, while improving the production structure and energy consumption structure, the efficiency gap with developed regions should be narrowed.

Spatial Correlation Network Structure of Carbon Emission Efficiency in China’s Construction Industry and Its Formation Mechanism

In the context of “carbon peak, carbon neutrality”, it is important to explore the spatial correlation network of carbon emission efficiency in the construction industry and its formation mechanism to promote regional synergistic carbon emission reduction. This paper analyzes the spatial correlation network of carbon emission efficiency in China’s construction industry and its formation mechanism through the use of the global super-efficiency EBM model, social network analysis, and QAP model. The results show that (1) the national construction industry’s overall carbon emission efficiency is steadily increasing, with a spatial distribution pattern of “high in the east and low in the west”. (2) The spatial correlation network shows a “core edge” pattern. Provinces such as Jiangsu, Zhejiang, Shanghai, Tianjin, and Shandong are at the center of the network of carbon emission efficiency in the construction industry, playing the role of “intermediary” and “bridge”. At the same time, the spatial correlation network is divided into four plates: “bidirectional spillover plate”, “main inflow plate”, “main outflow plate”, and “agent plate”. (3) Geographical proximity, regional economic differences, and urbanization differences have significant positive effects on the formation of a spatial correlation network. At the same time, the industrial agglomeration gap has a significant negative impact on the formation of such a network, while energy-saving technology level and labor productivity differences do not show any significant effect.