Carbon Emission Efficiency Research Articles

Evaluation of Carbon Emission Efficiency and Analysis of Influencing Factors of Chinese Oil and Gas Enterprises

Evaluation of Carbon Emission Efficiency and Analysis of Influencing Factors of Chinese Oil and Gas Enterprises

Agricultural Carbon Emission Efficiency Measurement, Space Spillover and Influencing Factors

Agricultural Carbon Emission Efficiency Measurement, Space Spillover and Influencing Factors

Regional carbon emission efficiency evaluation combining gray forecasting and game cross-efficiency analysis: The case of Yangtze River Delta

Carbon emission efficiency (CEE) reflects the interplay between carbon emissions and the economy, which refers to achieving more economic benefits and lower carbon emissions while considering energy, labor, and capital inputs. Assessing regional CEE is crucial for evaluating the level of China's regional low-carbon economic development. Thus, this paper proposes a scenario-based hybrid model with a foresight perspective and game cross-efficiency (GCE) analysis. It measures the future CEE of 41 Yangtze River Delta (YRD) cities from 2023 to 2030. The improved gray forecasting models generate the input and output datasets for GCE analysis, and the assurance region constraint simulates the carbon emission and energy consumption dual-control policy. The results show that: (1) the CEEs of 41 cities are generally low, with an average of 0.2142. Shanghai has the highest CEE, 0.8089, while Tongling has the lowest, 0.0307, under the current policy constraint. (2) Under the four control policy scenarios, the CEE of the YRD urban agglomeration generally follows a U-shaped trend. It indicates that the dual-control policy may lead to a short-term decline in the CEE of YRD, but in the long term, it may gradually increase in 2025 or 2026. (3) Spatial–temporal analysis reveals that the government should flexibly optimize and update the carbon intensity constraint value based on regional development differences and focus more on energy consumption. These results provide forward-looking guidance for China's regional low-carbon and high-quality development.

Carbon emission and energy risk management in mega sporting events: challenges, strategies, and pathways

Carbon emissions from mega sporting events pose a serious challenge to the sustainable development of the global environment, and the management of carbon emissions and energy efficiency in sporting events has become a focus of attention for both countries and international organizations. However, most existing research focuses on carbon emissions in sporting events is limited by a narrow focus on individual cases, limited attention to indirect emissions, insufficient integration of socioeconomic dimensions, a lack of broader data coverage, the adoption of interdisciplinary methodologies, and an emphasis on lifecycle energy risk management to provide robust support for sustainable event practices and policy development. To remedy these deficiencies, this study systematically compiles the current situation of carbon emissions in sports activities, analyzes the carbon emission characteristics and energy-saving potential of different types of sporting events, and summarizes the excellent cases of carbon emission and energy efficiency management in sports activities. The study reveals that large-scale sporting events generate substantial carbon emissions and energy consumption in transportation, venue construction, and event operation. However, carbon emissions and energy usage can be significantly reduced by optimizing venue locations, promoting green transportation, and implementing energy-saving measures at all stages. This study not only provides empirical data and theoretical support for the management of carbon emissions and energy efficiency in sporting events but also proposes practical and feasible suggestions that are highly important for the sustainable development of future sporting events. The findings have reference value for policymakers and event organizers in planning and implementing energy-saving and low-carbon events, helping promote environmental governance and sustainable development in the sports sector.

Facilitating or Hindering? The Impact of Low-Carbon Pilot Policies on Socio-Ecological Resilience in Resource-Based Cities

Low-carbon pilot policies are essential for the green transformation of resource-based cities, helping them mitigate the “carbon curse” and the “resource curse” while promoting sustainable socio-ecological development. Focusing on a panel of 114 resource-based cities in China, spanning from 2003 to 2022, this study employs a range of methodologies, including kernel density estimation, the Difference-in-Differences Model, Spatial Difference-in-Differences, Mediation Analysis, K-means Clustering, and Dual Machine Learning to assess the consequences of low-carbon pilot policies on socio-ecological resilience. The findings indicate that the socio-ecological resilience of the study area has generally improved, though there is noticeable polarization. Low-carbon pilot policies significantly enhance the resilience of resource-based cities by 0.4%, and they exhibit a positive spatial spillover effect of 1.1%. However, the long-term effects of the policies on economic resilience were not significant, and the policies did not have a direct impact on the social resilience of the pilot cities; however, they did promote social resilience in neighboring regions. Finally, the effectiveness of low-carbon pilots varies, with more pronounced benefits in declining and mature resource cities, particularly in those with medium ecological and economic resilience, and low social resilience. Green finance, industrial transformation, and carbon emission efficiency are identified as key strategies for improving socio-ecological resilience. The above findings provide insights for policymakers seeking to foster inclusive, resilient, and sustainable urban development in China.

A novel DEA-Tobit-SD assessment framework and application of provincial-level carbon emission embracing regional heterogeneity

Formulating tailored emission reduction policies for each Chinese province is crucial due to regional differences in carbon emission evolution patterns. This paper proposes a novel and comprehensive research framework that integrates data envelopment analysis (DEA), Tobit regression, and system dynamics (SD) model to analyze the influence factors and evaluate provincial emission reduction policies while considering regional differences. The DEA method assesses each province's development resource allocation and carbon emission efficiency. Based on the DEA results, each provinces’ key emission influencing factors can be derived combining with Tobit regression and sensitivity analysis of SD. Policies are then selected based on these factors to gauge their effectiveness. SD method is used to simulate carbon emissions under different policy scenarios in the future. The analysis results present obvious differences in resource allocation and regional characteristics among provinces. Qinghai's emission reduction potential has been preliminarily explored as an example. Energy structure, industry structure, energy intensity, forest coverage, and R&D input intensity are its main influencing factors for carbon emission. The forest carbon sink plays a significant role. The emission reduction of the integrated scenario is not a linear sum of all other scenarios. To ensure the completion of the neutralization goal, further adjustments to the long-term policy and extra measures are needed.

The chain reaction of carbon emission trading policy in efficiency and rebound: evidence from spatial perspective in China

Evaluating and improving environmental policies are significantly impacted by the question of whether increased energy efficiency results in an energy rebound effect. Utilizing carbon emission trading policies (CETP) as a quasi-natural experiment, this study created a spatial difference-in-differences (SDID) model with an endogenous spatio-temporal weight matrix. The analysis applied Chinese provincial panel data from 2008 to 2018 to assess the direct and spatial spillover effects of CETP on total factor carbon emissions efficiency (TCEE). On this premise, it is further explored to see if CETP causes the energy rebound (ER) effect via TCEE. It revealed that CETP exhibited positive spatial spillover effects on TCEE. Specifically, CETP was observed to enhance TCEE in pilot areas through green innovation (GI) but impede TCEE in neighboring regions due to siphoning and pollution haven effects. Further research, however, revealed that the CETP increased the ER in the pilot areas by improving the TCEE, causing the policy to fall into the trap of ineffective emission reduction, but it had a warning effect on the nearby areas. This study is of great significance for improving the emission reduction policy system and promoting the “dual carbon” goals.

Digital Inclusive Finance and Carbon Emission Efficiency: Evidence from China’s Economic Zones

In the current tide of technological revolution and industrial transformation, digitalization and greening have surfaced as two prominent trends, serving as the inherent requisites and impelling forces of new productive forces. Augmenting carbon emission efficiency is a crucial pathway for attaining green development. Digital inclusive finance, as an innovative financial paradigm, is a significant determinant influencing carbon emission efficiency and the progression of green development. By harnessing panel data from 108 cities within the Yangtze River Economic Belt spanning from 2011 to 2021, this study deploys the super-efficiency SBM model to gauge carbon emission efficiency. Additionally, it employs fixed effects and mediating mechanism models to empirically scrutinize the impact of digital inclusive finance on carbon emission efficiency within urban agglomerations in the Yangtze River Economic Belt. The study further probes its spatial effects through a spatial Durbin model. The research findings disclose that digital inclusive finance can substantially augment carbon emission efficiency in regional cities along the Yangtze River Economic Belt, manifesting notable regional correlations. This enhancement is accomplished by propelling industrial structure upgrades and augmenting scientific and technological capabilities. To enhance carbon emission efficiency in the Yangtze River Economic Belt, efforts should be centered around advancing digital inclusive finance, expediting industrial structural transformation, and fortifying scientific and technological development.

Regional Differences and Spatial Spillover Effects of Urban Carbon Emission Efficiency in Yangtze River Delta, China

Based on the super-efficiency SBM model with unexpected output, this study calculated the urban carbon emission efficiency in order to study the regional differences and spatial spillover effects of urban carbon emission efficiency in the Yangtze River Delta. From 2001 to 2020, the highest average of urban carbon emission efficiency in the Yangtze River Delta was only 0.698, with a low overall level and a flat "Inverted-U" evolution trend. Pure technical efficiency became the main factor driving the change in carbon emission efficiency. The Gini coefficient was relatively high, fluctuating in different years and still dominated by growth. The regional differences in urban carbon emission efficiency increased. The contribution of inter group differences among the four provinces （city） was the main source of the differences in urban carbon emission efficiency. The Moran's I index was positive, which indicated that the positive autocorrelation of carbon emission efficiency was significant, and the spatial spillover effect was obvious. The population density, economic level, and technical level had significant effects. The direct, indirect, and total effects of industrial structure, road network density, and foreign capital intensity on carbon emission efficiency did not pass the significance test. Based on the perspective of factor input-output, the study accurately measured urban carbon emission efficiency in the Yangtze River Delta, identified the causes of the differences in urban carbon efficiency, and introduced the "spatial spillover effect" to analyze the mechanism of carbon emission efficiency. This study compensated for the lack of research on the urban carbon emission efficiency in the Yangtze River Delta and provided quantitative support and scientific inspiration for the low-carbon development.

Spatio-temporal Evolution of Carbon Emissions and Emission Reduction Paths in the Northern Farming-pastoral Ecotone

The farming-pastoral ecotone has an important strategic place in the energy supply and ecological layout of China. Thus, exploring the spatial and temporal variation characteristics of carbon emissions in this region will help to deeply understand the information on the historical carbon emissions in China's energy production bases and provide data references for the formulation of differentiated emission reduction policies and the promotion of regional energy-saving and carbon-reducing measures, which is of great significance for the realization of low-carbon economic development. This study constructed a spatialization model of carbon emissions based on land use, night lighting, and provincial energy consumption data； explored the spatiotemporal changes and aggregation characteristics of carbon emissions in the farming-pastoral ecotone from 1995 to 2020 using the global Moran's index and hotspot analysis； and then combined it with the slack-based measure model to calculate the carbon emission efficiency and emission reduction potential of each city from 2010 to 2020 and classify cities to propose a differentiated emission reduction path. The results showed that, firstly, the estimated results at the prefectural city level of the carbon emission spatialization model constructed in this study with multi-source data could reach an R2 of 0.92 for a linear fit. Secondly, the total carbon emissions in the farming-pastoral ecotone increased from 176.29 million tons in 1995 to 1 014.51 million tons in 2020. However, the carbon emission intensity and growth rate both decreased, which was related to adjusting the energy structure and improving energy efficiency. Regarding spatial distribution, the cities with high carbon emissions over time were Datong, Baotou, and Yulin in order. Thirdly, the carbon emissions in the study area showed a significant global spatial positive correlation at the county level, with the hot spots mainly located at the junction of Shanxi, Shaanxi, and Inner Mongolia, while the cold spots were extended from Yanan City to Qingyang and Guyuan City after 2010. Finally, based on the differences in carbon emission efficiency and reduction potential, cities could be classified into four types： "high-efficiency and high potential," "low-efficiency and high potential," "high-efficiency and low potential," and "low-efficiency and low potential" to implement targeted emission reduction strategies.

Did digitalization of manufacturing industry improved the carbon emission efficiency of exports: Evidence from China

Did digitalization of manufacturing industry improved the carbon emission efficiency of exports: Evidence from China

Can the digital economy enhance carbon emission efficiency? Evidence from 269 cities in China

Can the digital economy enhance carbon emission efficiency? Evidence from 269 cities in China

Urban expansion and agricultural carbon emission efficiency: The moderating role of land property rights stability

Urban expansion and agricultural carbon emission efficiency: The moderating role of land property rights stability

Characteristics and formation mechanism of carbon emission efficiency spatial correlation network: Perspective from Shandong Province

Characteristics and formation mechanism of carbon emission efficiency spatial correlation network: Perspective from Shandong Province

Energy inequality and carbon emission efficiency: from the perspective of environmental regulation

Energy inequality and carbon emission efficiency: from the perspective of environmental regulation

Research on the Spatial Spillover Effect of Carbon Emission Trading on Carbon Emission Intensity

This article is based on panel data from 30 provinces in China from 2006 to 2019, analyzing the spatial spillover effects of carbon emission trading on carbon emission intensity through spatial econometric models. The empirical results indicate that China's carbon emission efficiency has significant regional differences and spatial correlations. Carbon emission trading can significantly reduce the carbon emission intensity of the treatment area, while also having a suppressive effect on the carbon emission intensity of surrounding areas. Based on the above conclusions, this article suggests promoting the opening and innovation of the carbon emissions trading market, increasing monitoring and control of the negative impact of carbon emissions trading, and promoting the optimization and improvement of carbon emissions trading policies.

Metropolitan Construction Carbon Emission Efficiency: An Integrative Static-Dynamic Analytical Framework

Abstract Taking urgent action to combat climate change and promote sustainable cities are part of the 17 Sustainable Development Goals, which demand immediate action from all countries. Enhancing the carbon emission efficiency of the construction industry (CICEE) from the "static-dynamic" perspective is crucial in addressing climate change. This study utilized the super slack-based measure model to analyze static efficiency. The Malmquist-Luenberger index model, multiple non-linear regression model, and principal component analysis were used to research the driving factors dynamically. Factors included technical change (TC), pure technical efficiency change (PEC), and scale efficiency change (SEC), which were subdivided into 21 variables. The panel data was from 11 cities in Zhejiang from 2007 to 2019. The results indicated that the static efficiency values were generally effective, but in 2019, more than half of the cities experienced an average decrease of 52%. Efficiency levels were higher in northern Zhejiang and lower in the southeastern region. CICEE values over one were primarily attributed to high TC levels and stable PEC and SEC conditions. Additionally, a generalized framework for improving CICEE was constructed, assisting policymakers in identifying reasons for low CICEE and providing targeted strategies to reduce emissions and increase efficiency.

Spatiotemporal Patterns and Driving Factors of Carbon Footprint in Coastal Saline Cropland Ecosystems: A Case Study of the Yellow River Delta, China

Coastal saline cropland ecosystems are becoming increasingly vital for food security in China, driven by the decline in arable land and the growing demand for resource-intensive diets. Although developing and utilizing saline land can boost productivity, it also impacts greenhouse gas (GHG) emissions. This study uses the Yellow River Delta as a case study to analyze the spatial-temporal patterns of carbon footprints in saline croplands from 2001 to 2020 and their correlations with climate factors, cropland management scale, and agricultural mechanization. The results reveal that agricultural production in this region is characterized by high inputs, emissions, and outputs, with carbon emission efficiency improving significantly due to a reduction in net carbon emissions. Major sources of carbon emissions include electricity, chemical nitrogen fertilizers, nitrogen input, and straw return, which together account for 65.06% of total emissions. Based on these findings, three key principles have been proposed for policy recommendations to enhance carbon emission efficiency. First, adopt tailored strategies for regions with different salinization levels. Second, strengthen cropland drainage infrastructure to mitigate the adverse effects of heavy rainfall. Third, expand the scale of cropland management through land transfers and promote agricultural mechanization. These insights offer valuable guidance for mitigating GHG emissions in coastal saline cropland ecosystems.

How Does the Intensive Use of Urban Construction Land Improve Carbon Emission Efficiency?—Evidence from the Panel Data of 30 Provinces in China

China actively explores the green development road by promoting the intensive use of urban construction land and low carbon emissions. This research evaluates urban construction land intensity and carbon emission efficiency by using a multifactor comprehensive evaluation method and SBM model and measures the spatial characteristics of between urban construction land intensity and carbon emission efficiency by Moran’s I and LISA scattering. The result shows that (1) the average value of urban construction land intensity was constantly improving from 0.11 in 2000 to 0.29 in 2021, (2) the value of carbon emission efficiency generally showed a fluctuating downward from 0.89 in 2000 to 0.66 in 2009 and then a fluctuating upward to 0.84 in 2021, with many provinces located in the Level III and IV carbon emission efficiency intervals, (3) the land development, road density, technology development, and fixed asset investment have a negative impact on carbon emission efficiency, while the greening level and tax have a positive promotion effect, and (4) the correlation and spatial features between urban construction land intensity and carbon emission efficiency in 30 provinces have consistency, as well as heterogeneity in the temporal and spatial development trends. Policy implications are accordingly proposed.

Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory.

Land use in urban agglomerations is the main source of carbon emissions, and reducing them and improving land use efficiency are the keys to achieving sustainable development goals (SDGs). To advance the literature on densely populated cities and highly commercialized regions, this research evaluates the total-factor carbon emission efficiency index (TCEI) of 27 cities in China's Yangtze River Delta (YRD) urban agglomeration at different stages from 2011 to 2020 using two-stage dynamic data envelopment analysis (DEA). The study carries out regression analysis and a long-short-term memory model (LSTM) to respectively filter out the factors and predict TCEI. The results indicate the following. (1) The total efficiency of 27 cities has significantly improved from 2011 to 2020, and there are obvious spatial heterogeneity characteristics. (2) In terms of stages, most cities' efficiency values in the initial stage (energy consumption) exceed those in the second stage (sustainable land utilization). (3) In terms of influencing factors, urban green space's ability to capture carbon has a notably positive correlation with carbon emission efficiency. In contrast, the substantial carbon emissions resulting from human respiration are a negative factor affecting carbon emission efficiency. (4) Over the forthcoming six years, the efficiency value of land use TCEI in the YRD urban cluster is forecasted to range between 0.65 and 0.75. Those cities with the highest performance are projected to achieve an efficiency value of 0.9480. Lastly, this research investigates the interaction between actors and land resources on TCEI, resulting in a beneficial understanding for the former to make strategic adjustments during the urbanization process.