Convergence of energy carbon emission efficiency: evidence from manufacturing sub-sectors in China

Aims: To correlate the energy and carbon emission efficiency relative to research income, gross internal area, and population for all the Higher Education Institutions (HEIs) in the UK and to assess the comparative carbon emission efficiency of HEIs relative to economic metrics. Study Design: Analytical panel data study. Place and Duration of Study: This paper evaluates the energy efficiency of 131 HEIs in the UK subdivided into Russell and non-Russell groups from 2008 to 2015. Methodology: Data Envelopment Analysis (DEA) and Malmquist productivity indexes (MPI) are used for the efficiency calculations. Results: The empirical results indicate that UK HEIs have relatively high energy efficiency scores of 96.9% and 77.6% (CRS) and 98.5%, 86.3% (VRS) for Russell and non-Russell groups respectively. Conclusion: The evidence from this study reveals that HEIs are not significantly suffering from scale effects, hence, an increase in energy efficiency of these institutions is feasible with the present operating scale but would need to work on their technical improvements in energy use. Malmquist index analysis confirms the lack of substantial technological innovation, which impedes their energy efficiency and productivity gain. Findings show that pure technical efficiency accounts for the annual efficiency obtained in the DEA model, the technological progress in contrast is the source of their energy inefficiency.

Download Full-text

Does Emission Trading Improve Carbon Emission Efficiency of China’s Iron and Steel Industry? An Exploration Using a DEA-SBM and Difference-In-Differences Approach

10.21203/rs.3.rs-626027/v1 ◽

2021 ◽

Author(s):

Xiping Wang ◽

Sujing Wang

Keyword(s):

Steel Industry ◽

Carbon Emission ◽

Emission Trading ◽

Energy Structure ◽

Iron And Steel Industry ◽

Low Carbon ◽

Difference In Differences ◽

Iron And Steel ◽

Emission Efficiency ◽

The Impact

Abstract As an effective tool of carbon emission reduction, emission trading has been widely used in many countries. Since 2013, China implemented carbon emission trading in seven provinces and cities, with iron and steel industry included in the first batch of pilot industries. This study attempts to explore the policy effect of emission trading on iron and steel industry in order to provide data and theoretical support for the low-carbon development of iron and steel industry as well as the optimization of carbon market. With panel data of China’s 29 provinces from 2006 to 2017, this study adopted a DEA-SBM model to measure carbon emission efficiency of China’s iron and steel industry (CEI) and a difference-in-differences (DID) method to explore the impact of emission trading on CEI. Moreover, regional heterogeneity and influencing mechanisms were further investigated, respectively. The results indicate that: (1) China's emission trading has a significant and sustained effect on carbon abatement of iron and steel industry, increasing the annual average CEI by 12.6% in pilot provinces. (2) The policy effects are heterogeneous across diverse regions. Higher impacts are found in the western and eastern regions, whereas the central region is not significant. (3) Emission trading improves CEI by stimulating technology innovation, reducing energy intensity, and adjusting energy structure. (4) Economic level and industrial structure are negatively related to CEI, while environmental governance and openness degree have no obvious impacts. Finally, according to the results and conclusions, some specific suggestions are proposed.

Download Full-text

Analysis of Carbon Emission Efficiency in Belt and Road Agriculture

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/513/1/012024 ◽

2020 ◽

Vol 513 ◽

pp. 012024

Author(s):

Du Ye ◽

Zhu Poyi ◽

Gong Yan ◽

Zhao Keshan ◽

Chen Shiwen ◽

...

Keyword(s):

Carbon Emission ◽

Emission Efficiency ◽

Belt And Road

Download Full-text

Spatial Differentiation of Carbon Emission Efficiency of “Silk Road Economic Belt” based on Environmental Regulation

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/208/1/012027 ◽

2018 ◽

Vol 208 ◽

pp. 012027 ◽

Cited By ~ 1

Author(s):

Xuhui Ding ◽

Juhua He ◽

Xinyuan Yu

Keyword(s):

Environmental Regulation ◽

Carbon Emission ◽

Spatial Differentiation ◽

Silk Road ◽

Emission Efficiency ◽

Silk Road Economic Belt

Download Full-text

Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Energies ◽

10.3390/en12163081 ◽

2019 ◽

Vol 12 (16) ◽

pp. 3081 ◽

Cited By ~ 5

Author(s):

Zeng ◽

Lu ◽

Liu ◽

Zhou ◽

Hu

Keyword(s):

Influencing Factors ◽

Regional Differences ◽

Carbon Emission ◽

Industrial Structure ◽

Tobit Model ◽

Policy Implications ◽

Dea Model ◽

Emission Efficiency ◽

Unit Root Testing ◽

Technology Level

With the challenge to reach targets of carbon emission reduction at the regional level, it is necessary to analyze the regional differences and influencing factors on China’s carbon emission efficiency. Based on statistics from 2005 to 2015, carbon emission efficiency and the differences in 30 provinces of China were rated by the Modified Undesirable Epsilon-based measure (EBM) Data Envelopment Analysis (DEA) Model. Additionally, we further analyzed the influencing factors of carbon emission efficiency’s differences in the Tobit model. We found that the overall carbon emission efficiency was relatively low in China. The level of carbon emission efficiency is the highest in the East region, followed by the Central and West regions. As for the influencing factors, industrial structure, external development, and science and technology level had a significant positive relationship with carbon emission efficiency, whereas government intervention and energy intensity demonstrated a negative correlation with carbon emission efficiency. The contributions of this paper include two aspects. First, we used the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods. Secondly, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression. Based on the results, we also provide several policy implications for policymakers to improve carbon emission efficiency in different regions.

Download Full-text

Total Factor Energy Efficiency, Carbon Emission Efficiency, and Technology Gap: Evidence from Sub-Industries of Anhui Province in China

Sustainability ◽

10.3390/su12041402 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1402 ◽

Cited By ~ 3

Author(s):

Ya Chen ◽

Wei Xu ◽

Qian Zhou ◽

Zhixiang Zhou

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Carbon Emission ◽

Energy Use ◽

Industrial Sector ◽

Policy Implications ◽

Light Industry ◽

Heavy Industry ◽

Technology Gap ◽

Emission Efficiency

The phenomena of “large energy consumption, high carbon emission, and serious environmental pollution” are against the goals of “low energy consumption, low emissions” in China’s industrial sector. The key to solving the problem lies in improving total factor energy efficiency (TFEE) and carbon emission efficiency (TFCE). Considering the heterogeneity of different sub-industries, this paper proposes a three-stage global meta-frontier slacks-based measure (GMSBM) method for measuring TFEE and TFCE, as well as the technology gap by combining meta-frontier technology with slacks-based measure (SBM) using data envelopment analysis (DEA). DEA can effectively avoid the situation where the technology gap ratio (TGR) is larger than unity. This paper uses the three-stage method to empirically analyze TFEE and TFCE of Anhui’s 38 industrial sub-industries in China from 2012 to 2016. The main findings are as follows: (1) Anhui’s industrial sector has low TFEE and TFCE, which has great potential for improvement. (2) TFEE and TFCE of light industry are lower than those of heavy industry under group-frontier, while they are higher than those of heavy industry under meta-frontier. There is a big gap in TFEE and TFCE among sub-industries of light industry. Narrowing the gap among different sub-industries of light industry is conducive to the overall improvement in TFEE and TFCE. (3) The TGR of light industry is significantly higher than that of heavy industry, indicating that there are sub-industries with the most advanced energy use and carbon emission technologies in light industry. And there is a bigger carbon-emitting technology gap in heavy industry, so it needs to encourage technology spillover from light industry to heavy industry. (4) The total performance loss of industrial sub-industries in Anhui mainly comes from management inefficiency, so it is necessary to improve management and operational ability. Based on the findings, some policy implications are proposed.

Download Full-text