scholarly journals Research on Carbon Emission Efficiency in the Chinese Construction Industry based on a Three-stage DEA-Tobit Model

2021 ◽  
Author(s):  
Mengna Zhang ◽  
Lianshui Li ◽  
Zhonghua Cheng
Keyword(s):  
Environmental Factors ◽  
Construction Industry ◽  
Regional Differences ◽  
Carbon Emission ◽  
Tobit Model ◽  
Total Power ◽  
Scale Efficiency ◽  
Energy Structure ◽  
Dea Model ◽  
Emission Efficiency

Abstract The traditional data envelopment analysis (DEA) model usually ignores the influence of external environmental factors and random interference. This can easily lead to deviations in efficiency estimates. In order to solve this problem, a three-stage DEA model was used to better reflect the carbon emission efficiency of Chinese construction industry (CEECI) (2006–2017) from the perspective of non-management factors. The internal influencing factors of CEECI are analyzed by the Tobit model, which provides a more accurate basis for formulating policies. It is found that the CEECI is significantly affected by the GDP, the level of industrialization, the degree of opening-up, technological innovation and energy structure. After excluding environmental factors and random interference, the average CEECI increased by 16%. The resulting calculations were noteworthy in three aspects. First, there are significant regional differences in the CEECI. Both the multi-polarization phenomenon of CEECI and regional differences also reduced gradually over time. Second, the CEECI can be decomposed into pure carbon emission efficiency (PCEE) and scale efficiency (SE), which is mainly caused by SE. Excluding external environmental factors and random interference will have a specific impact on the CEECI. All the 30 provinces are divided into four categories to analyze the reasons and solutions of the differences in the CEECI in provinces. Third, many factors had inhibitory effects on the CEECI, PCEE and SE; these included energy structure optimization, labor force number, total power of construct ion equipment and construction intensity in the construction industry. Nevertheless, the development level of the construction industry did have a significant positive effect.

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

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3081 ◽  
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.

scholarly journals 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

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.

Applying a Ruggiero three-stage super-efficiency DEA model to gauge regional carbon emission efficiency: evidence from China

2017 ◽  
Vol 87 (3) ◽  
pp. 1453-1468 ◽  
Author(s):  
Feng Dong ◽  
Ruyin Long ◽  
Zhengfu Bian ◽  
Xihui Xu ◽  
Bolin Yu ◽  
...  
Keyword(s):  
Carbon Emission ◽  
Super Efficiency ◽  
Dea Model ◽  
Emission Efficiency

scholarly journals Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China

2020 ◽  
Vol 12 (8) ◽  
pp. 3138 ◽  
Author(s):  
Jinkai Li ◽  
Jingjing Ma ◽  
Wei Wei
Keyword(s):  
Carbon Dioxide Emissions ◽  
Carbon Emission ◽  
Government Regulation ◽  
Industrial Structure ◽  
Western China ◽  
Energy Structure ◽  
Analysis And Evaluation ◽  
Emission Efficiency ◽  
Technological Efficiency ◽  
The Impact

To promote economic and social development with reduced carbon dioxide emissions, the key lies in determining how to improve carbon emission efficiency (CEE). We first measured the CEE of each province by using the input-oriented three-stage Data Envelopment Analysis (DEA) and DEA-Malmquist model for the panel data of 30 provinces in China during 2000–2017. Then we explored the CEE differences and characteristics of different regions obtained by using hierarchical clustering of each province’s CEE. Finally, based on the regression model, we conducted an empirical analysis of the impact of each factor of total factor productivity (TFP) on CEE. The main findings of this research are as follows: (1) The industrial structure, energy structure, government regulation, technological innovation, and openness had a significant impact on CEE; (2) The variation trends of CEE and TFP in the eight regions we studied were convergent, while the variations of CEE among regions were diverse and all distributed stably in different ranges; (3) The eight regions’ efficiency basically showed a downward trend of eastern, central and western China; (4) Technological regression was the main reason for the decline in TFP. Technological progress and technological efficiency can contribute to an improvement in CEE. Based on the findings above, we provide decision-making references for comprehensively improving the efficiency of various regions and accelerating China’s energy conservation, emissions reduction, and coordinated development.

scholarly journals Tracking the Domestic Carbon Emission Intensity of China’s Construction Industry: A Global Value Chain Perspective

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaoping Li ◽  
Yuan Yu ◽  
Xunpeng Shi ◽  
Xin Hu
Keyword(s):  
Construction Industry ◽  
Value Chain ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Value Added ◽  
Intermediate Input ◽  
Direct Production ◽  
Emission Efficiency ◽  
Carbon Emission Intensity ◽  
Input Structure

China is the largest producer of carbon in the world. China’s construction industry has received widespread attention in recent years due to its environmental issues. However, little research has been conducted to investigate the environmental efficiency of the domestic part of this industry. As the foreign contribution is beyond China’s control, identification of domestic carbon emissions is necessary to formulate effective policy interventions. Based on a multi-regional input‐output model, this study attempts to reduce the statistical bias associated with international trade, thereby obtaining a more accurate indicator of domestic carbon emission intensity. This study aims to reveal the change in the domestic carbon emission intensity of China’s construction industry during 2000–2014 and analyze the reason behind it. The results show that, first, both the constructed intensity indicator and commonly used measures of carbon emission intensity have exhibited a decreasing trend over the study period. However, the former has been consistently larger than the latter. Moreover, this difference first increased and then suddenly decreased after a particular year. Second, although the domestic carbon emission intensity shows a gradually declining trend, it has moved from second to first in global rankings, implying that China’s domestic construction industry’s carbon emission efficiency, while falling, lags behind other major economies. Third, the structural decomposition results reveal that changes in direct production emission intensity are the leading causes of the decline in domestic carbon emission intensity. In contrast, a change in the intermediate input structure led to an increase in the emission intensity in China’s construction industry. In addition, the enormous gaps of domestic carbon emission intensity in the construction industry between China and the selected countries are mainly attributable to the difference in the intermediate input structure. The study suggests that China’s construction industry needs to promote high value-added output, optimize intermediate input structure, and improve energy and emission efficiency.

scholarly journals What Is the Regional Differences in Carbon Emission Performance: Evidences From Energy-intensive Industries in China?

2021 ◽  
Author(s):  
Yao Chen ◽  
Jing Wu
Keyword(s):  
Growth Rate ◽  
Emission Reduction ◽  
Regional Differences ◽  
Carbon Emission ◽  
Reduction Potential ◽  
Carbon Emission Reduction ◽  
Emission Efficiency ◽  
Emission Reduction Potential ◽  
Economic Regions ◽  
Energy Intensive Industries

Abstract As the major energy consumers, energy-intensive industries are the key players in achieving carbon emission reduction targets. Grasping the carbon emission reduction potential has a direct impact on the implementation of the carbon emission reduction policies of China. The paper builds a super-Slack Based Model(SBM) considering this undesirable output, and calculates the carbon emission efficiency. Then, the Meta-Frontier Malmquist-Luenberger productivity index (MF-MLPI) is constructed to dynamically analyse the growth rate changes of the carbon emission efficiency and the regional differences in energy-intensive industries. Furthermore, the carbon emission reduction potential of the energy-intensive industries in various economic regions of China is discussed and the conclusions are as follows: there is a big difference in the carbon emission Technology Gap Ratios (TGRs) of the energy-intensive industries in different economic regions; the growth rate of the carbon emission efficiency of energy-intensive industries shows a trend of first declining and then slowly recovering while the carbon reduction potential generally shows a trend of decreasing and then rising; and the carbon emission reduction potential in the eastern region keeps decreasing. The following is recommended: the government should rationally distribute energy-intensive industries, promote industrial structure adjustment, optimize the energy structure according to the regional industrial advantages; increase investment in R&D, promote energy technology innovation in energy-intensive industries; prioritize the promotion of carbon peaks on key emission industries and regional, formulate differentiated plans for the regions and industries with different carbon emission reduction potentials.

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