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From the Kyoto Protocol to the Copenhagen Conference and the Paris Agreement, eco-environmental problems have gradually become a matter of common concern worldwide. Eco-efficiency (EE) is an essential indicator for measuring levels of sustainable development. This study uses an epsilon-based measure (EBM) model with undesirable outputs to evaluate the EEs of 30 Chinese provinces during the research period 2008 to 2017, and a spatial Durbin model (SDM) to search for the impact factors of EE. The results indicate that most provinces in China have a low EE level. The EE value of the eastern area is higher than are those for the central, western, or northeastern areas. The EE in China as a whole demonstrates an inverted V-shaped trend with a high point in 2011. The SDM shows that economic development level, foreign trade dependence, and technological progress exert significant positive effects on EE, while population density exerts significant negative influences on EE. This paper provides scientific bases for the formulation of policies resulting in sustainable development.
Liangen Zeng. China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach. Sustainability 2021, 13, 3143 .
AMA StyleLiangen Zeng. China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach. Sustainability. 2021; 13 (6):3143.
Chicago/Turabian StyleLiangen Zeng. 2021. "China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach." Sustainability 13, no. 6: 3143.
Due to the pressure of global ecological degradation, the coordination of economic increase and ecological protection has drawn attention from policymakers and practitioners. Green economic efficiency (GEE) is a comprehensive index to measure economic, social, and environmental development. As China is the second-biggest economy in the world with high-energy consumption, it is necessary to investigate its green economy efficiency. In this paper, we innovatively adopt a super-SBM (slacks-based measure) model with undesirable outputs to calculate the GEE in 30 provinces of China between 2008 and 2017, and then comprehensively apply a spatial Dubin model (SDM) to investigated its influencing factors. The results showed that the overall GEE in China during the study period was at a low level with significant regional differences. The inter-regional GEE generally showed a gradient decreasing pattern of “East-Middle-West”, which demonstrates a gradual decline from the East to the West in China. The trend of the national GEE initially dropped and then gradually stabilized over the study period. Foreign trade dependence and direct investment had significant positive effects on the GEE, while the secondary industry and urbanization level had a significant negative effect.
Peng-Jun Zhao; Liang-En Zeng; Hai-Yan Lu; Yang Zhou; Hao-Yu Hu; Xin-Yuan Wei. Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model. Science of The Total Environment 2020, 741, 140026 .
AMA StylePeng-Jun Zhao, Liang-En Zeng, Hai-Yan Lu, Yang Zhou, Hao-Yu Hu, Xin-Yuan Wei. Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model. Science of The Total Environment. 2020; 741 ():140026.
Chicago/Turabian StylePeng-Jun Zhao; Liang-En Zeng; Hai-Yan Lu; Yang Zhou; Hao-Yu Hu; Xin-Yuan Wei. 2020. "Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model." Science of The Total Environment 741, no. : 140026.
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.
Liangen Zeng; Haiyan Lu; Yenping Liu; Yang Zhou; Haoyu Hu; Zeng; Lu; Liu; Zhou; Hu. Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015. Energies 2019, 12, 3081 .
AMA StyleLiangen Zeng, Haiyan Lu, Yenping Liu, Yang Zhou, Haoyu Hu, Zeng, Lu, Liu, Zhou, Hu. Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015. Energies. 2019; 12 (16):3081.
Chicago/Turabian StyleLiangen Zeng; Haiyan Lu; Yenping Liu; Yang Zhou; Haoyu Hu; Zeng; Lu; Liu; Zhou; Hu. 2019. "Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015." Energies 12, no. 16: 3081.