Abstract
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.
Highlights
Global warming has attracted the attention of politicians and scholars as it has severely affected the survival and development of human beings
Based on the above results, this paper evaluated the impact of above influencing factors on the carbon emission efficiency in China
CEEi,t = β0 + β1 GIEit + β2 ISit + β3 FTLit + β4 FCULit + β5 EIit + β6 STLit + uit where CEEi,t represents the carbon emission efficiency value of the ith province in the tth year, β0, β1, β2, . . . , β6 stands for the unknown coefficients, and ui,t is a random disturbance term
Summary
Global warming has attracted the attention of politicians and scholars as it has severely affected the survival and development of human beings. According to the assessment results of the UN. Intergovernmental Panel on Climate Change (IPCC), greenhouse gases, CO2 , are the main cause of global warming [1,2]. Carbon emission reduction has become a consensus by the international community. As the largest carbon emitter, China has more pressure to reduce its carbon emission. According to the 2018 BP Statistical Review of World Energy, the average growth rate per annum of CO2 emission is 3.2% in mainland China from 2006 to 2016.
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