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Open AccessArticle

Spatial-Temporal Analysis and Driving Factors Decomposition of (De)Coupling Condition of SO2 Emissions in China

by Yue Wang 1, Lei Shi 1,*, Di Chen 1,2 and Xue Tan 3
1
School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China
2
Division of the Social Science, The University of Chicago, Chicago, IL 60615, USA
3
State Grid Energy Research Institute Co., LTD, Beijing 102209, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(18), 6725; https://doi.org/10.3390/ijerph17186725
Received: 24 July 2020 / Revised: 10 September 2020 / Accepted: 11 September 2020 / Published: 15 September 2020
(This article belongs to the Section Environmental Science and Engineering)
China has a fast-growing economy and is one of the top three sulfur dioxide (SO2) emitters in the world. This paper is committed to finding efficient ways for China to reduce SO2 emissions with little impact on its socio-economic development. Data of 30 provinces in China from 2000 to 2017 were collected to assess the decoupling relationship between economic growth and SO2 emissions. The Tapio method was used. Then, the temporal trend of decoupling was analyzed and the Moran Index was introduced to test spatial autocorrelation of the provinces. To concentrate resources and improve the reduction efficiency, a generalized logarithmic mean Divisia index improved by the Cobb–Douglas function was applied to decompose drivers of SO2 emissions and to identify the main drivers. Results showed that the overall relationship between SO2 emissions and economic growth had strong decoupling (SD) since 2012; provinces, except for Liaoning and Guizhou, have reached SD since 2015. The decoupling indexes of neighboring provinces had spatial dependence at more than 95% certainty. The main positive driver was the proportion of the secondary sector of the economy and the main negative drivers were related to energy consumption and investment in waste gas treatment. Then, corresponding suggestions for government and enterprises were made. View Full-Text
Keywords: decoupling analysis; driving factors decomposition; Moran Index; generalized logarithmic mean Divisia index; SO2 emissions; China decoupling analysis; driving factors decomposition; Moran Index; generalized logarithmic mean Divisia index; SO2 emissions; China
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Wang, Y.; Shi, L.; Chen, D.; Tan, X. Spatial-Temporal Analysis and Driving Factors Decomposition of (De)Coupling Condition of SO2 Emissions in China. Int. J. Environ. Res. Public Health 2020, 17, 6725.

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