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Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach.
- Source :
-
Ecological Indicators . Oct2021, Vol. 129, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
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Abstract
- • SO 2 emissions in China have been found to exhibit spatial heterogeneity. • A random effect eigenvector spatial filtering (RE-ESF) approach is suggested to deal with spatial dependence in empirical studies. • The global and local relationships between factors and SO 2 emissions can be captured. • Centralized and province-specific policies for reducing SO 2 emissions can be developed. Sulfur dioxide (SO 2) emissions have been a great challenge in China over the last few decades due to their serious impact on the environment and human health. In this paper, a random effect eigenvector spatial filtering (RE-ESF) approach without and with non-spatially varying coefficients (SNVC) is identified to examine spatial heterogeneity and economic driving factors of SO 2 emissions in China from 2011 to 2017. Using the Moran eigenvectors to extract information on spatial dependence, the main findings of the RE-ESF approach are as follows: First, after comparing different approaches for dealing with spatial dependence, it is found that the RE-ESF approach demonstrates the best fit to the dataset. Second, the global investigation shows that SO 2 emissions are negatively determined by economic growth and government expenditure for environment protection, but are positively determined by road freight transport, coal consumption and oil consumption. Third, the local investigation indicates that the spatially varying coefficients of economic growth and coal consumption range from 0.1401 to 0.2732 with the median value of 0.2478 and from 0.2406 to 0.3611 with the median value of 0.3210, respectively, revealing significant spatial heterogeneity of SO 2 emissions driven by economic growth and coal consumption. These findings provide meaningful insights into centralized and province-specific policies for reducing SO 2 emissions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1470160X
- Volume :
- 129
- Database :
- Academic Search Index
- Journal :
- Ecological Indicators
- Publication Type :
- Academic Journal
- Accession number :
- 151684936
- Full Text :
- https://doi.org/10.1016/j.ecolind.2021.108001