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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Spatial-Temporal Analysis and Driving Factors Decomposition of (De)Coupling Condition of SO
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Spatial-Temporal Analysis and Driving Factors Decomposition of (De)Coupling Condition of SO

机译：（DE）耦合条件的空间时间分析和驱动因子分解

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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.
机译：中国经济增长快，是世界上三大二氧化硫（SO2）发射者之一。本文致力于为中国寻找有效的方式，以减少SO2排放，影响其社会经济发展的影响。收集了2000年至2017年中国30个省份的数据，以评估经济增长与SO2排放之间的解耦关系。使用Tapio方法。然后，分析了去耦的时间趋势，并引入了莫兰指数以测试省份的空间自相关。为了集中资源并提高减少效率，通过COBB-DONGLAS函数改善的广义对数平均Divisia指数用于分解SO2排放的驱动因素，并识别主司机。结果表明，自2012年以来，SO2排放量和经济增长之间的整体关系有强烈的解耦（SD）;除辽宁和贵州外，省份自2015年以来已达到SD。邻近省份的解耦指数依靠95％以上的空间依赖。主要阳性司机是经济二级部门的比例，主要的负面司机与消防瓦斯治疗中的能耗和投资有关。然后，制定了对政府和企业的相应建议。

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