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PLS模型下空气污染影响因素的统计测度——以中原五省为例
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  • 英文篇名:The Statistioal Measurement of Factors influencing Air Pollution under the PLS Model ——Take the Five Provinces in Central China as an Example
  • 作者:常思远
  • 英文作者:CHANG Siyuan;School of Finance and Economics, Jiangsu University;
  • 关键词:空气污染 ; 影响因素 ; 偏最小二乘回归 ; 预测
  • 英文关键词:Air Pollution;;Factor of influence;;Partial Least Squares Regression;;Prediction
  • 中文刊名:安阳师范学院学报
  • 英文刊名:Journal of Anyang Normal University
  • 机构:江苏大学财经学院;
  • 出版日期:2019-04-15
  • 出版单位:安阳师范学院学报
  • 年:2019
  • 期:02
  • 语种:中文;
  • 页:25-30
  • 页数:6
  • CN:41-1331/Z
  • ISSN:1671-5330
  • 分类号:X51
摘要
"十三五"以来,各级政府越来越重视空气污染等环境污染问题。利用2011年至2017年中原五个省份的面板数据,建立偏最小二乘回归(PLS)模型,从经济、能耗、交通、技术创新和政府响应五个方面分析影响空气污染的因素。实证表明:经济、能耗和交通对空气污染有正向影响,而技术创新和政府响应则具有抑制作用。模型预测精度达到0.837,预测结果较好。
        Since the 13 th five-year plan, governments at all levels have paid increasing attention to air pollution and other problems of environmental pollution. The partial least squares regression(PLS) was established by using panel data of five provinces in central China from 2011 to 2017. Factors affecting air pollution were analyzed from five aspects including economy, energy consumption, transportation, technological innovation and government response. The empirical results show that economy, energy consumption and transportation have positive effects on air pollution, while technological innovation and government response have inhibitory effects. The predictive accuracy of the model reaches 0.837, and the predictiove result is good.
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