基于SML指数的环境回弹效应分析：模型与测算

本文通过搭建环境回弹效应理论框架,采用熵权法构造各地区综合环境污染指数,利用Sequential Malmquist-Luenberger指数全要素生产率模型测度技术进步对经济增长的贡献率,定量评估了1999—2017年我国30个省份的环境回弹效应,识别了环境回弹效应时序演变和区域差异,并进一步利用空间分析技术探究环境回弹效应的空间分布特征。研究表明:样本期内各省份环境回弹效应均值集中在-13.23%~29.63%,全国平均环境回弹效应为10.42%,实际减污率仅为65.74%;时序特征上,环境回弹效应与技术进步有部分相关性,但存在滞后作用,经济发展水平和技术溢出效应是主要的异质性来源;空间特征上,环境回弹效应的区域差异显著,但具有空间相关性,地理因素在解释环境回弹效应的影响因素中不容忽视;全局自相关检验表明2006—2011年各省份环境回弹效应呈显著的正自相关,空间集聚特征明显。建议进一步挖掘环境降污空间,加快经济增长与环境污染的脱钩,关注区域协同减排,是改善污染治理的有效措施。

Analysis of Environmental Rebound Effect Based on the SML Index: Model and Measurement

By building theoretical framework of environment rebound effect, this paper used the entropy method to construct regional comprehensive environmental pollution index, estimated the contribution rate of technological progress on economic growth with the Sequential Malmquist-Luenberger total factor productivity (SML) index model, quantitatively evaluated the environmental rebound effects of 30 provinces in China from 1999 to 2017, identified the temporal evolution and regional differences of environmental rebound effects, and further explored the spatial distribution characteristics of environmental rebound effects using spatial analysis techniques. The results showed that the mean environmental rebound effect of all provinces in China was between -13.23%-29.63%, the national average environmental rebound effect was 10.42%, and the actual pollution reduction rate was only 65.74%. In terms of temporal characteristics, the environmental rebound effect was partially correlated with technological progress, but there was a lagging effect. Economic development level and technological spillover effect were the main sources of heterogeneity. In terms of spatial characteristics, the regional differences of environmental rebound effect were significant, but there was a spatial correlation. Geographical factors can not be ignored in the explanation of environmental rebound effect. The global autocorrelation test showed that the environmental rebound effect of China''s provinces presented a significant positive autocorrelation from 2006 to 2011, and the spatial agglomeration characteristics were obvious. The effective measures to improve pollution control are to further excavate the space of environmental pollution reduction, accelerate the decoupling of economic growth and environmental pollution, and pay attention to regional cooperative emission reduction.