摘要
基于扩展的传统环境库茨涅茨曲线(EKC),利用2008年中国31个省域截面数据和空间计量经济学模型,分析省域环境污染的空间相关性、EKC的形状及决定因素。结果发现:我国省域环境污染存在明显空间依赖性和空间溢出效应,高—高和低—低集聚区居主导地位;全域截面数据模拟的EKC形状为"U+倒U"型,其中30个省域的EKC曲线为"倒U"型,EKC假说在省域尺度得到了证实;29个省域的人均GDP位于"倒U"型曲线左侧区域,表明省域人均收入越高,环境污染越严重;上海已率先跨过"倒U"型曲线拐点,其人均收入的提高与环境保护较为协调;人口规模、城市化水平及中等级人力资本,与环境污染损失成正相关;升级产业结构、积累高等级人力资本及提高对外开放程度,有利于控制环境污染。
Based on the extension of the traditional Environmental Kuznets Curve（EKC）,employing the cross-sectional data of Chinese 31 provinces in 2008 and spatial econometrics model,the paper analyzes the spatial autocorrelation of environmental pollution of Chinese provinces,as well as the shape and determinants of the EKC.The empirical results are shown as follows.（1） There exists evident spatial dependence and spillover effects of the provincial environmental pollution,and the high-high and low-low clustering areas are playing the leading roles.（2） The simulation shape between per capita GDP and environmental degradation presents a ＂U-shaped and inverted-U-shaped＂ pattern based on the global cross-sectional data,and the EKC pattern fitted by Chinese 30 provincial data shows an ＂inverted U-shaped＂ pattern,so the EKC hypothesis at provincial level is confirmed.（3） Per capita GDP of 29 provinces is observed in the left-hand area of the ＂inverted U-shaped＂ curve,which demonstrates that the more income people get,more serious environment pollution will be.（4） Shanghai has transcended the ＂inverted U-shaped＂ curve＇s inflection point in advance,which means that the increase of per capita income is coordinated with its environmental protection.（5） The population size,urbanization level and middle-grade human capital are positively correlated with losses induced by environmental pollution of the provinces.（6） The upgrading of industrial structure,the accumulation of the high-grade human capital,and the widening of opening up are in favor of dealing with the environmental problems.
出处
《地理研究》
CSSCI
CSCD
北大核心
2012年第4期627-640,共14页
Geographical Research
基金
广西自然科学基金项目(桂科自0832094)
教育部人文社会科学研究基金一般项目(08JA790030)
国家社会科学基金项目(10XFX0022)
