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首页> 外文期刊>Journal of Cleaner Production >Is there an EKC between economic growth and smog pollution in China? New evidence from semiparametric spatial autoregressive models
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Is there an EKC between economic growth and smog pollution in China? New evidence from semiparametric spatial autoregressive models

机译：在中国经济增长和烟雾污染之间是否存在EKC？来自Semiparametric空间自回归模型的新证据

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摘要

Although China has achieved rapid economic growth, it suffers from severe smog conditions. Determining how to promote cleaner production and environmental sustainability is of great significance in clarifying the relationship between income and pollutants. Within an extension of the stochastic impacts by regression on population, affluence and technology (STIRPAT) framework, the effects of economic growth on PM2.5 contamination are explicitly investigated based on a recent large sample of 249 Chinese cities in 2015. From a nonlinear spatial perspective, this study marks the first attempt at comprehensively exploring the nexus between the selected variables and PM2.5 emissions using semiparametric spatial autoregressive models, which overcome the misspecification issues of conventional parametric models. To avoid the limitations of a single method, this study also applies different estimation techniques such as the spatial lag model (SLM), the spatial autoregressive model with spatial autoregressive disturbances (SARAR), two-stage least squares regression (2SLSR), quantile regression (QR) and nonparametric regression (NPR). The results reveal that PM2.5 pollutants show strong positive characteristics of spatial spillover. The results also suggest that there is a significant inverted U-shaped relationship between economic growth and PM2.5 concentrations, which confirms the environmental Kuznets curve (EKC) hypothesis. Additionally, the empirical findings highlight the influence that the population density, industrialization, urbanization and traffic development factors have on increasing PM2.5 emissions. In contrast, technological innovation and green coverage do not play important roles in reducing PM2.5 concentrations. The distribution of PM2.5 concentrations displays apparent geographical features. To provide an in-depth understanding of these impacts, marginal analysis is performed to distinguish the local and neighbouring effects of determinants on PM2.5 pollution. (C) 2019 Elsevier Ltd. All rights reserved.
机译：虽然中国已经实现了迅速的经济增长，但它受到严重烟雾条件。确定如何促进清洁生产和环境可持续性在澄清收入与污染物之间的关系方面具有重要意义。在随机影响的延长内，通过回归人口，富裕和技术（STICPAT）框架，基于2015年最近的249个中国城市的大量大型样本，明确调查了经济增长对PM2.5污染的影响。来自非线性空间透视，这项研究标志着使用Semiparametric Spatial自动评级模型全面探索所选变量和PM2.5排放的Nexus的首次尝试，这克服了传统参数模型的误操作问题。为了避免单一方法的局限性，本研究还应用不同的估计技术，例如空间滞后模型（SLM），空间自回归模型，具有空间自回归紊乱（Sarar），两级最小二乘回归（2SLSR），定量回归（QR）和非参数回归（NPR）。结果表明，PM2.5污染物表现出较强的空间溢出阳性特征。结果还表明，经济增长和PM2.5浓度之间存在显着的倒U形关系，这证实了环境库兹涅茨曲线（EKC）假设。此外，经验研究结果突出了人口密度，产业化，城市化和交通发展因素对增加PM2.5排放的影响。相比之下，技术创新和绿色覆盖率在减少PM2.5浓度时不会发挥重要作用。 PM2.5浓度的分布显示表观地理特征。为了提供对这些影响的深入理解，进行边际分析以区分决定因素对PM2.5污染的局部和邻近效应。 （c）2019 Elsevier Ltd.保留所有权利。

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