Is there an EKC between economic growth and smog pollution in China? New evidence from semiparametric spatial autoregressive models

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.