The purpose of this paper is to analyse the impact of high-speed railway (HSR) on industrial pollution emissions using the data for 285 prefecture-level cities in China from 2004 to 2016.
The research method used in this paper is the multi-period difference-in-differences (DID) model, which is an effective policy effect assessment method. To further address the issue of endogeneity, the DID integrated with the propensity score matching (PSM-DID) approach is employed to eliminate the potential self-selection bias.
The results show that the HSR has significantly reduced industrial pollution emissions, which is validated by several robustness tests. Compared with peripheral cities, HSR exerts a greater impact on industrial pollution emissions in central cities. In addition, the mechanism test reveals that the optimised allocation of inter-city industries is an important channel for HSR to mitigate industrial pollution emissions, and this is closely related to the location of HSR stations.
Previous studies have paid more attention to evaluating the economic effects of HSR, however, most of these studies overlook its environmental effects. Consequently, the impact of HSR on industrial pollution emissions is led by using multi-period DID models in this paper, in which the environmental effects are measured. The results of this paper can provide a reference for the pollution reduction policies and also the coordinated development of economic growth and environmental quality.
The research is financially supported by the Key Projects of the National Social Science Foundation of China (19AJY011), the National Social Science Foundation of China (17ZDA038), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX19_0133), and the Fundamental Research Funds for the Central Universities (2242019S10007).
Fan, X., Xu, Y., Nan, Y., Li, B. and Cai, H. (2020), "Impacts of high-speed railway on the industrial pollution emissions in China: Evidence from multi-period difference-in-differences models", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-07-2019-0499Download as .RIS
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