Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

No abstract

No abstract

No abstract

As the world’s largest emitter of sulfur dioxide, China is facing mounting domestic and international pressures to tackle the increasingly serious atmospheric pollution. Convergence is an important inherent characteristic of sulfur dioxide discharge. This study examines the convergence of per capita sulfur dioxide emissions across 280 Chinese prefecture-level cities from 2003 to 2016. Due to the spatial autocorrelation of air pollutants, conventional estimation methods for β convergence ignore the spatial effects and produce biased results. Consequently, spatial econometric models with different weight matrices are employed to control for spatial effects. The empirical results indicate that per capita sulfur dioxide emissions exhibit both absolute β convergence and conditional β convergence, and spatial effect and other socioeconomic factors accelerate the convergence speed. In addition, this study verifies the environmental Kuznets curve hypothesis between sulfur dioxide and gross domestic product. The results highlight the importance of regional cooperation and coordination when formulating environmental and industrial policies.