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High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach

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  • Jia, Ruining
  • Shao, Shuai
  • Yang, Lili

Abstract

As the most important emerging transportation technology, high-speed rail (HSR) can reshape regional economic development patterns and exert an important effect on the ecological environment. Using a panel data set of 275 Chinese cities at the prefecture level and above from 2003 to 2014, this study is the first to adopt a continuous spatial difference-in-differences (SDID) model to investigate the effect and its mechanism of HSR service intensity on CO2 emissions. A series of robustness tests are performed, including the placebo test and using the propensity score matching method combined with the SDID (PSM-SDID) model. We also conduct a heterogeneity analysis using a spatial difference-in-difference-in-differences (SDDD) model. The results show that an increase in HSR service intensity significantly reduces urban CO2 emissions, resulting from the effects of transportation substitution, market integration, industrial structure, and technological innovation. Meanwhile, such an increase inhibits CO2 emissions in neighboring cities with a spatial attenuation boundary of 1000 km. On average, for every addition of 100 HSR trains in a city, the total CO2 emissions can be reduced by 0.14%. Moreover, the CO2 emission reduction effect of HSR is more significant in eastern China, large cities, and resource-based cities. However, higher levels of HSR service intensity in large cities and resource-based cities are not conducive to reducing CO2 emissions in neighboring cities. These findings can help to accurately evaluate the social benefits of expanding HSR networks and provide an important decision-making reference for climate governance during the era of HSR.

Suggested Citation

  • Jia, Ruining & Shao, Shuai & Yang, Lili, 2021. "High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach," Energy Economics, Elsevier, vol. 99(C).
  • Handle: RePEc:eee:eneeco:v:99:y:2021:i:c:s0140988321001766
    DOI: 10.1016/j.eneco.2021.105271
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