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<p>High-speed rail (HSR), as a critical pillar of sustainable transportation, plays an essential role in enhancing ecological quality and fostering sustainable economic and social development. Leveraging HSR implementation as a quasi-natural experiment, this study applies a multi-period difference-in-differences approach, supplemented by the double machine learning model, to rigorously evaluate its impact on the reduction of pollution and carbon emissions (RPCE) across 284 Chinese cities. The empirical findings reveal that HSR remarkably facilitates RPCE, with pronounced effects observed in growing resource-based cities, non-provincial capitals, and those centrally positioned within the HSR network. Mechanism analysis further demonstrates that these synergistic environmental benefits are primarily driven by improvements in urban energy efficiency, technological innovation capacity, and industrial co-agglomeration. Notably, the impact of HSR on RPCE exhibits nonlinear threshold effects, with its effectiveness closely tied to whether cities exceed specific thresholds in energy efficiency, innovation capability, and industrial synergy. These findings offer valuable theoretical insights into the environmental governance potential of sustainable transportation infrastructure and provide practical policy implications for tailoring HSR investments to local conditions and optimizing transportation strategies to achieve coordinated pollution and carbon reduction goals.</p>

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Exploring the dual environmental impact of high-speed rail: can it effectively reduce both pollution and carbon emissions?

  • 期刊论文

  • 发表日期：

    2025年8月13日

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  • Yue Yang,
  • Fan Luo

摘要

High-speed rail (HSR), as a critical pillar of sustainable transportation, plays an essential role in enhancing ecological quality and fostering sustainable economic and social development. Leveraging HSR implementation as a quasi-natural experiment, this study applies a multi-period difference-in-differences approach, supplemented by the double machine learning model, to rigorously evaluate its impact on the reduction of pollution and carbon emissions (RPCE) across 284 Chinese cities. The empirical findings reveal that HSR remarkably facilitates RPCE, with pronounced effects observed in growing resource-based cities, non-provincial capitals, and those centrally positioned within the HSR network. Mechanism analysis further demonstrates that these synergistic environmental benefits are primarily driven by improvements in urban energy efficiency, technological innovation capacity, and industrial co-agglomeration. Notably, the impact of HSR on RPCE exhibits nonlinear threshold effects, with its effectiveness closely tied to whether cities exceed specific thresholds in energy efficiency, innovation capability, and industrial synergy. These findings offer valuable theoretical insights into the environmental governance potential of sustainable transportation infrastructure and provide practical policy implications for tailoring HSR investments to local conditions and optimizing transportation strategies to achieve coordinated pollution and carbon reduction goals.