The nonlinear association between high-speed rail expansion and carbon emissions in China: an empirical analysis from theoretical-driven model to data-driven approach

Abstract

This study extends high-speed rail (HSR) environmental impacts by investigating the nonlinear association between HSR expansion and carbon emissions in mainland China. We adopt ordinary least squares method (OLS, i.e., difference-in-difference, hereafter DID) and machine learning algorithms (i.e., Extreme Gradient Boosting, hereafter XGBoost) to address the HSR-carbon nexus in 258 Chinese prefectures from 2003 to 2018. Our findings provide new evidence on the IPAT theory and environmental Kuznets curve hypothesis. That is, carbon reduction benefits from HSR-embodied technological innovation. Specifically, the carbon emission reaches its peak when a HSR-connected city's degree centrality equals around 300, followed by a sharp decrease till the level at 600 of the degree centrality. It then gradually decreases until the centrality reaches 3000, beyond which the emissions stay at a low and stable level, where we define it as a green threshold. It is crucial for policymakers and urban planners to prioritize the implementation of policies that foster HSR development, was they possess the potential to not only facilitate a rapid carbon reduction but also enhance long-term sustainable development, ultimately leading to carbon neutral.

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