Evaluating the long-term urban effects of high-speed rail in Japan: An integrated approach using synthetic difference-in-differences and double/debiased machine learning

Jingyuan Wang, Shintaro Terabe and Hideki Yaginuma

Transportation Research Part A: Policy and Practice, 2026, vol. 203, issue C

Abstract: This study evaluates the long-term impacts of Japan’s high-speed rail (HSR) network on urban development, using long panel data from 1960 to 2020. To address methodological limitations of traditional econometric approaches, such as difference-in-differences (DID) and propensity score matching-DID, especially in heterogeneous urban contexts, we adopt two advanced causal inference techniques: synthetic difference-in-differences (SDID) and double/debiased machine learning (DDML). To increase estimation robustness, our DDML framework uses stacked generalization, combining multiple machine-learning models into a single predictive ensemble. This method captures complex, nonlinear relationships and strengthens causal identification in high-dimensional settings. Our findings demonstrate that, overall, the introduction of HSR has greatly promoted long-term urban expansion. However, further analyses uncover considerable heterogeneity in treatment effects depending on the timing of network expansion, specific Shinkansen routes, and station typologies. Importantly, SDID proves to be highly robust across scales, from national multi-city evaluations to micro-level assessments of individual metropolitan areas, making SDID a powerful methodological tool for both macro and localized urban impact studies. These findings highlight the value of advanced causal inference techniques in capturing the nuanced, dynamic, and context-dependent effects of large-scale infrastructure investments, offering practical implications for future HSR planning and spatial policy evaluation.

Keywords: Causal inference; High-speed rail; Synthetic difference-in-differences; Double/debiased machine learning; Stacked generalization (search for similar items in EconPapers)
Date: 2026
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0965856425003763
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transa:v:203:y:2026:i:c:s0965856425003763

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.tra.2025.104743

Access Statistics for this article

Transportation Research Part A: Policy and Practice is currently edited by John (J.M.) Rose

More articles in Transportation Research Part A: Policy and Practice from Elsevier
Bibliographic data for series maintained by Catherine Liu ().