No impact without details: Quasi-experimental evidence on carpooling regulations and usage

Carpooling is widely regarded as an effective approach to mitigating urban traffic congestion and environmental pollution. However, adoption remains limited, and rigorous causal evidence on carpooling regulatory policies (CRP) is scarce. Using high-resolution order data from 28 cities in three major Chinese urban agglomerations in 2017, this study exploits staggered city-level CRP implementation as a quasi-natural experiment to estimate its effects on carpooling activity. We develop a spatiotemporal feature-based classification algorithm (SFBCA) to distinguish LF-HSC drivers, characterized by lower carpooling frequency or higher spatiotemporal concentration, from HF-LSC drivers, characterized by higher carpooling frequency and lower spatiotemporal concentration. We estimate average effects using a two-way fixed-effects staggered difference-in-differences model and dynamic effects using heterogeneity-robust event-study estimators, supplemented by robustness checks. Under the monthly specification, SFBCA achieves consistency rates of 93.8% for LF-HSC drivers and 73.0% for HF-LSC drivers, yielding a harmonic consistency of 82.1%. CRP reduce total carpooling orders by 10.2% and HF-LSC drivers’ orders by 25.0%, while having no statistically significant average effect on LF-HSC drivers’ orders. These findings suggest that CRP primarily constrain more intensive and potentially quasi-commercial carpooling activity without producing a detectable average reduction in lower-frequency or more spatiotemporally concentrated carpooling. Heterogeneity analyses reveal variation across urban agglomerations, policy designs, and market environments, with statistically detectable effects concentrated in cities with standalone and detailed regulations. Overall, the findings provide implications for governments and transportation network companies (TNCs) to refine carpooling regulation through targeted and context-sensitive implementation.