Public Charging Infrastructure Deployment and Credit Risk of Rural Commercial Banks in China: Evidence from Sustainable Mobility Infrastructure

This study examines the relationship between public electric-vehicle charging-infrastructure deployment and the credit risk of rural commercial banks in China. Using an unbalanced panel dataset of 180 RCBs across 10 provinces from 2016 to 2022, this paper employs a two-way fixed-effects model and uses the provincial number of public charging piles as a proxy for the deployment scale of public charging infrastructure. The results show that the rapid expansion of public charging infrastructure is statistically associated with higher non-performing loan ratios among local RCBs and this association remains under alternative standard-error specifications and robustness tests. The theoretical analysis suggests that this association may be related to policy uncertainty, technological upgrading pressure, utilization uncertainty, maturity mismatch, and market competition in the new energy vehicle charging sector. Heterogeneity results indicate that the positive association is weaker in economically stronger regions, while higher provision coverage is associated with a stronger positive relationship, possibly reflecting risk-taking incentives, historical asset-quality differences, or delayed non-performing asset disposal. By connecting sustainable transport infrastructure, green finance, infrastructure governance, and local banking risk, this study contributes to debates on how low-carbon mobility transitions can be financed without weakening regional financial stability. The findings highlight the need for coordinated transport–energy planning, clearer regulatory standards, regionally differentiated risk-sharing mechanisms, and stronger financial governance frameworks for public charging infrastructure. Because the analysis relies on provincial-level infrastructure data rather than project-bank matched loan-level data, the documented findings should be interpreted as statistical associations rather than definitive causal relationships.