Exploring the Nonlinear and Heterogeneous Effects of Accessibility on Intercity Passenger Transport Carbon Emissions

Reducing carbon emissions while ensuring travel efficiency is a major challenge in sustainable transportation planning. Accessibility plays a key role in achieving both goals, yet its impact on carbon emissions remains unclear. This study investigates the nonlinear and spatially heterogeneous effects of accessibility on intercity road and railway passenger transport carbon emissions using gradient boosting decision tree (GBDT) and multiscale geographically weighted regression (MGWR) models. Accessibility indicators are determined from both travel-based and network-based dimensions, with the Guanzhong Plain urban agglomeration in China as the case study. The results show that accessibility accounts for over 60% of the variation in carbon emissions, with potential accessibility identified as the most influential factor. GBDT results reveal a positive, nonlinear relationship with threshold effects, while MGWR identifies significant spatial heterogeneity in the impacts of potential accessibility, cumulative accessibility, and betweenness centrality. Notably, enhanced rail accessibility has a greater impact on surrounding counties than on core cities. These findings offer valuable guidance for developing targeted, region-specific transportation and carbon reduction strategies.