ANALYSIS OF THE IMPACT OF RAILWAY STATION DEVELOPMENT POLICIES ON LOCAL AIR POLLUTION CONCENTRATIONS: PROPESINTY SCORE MATCHING (PSM) ESTIMATES USING IFLS-5 DATA

Abstract

Railway infrastructure development is often hailed as an environmentally friendly transportation mode; however, the presence of stations may trigger economic activity agglomerations that potentially increase local air pollution. This study aims to evaluate the causal impact of train stations on air pollution levels while controlling for confounding variables such as industrial activity and rainfall intensity. Utilizing data from 534 territorial observations, this research employs Propensity Score Matching (PSM) to address selection bias and establish a balanced counterfactual framework. Baseline estimation results indicate that the presence of train stations does not have a statistically significant effect on air pollution levels (ATE=0.009; p=0.564). These findings remain robust across various sensitivity analyses, including the Average Treatment Effect on the Treated (ATET; p=0.189), heteroscedasticity correction via hetprobit (p=0.769), and k-nearest neighbor matching (n=4; p=0.498). Covariate balance tests demonstrate superior matching quality, with a Mean Bias of 3.5% and a Pseudo R2 of 0.000 post-matching. This study concludes that train stations are environmentally neutral regarding regional air quality, suggesting that emissions from activity clusters around stations may be offset by the efficiency of mass transit.