Abstract
<title>Abstract</title> <p>Infrastructure appraisal requires transparent integration of economic returns, spatial coverage, service use, and capital cost. We conducted a secondary methodological analysis of a station-level dataset assembled from public statistical releases, city reports, satellite-derived catchment estimates, and database exports. Eighteen Chinese high-speed rail stations with complete data were evaluated using four indicators: economic benefit, catchment area, annual passenger volume, and construction cost. Analytic Hierarchy Process (AHP) weights were combined equally with entropy weights, after min–max normalization and reverse scaling of cost. The resulting weights were 0.435, 0.165, 0.290, and 0.110, respectively. Composite scores ranged from 15.03 to 70.31 (mean 38.40; median 33.03). Guangzhou South ranked first, followed by Shanghai Hongqiao and Xi’an North; Lanzhou West and Yangshuo had the lowest relative scores. In 10,000 weight-perturbation analyses in which each weight varied by ± 20% before renormalization, the median rank correlation with the baseline ranking was 0.996 and the fifth percentile was 0.990; top-five membership was preserved in at least 95% of simulations. Beijing indicators for 2002–2014 were examined descriptively as temporal demand context, not as a validated forecasting exercise. The framework provides an auditable relative comparison, but it does not measure causal returns, social welfare, or the absolute necessity of a station. Policy application requires source-level provenance, uncertainty in indicator construction, out-of-sample validation, and stakeholder-defined decision thresholds.</p>