Climate change poses severe threats to cultural heritage, yet thermal performance impacts on traditional architecture in conflict-affected regions remain poorly quantified. This study provides one of the first comprehensive assessments of climate change effects on UNESCO World Heritage architecture in Yemen’s Old City of Sana’a. We employed building energy simulation (DesignBuilder/EnergyPlus) to evaluate the thermal performance of a representative five-story traditional adobe tower house under three climate scenarios: baseline (1974–2017), SSP2-4.5 (moderate emissions, 2041–2060), and SSP5-8.5 (high emissions, 2041–2060). Climate projections were derived from five CMIP6 models using the morphing methodology, with natural ventilation-only operation (no mechanical cooling). The results demonstrate dramatic thermal performance degradation: annual overheating hours (>30 °C) increase more than 10-fold from 111 h (baseline) to 1264 h (SSP2-4.5) on the most vulnerable floor, representing escalation from 1.3% to 14.4% of the year. Extreme heat exposure (>32 °C) emerges under climate scenarios (324–423 h annually) and is absent under baseline conditions. Thermal comfort declines 27–30 percentage points across all floors. The findings reveal the systematic failure of passive cooling mechanisms under elevated temperatures, particularly when nighttime temperatures exceed 20 °C, eliminating nocturnal heat purging opportunities. The results necessitate the urgent development of heritage-sensitive adaptation strategies for Old Sana’a and similar UNESCO sites in arid regions facing compound climate-conflict vulnerability.

Amid growing demands for urban regeneration, metro station areas (MSAs) have emerged as critical spatial units for assessing renewal potential. However, their highly heterogeneous functional and spatial attributes pose challenges to precise classification and targeted strategy development. This study introduces the RFM (recency, frequency, and monetary) model—originally used in marketing—to the urban renewal domain. By mapping POI (point of interest) data, population density, and land price to the RFM dimensions, a three-dimensional evaluation framework is constructed. Using QGIS to process multi-source data for 118 MSAs in Beijing, we apply an improved five-quantile stratification method to classify station areas into eight renewal potential types. The results reveal a concentric spatial gradient: 24% of core-area MSAs are identified as Key-Value MSAs, while 23% of peripheral MSAs are categorized as General-Retention MSAs. Based on the classification, differentiated renewal strategies are proposed: high-potential MSAs should prioritize public space enhancement and walkability improvements, whereas low-potential MSAs should focus on upgrading basic transit infrastructure. The study provides a replicable method for classifying MSAs based on spatial and economic indicators, offering new theoretical insights and practical tools to guide evidence-based urban regeneration and station–city integration in high-density metropolitan areas such as Beijing.