The land surface temperature (LST) in the central urban area has shown a consistent upward trend over the years, exacerbating the surface urban heat island (SUHI) effect. Therefore, this study focuses on the central urban area of Chengdu, using blocks as the research scale. The Gradient Boosting Decision Tree (GBDT) model and SHAP values are employed to explore the nonlinear effects of human settlements (HS) on LST across different seasons. The results show that (1) At the block scale, the overall impact of HS on LST across all four seasons tracks the following order: built environment (BE) > landscape pattern (LP) > socio-economic development (SED). (2) LP is the most important factor affecting LST in summer, while the BE has the greatest influence on LST during spring, autumn, and winter. (3) Most HS indicators exhibit seasonal variations in their impact on LST. The impervious surface area (ISA) exhibits a significant positive impact on LST during spring, summer, and autumn. In contrast, the nighttime light index (NTL) and functional mix degree (FMD) exert a significant negative influence on LST in spring, autumn, and winter. Additionally, the normalized difference vegetation index (NDVI) negatively affects LST in both spring and summer. Moreover, connectivity (CNT) and functional density (FPD) demonstrate notable threshold effects in their influence on LST. (4) Certain HS indicators exhibit interaction effects, and some combinations of these indicators can effectively reduce LST. This study reveals HS–LST interactions through multidimensional analysis, offering block-scale seasonal planning strategies for sustainable urban thermal optimization.

With the progress of China’s economy, there is an increasing focus on accessibility systems. Enhancements to accessibility infrastructure are being implemented in all types of structures, with a particular focus on educational institutions such as college campuses. This research was carried out to examine the campus accessibility system of Northeastern University’s Nanhu and Hunnan Campuses in Shenyang City, China, using failure mode and effects analysis (FMEA) as a methodology and using incorporating fuzzy control, which overcomes the limitations of traditional FMEA. Fuzzy-FMEA integrates the fuzzy linguistic assessment to assist the analysis process, in contrast to standard analysis which mainly relies on subjective judgment. Through calculations, it is known that ramps, barrier-free toilets, and barrier-free entrances are the items with the highest failure modes. The construction of the campus accessible environment needs to prioritize solving the problems of these facilities. The research results also found that there is a lack of research specifications for accessible environments in China’s cold regions, and universal specifications are not fully suitable. The accessibility of the new campus was improved and management was improved, reflecting the school’s increased awareness of accessible environment construction in recent years. However, there are still many common problems in the old and new campuses, proving that they are not aware of the importance and urgency of improving these problems. In addition, the construction of barrier-free facilities in the administrative office buildings where management and faculty are located on both campuses is generally in good condition. The accessible design of dormitories and canteens commonly used by students is often ignored. Moreover, the rough detailing in many buildings prevents these facilities from being used properly. These are the challenges faced by the Northeastern University in building an accessible environment.