Vegetation greenness helps cool cities, but how its cooling effect changes over time in newly expanded and existing old built-up areas (BUAs) across different urban development stages remains poorly understood. This study investigated the spatiotemporal trends of the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) within newly expanded and old BUAs across cities of different development stages from 1992 to 2020. Utilizing the ESA CCI dataset for BUA delineation and Landsat-derived NDVI and LST, we applied the Mann–Kendall test and Sen’s slope estimator to analyze 26 core cities. City sizes were classified into five categories from small cities to super megacities based on their status, and then assigned to urban development stages ranging from Stage 4 (least developed) to Stage 1 (most developed). The results revealed a significant decrease in NDVI across all areas in both new BUA (average significant slope = −0.0009, p < 0.05) and old BUA (average significant slope = −0.0002, p < 0.05), and a significant increase in LST in both new BUA (average significant slope = 0.0818, p < 0.05) and old BUA (average significant slope = 0.0245, p < 0.05). In addition, NDVI trends in BUA expansion varied significantly across urban development stages (p for trend = 0.016). Excluding Stage 1 (Shanghai only), vegetation loss attenuated from the sharpest decline in Stage 4 (average significant slope = −0.0021, 36.60% significant, p < 0.05) to a moderate decline in Stage 3 (−0.0011, 26.03%), and reversed to a slight vegetation gain in Stage 2 (0.0031, 19.14%), whereas LST showed no significant stage-dependent pattern. These findings underscore the widespread decreasing NDVI and increasing LST in both newly expanded and old BUAs, with the stage-wise vegetation differences potentially reflecting varying policy and economic contexts.

China has implemented a series of clean air policies, resulting in improved air quality since 2013. However, there remains a paucity of national prospective evidence regarding the relationship between fine particulate matter (PM_2.5) and kidney disease (KD) incidence in China, as well as the potential mediating effects of lipid profiles in this association. This study aimed to assess the association of decreased PM_2.5 concentration and KD incidence in China from 2013 to 2020. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), we included 15,368 participants who were free of KD in 2013 and followed up until 2020. For each participant, we calculated the 3-year and 2-year average PM_2.5 concentrations. The Cox proportional hazards model was employed to estimate the association between PM_2.5 exposure and KD incidence. Mediation analyses were conducted using eight lipid indices, and subgroup analyses were performed. The annual average PM_2.5 concentration for CHARLS participants reduced from 61.72 μg/m³ in 2013 to 32.75 μg/m³ in 2020. A reduction of 5 μg/m³ in 3-year and 2-year average PM_2.5 concentrations was associated with 14.3% (hazard ratio [HR]: 0.857, 95% confidence interval [CI]: 0.841, 0.873) and 14.4% (HR: 0.856, 95% CI: 0.840, 0.873) reductions in KD incidence in the fully adjusted models. The TyG-BMI and TyG-WHtR indices exhibited small mediating effects of 7.36% (95% CI: 2.35%, 12.38%) and 4.48% (95% CI: 0.51%, 8.45%) on the relationship of PM_2.5–KD, while other indicators did not demonstrate significant mediation. The findings of this study suggest that reductions in PM_2.5 concentration were associated with a decreased incidence of KD during the period from 2013 to 2020. The implementation of clean air policies since 2013 may have contributed to the decrease in chronic diseases like KD.