This study focuses on the Triangle of Central China and investigates the spatiotemporal evolution, driving factors, and impacts of population aging on regional sustainable development from 2000 to 2020. The study adopts an innovative two-scale analytical framework at the prefecture and district/county level, integrating spatial autocorrelation analysis, the Geodetector model, and geographically weighted regression. The results show a significant acceleration in population aging across the study area, accompanied by pronounced spatial clustering, particularly in western Hubei and the Wuhan metropolitan area. Over time, the spatial distribution has evolved from a relatively dispersed pattern to one of high concentration. Key drivers of the spatial heterogeneity of aging include economic disparities, demographic transitions, and the uneven spatial allocation of public services such as healthcare and education. These aging patterns profoundly affect the region’s potential for sustainable development. Accordingly, the study proposes a multi-scale collaborative governance strategy: At the prefecture level, efforts should focus on promoting the coordinated development of the silver economy and optimizing the spatial redistribution of healthcare resources; At the district and county level, priorities should include strengthening infrastructure, curbing the outflow of young labor, and improving access to basic public services. By integrating spatial analysis techniques with sustainable development policy recommendations, this study provides a basis for scientifically measuring, understanding, and managing demographic transitions. This is essential for achieving long-term socioeconomic sustainability in rapidly aging regions.

This study categorizes 45 cities into four types based on population dynamics using census data (2000–2020). Methods such as ArcGIS10.8, carbon emission estimation, LISA clustering, and association analysis are employed to explore the spatiotemporal distribution of shrinking cities and carbon emissions. This study analyzes carbon emission patterns and influencing factors for the four city types and provides policy recommendations. The findings are as follows: (1) Lasting-growth cities show a “two-end mass, middle-point” pattern, while stage-growth and stage-shrinking cities are “point” distributed. Lasting-shrinking cities are mainly distributed in the middle and lower reaches of the Yangtze River. (2) Total carbon emissions are rising, showing two clusters of high-value areas. Carbon emission intensity is falling quickly, being higher in the west and lower in the east. (3) Lasting-growth cities have the fastest direct carbon emission growth rate, stage-growth cities have the fastest energy-related indirect emission growth rate, and cities undergoing population increase have the fastest growth rate for other indirect carbon emissions. In terms of carbon reduction, lasting-growth cities perform best, whereas stage-growth cities perform worst. (4) Regional GDP, per capita regional GDP, urban construction area, and hospital beds per 10,000 people promote carbon emission reduction in the four city types, while a higher number of industrial enterprises inhibits it. Birth rate, aging rate, and mortality rate have no significant impact. This study addresses the gaps in previous research on shrinking cities and carbon emission reduction by considering the dynamic nature of shrinking processes and analyzing carbon emission patterns.