Under the background of social transformation and space reconstruction, population aging in China is becoming more and more diverse and complex. “Aging before getting rich”, a notion in population and economic development, has been a serious threat to sustainable development in China. On the basis of town- and street block-scale data from the Fifth and Sixth National Census in the Yangtze River Delta, we studied spatial distribution characteristics of population aging using global Moran’s I and hotspot analysis, and applied the geographical detector technique to explain the spatial heterogeneity of population aging. Several conclusions were drawn. (1) The promotion and replacement of aging coexist. Cluster of aging degree exhibits an increasing trend. Population aging is more severe in suburban areas than urban areas. (2) Migration is the main factor affecting the spatial heterogeneity of population aging. Per capital GDP and road network density are the second most influential factors. By contrast, the relief degree of land surface and the air quality index minimally influence population aging. The mechanisms of aging in various regions are affected by varying development levels. (3) The interaction among social, economic, and environmental factors enhances their effects and, thus, interacting factors have a greater influence on population aging than any single factor. The findings of this study have significant implications for local inhabitants and policy makers to address the population aging challenge in achieving sustainability of society, economy, and environment.

Carbon emissions research based on regional perspective is necessary and helpful for China to achieve its reduction targets. This research aims at analyzing the energy-related carbon emissions and finding out the most important driving forces for the carbon emissions increments in Guangdong province. LMDI (Logarithmic Mean Divisia Index) method based on the extended Kaya identity has been used to explore the main driving factors for energy-related carbon emissions in Guangdong province annually between 1990 and 2014. Research results show that the impacts and influences of various factors on carbon emissions are different in the different development stages. Economic growth effect and population size effect are the two most important driving factors for the increased carbon emissions. Energy intensity effect played the dominant role in curbing carbon emissions. Energy structure effect and technical progress effect had different but relatively minor effects on carbon emissions during the five different development stages.