Integrated framework for identifying shrinking cities using nighttime light and socioeconomic data: a case study of China

The shrinking of cities has become a widespread global phenomenon. The accurate identification of the dynamics of shrinking cities, as well as quantification of the degree of shrinkage, is important for promoting sustainable urban development and formulating related policies. This study proposes an innovative integrated framework for shrinking-city identification that comprises the initial detection of potential urban shrinkage through the analysis of nighttime-light data, followed by a refined screening process using demographic and GDP (Gross Domestic Product) metrics. Furthermore, we quantified the degree and types of shrinkage in these cities. Between 2000 and 2022, we identified 77 shrinking cities in China, representing 11.44% of the studied urban entities. These cities were dispersed across diverse economic systems, with notable concentrations in the northeastern and central economic regions. Most analyzed cities exhibited a low shrinking intensity, with shrinkage ratios predominantly falling within two ranges: 30–45% and over 60%. We classified shrinking cities into primary categories by integrating their demographic, economic, and geospatial metrics. Shrinking urban agglomerations were classified into three primary categories: “Continuous Shrinkage,” “Episodic Shrinkage,” and “Temporary Shrinkage.” Our findings support a transition from conventional growth-centric paradigms toward adaptive governance frameworks that emphasize resilience and structural readjustment.