人口收缩背景下城镇人口变化趋势预测与建设用地风险识别——以东北地区为例

Under the background of population decline, the number of cities in China with shrinking urban population continues to increase, posing challenges to the allocation of urban construction land resources. This study, based on multi-source population and land use data and taking Northeast China as a case example, constructed a hierarchical constraint plus multi-scenario simulation forecasting method to predict urban population changes from 2020 to 2035. It further identified construction land development risks from the perspectives of "static coordination" and "dynamic adaptation", proposing ideas for optimized land allocation. The study found that: 1) Urban population forecasts reveal a continued decline in Northeast China, with a growing number and intensifying severity of shrinking cities. The spatial pattern exhibits a clear "core growth-peripheral shrinkage" characteristic, with resource-based and old-industrial cities facing particularly severe shrinkage. 2) Urban population changes show diverse types, with differentiated pressures on land allocation transformation, dominated by "deepening shrinkage" and "falling into shrinkage" types, and significant interprovincial differences. 3) The region is characterized mainly by "static scale redundancy" and "insufficient dynamic adaptation" in the human-land relationship, with widespread redundancy in prefecture-level cities' construction land and most cities still following expansion-based land allocation patterns at the baseline period. 4) Risk identification of construction land highlights deep-seated conflicts; different levels of land allocation risks exist in 30 shrinking cities. High-risk cities form a "land-asset decline cycle" and urgently need to explore "value pathways" within "land reduction". By constructing a systematic logical framework linking population forecasting and land use risk, this study innovated population prediction methods and provided a comprehensive diagnosis of land use risks, offering valuable exploration for guiding optimized construction land allocation in shrinking cities.