Spatial Matching and Policy-Planning Evaluation of Urban Elderly Care Facilities Based on Multi-Agent Simulation: Evidence from Shanghai, China

Abstract

China is about to enter the stage of deep aging, and the number of disabled elderly is growing continuously. Although the government continues to increase its investment in elderly care institutions and facilities, there is still an unreasonable and unbalanced structure of resource allocation, and the configuration of elderly care institutions and facilities is not well coupled with the geographical layout of the population. Taking Shanghai, China, as an example, based on the multi-agent simulation method, this paper forecasts the supply and demand of elderly care institutions, and evaluates the rationality of relevant policy planning by comprehensively analyzing the behavior of the elderly, the behavior of elderly care institutions, and the interaction between the actors and the environment. The study concludes that: (1) With the increase of aging in Shanghai, the disability rate and chronic diseases will reach 18.7% and 42.0%, respectively, before the arrival of the peak of the elderly population in 2030, and 32.2% of the elderly will be willing to receive nursing services in elderly care institutions, which may make it difficult for the incremental reform of institutional beds in the existing policy planning to meet the actual needs. (2) The index of the number of beds for 100 people in the spatial configuration of elderly care institutions continued to increase, but the actual utilization rate of beds continued to decline, which was only 61.3% in 2030. (3) From the regional perspective, there is an obvious imbalance. The research shows that, with the increase in the disability rate of the elderly and the high incidence of chronic diseases, increasing the supply of facilities for elderly care institutions is an inevitable choice to meet the differentiated needs of the elderly. The demographic characteristics, psychology, economic costs, and spatial distribution of the elderly have a comprehensive impact on the decision-making of institutional elderly care. Institutional facility planning needs to combine urban development strategies, comprehensively consider economic costs, population characteristics and spatial distribution, and achieve a reasonable layout of different types of elderly care institutions. In summary, this paper provides a reference for the scientific layout of urban elderly care institutions through multi-agent simulation, and provides empirical support for how to achieve a good configuration of “population–facilities” in space.