In recent years, urban policy-makers have been urged to adopt the development of urban policies for shrinking cities from an urban management perspective. However, urban management in shrinking cities is a difficult problem because it is fiscally restrictive compared with cities with growing populations. This study aims to clarify the types of effective per capita municipal expenditures correlated with population changes in small and medium-sized cities. The research design adopted cross-sectional studies from 2007 to 2022 for all Japanese small and medium-sized cities with populations of less than 200,000. The nonlinear relationship was analyzed via the eXtreme gradient boosting algorithm. As a result, this study revealed 1288 shrinking cities, accounting for 82.56% of all small and medium-sized cities in Japan. For the shrinking cities, this study identified the types of per capita municipal expenditures that correlate with population change: population change was correlated with welfare expenditures. Specifically, the population grew in cities that increased in per capita expenditures on children and decreased in those on welfare recipients and the older population. Our findings suggest that municipal policy-makers should prioritize per capita expenditures on child welfare to sustain the population in small and medium-sized cities. 

Many developed countries need to plan urban policies based on multidimensional factors related to population change. However, empirical research has been inconsistent with respect to identifying these factors, including economic-, social-, and urban-planning-related factors. The purpose of this study is to clarify the nonlinear multidimensional factors that are correlated with population changes according to the city size. In the analysis, the population change rate was defined as the outcome variable, and 269 economic, social, and educational index (ESE index) were used as predictor variables. Data were stratified according to three city sizes. Using the ESE index, the XGBoost algorithm was used to analyze the nonlinear relationship between the population change rate and multidimensional data. As a key result, population changes were strongly correlated with social-related indicators, such as the population change rate among persons ages 0–14 years in small-sized cities, the natural population change rate in medium-sized cities, and the migration change rate in large-sized cities. Regarding the population decline, Japan has 1304 shrinking cities, which are primarily comprised of medium-sized and small-sized cities. In such cities, other than social-related factors, population changes correlated with the financial strength index as an economic-related factor in medium-sized cities and the designation of underpopulated areas as an urban-planning-related factor in small-sized cities. Among the multidimensional factors, cities of different sizes were characterized by factors other than social-related indicators. These multifaceted factors could provide preliminary insights for urban policymakers to explore various policy measures on which they need to focus, depending on the city’s size.

Shrinking cities due to low birthrates and aging populations represent a significant urban planning issue. The research question of this study is: which economic, social, and educational factors affect population decline in Japanese shrinking cities? By modeling shrinking cities using the case of Japanese cities, this study aims to clarify the indicators that affect the population change rate. The study employed Bayesian network analysis, a machine learning technique, using a dataset of economic, social, and educational indicators. In conclusion, this study demonstrates that social and educational indicators affect the population decline rate. Surprisingly, the impact of educational indicators is more substantial than that of economic indicators such as the financial strength index. Considering the limitations in fiscal expenditures, increasing investment in education might help solve the problem of shrinking cities because of low birthrates and aging populations. The results provide essential insights and can function as a planning support system.

This study aims to clarify the statistical causal relationship between the locations of urban facilities and forecasted population changes according to types of residential clusters in the Osaka Metropolitan Fringe areas. This paper’s background is the location optimization plan policy formulated by the Japanese MLIT (Ministry of Land, Infrastructure, Transport, and Tourism) in 2015. The methods combined urban ecological analysis, cohort analysis, and Bayesian network analysis. Using the Bayesian network analysis, the causal relationship between the forecasted population change ratio and the urban facility location was analyzed. The results suggest the location of urban facilities for each residential cluster that will prevent a rapid population decline in the future. Specifically, in the sprawl cluster, this study found that residential areas closer to medical facilities will sustain the future population, while in the old new-town cluster, this study found that residential areas closer to train stations will best sustain the future population. However, in the public housing cluster, residential areas more distant from regional resources will best sustain the future population. Therefore, it is worth considering different urban designs in the old new-town and public housing clusters, rather than the location optimization plan policy.