从何以见“智”到足以见“智”——应对人口老龄化国家战略下上海智慧养老服务需求分析

From “How to Be Smart” to “Enough to Be Smart”—Analysis of Shanghai’s Demand for Smart Elderly Services under the National Strategy of Coping with an Aging Population

摘要: 背景：如今我国人口老龄化进程不断加快，传统的养老模式已无法有效应对当下的养老问题。利用数字化，结合新技术，智慧养老这一新兴养老模式正渐渐映入大众的视线，为人口老龄化社会治理提供新的路径。作为我国经济体量最大的城市之一，上海的人口老龄化程度高，范围广。但上海利用合理的资源配置，积极发展智慧养老产业结构，并不断更新改善智慧养老模式使其适应城市发展，有效地缓解了当前人口老龄化问题带来的冲击。目的：以上海市为例，探讨老年人对智慧养老服务的需求现状及其影响因素。为进一步应对人口老龄化问题，完善上海市智慧养老服务供给提供参考。方法：本文使用多元线性回归模型作为分析工具，并使用CGSS2018年数据作为主要数据来源。对回归模型进行诊断和改进，提高模型的拟合度和稳健性。结果：上海市老年人对智慧养老服务的需求程度整体较低；老年人对智慧养老服务的需求与其年龄、性别、教育水平、收入水平、子女数量、子女同住情况等变量有显著关系。结论:上海市智慧养老服务需求存在较大的潜力和空间，应从提高老年人对智慧养老服务的认知度、培养老年人对智慧养老服务的使用能力、降低智慧养老服务的费用负担、增加智慧养老服务的供给和质量等方面，促进上海市智慧养老服务的发展和普及。

Abstract: Background: Nowadays, the process of population aging in China is accelerating, and the traditional pension model can not effectively deal with the current pension problem. Using digitization and combined with new technologies, the smart elderly care is gradually coming into the public’s sight, providing a new path for the social governance of population aging. As one of the cities with the largest economic volume in China, Shanghai has a high degree of population aging and a wide range. However, Shanghai uses reasonable resource allocation, actively develops the industrial structure of smart elderly care and constantly updates and improves the smart elderly care model to adapt to urban development, effectively alleviating the impact of the current population aging problem. Objective: Taking Shanghai as an example, this paper discusses the current situation and influencing factors of the elderly’s demand for smart elderly care services. In order to further cope with the problem of population aging and improve the supply of smart elderly care services in Shanghai, it provides a reference. Methods: The multiple linear regression model was used as the analysis tool, and the CGSS2018 data was used as the main data source. The regression model is diagnosed and improved to improve the fitting degree and robustness of the model. Results: The elderly in Shanghai have a low demand for smart elderly care services. The elderly’s demand for smart elderly care services is significantly related to variables such as age, gender, education level, income level, number of children, and co-residence of children. Conclusion: There is great potential and space for the demand of smart elderly care services in Shanghai. It is necessary to promote the development and popularization of smart elderly care services in Shanghai from the aspects of improving the elderly’s awareness of smart elderly care services, cultivating the elderly’s ability to use smart elderly care services, reducing the cost burden of smart elderly care services, and increasing the supply and quality of smart elderly care services.