摘要
智慧养老产业政策对于优化调配智慧养老产业整体布局,促进产业有序发展,有效应对人口老龄化等,具有重要意义。从政策工具、政策目标和政策效力三个维度构建分析框架,运用内容分析法和政策一致性(PMC)指数模型等,对2011—2021年中央及各部委颁布的88份智慧养老产业政策文件进行量化分析。研究发现:中国智慧养老产业政策呈现“波动发展—缓慢推进—爆发性增长”的发展趋势;政策工具结构失衡,表现为供给型政策工具的使用频数占比偏多,而需求型和环境型政策工具的使用频数占比相对较少;政策目标引领作用发挥不足,呈现轻技术、弱标准等特征;政策效力整体表现良好。基于此,建议国家合理配置政策工具、完善政策目标体系、优化政策效力机制,以推动智慧养老产业健康发展。
Policies on smart elderly care industry are of great significance for optimizing its overall layout,promoting its orderly development and effectively coping with the aging of the population.The paper has constructed an analysis framework from three dimensions of policy tools,policy objectives and policy effectiveness,and then made quantitative analysis of 88 policy documents on smart elderly care industry promulgated by the central departments from 2011 to 2021 using content analysis and the PMC index model.The results show that China’s policies on smart elderly care industry present the development pattern of“fluctuating development-slow progress-explosive growth”.The structure of policy tools is unbalanced,with a large proportion of supply-based policy tools and a relatively small proportion of demand-based and environment-based policy tools.The policy objectives haven’t played the guiding role effectively,with less emphasis on technology and standards,but policy effectiveness works well.Based on this,the paper proposes that the government should rationally allocate policy tools,improve policy objective system and optimize policy effectiveness mechanism to promote the healthy development of smart elderly care industry.
作者
胡扬名
刘鲜梅
宫仁贵
HU Yangming;LIU Xianmei;GONG Rengui(College of Public Administration and Law,Hunan Agricultural University,Changsha Hunan 410128,China)
出处
《北京航空航天大学学报(社会科学版)》
2023年第2期67-77,共11页
Journal of Beijing University of Aeronautics and Astronautics:Social Sciences edition Edition
基金
国家社会科学基金重点项目(20AGL035)。
关键词
智慧养老产业
政策量化
政策工具
政策目标
政策效力
内容分析法
PMC指数模型
smart elderly care industry
policy quantification
policy tool
policy objective
policy effectiveness
content analysis
PMC index model