Work

Capacity utilization of China’s elderly care service industry during 2010–2022

Year: 2026

Type: article

Abstract: BACKGROUND: China's elderly care service industry (ECSI) plays a critical role in addressing the needs of an aging population. However, the industry faces both resource shortages and waste, particularly of fixed assets, while capacity utilization (CU), which reflects fixed asset efficiency, has received limited attention. METHODS: This study develops a CU measurement model based on the data envelopment analysis framework to evaluate the CU of ECSI across 30 provinces in China from 2010 to 2022. In addition, the Dagum Gini ratio and Moran's I are employed to analyze the spatial disparities and correlations. FINDINGS: The CU of China's ECSI showed a downward trend with fluctuations, declining from 0.7426 in 2010 to 0.5889 in 2022. Central China generally exhibit higher CU than eastern and western China. Western China had the lowest CU and the most pronounced fluctuations. The Dagum Gini ratio indicated that the difference between subregions was the primary driver of the total difference (43%), followed by the difference within subregions (28%). Moran's I analysis showed that CU in China's ECSI exhibited increasing spatial agglomeration, with weak but significant spatial correlation. Economically developed provinces, such as Jiangsu, Zhejiang, and Hubei, formed High-High clusters. In contrast, underdeveloped western provinces, including Gansu and Xinjiang, formed Low-Low clusters. CONCLUSIONS: China's ECSI faces declining CU and pronounced regional imbalances. The increasing spatial agglomeration and distinct clustering patterns highlight the need for targeted policies to reduce regional disparities and improve resource allocation.

Source: Cost Effectiveness and Resource Allocation

DOI: 10.1186/s12962-026-00769-2

Authors: Xiuquan Huang, Tao Zhang, Xi Wang, Hanxiang Gong, Wenshuo Zhang

Institutions: Nanjing University of Chinese Medicine, Macao Polytechnic University, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University

Language: English

Topic: Efficiency Analysis Using DEA

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Industry, innovation and infrastructure, Decent work and economic growth, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,790