Spatial and temporal characteristics and evolutionary prediction of urban health development efficiency in China: Based on super-efficiency SBM model and spatial Markov chain model

Construction healthy city is a global strategy advocated by the WHO to address the social and environmental problems brought about by urbanization worldwide, and is also an important grip for China to promote the strategy of Beautiful China and Healthy China in the new era. This study constructed an evaluation system for health development efficiency (HDE) of Chinese cities from an input-output perspective, evaluated the HDE of 284 prefecture-level and above cities in China from 2009 to 2019 using the super-efficient SBM model, and further explored the dynamic transfer characteristics, spatial spillover effects, and future development trends of urban HDE in depth using Markov chain model and spatial Markov chain model. The main findings were as follows. (1) From 2009 to 2019, the average level of HDE in Chinese cities generally showed a "W-shaped" fluctuating upward trend, increasing from 0.6106 to 0.6538, an overall increase of 7 %. However, the overall input-output efficiency was still relatively low, with only 27.11 % of cities in a high efficiency state in 2019. (2) The spatial distribution pattern of HDE of cities nationwide showed a gradually decreasing pattern with the urban cluster cities as the core, specifically "eastern cities > western cities > central cities" and "mega cities > super cities > large cities > medium cities > small cities". (3) There was a significant spatial spillover effect on the HDE of Chinese cities. When a city was adjacent to a region with low HDE, the probability of its HDE type shifting downward increased, and when it was adjacent to a region with high HDE, the probability of its HDE type shifting upward increased. (4) In the long term, the distribution of urban HDE in China gradually shifted from intermediate (ML and MH) to high (H) or low (L) state over time, with the greatest possibility (27.40 %) of dynamic shift to high (H) states. After further classification and prediction based on spatial neighborhood states, heterogeneity in the future evolutionary impact of different neighborhood contexts on urban HDE was found. These findings can provide references for improving the quality of healthy cities work and optimizing healthy cities guidance policies.