Spatial Correlation Network and Driving Factors of Urban Energy Eco-Efficiency from the Perspective of Human Well-Being: A Case Study of Shaanxi Province, China

Meixia Wang (), Qingyun Zheng and Yunxia Wang ()
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Meixia Wang: School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China
Qingyun Zheng: School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China
Yunxia Wang: Business School, Shenzhen Technology University, Shenzhen 518118, China

IJERPH, 2023, vol. 20, issue 6, 1-20

Abstract: It is very important to seek a sustainable improvement in human well-being under a limited resource supply and to promote the scientific and coordinated development of urban economic development, ecological environment protection, and human well-being. This paper constructs a human well-being index that includes economic well-being, culture and education well-being, and social development well-being as factors, and it incorporates the human well-being index into the evaluation system for urban well-being energy eco-efficiency (WEE). It uses the super-slack-based measure (SBM) model, which considers undesirable output, to measure the WEE of 10 prefecture-level cities in Shaanxi Province, China, from 2005 to 2019. The social network analysis (SNA) is used to describe the characteristics of the spatial correlation network of WEE and its spatiotemporal evolutionary trend, and the quadratic assignment procedure (QAP) analysis method is used to identify the driving factors that affect the spatial correlation network. The results show that, first, the WEE in Shaanxi is relatively low as a whole and varies greatly among regions, with the highest level in northern Shaanxi, followed by Guanzhong; the lowest level is in southern Shaanxi. Second, in Shaanxi, WEE has transcended geographical proximity into a complex, multi-threaded spatial correlation network, and Yulin is at the center of the network. Third, the network shows four sectors: the net overflow, main benefit, two-way overflow, and broker. Members in each sector have not fully exploited their advantages, and the whole network can be improved. Fourth, the differences in the economic development level, openness, industrial structure, and population are the main driving factors influencing the formation of the spatial correlation network.

Keywords: human well-being index; spatial correlation network structure; social network analysis; well-being energy eco-efficiency (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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