Spatial Correlation Network Structure of Carbon Emission Efficiency in China’s Construction Industry and Its Formation Mechanism

Haidong Gao (), Tiantian Li, Jing Yu, Yangrui Sun and Shijie Xie
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Haidong Gao: State Key Laboratory of Northwest Arid Zone Ecological Water Resources, Xi’an University of Technology, Xi’an 710048, China
Tiantian Li: State Key Laboratory of Northwest Arid Zone Ecological Water Resources, Xi’an University of Technology, Xi’an 710048, China
Jing Yu: State Key Laboratory of Northwest Arid Zone Ecological Water Resources, Xi’an University of Technology, Xi’an 710048, China
Yangrui Sun: State Key Laboratory of Northwest Arid Zone Ecological Water Resources, Xi’an University of Technology, Xi’an 710048, China
Shijie Xie: School of Civil Engineering, Southeast University, Nanjing 210096, China

Sustainability, 2023, vol. 15, issue 6, 1-23

Abstract: In the context of “carbon peak, carbon neutrality”, it is important to explore the spatial correlation network of carbon emission efficiency in the construction industry and its formation mechanism to promote regional synergistic carbon emission reduction. This paper analyzes the spatial correlation network of carbon emission efficiency in China’s construction industry and its formation mechanism through the use of the global super-efficiency EBM model, social network analysis, and QAP model. The results show that (1) the national construction industry’s overall carbon emission efficiency is steadily increasing, with a spatial distribution pattern of “high in the east and low in the west”. (2) The spatial correlation network shows a “core edge” pattern. Provinces such as Jiangsu, Zhejiang, Shanghai, Tianjin, and Shandong are at the center of the network of carbon emission efficiency in the construction industry, playing the role of “intermediary” and “bridge”. At the same time, the spatial correlation network is divided into four plates: “bidirectional spillover plate”, “main inflow plate”, “main outflow plate”, and “agent plate”. (3) Geographical proximity, regional economic differences, and urbanization differences have significant positive effects on the formation of a spatial correlation network. At the same time, the industrial agglomeration gap has a significant negative impact on the formation of such a network, while energy-saving technology level and labor productivity differences do not show any significant effect.

Keywords: carbon emission efficiency of the construction industry; spatial correlation network; social network analysis; QAP model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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