Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity

Changwei Yuan, Jinrui Zhu (), Shuai Zhang (), Jiannan Zhao and Shibo Zhu
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Changwei Yuan: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Jinrui Zhu: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Shuai Zhang: College of Economics and Management, Chang’an University, Xi’an 710064, China
Jiannan Zhao: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Shibo Zhu: BYD Automobile Co., Ltd., Xi’an 710119, China

Sustainability, 2024, vol. 16, issue 7, 1-23

Abstract: From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China’s transportation carbon emission intensity. This study found that China’s transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: “two-way spillover”, “net benefit”, “broker”, and “net spillover”. The “net benefit” plate was mainly located in developed regions, and the “net spillover” plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity.

Keywords: transportation carbon emission intensity; spatial correlation; gravity model; social network analysis; TERGM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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