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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Changwei Yuan & Jinrui Zhu & Shuai Zhang & Jiannan Zhao & Shibo Zhu, 2024. "Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity," Sustainability, MDPI, vol. 16(7), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:3086-:d:1371834
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    References listed on IDEAS

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    1. Kevin Robert Gurney, 2009. "China at the carbon crossroads," Nature, Nature, vol. 458(7241), pages 977-979, April.
    2. Zhao Liu & Ling Li & Yue-Jun Zhang, 2015. "Investigating the CO 2 emission differences among China’s transport sectors and their influencing factors," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 1323-1343, June.
    3. Tom Broekel & Pierre-Alexandre Balland & Martijn Burger & Frank Oort, 2014. "Modeling knowledge networks in economic geography: a discussion of four methods," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(2), pages 423-452, September.
    4. Hunter, David R. & Handcock, Mark S. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i03).
    5. Cody J. Dey & James S. Quinn, 2014. "Individual attributes and self-organizational processes affect dominance network structure in pukeko," Behavioral Ecology, International Society for Behavioral Ecology, vol. 25(6), pages 1402-1408.
    6. Zhu Liu & Dabo Guan & Wei Wei & Steven J. Davis & Philippe Ciais & Jin Bai & Shushi Peng & Qiang Zhang & Klaus Hubacek & Gregg Marland & Robert J. Andres & Douglas Crawford-Brown & Jintai Lin & Hongya, 2015. "Reduced carbon emission estimates from fossil fuel combustion and cement production in China," Nature, Nature, vol. 524(7565), pages 335-338, August.
    7. Yan, Ran & Ma, Minda & Zhou, Nan & Feng, Wei & Xiang, Xiwang & Mao, Chao, 2023. "Towards COP27: Decarbonization patterns of residential building in China and India," Applied Energy, Elsevier, vol. 352(C).
    8. Ningxin Zhang & Yu Zhang & Hanli Chen, 2023. "Spatial Correlation Network Structure of Carbon Emission Efficiency of Railway Transportation in China and Its Influencing Factors," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
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