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Spatial connection strength and endogenous and exogenous interactive driving factors of carbon efficiency in China's metropolitan areas with higher energy consumption

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  • Shang, Hua
  • Jiang, Li
  • Di, Yuhang

Abstract

Investigating the spatial effects and driving factors of carbon efficiency is meaningful for the sustainable development of metropolitan areas. However, research on the spatial connection strength and endogenous and exogenous interactive driving factors for metropolitan areas' carbon efficiency are limited. This research studies the carbon efficiency in China's metropolitan areas within year 2003–2019. The spatial connection strength using the gravity model with the “dual” driving factors and the geographical detector are analyzed. The results show that: 1) Shanghai metropolitan area performs well and promotes the development of neighboring cities, but Shenyang and Taiyuan metropolitan areas have negative effects on the neighboring cities; 2) The interaction between land resource consumption and pollution index, energy consumption and economic development level is the main endogenous driving factor with the determinant force values of 0.712, 0.674 and 0.634. Energy consumption is a secondary endogenous factor. Capital input, labor input and pollution play weaker roles. 3) Openness degree and education development level and their interaction are the main exogenous driving factors of carbon efficiency in China's metropolitan areas with the determinant force value of 0.673. Based on the results of interactive driving factors of China's metropolitan areas, this paper gives policy suggestions accordingly.

Suggested Citation

  • Shang, Hua & Jiang, Li & Di, Yuhang, 2024. "Spatial connection strength and endogenous and exogenous interactive driving factors of carbon efficiency in China's metropolitan areas with higher energy consumption," Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:energy:v:311:y:2024:i:c:s0360544224031268
    DOI: 10.1016/j.energy.2024.133350
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    as
    1. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    2. Wen, Lanjiao & Chatalova, Lioudmila & Gao, Xin & Zhang, Anlu, 2021. "Reduction of carbon emissions through resource-saving and environment-friendly regional economic integration: Evidence from Wuhan metropolitan area, China," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Song, Yanwu & Zhang, Jinrui & Song, Yingkang & Fan, Xinran & Zhu, Yuqing & Zhang, Chen, 2020. "Can industry-university-research collaborative innovation efficiency reduce carbon emissions?," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    4. Gao, Xin & Zhang, Anlu & Sun, Zhanli, 2020. "How regional economic integration influence on urban land use efficiency？ A case study of Wuhan metropolitan area, China," Land Use Policy, Elsevier, vol. 90(C).
    5. Cheng, Lu & Mi, Zhifu & Sudmant, Andrew & Coffman, D'Maris, 2022. "Bigger cities better climate? Results from an analysis of urban areas in China," Energy Economics, Elsevier, vol. 107(C).
    6. Liu, S. & Xiao, Q., 2021. "An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model," Energy, Elsevier, vol. 224(C).
    7. Long, Yin & Dong, Liang & Yoshida, Yoshikuni & Li, Zhaoling, 2018. "Evaluation of energy-related household carbon footprints in metropolitan areas of Japan," Ecological Modelling, Elsevier, vol. 377(C), pages 16-25.
    8. Sovacool, Benjamin K. & Brown, Marilyn A., 2010. "Twelve metropolitan carbon footprints: A preliminary comparative global assessment," Energy Policy, Elsevier, vol. 38(9), pages 4856-4869, September.
    9. Shang, Hua & Jiang, Li & Pan, Xianyou & Pan, Xiongfeng, 2022. "Green technology innovation spillover effect and urban eco-efficiency convergence: Evidence from Chinese cities," Energy Economics, Elsevier, vol. 114(C).
    10. Filippo Simini & Gianni Barlacchi & Massimilano Luca & Luca Pappalardo, 2021. "A Deep Gravity model for mobility flows generation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    11. Chen, Changhua & Luo, Yuqing & Zou, Hong & Huang, Junbing, 2023. "Understanding the driving factors and finding the pathway to mitigating carbon emissions in China's Yangtze River Delta region," Energy, Elsevier, vol. 278(PB).
    12. Clark, Thomas A., 2013. "Metropolitan density, energy efficiency and carbon emissions: Multi-attribute tradeoffs and their policy implications," Energy Policy, Elsevier, vol. 53(C), pages 413-428.
    13. Liu, Jiaguo & Li, Sujuan & Ji, Qiang, 2021. "Regional differences and driving factors analysis of carbon emission intensity from transport sector in China," Energy, Elsevier, vol. 224(C).
    14. Weijia Cui & Xueqin Lin & Dai Wang & Ying Mi, 2022. "Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China," Land, MDPI, vol. 12(1), pages 1-21, December.
    15. Liu, Xiaoxiao & Niu, Qian & Dong, Shuli & Zhong, Shuiying, 2023. "How does renewable energy consumption affect carbon emission intensity? Temporal-spatial impact analysis in China," Energy, Elsevier, vol. 284(C).
    16. Yan, Yunfeng & Wang, Ran & Zheng, Xiuxiu & Zhao, Zhongxiu, 2020. "Carbon endowment and trade-embodied carbon emissions in global value chains: Evidence from China," Applied Energy, Elsevier, vol. 277(C).
    17. Hongtao Jiang & Jian Yin & Yuanhong Qiu & Bin Zhang & Yi Ding & Ruici Xia, 2022. "Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces," Land, MDPI, vol. 11(8), pages 1-22, July.
    18. Wang, Juan & Li, Jing & Zhang, Qingjun, 2021. "Does carbon efficiency improve financial performance? Evidence from Chinese firms," Energy Economics, Elsevier, vol. 104(C).
    19. Liqiang Zhang & Weiwei Liu & Kun Hou & Jintai Lin & Changqing Song & Chenghu Zhou & Bo Huang & Xiaohua Tong & Jinfeng Wang & William Rhine & Ying Jiao & Ziwei Wang & Ruijing Ni & Mengyao Liu & Liang Z, 2019. "Air pollution exposure associates with increased risk of neonatal jaundice," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    20. Jan Ubøe, 2004. "Aggregation of Gravity Models for Journeys to Work," Environment and Planning A, , vol. 36(4), pages 715-729, April.
    21. Xia, Chang & Zhang, Anqi & Wang, Haijun & Zhang, Boen & Zhang, Yan, 2019. "Bidirectional urban flows in rapidly urbanizing metropolitan areas and their macro and micro impacts on urban growth: A case study of the Yangtze River middle reaches megalopolis, China," Land Use Policy, Elsevier, vol. 82(C), pages 158-168.
    22. Liang, Xiaoying & Min Fan, & Xiao, Yuting & Yao, Jing, 2022. "Temporal-spatial characteristics of energy-based carbon dioxide emissions and driving factors during 2004–2019, China," Energy, Elsevier, vol. 261(PA).
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