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Rethinking Regional High-Quality Development Pathways from a Carbon Emission Efficiency Perspective

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Listed:
  • Chao Wang

    (School of Labor Economics, Capital University of Economics and Business, Beijing 100070, China)

  • Yuxiao Kong

    (State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China)

  • Xingliang Lu

    (State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China)

  • Hongyi Xie

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China)

  • Yanmin Teng

    (Research Center for Eco-Environmental Engineering, Dongguan University of Technology, Dongguan 523808, China)

  • Jinyan Zhan

    (State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China)

Abstract

Optimizing resource efficiency and mitigating climate change have become consensuses of human society. However, there is still a gap in assessing the carbon emission efficiency (CEE) and identifying the influence of various factors, especially in rapid urbanizing regions. In this paper, we built a stochastic frontier analysis model to assess CEE and conducted a case study in the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA), a typical area of collaborative development in China. A comprehensive influencing factor index was constructed to analyze and identify the key influencing factors of CEE. The results revealed that the average CEE among the 13 cities increased in volatility from 2000 to 2019. The average CEE in Langfang was lowest, while that in Tangshan was highest. The input-related factors had a negative effect on CEE, including carbon emissions per capita, employment per ten thousand people, total assets per capita, and energy intensity. GDP per capita, the urbanization level, and the proportion of the tertiary sector’s GDP had positive impacts on CEE. Future policy formulation should focus on the transition from labor- and material-intensive industries to knowledge- and technology-intensive industries. All the results can contribute to achieving high-quality development and dual-carbon target of rapid-urbanizing areas.

Suggested Citation

  • Chao Wang & Yuxiao Kong & Xingliang Lu & Hongyi Xie & Yanmin Teng & Jinyan Zhan, 2024. "Rethinking Regional High-Quality Development Pathways from a Carbon Emission Efficiency Perspective," Land, MDPI, vol. 13(9), pages 1-18, September.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:9:p:1441-:d:1472288
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    References listed on IDEAS

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    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. Ester Gutiérrez & Sebastián Lozano, 2022. "Cross-country comparison of the efficiency of the European forest sector and second stage DEA approach," Annals of Operations Research, Springer, vol. 314(2), pages 471-496, July.
    3. Christensen, Laurits R & Jorgenson, Dale W & Lau, Lawrence J, 1973. "Transcendental Logarithmic Production Frontiers," The Review of Economics and Statistics, MIT Press, vol. 55(1), pages 28-45, February.
    4. Zhao, Zhe & Bai, Yuping & Wang, Guofeng & Chen, Jiancheng & Yu, Jiangli & Liu, Wei, 2018. "Land eco-efficiency for new-type urbanization in the Beijing-Tianjin-Hebei Region," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 19-26.
    5. Liu, Haiying & Pata, Ugur Korkut & Zafar, Muhammad Wasif & Kartal, Mustafa Tevfik & Karlilar, Selin & Caglar, Abdullah Emre, 2023. "Do oil and natural gas prices affect carbon efficiency? Daily evidence from China by wavelet transform-based approaches," Resources Policy, Elsevier, vol. 85(PB).
    6. Yuan Zeng & Wengang Zhang & Jingwen Sun & Li’ao Sun & Jun Wu, 2023. "Research on Regional Carbon Emission Reduction in the Beijing–Tianjin–Hebei Urban Agglomeration Based on System Dynamics: Key Factors and Policy Analysis," Energies, MDPI, vol. 16(18), pages 1-20, September.
    7. Song, Malin & Fisher, Ron & Kwoh, Yusen, 2019. "Technological challenges of green innovation and sustainable resource management with large scale data," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 361-368.
    8. Pankaj Dutta & Aayush Jain & Asish Gupta, 2020. "Performance analysis of non-banking finance companies using two-stage data envelopment analysis," Annals of Operations Research, Springer, vol. 295(1), pages 91-116, December.
    9. Ma-Lin Song & Ron Fisher & Jian-Lin Wang & Lian-Biao Cui, 2018. "Environmental performance evaluation with big data: theories and methods," Annals of Operations Research, Springer, vol. 270(1), pages 459-472, November.
    10. Fan, Ying & Liu, Lan-Cui & Wu, Gang & Tsai, Hsien-Tang & Wei, Yi-Ming, 2007. "Changes in carbon intensity in China: Empirical findings from 1980-2003," Ecological Economics, Elsevier, vol. 62(3-4), pages 683-691, May.
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