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Temporal-Spatial Evolution and Driving Factors of Global Carbon Emission Efficiency

Author

Listed:
  • Ping Cao

    (School of Management Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Xiaoxiao Li

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China)

  • Yu Cheng

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China)

  • Han Shen

    (School of Foreign Languages, Shandong Jianzhu University, Jinan 250101, China)

Abstract

With global warming, the continuous increase of carbon emissions has become a hot topic of global concern. This study took 95 countries around the world as the research object, using the Gini coefficient, spatial autocorrelation, spatial econometric model and other methods to explore temporal and spatial evolution, and spatial agglomeration characteristics from 2009 to 2018. The results are as follows: First, global carbon emission efficiency (CEE) showed an overall upward trend, and the average value fluctuated from 0.3051 in 2009 to 0.3528 in 2018, with an average annual growth rate of 1.63%. Spatially, the areas with higher CEE are mainly located in Western Europe, East Asia, and North America, and the areas with lower values are mainly located in the Middle East, Latin America, and Africa. Second, the Gini coefficient increased from 0.7941 to 0.8094, and regional differences showed a gradually expanding trend. The Moran’s I value decreased from 0.2389 to 0.1860, showing a positive fluctuation characteristic. Third, judging from the overall sample and the classified sample, the correlations between the influencing factors and CEE were different in different regions. Scientific and technological innovation, foreign direct investment and CEE in all continents are significantly positively correlated while industrial structure is significantly negatively correlated, and urbanization, economic development level, and informatization show obvious heterogeneity. The research is aimed at strengthening exchanges and cooperation between countries, adjusting industrial structure; implementing emission reduction policies according to local conditions; and providing guidance and reference for improving CEE and mitigating climate change.

Suggested Citation

  • Ping Cao & Xiaoxiao Li & Yu Cheng & Han Shen, 2022. "Temporal-Spatial Evolution and Driving Factors of Global Carbon Emission Efficiency," IJERPH, MDPI, vol. 19(22), pages 1-20, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:22:p:14849-:d:969893
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    References listed on IDEAS

    as
    1. Li, Jinying & Li, Sisi, 2020. "Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model," Energy Policy, Elsevier, vol. 140(C).
    2. Guiliang Tian & Suwan Yu & Zheng Wu & Qing Xia, 2022. "Study on the Emission Reduction Effect and Spatial Difference of Carbon Emission Trading Policy in China," Energies, MDPI, vol. 15(5), pages 1-20, March.
    3. Shuangjie Li & Wei Wang & Hongyu Diao & Liming Wang, 2022. "Measuring the Efficiency of Energy and Carbon Emissions: A Review of Definitions, Models, and Input-Output Variables," Energies, MDPI, vol. 15(3), pages 1-21, January.
    4. Yamaji, Kenji & Matsuhashi, Ryuji & Nagata, Yutaka & Kaya, Yoichi, 1993. "A study on economic measures for CO2 reduction in Japan," Energy Policy, Elsevier, vol. 21(2), pages 123-132, February.
    5. Vujović, Tanja & Petković, Zdravka & Pavlović, Miloš & Jović, Srdjan, 2018. "Economic growth based in carbon dioxide emission intensity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 179-185.
    6. Zhao, Pengjun & Zeng, Liangen & Li, Peilin & Lu, Haiyan & Hu, Haoyu & Li, Chengming & Zheng, Mengyuan & Li, Haitao & Yu, Zhao & Yuan, Dandan & Xie, Jinxin & Huang, Qi & Qi, Yuting, 2022. "China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model," Energy, Elsevier, vol. 238(PC).
    7. Ferreira, Ana & Pinheiro, Manuel Duarte & de Brito, Jorge & Mateus, Ricardo, 2018. "Combined carbon and energy intensity benchmarks for sustainable retail stores," Energy, Elsevier, vol. 165(PB), pages 877-889.
    8. Sun, J. W., 2005. "The decrease of CO2 emission intensity is decarbonization at national and global levels," Energy Policy, Elsevier, vol. 33(8), pages 975-978, May.
    9. Weizhong Zhou & Wenhua Yu & Ahmed Farouk, 2021. "Regional Variation in the Carbon Dioxide Emission Efficiency of Construction Industry in China: Based on the Three-Stage DEA Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-13, August.
    10. Zhang, Ning & Wang, Bing & Liu, Zhu, 2016. "Carbon emissions dynamics, efficiency gains, and technological innovation in China's industrial sectors," Energy, Elsevier, vol. 99(C), pages 10-19.
    11. Fu Hua & Majed Alharthi & Weihua Yin & Muhammad Saeed & Ishtiaq Ahmad & Syed Ahtsham Ali, 2022. "Carbon Emissions and Socioeconomic Drivers of Climate Change: Empirical Evidence from the Logarithmic Mean Divisia Index (LMDI) Base Model for China," Sustainability, MDPI, vol. 14(4), pages 1-12, February.
    12. Pan, Yuling & Dong, Feng, 2022. "Design of energy use rights trading policy from the perspective of energy vulnerability," Energy Policy, Elsevier, vol. 160(C).
    13. Soytas, Ugur & Sari, Ramazan, 2009. "Energy consumption, economic growth, and carbon emissions: Challenges faced by an EU candidate member," Ecological Economics, Elsevier, vol. 68(6), pages 1667-1675, April.
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