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Spatial Heterogeneity of Energy-Related CO 2 Emission Growth Rates around the World and Their Determinants during 1990–2014

Author

Listed:
  • Yebing Fang

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    College of Territorial Resources and Tourism, Anhui Normal University, Wuhu 241003, China)

  • Limao Wang

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Zhoupeng Ren

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yan Yang

    (The CAE Center for Strategic Studies, Chinese Academy of Engineering, Beijing 100088, China
    Department of Chemical Engineering, Tsinghua University, Beijing 100084, China)

  • Chufu Mou

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Qiushi Qu

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Understanding the spatial heterogeneity and driving force identification of energy-related CO 2 emissions (ECEs) can help build consensus for mitigating CO 2 emissions and designing appropriate policies. However, previous studies on ECEs that focus on both the global-regional scale and the interaction of factors have been seldom conducted. In this paper, ECE data from 143 countries from 1990 to 2014 were selected to analyze regional differences in ECE growth rates by using the coefficient of variation. Then a geographical detector was used to analyze the key determinant factors on ECE growth rates around the world and in eight types of regions. The results show that: (1) the ECE growth rate in the Organization for Economic Cooperation and Development (OECD) region is low and tended to decrease, while in the non-OECD region it is high and tended to increase; (2) the coefficient of variation and detection factor of ECE growth rates at a regional scale are higher than those at a global scale; (3) in terms of the key determinant factors, population growth rate, growth rate of per capita GDP, and energy intensity growth rate are the three key determinant factors of ECE growth rates in the OECD region and most of the non-OECD regions such as non-OECD European and Eurasian (NO-EE), Asia (NO-AS), non-OECD Americas (NO-AM). The key determinant factors in the African (NO-AF) region are population growth rates and natural gas carbon intensity growth rates. The key determinant factors of the Middle East (NO-ME) are population growth rate, coal carbon intensity growth rate and per capita GDP growth rate; (4) the determinant power of the detection factor, the population growth rate at the global scale and regional scale is the strongest, showing a significant spatial consistency. The determinant power of per capita GDP growth rate and energy intensity growth rate in the OECD region, respectively, rank second and third, also showing a spatial consistency. However, the carbon intensity growth rates of the three fossil fuels contribute little to the growth rate of ECEs, and their spatial coherence is weak; (5) from the perspective of the interaction of detection factors, six detection factors showed bilinear or non-linear enhancement at a global and a regional scale, and the determinant power of the interaction of factors was significantly enhanced; and (6) from the perspective of ecological detection, the growth rate of carbon intensity and the growth rate of natural gas carbon intensity at the global scale and NO-ME region are significantly stronger than other factors, with a significant difference in the spatial distribution of its incidence. Therefore, the OECD region should continue to reduce the growth of energy intensity, and develop alternative energy resources in the future, while those that are plagued by carbon emissions in non-OECD regions should pay more attention to the positive influence of lower population growth rates on reducing the growth rate of energy-related CO 2 emissions. Reducing energy intensity growth rates and reducing, fossil energy consumption carbon intensity.

Suggested Citation

  • Yebing Fang & Limao Wang & Zhoupeng Ren & Yan Yang & Chufu Mou & Qiushi Qu, 2017. "Spatial Heterogeneity of Energy-Related CO 2 Emission Growth Rates around the World and Their Determinants during 1990–2014," Energies, MDPI, vol. 10(3), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:367-:d:93128
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    2. Sidong Zhao & Weiwei Li & Kaixu Zhao & Ping Zhang, 2021. "Change Characteristics and Multilevel Influencing Factors of Real Estate Inventory—Case Studies from 35 Key Cities in China," Land, MDPI, vol. 10(9), pages 1-29, September.

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