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The efficient, the intensive, and the productive: Insights from urban Kaya scaling

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  • Gudipudi, Ramana
  • Rybski, Diego
  • Lüdeke, Matthias K.B.
  • Zhou, Bin
  • Liu, Zhu
  • Kropp, Jürgen P.

Abstract

Urban areas play an unprecedented role in potentially mitigating climate change and supporting sustainable development. In light of the rapid urbanisation in many parts on the globe, it is crucial to understand the relationship between settlement size and CO2 emission efficiency of cities. Recent literature on urban scaling properties of emissions as a function of population size has led to contradictory results and more importantly, lacked an in-depth investigation of the essential factors and causes explaining such scaling properties. Therefore, in analogy to the well-established Kaya Identity, we develop a relation combining the involved exponents. We demonstrate that application of this Urban Kaya Relation will enable a comprehensive understanding about the intrinsic factors determining emission efficiencies in large cities by applying it to a global dataset of 61 cities. Contrary to traditional urban scaling studies which use Ordinary Least Squares (OLS) regression, we show that the Reduced Major Axis (RMA) is necessary when complex relations among scaling exponents are to be investigated. RMA is given by the geometric mean of the two OLS slopes obtained by interchanging the dependent and independent variable. We discuss the potential of the Urban Kaya Relation in mainstreaming local actions for climate change mitigation.

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  • Gudipudi, Ramana & Rybski, Diego & Lüdeke, Matthias K.B. & Zhou, Bin & Liu, Zhu & Kropp, Jürgen P., 2019. "The efficient, the intensive, and the productive: Insights from urban Kaya scaling," Applied Energy, Elsevier, vol. 236(C), pages 155-162.
    • Handle: RePEc:eee:appene:v:236:y:2019:i:c:p:155-162
    DOI: 10.1016/j.apenergy.2018.11.054
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    Cited by:

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    2. 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).
    3. Julia Kurek & Justyna Martyniuk-Pęczek, 2020. "Looking for the Optimal Location of an Eco-District within a Metropolitan Area: The Case of Tricity Metropolitan Area," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    4. Joao Meirelles & Fabiano L. Ribeiro & Gabriel Cury & Claudia R. Binder & Vinicius M. Netto, 2021. "More from Less? Environmental Rebound Effects of City Size," Sustainability, MDPI, vol. 13(7), pages 1-20, April.
    5. Daiva Makutėnienė & Algirdas Justinas Staugaitis & Valdemaras Makutėnas & Gunta Grīnberga-Zālīte, 2023. "The Impact of Economic Growth and Urbanisation on Environmental Degradation in the Baltic States: An Extended Kaya Identity," Agriculture, MDPI, vol. 13(9), pages 1-25, September.
    6. Xu, Gang & Xu, Zhibang & Gu, Yanyan & Lei, Weiqian & Pan, Yupiao & Liu, Jie & Jiao, Limin, 2020. "Scaling laws in intra-urban systems and over time at the district level in Shanghai, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    7. Jiang, Shan & Zhu, Yongnan & He, Guohua & Wang, Qingming & Lu, Yajing, 2020. "Factors influencing China’s non-residential power consumption: Estimation using the Kaya–LMDI methods," Energy, Elsevier, vol. 201(C).
    8. Ramana Gudipudi & Diego Rybski & Matthias KB Lüdeke & Jürgen P Kropp, 2019. "Urban emission scaling — Research insights and a way forward," Environment and Planning B, , vol. 46(9), pages 1678-1683, November.
    9. Bin Zhou & Stephan Thies & Ramana Gudipudi & Matthias K B Lüdeke & Jürgen P Kropp & Diego Rybski, 2020. "A Gini approach to spatial CO2 emissions," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.

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