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Published February 15, 2019 | Submitted
Journal Article Open

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

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 CO_2 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.

Additional Information

© 2018 Elsevier Ltd. Received 13 April 2018, Revised 15 November 2018, Accepted 16 November 2018, Available online 29 November 2018. Data accessibility: All data used are publicly available, sources are referenced in Sec. 3. Authors' contributions: RG, DR, MKBL, BZ, JPK designed the study; ZL, RG collected the data; RG carried out the data analysis; RG, DR, MKBL, BZ validated the results; RG prepared the Figures; RG, DR, MKBL, JPK wrote and revised the manuscript. We declare we have no competing interests. Research Ethics: No experiments were carried out during this study and no prior ethical assessment was required. The research leading to these results has received funding from the European Community's Seventh Framework Programme under Grant Agreement No. 308497 (Project RAMSES). Zhu Liu acknowledges support by the Green Talents Program held by the German Federal Ministry of Education and Research (BMBF). We thank M. Barthelemy, H. V. Ribeiro, and L. Costa for useful discussions. This work emerged from ideas discussed at the symposium Cities as Complex Systems (Hanover, July 13th-15th, 2016) which was generously funded by Volkswagen Foundation.

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