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

TL;DR

This paper introduces the Urban Kaya Relation, a new framework for understanding how city size influences CO2 emission efficiency, using a global dataset and emphasizing the importance of RMA regression over traditional methods.

Contribution

It develops the Urban Kaya Relation to analyze emission efficiencies in cities, providing a comprehensive approach and highlighting the need for RMA regression in scaling studies.

Findings

Urban Kaya Relation enables better understanding of emission efficiency factors.

RMA regression is essential for analyzing complex scaling relations.

Application to 61 cities demonstrates the relation's practical utility.

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 have 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 main-streaming local actions for climate change mitigation.