Scaling: Lost in the smog

TL;DR

This paper critiques the use of scaling laws in urban systems, emphasizing the need for better theoretical models to understand underlying mechanisms and improve policy relevance.

Contribution

It highlights the importance of theoretical understanding alongside data in developing a science of cities, especially regarding scaling laws and emissions.

Findings

Errors in data estimation affect scaling law conclusions.

City definitions significantly impact scaling analyses.

Current models lack predictive accuracy without theoretical grounding.

Abstract

In this commentary we discuss the validity of scaling laws and their relevance for understanding urban systems and helping policy makers. We show how the recent controversy about the scaling of CO2 transport-related emissions with population size, where different authors reach contradictory conclusions, is symptomatic of the lack of understanding of the underlying mechanisms. In particular, we highlight different sources of errors, ranging from incorrect estimate of CO2 to problems related with the definition of cities. We argue here that while data are necessary to build of a new science of cities, they are not enough: they have to go hand in hand with a theoretical understanding of the main processes. This effort of building models whose predictions agree with data is the prerequisite for a science of cities. In the meantime, policy advice are, at best, a shot in the dark.