Abstract
We study how urban quality evolves as a result of carbon dioxide
emissions as urban agglomerations grow. We employ a bottom-up approach
combining two unprecedented microscopic data on population and carbon
dioxide emissions in the continental US. We first aggregate settlements
that are close to each other into cities using the City Clustering
Algorithm (CCA) defining cities beyond the administrative boundaries.
Then, we use data on CO2 emissions at a fine geographic scale to
determine the total emissions of each city. We find a superlinear
scaling behavior, expressed by a power-law, between CO2 emissions and city population with average allometric exponent beta = 1.46 across all
cities in the US. This result suggests that the high productivity of
large cities is done at the expense of a proportionally larger amount of
emissions compared to small cities. Furthermore, our results are
substantially different from those obtained by the standard
administrative definition of cities, i.e. Metropolitan Statistical Area
(MSA). Specifically, MSAs display isometric scaling emissions and we
argue that this discrepancy is due to the overestimation of MSA areas.
The results suggest that allometric studies based on administrative
boundaries to define cities may suffer from endogeneity bias.
