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Proximity-based cities emit less mobility-driven CO$_2$

Francesco Marzolla, Matteo Bruno, Hygor P. M. Melo, Vittorio Loreto

Abstract

In the quest for more environmentally sustainable urban areas, the concept of the 15-minute city has been proposed to encourage active mobility, primarily through walking and cycling. An urban area is considered a ``15-minute city" if every resident can access essential services within a 15-minute walk or bike ride from their home. However, there is an ongoing debate about the effectiveness of this model in reducing car usage and carbon emissions. In this study, we conduct a large-scale data-driven analysis to evaluate the impact of service proximity to homes on CO$_2$ emissions. By examining nearly 400 cities worldwide, we discover that, within the same city, areas with services located closer to residents produce less CO$_2$ emissions per capita from transportation. We establish a clear relationship between the proximity of services and CO$_2$ emissions for each city. Additionally, we quantify the potential reduction in emissions for 30 cities if they optimise the location of their services. This optimisation maintains each city's total number of services while redistributing them to ensure equal accessibility throughout the entire urban area. Our findings indicate that improving the proximity of services can significantly reduce expected urban emissions related to transportation.

Proximity-based cities emit less mobility-driven CO$_2$

Abstract

In the quest for more environmentally sustainable urban areas, the concept of the 15-minute city has been proposed to encourage active mobility, primarily through walking and cycling. An urban area is considered a ``15-minute city" if every resident can access essential services within a 15-minute walk or bike ride from their home. However, there is an ongoing debate about the effectiveness of this model in reducing car usage and carbon emissions. In this study, we conduct a large-scale data-driven analysis to evaluate the impact of service proximity to homes on CO emissions. By examining nearly 400 cities worldwide, we discover that, within the same city, areas with services located closer to residents produce less CO emissions per capita from transportation. We establish a clear relationship between the proximity of services and CO emissions for each city. Additionally, we quantify the potential reduction in emissions for 30 cities if they optimise the location of their services. This optimisation maintains each city's total number of services while redistributing them to ensure equal accessibility throughout the entire urban area. Our findings indicate that improving the proximity of services can significantly reduce expected urban emissions related to transportation.

Paper Structure

This paper contains 16 sections, 8 equations, 3 figures, 1 table.

Figures (3)

  • Figure 1: Correlation between proximity and CO$_2$ emissions within cities.Panels (a) and (b) show, for Tokyo and Madrid, the relationship between population-weighted proximity time $s$ and per capita CO$_2$ emissions from road transport ($C_\text{pc}$) in 2021. Each dot represents a 0.1° $\times$ 0.1° grid cell; maps on the right display the same grid coloured by proximity time and emissions. Orange lines are power-law fits (Eq. \ref{['powerlaw_methods']}). Panel (c) shows the distribution ($H_1$) of correlation coefficients between $\log C_\text{pc}$ and $\log s$ across all cities, compared to a population-controlled randomisation ($H_0$, grey). Panel (d) shows the distribution of power-law exponents $\gamma$, with the mean (solid) and 68% confidence interval (dashed).
  • Figure 2: Optimising walking accessibility and its expected impact on transport emissions in Boston. Panels (a) and (b) show Boston maps with colour gradients indicating, respectively, the percentage change in proximity time and in expected transport-related CO$_2$ emissions after optimisation. Panel (c) compares current (circles) and optimised (squares) grid elements, linked by arrows; the orange line is a power-law fit of emissions versus proximity time.
  • Figure 3: Emissions of accessibility-optimised cities. Panel (a) shows the trajectories of cities in the log-log plane of per capita transport CO$_2$ emissions versus proximity time, before and after service relocation. Panel (b) shows the expected change in cumulative transport CO$_2$ emissions for the cities included in the optimisation process.