Using crowdsourced data to estimate the carbon footprints of global cities
Xinlu Sun, Zhifu Mi, Andrew Sudmant, D’Maris Coffman, Yang Pu, Richard Wood
Abstract
Cities are at the forefront of the battle against climate change. However, intercity comparisons and responsibility allocations among cities are hindered because cost- and time-effective methods to calculate the carbon footprints of global cities have yet to be developed. Here, we establish a hybrid method integrating top-down input–output analysis and bottom-up crowdsourced data to estimate the carbon footprints of global cities. Using city purchasing power as the main predictor of the carbon footprint, we estimate the carbon footprints of 465 global cities in 2020. Those cities comprise 10% of the global population but account for 18% of the global carbon emissions showing a significant concentration of carbon emissions. The Gini coefficients are applied to show that global carbon inequality is less than income inequality. In addition, the increased carbon emissions that come from high consumption lifestyles offset the carbon reduction by efficiency gains that could result from compact city design and large city scale. Large climate benefits could be obtained by achieving a low-carbon transition in a small number of global cities, emphasizing the need for leadership from globally important urban centres.