Sustained Reductions of Bay Area CO₂ Emissions 2018–2022

Abstract

Cities represent a significant and growing portion of global carbon dioxide (CO₂) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO₂ emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO₂ Network (BEACO₂N), in combination with Bayesian inversions, result in a synthesis of measured CO₂ concentrations and meteorology to yield an improved estimate of CO₂ emissions and provide a cost-effective and accurate assessment of CO₂ emissions trends over time. We describe nearly 5 years of continuous CO₂ observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO₂ emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.