A High-Resolution, Data-Driven Model of Urban Carbon Emissions (Papers Track) Best Pathway to Impact

Bartosz Bonczak (New York University); Boyeong Hong (New York University); Constantine E. Kontokosta (New York University)

Hybrid Physical Models Climate Science & Modeling Data Mining


Cities represent both a fundamental contributor to greenhouse (GHG) emissions and a catalyst for climate action. Many global cities have outlined sustainability and climate change mitigation plans, focusing on energy efficiency, shifting away from fossil fuels, and prioritizing environmental and social justice. To achieve broad-based and equitable carbon emissions reductions and sustainability goals, new data-driven methodologies are needed to identify and target efficiency and carbon reduction opportunities in the built environment at the building, neighborhood, and city-scale. Our methodology integrates data from numerous data sources and develops data-driven and physical models of energy use and carbon emissions from buildings and transportation to generate a high spatiotemporal resolution model of urban greenhouse gas emissions. The method and data tool are designed to support city leaders and urban policymakers with an unprecedented view of localized carbon emissions to enable data-driven and evidenced-based climate action.