The reduction of energy use and greenhouse gas (GHG) emissions in the urban built environment has emerged as one of the primary grand challenges facing society in the 21st century. The Paris Climate Agreement calls on the global community to limit global temperature rise to 1.5 degrees Celsius through significant reductions in carbon emissions. Given the need for immediate action, cities and urban areas are increasingly taking the lead in addressing this challenge, as cities are positioned to make substantial impacts through improvements to building and transit efficiency, and face dramatic consequences of inaction through increased risk from sea-level rise and extreme events.
To achieve these 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 approach combines data science, engineering, and urban planning to harness and interpret this “data deluge” coming from cities, including public, private, and citizen-generated sources, with the aim of advancing carbon reduction goals in a way that is efficient, scalable, and rapidly deployable. Our model integrates data from numerous "big" data sources and develops data-driven and physical models of energy and carbon emissions in buildings and transportation to generate a first-of- its-kind high resolution hourly model of urban carbon emissions. This tool is designed as a prototype to support city leaders and urban policymakers with an unprecedented view of localized carbon emissions to enable data-driven and evidenced-based climate action based on rigorous scientific models.
For the purpose of this study, we process and integrate large-scale, heterogeneous data from numerous public and private sources – including Earth Networks, Plume Labs, Crimson Hexagon, Waze, the NYC Mayor’s Office of Sustainability, the NYC Department of City Planning, the NYC and New York State Departments of Transportation, the U.S. Department of Energy, the NYC Department of Parks and Recreation, and the U.S. Census, among others
To develop a model of hourly GHG greenhouse gas emissions (in CO2 equivalent units) for every building and street segment (point and non-point sources) in the City, we account for local variations in temperature and the urban heat island, building energy efficiency, building type and land use, traffic, street trees, population density, and household income.
Our model provides a scalable platform and methodology for estimating and evaluating carbon emissions in cities. The web-based, interactive visualization tool is designed to provide city leaders and urban policy-makers, working across sectors and agencies, to understand highly-localized patterns of carbon emissions across their city. Our prototype model uses the data-rich environment of New York City as a test case for a generalizable methodology that can used in almost any city, thus expanding potential insights through comparative studies and analysis.
We use data to solve problems facing cities and society. Through an inter-disciplinary approach centered around computational methods, we apply analytics to the study of systems dynamics at the building, district, and city scales to advance the well-being, sustainability, and resilience of urban environments. Our research confronts the social, economic, and political realities facing cities, and seeks to understand how bias and inequality influence data-driven models and their application to urban operations, policy, and planning.