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.
Our aim is to advance energy and carbon modeling and subsequent public policy and investment decisions in cities in a way that is efficient, equitable, scalable, and rapidly deployable. This project, in collaboration with the City of New York, the United Nations, Lawrence Berkeley National Lab, and others integrates numerous big data sources and develops methods and tools that combine data-driven statistical and physical models to generate a first-of-its-kind dynamic, hyperlocal model of urban carbon emissions. Our work enables city leaders and urban policymakers to implement evidenced-based climate action policies based on rigorous scientific models that can help to achieve long-term sustainability goals.