In an era where governments are developing goals for decarbonizing their economies and many cities are developing greenhouse gas inventories and strategies to reduce the use of hydrocarbon based fuels, understanding the patterns of energy use across cities seems increasingly foundational to those goals. Yet, often little is known about building energy use, building characteristics (such as age, size, and use, coupled to energy use), and socio-demographic characteristics of energy use. Further, grid infrastructure, power generation and the potential for non-hydrocarbon power generation are also poorly known. In part this is due to concerns about customer privacy and utility control of power generation and distribution, but such historic lack of data impedes transition.

I present an example of the power of big data in this sector for decarbonization strategies. My Center – The California Center for Sustainable Communities – has the biggest dataset of actual building energy use that I know of. We show energy use by using GIS and mapping (www.energyatlas.ucla.edu) for public consumption, and mine the back end databases for specific analysis.

This research demonstrates the importance of empirical data for urban transformation. For cities to reduce their carbon footprints, we need to know how resource flows are used in cities, and their supply chain impacts. Guided by the concept of “Urban Metabolism” this research attempts to begin to develop the types of data necessary for understanding cities and their dependencies on the resources of nature.