Collaborative Research: CAS-Climate: Linking Activities, Expenditures and Energy Use into an Integrated Systems Model to Understand and Predict Energy Futures

Rochester Institute Of Tech, Rochester NY

Investigators

Abstract

Predicting and managing energy demand are crucial tasks for addressing climate change and other environmental impacts of energy use. The mainstream models of energy demand are reductionist, dividing demand into separate categories such as residential, commercial, and transportation, and analyzing each separately. This collaborative research will develop a holistic model of energy demand, one that considers how consumer actions affect multiple sectors at the same time, including residences, vehicles, commercial buildings, server/data networks, and the production of purchased goods. The model will include consumer ownership and use of energy- consuming technologies such as vehicles or home furnaces, and accounts for how people use time and spend money. The new model will be constructed for the U.S. by integrating government micro-data on consumer behavior (American Time Use Survey, Residential Energy Consumption Survey, National Household Travel Survey, Consumer Expenditure Survey), using modern data analysis methods to integrate them. The integrated dataset will provide information about energy device ownership and use, internet use, time spent in commercial buildings, and expenditures on goods. A set of models will map these consumer attributes to energy use and carbon emissions. For commercial buildings, a regression model will be built that links the area of different building types with consumers’ and employees’ use of them. An Economic Input Output Life Cycle Assessment (EIOLCA) will estimate the energy use and emissions from consumer expenditures. The holistic model will help understanding of the broader effects of demand interventions. For example, how are the carbon benefits of electric vehicles affected by induced changes in consumer purchases and activity choices? The model will help assess the effect of behavioral changes not typically considered in policy (e.g. encouraging telework), and thus could broaden the scope of policy options considered. The model advances the state of energy demand modeling on a number of fronts. First, the model combines data on personal expenditures, time, and technology use to provide a household-level estimate of life cycle carbon emissions, accounting for energy use in residences, commercial buildings, servers/networks, transportation, and manufacturing of goods. Part of the contribution is in integrating existing models into a larger holistic framework. Missing modeling elements will be developed, in particular linking consumers’ use of commercial buildings to energy demand, and attributing use of information technology to energy demand in data networks and servers. In methods, the research develops a new approach for assessing how change in time use of a given activity (e.g. shift from driving to biking) leads to changes in other activities as well as the overall energy use from the primary shift in activity. The research involves merging multiple data sources on consumers via representative populations, and this dataset will be made publicly available. Outreach activities include a workshop with energy efficiency modelers and policy advocates, development of an energy demand game, and a summer camp for high school students from underrepresented groups. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.