U.S. Department of Energy

Design of Social and Economic Incentives and Information Campaigns to Promote Solar Technology Diffusion through Data-Driven Behavior Modeling

Logos of Sandia National Laboratories, the Center of Sustainable Energy California, National Renewable Energy Laboratory, and the University of Pennsylvania Wharton School. A graph that highlights the solar social networks and helps to forecast how solar adoption patterns change under different business or policy scenarios.

Solar social networks: The adoption rate for rooftop solar and other innovations depends upon community structure. A "tipping point" will be reached when a variety of factors align, including the leveraging of social linkages that amplify the value of solar electricity.

Sandia National Laboratories, along with partners at the California Center for Sustainable Energy, the National Renewable Energy Laboratory, the University of Pennsylvania Wharton School, and Vanderbilt University, under theĀ Solar Energy Evolution and Diffusion Studies (SEEDS) program, is developing powerful computer algorithms that forecast how solar adoption patterns change under different business or policy scenarios.


Technology diffusion is influenced by many factors, including economic and social considerations and the availability and framing of information. Economic incentives affect people's willingness to invest in solar technology in a relatively uniform way. Social incentives, in contrast, can spur adoption in some fragments of the population and dull it in others. The aim of this project is to integrate social and economic incentives, as well as information, into a single comprehensive framework.


The central component of this project is the development, calibration, and validation of a "crystal ball" for solar's spread. First, individual-level data will be collected via targeted surveys and laboratory experiments that elicit patterns of consumer decision making. The data will be incorporated into a predictive agent-based simulation that models social and economic interactions. The simulation output will be compared against the current state of the art to measure the improvement in predictive capability. Next, a series of policies will be evaluated in the model and ranked in terms of an increased solar diffusion rate. The most promising candidate policy will be implemented via a pilot program in southern California.


Solar energy policies have direct and indirect technological and socioeconomic implications. The computational policy design framework developed under this project will provide a rigorous assessment tool for comparing predicted outcomes with desired results.