U.S. Department of Energy

Influence of Novel Behavioral Strategies in Promoting the Diffusion of Solar Energy

Logos of Yale University, New York University Stern School of Business, SmartPower, Clean Energy Finance and Investment Authority, and Solarize Connecticut. A line graph that highlights the social interactions for accelerating adoption of solar energy technologies.

Optimizing the solar adoption accelerator: By leveraging social interactions, Solarize Connecticut is speeding solar markets across the state. The adoption rate in the town represented in the graph saw a 70-fold increase during the program period (distinguished by the a dark shade in the plot).

Yale University, along with partners at the Clean Energy Finance and Investment Authority, SmartPower, and the NYU Stern School of Business, under theĀ Solar Energy Evolution and Diffusion Studies (SEEDS) program, is investigating the effectiveness, cost-effectiveness, scalability, and persistence of novel strategies that leverage social interactions for accelerating adoption of solar energy technologies.


Solarize group-buy programs are rapidly spreading from community to community across the United States. The combination of simple tiered pricing, competitive bidding from solar installers, trusted solar "coaching," community rewards, and fixed decision periods seems to be a win-win for reducing consumer prices and increasing adoption rates. This project will rigorously evaluate what works and what doesn't so that community solar programs can scale up effectively.


Multiple waves of randomized field trials of specifically tailored Solarize programs will be run in conjunction with the 2013 and 2014 Solarize Connecticut programs. Each trial will be designed to quantify the effects of subsets of strategies within the suite of Solarize activities. The full study will compare the efficacy of Solarize components to one another and to a control group. As part of the study, data will be collected that trace out the dynamic social network structure that underlies technology adoption decision patterns. A numerical model will incorporate this data to predict how technologies, like solar, spread through a community given a series of interventions.


A slew of strategies for increasing the diffusion rate of renewable energy and energy efficiency technologies pervade at the local, state, national, and international scales. This project will assess the value of community level solar programming with a generalizable framework that can be applied in other settings.