Diffusion of Residential Solar Power Systems: A Dynamic Discrete Choice Approach

49 Pages Posted: 29 Dec 2022

See all articles by Sebastian Souyris

Sebastian Souyris

Lally School of Management, Rensselaer Polytechnic Institute (RPI)

Jason A. Duan

University of Texas at Austin

Anant Balakrishnan

McCombs School of Business, University of Texas at Austin

Varun Rai

University of Texas at Austin - LBJ School of Public Affairs; University of Texas at Austin - Walker Department of Mechanical Engineering

Date Written: December 13, 2022

Abstract

Solar electricity generation is a strategic component in the energy portfolio of many countries to replace fossil fuels and reduce green-house gas emissions. Installations of solar photovoltaic (PV) systems by residential homes in the United States have grown rapidly in the past decade, driven by decreasing costs of PV systems and government incentives. However, PV systems currently still produce only a small fraction of the total electricity generated in the U.S. So, it is important to study how federal and local incentives can be made more effective to achieve the goal of widespread residential adoption of PV systems. We study the diffusion of PV systems by modeling the adoption decisions of individual households who are forward-looking and consider installing PV systems as an investment with its future economic benefits. Moreover, the space-time dynamics of installations reveal a clear pattern that a household’s adoption decision is strongly influenced by the adopters in their neighborhood. We propose a dynamic discrete choice model in which a household considers the inter-temporal trade-off between installing the system now and waiting by analyzing the current and future return on investment and peer effects. Our model incorporates unobserved household heterogeneity in addition to segmenting households by their home values and geographic locations. We collect a novel data set with detailed information on PV adoptions in Austin, Texas to estimate the model parameters. For inference, we use a Bayesian method to overcome the computational burden. Our estimation reveals a strong peer effect in a household’s adoption decision. Our structural model permits counterfactual analysis to generate insights to design incentive programs that are effective in accelerating the PV diffusion process. As an illustration, counterfactual policy simulations for local rebate programs demonstrate how policy-makers can achieve higher adoption rates with a lower budget.

Keywords: solar photovoltaic, technology diffusion, peer effects, dynamic discrete choice modeling, structural estimation, Bayesian econometrics, counterfactual analysis, energy policy

Suggested Citation

Souyris, Sebastian and Duan, Jason A. and Balakrishnan, Anantaram and Rai, Varun, Diffusion of Residential Solar Power Systems: A Dynamic Discrete Choice Approach (December 13, 2022). Available at SSRN: https://ssrn.com/abstract=4301666 or http://dx.doi.org/10.2139/ssrn.4301666

Sebastian Souyris (Contact Author)

Lally School of Management, Rensselaer Polytechnic Institute (RPI) ( email )

110 8th St
Troy, NY 12180
United States

Jason A. Duan

University of Texas at Austin ( email )

2317 Speedway
Austin, TX 78712
United States

Anantaram Balakrishnan

McCombs School of Business, University of Texas at Austin ( email )

Austin, TX 78712
United States

Varun Rai

University of Texas at Austin - LBJ School of Public Affairs ( email )

2300 Red River St., Stop E2700
PO Box Y
Austin, TX 78713
United States

University of Texas at Austin - Walker Department of Mechanical Engineering ( email )

United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
17
Abstract Views
82
PlumX Metrics