Adoption Model Choice Affects the Optimal Subsidy for Residential Solar

Tiruwork B. Tibebu, Eric Hittinger, Qing Miao and Eric Williams ()
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Tiruwork B. Tibebu: Department of Environmental Science, American University, Washington, DC 20016, USA
Eric Hittinger: Department of Public Policy, Rochester Institute of Technology, Rochester, NY 14623, USA
Qing Miao: Department of Public Policy, Rochester Institute of Technology, Rochester, NY 14623, USA
Eric Williams: Golisano Institute for Sustainability, Rochester Institute of Technology, Rochester, NY 14623, USA

Energies, 2024, vol. 17, issue 3, 1-19

Abstract: Understanding the adoption patterns of clean energy is crucial for designing government subsidies that promote the use of these technologies. Existing work has examined a variety of adoption models to explain and predict how economic factors and other technology and demographic attributes influence adoption, helping to understand the cost-effectiveness of government policies. This study explores the impact of adoption modeling choices on optimal subsidy design within a single techno–economic framework for residential solar PV technology. We applied identical datasets to multiple adoption models and evaluated which model forms appear feasible and how using different choices affects policy decisions. We consider three existing functional forms for rooftop solar adoption: an error function, a mixed log-linear regression, and a logit demand function. The explanatory variables used are a combination of net present value (NPV), socio-demographic, and prior adoption. We compare how the choice of model form and explanatory variables affect optimal subsidy choices. Among the feasible model forms, there exist justified subsidies for residential solar, though the detailed schedule varies. Optimal subsidy schedules are highly dependent on the social cost of carbon and the learning rate. A learning rate of 10% and a social carbon cost of USD 50/ton suggest an optimal subsidy starting at USD 46/kW, while the initial subsidy is 10× higher (USD 540/kW) with a learning rate of 15% and social carbon cost of USD 70/ton. This work illustrates the importance of understanding the true drivers of adoption when developing clean energy policies.

Keywords: optimal subsidy; adoption models; modeling uncertainty; techno–economic framework; residential solar PV (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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