Original title: Accelerating the Adoption of Residential Solar Power Systems: Policy Analysis using a Dynamic Structural Model
Sebastián Souyris, Jason A. Duan, Anantaram Balakrishnan, Varun Rai · August 2026
Governments want more homes to install solar panels to cut carbon emissions, but they have limited money for rebates. Researchers built a computer model to study how families choose to buy solar panels. The model works by tracking forward-looking householdsHouseholds that think about future benefits and costs when deciding whether to install solar panels now or later. that weigh whether to buy panels today or wait. People decide based on their return on investment and whether their neighbors have installed solar. The team tested their model on real household data and then simulated different rebate designs. They found that a rebate available for only a limited time drives more solar adoption and cuts more emissions than a longer, more expensive program. Knowing the discount will end pushes families to act early, which also spreads solar through neighborhood influence. They also found that cutting rebates in two steps beats many small cuts, and targeting rebates by location works better than targeting by home value.
You are deciding whether to buy a new phone now or wait for a possible discount later. If the discount has no end date, you might keep waiting. But if the discount ends in a week, you are more likely to buy immediately. In this paper, the phone is a solar panel system, and the discount is a government rebate. The model shows that a rebate with a deadline pushes households to install sooner, leading to more total adoptions than a rebate that lasts indefinitely.
Governments spend millions on solar rebates but often get less adoption than hoped. This research shows that a rebate with a clear deadline pushes households to act quickly, leading to more installations and greater emissions cuts for the same budget. The insight can help cities design smarter incentives, not just for solar but for other green technologies too.
The model is estimated using data from Austin, Texas, so results may not generalize to other regions with different climates, electricity prices, or policies. It also assumes households are forward-looking and influenced by neighbors, but actual behavior may vary. The analysis focuses on rebates and does not consider other incentives like tax credits or net metering.
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Accelerating the Adoption of Residential Solar Power Systems: Policy Analysis using a Dynamic Structural Model — Sebastián Souyris, Jason A. Duan, Anantaram Balakrishnan et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.