Abstract

Renewable energy technologies can significantly reduce fossil fuel consumption and greenhouse gas emissions associated with the energy sector. In US, both federal and state governments have implemented numerous policies and programs to support these technologies. But these policies require a substantial amount of public spending. In this study, an integrated model to identify optimal subsidy schedules for clean energy technologies that maximize social benefits less subsidy costs is developed. The national flexible optimal subsidy schedule for residential solar PV begins at $585/kW and declines to zero in 14 years. An alternative analytical model is also presented to analyze technological features affecting subsidy design. Three important factors determining the social benefits of subsidizing the use of clean energy technology are examined: the price sensitivity of adoption, induced cost reductions through learning, and environmental benefits. Results show that optimal subsidy schedules for utility wind are roughly constant over time. In contrast, optimal residential solar subsidies either decline over time or are not desirable (subsidy of zero). The results imply that the optimal subsidy for utility wind is justified mainly through the direct environmental benefits, unlike residential solar PV, in which indirect technological progress benefits primarily justify the subsidy. The effects of multiple adoption modeling and parameter choice alternatives on optimal subsidy design are also explored. The study considers three different model structures for rooftop solar adoption consisting of a combination of single and multiple explanatory variables. Results show that the scale of sensitivity of optimal subsidy designs to technology learning rate assumptions depends on the model choice. This dissertation shows that analytical inputs can be instrumental in informing policymakers deciding on subsidy schedules promoting renewable technologies. These tools can integrate environmental benefits and the complex interaction between the subsidy, diffusion patterns, and technology cost trajectories to ensure socially optimal policy designs.

Publication Date

8-25-2022

Document Type

Dissertation

Student Type

Graduate

Degree Name

Sustainability (Ph.D.)

Department, Program, or Center

Sustainability (GIS)

Advisor

Eric Hittinger

Advisor/Committee Member

Qing Miao

Advisor/Committee Member

Eric Williams

Campus

RIT – Main Campus

Share

COinS