Roles of diffusion patterns, technological progress, and environmental benefits in determining optimal renewable subsidies in the US

Tiruwork B. Tibebu, Eric Hittinger, Qing Miao and Eric Williams

Technological Forecasting and Social Change, 2022, vol. 182, issue C

Abstract: In this paper we develop an integrated model to identify optimal subsidy schedules for clean energy technologies that maximize social benefits less subsidy costs. The model uses historical cost, adoption, and emissions data and accounts for both environmental and technological progress benefits of the subsidy. An alternative analytical model is also presented to analyze key technological features affecting subsidy design. We focus on three important factors in determining the social benefits of subsidizing the use of clean energy technology: the price (or cost) sensitivity of adoption, induced cost reductions through learning, and environmental benefits. We quantify how distinct profiles of these three factors result in qualitatively different optimal subsidy plans for utility wind and residential solar power in 13 electricity grid regions in the US. Results show that optimal subsidy schedules for utility wind depend on the region, starting at $20–60/MWh, and are roughly constant over time. In contrast, optimal residential solar subsidies either decline over time (starting from $8–70/MWh) 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 the subsidy is primarily justified by indirect technological progress benefits.

Keywords: Optimal subsidy policy; Clean energy technology; Diffusion sensitivity; Technological progress; Environmental benefits (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:182:y:2022:i:c:s004016252200364x

DOI: 10.1016/j.techfore.2022.121840

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