Abajian, Alexander and Pretnar, Nick (2021): An Aggregate Perspective on the Geo-spatial Distribution of Residential Solar Panels.
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Abstract
Residential solar panels in the United States (U.S.) are inefficiently distributed in terms of optimizing solar-electrical production. Controlling for local solar electricity generation potential (insolation), the residential solar share of electrical consumption is relatively higher in cloudier locales like the Pacific Northwest and Northeast than it is in sunnier areas like the Western U.S. and Florida. Rebates designed to increase residential solar adoption in places like Florida and Texas with relatively low solar-electrical shares are ineffective and may lead to net decreases in the residential solar share if housing and electrical consumption are complementary. This is because electrical consumption increases faster in response to a decline in effective residential solar prices than actual demand for panels themselves, thus driving down the solar share despite additional installations. Through the lens of a county-level structural model of demand for housing, electricity, and solar panels, we find that this phenomenon is especially prevalent in locales with high demand for cooling services (e.g., air conditioning, refrigeration, etc.) due to high numbers of cooling degree days. Inability to effectively store solar-produced electricity may be to blame. Our results thus suggest that future policies should subsidize nascent battery technologies in place of direct solar-panel installation rebates if the goal is to increase the residential solar share of electrical consumption.
Item Type: | MPRA Paper |
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Original Title: | An Aggregate Perspective on the Geo-spatial Distribution of Residential Solar Panels |
Language: | English |
Keywords: | subsidies, environmental subsidy, environmental economics, electricity, energy utilities, renewable energy, solar energy, neighborhood characteristics, diffu- sion, spatial pricing, industrial geography |
Subjects: | H - Public Economics > H2 - Taxation, Subsidies, and Revenue > H23 - Externalities ; Redistributive Effects ; Environmental Taxes and Subsidies Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q42 - Alternative Energy Sources R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R2 - Household Analysis > R23 - Regional Migration ; Regional Labor Markets ; Population ; Neighborhood Characteristics |
Item ID: | 105481 |
Depositing User: | Mr. Nick Pretnar |
Date Deposited: | 27 Jan 2021 08:44 |
Last Modified: | 27 Jan 2021 08:44 |
References: | Bakkensen, Laura, and Paul Schuler. 2020. “A preference for power: Willingness to pay for energy reliability versus fuel type in Vietnam”. Energy Policy 144:111696. (Cit. on p. 3). Barbose, Galen, et al. 2019. “Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States-2019 Edition”. (Cit. on p. 1). Bollinger, Bryan, and Kenneth Gillingham. 2012. “Peer effects in the diffusion of solar photovoltaic panels”. Marketing Science 31 (6): 900–912. (Cit. on p. 3). Borenstein, Severin. 2017. “Private net benefits of residential solar PV: The role of electricity tariffs, tax incentives, and rebates”. Journal of the Association of Environmental and Resource Economists 4 (S1): S85–S122. (Cit. on p. 3). Borenstein, Severin, and Lucas W Davis. 2016. “The distributional effects of US clean energy tax credits”. Tax Policy and the Economy 30 (1): 191–234. (Cit. on p. 3). Callaway, Duncan S, Meredith Fowlie, and Gavin McCormick. 2018. “Location, location, location: The variable value of renewable energy and demand-side efficiency resources”. Journal of the Association of Environmental and Resource Economists 5 (1): 39–75. (Cit. on p. 3). EIA. 2020. “Monthly energy review”. DOE/EIA-0035 (2020/9). Office of Energy Statistics, US Department of Energy Washington. (Cit. on p. 1). Flowers, Mallory E, et al. 2016. “Climate impacts on the cost of solar energy”. Energy Policy 94:264–273. (Cit. on p. 10). Freeman, Janine M, et al. 2018. System Advisor Model (SAM) General Description (Version 2017.9. 5). Tech. rep. National Renewable Energy Lab.(NREL), Golden, CO (United States). (Cit. on p. 2). Gillingham, Kenneth, and James H Stock. 2018. “The cost of reducing greenhouse gas emissions”. Journal of Economic Perspectives 32 (4): 53–72. (Cit. on p. 3). Gorman, William M. 1959. “Separable utility and aggregation”. Econometrica: Journal of the Econometric Society: 469–481. (Cit. on p. 17). Heal, Geoffrey M. 2020. “Economic Aspects of the Energy Transition”. NBER working paper, no. w27766. (Cit. on p. 3). Heng, Yan, et al. 2020. “The heterogeneous preferences for solar energy policies among US households”. Energy Policy 137:111187. (Cit. on p. 3). Hughes, Jonathan E, and Molly Podolefsky. 2015. “Getting green with solar subsidies: evidence from the California solar initiative”. Journal of the Association of Environmental and Resource Economists 2 (2): 235–275. (Cit. on p. 3). Imelda, Imelda, Matthias Fripp, and Michael Roberts. 2018. Variable pricing and the cost of renewable energy. Tech. rep. University of Hawaii Economic Research Organization, University of Hawaii at . . . (Cit. on p. 3). Jordan, Dirk C, and Sarah R Kurtz. 2013. “Photovoltaic degradation rates—an analytical review”. Progress in photovoltaics: Research and Applications 21 (1): 12–29. (Cit. on p. 10). Neal, Radford. 2011. “MCMC using Hamiltonian dynamics”. Chap. 5 in Handbook of Markov Chain Monte Carlo, ed. by Steve Brooks et al. Chapman & Hall. (Cit. on p. 18). Nomura, Noboru, and Makoto Akai. 2004. “Willingness to pay for green electricity in Japan as estimated through contingent valuation method”. Applied Energy 78 (4): 453– 463. (Cit. on p. 3). Sengupta, Manajit, et al. 2018. “The national solar radiation data base (NSRDB)”. Renew- able and Sustainable Energy Reviews 89:51–60. (Cit. on p. 2). Sexton, Steven E, et al. 2018. Heterogeneous environmental and grid benefits from rooftop solar and the costs of inefficient siting decisions. Tech. rep. National Bureau of Economic Research. (Cit. on p. 3). Vehtari, Aki, Andrew Gelman, and Jonah Gabry. 2017. “Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC”. Statistics and Computing 27:1413– 1432. (Cit. on pp. 18, 21, 22, 41). Vehtari, Aki, et al. 2019. “Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC”. arXiv:1903.08008. (Cit. on p. 22). Wiser, Ryan, and Dev Millstein. 2020. “Evaluating the economic return to public wind energy research and development in the United States”. Applied Energy 261:114449. (Cit. on p. 2). Wolak, Frank A. 2018. The Evidence from California on the Economic Impact of Inefficient Distribution Network Pricing. Tech. rep. National Bureau of Economic Research. (Cit. on p. 3). Yoo, Seung-Hoon, and So-Yoon Kwak. 2009. “Willingness to pay for green electricity in Korea: A contingent valuation study”. Energy policy 37 (12): 5408–5416. (Cit. on p. 3). Yu, Jiafan, et al. 2018. “DeepSolar: A machine learning framework to efficiently construct a solar deployment database in the United States”. Joule 2 (12): 2605–2617. (Cit. on p. 4). |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/105481 |