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Have State Renewable Portfolio Standards Really Worked?: Synthesizing Past Policy Assessments

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  • Gireesh Shrimali
  • Steffen Jenner
  • Felix Groba
  • Gabriel Chan
  • Joe Indvik

Abstract

Renewable portfolio standards (RPS) are the most popular U.S. state-level policies for promoting deployment of renewable electricity (RES-E). While several econometric studies have estimated the effect of RPS on in-state RES-E deployment, results are contradictory. We reconcile these studies and move toward a definitive answer to the question of RPS effectiveness. We conduct an analysis using time series cross sectional regressions - including the most nuanced controls for policy design features to date - and nonparametric matching analysis. We find that higher RPS stringency does not necessarily drive more RES-E deployment. We examine several RPS design features and market characteristics (including REC unbundling, RPS in neighboring states, out-of-state renewable energy purchases) that may explain the gap between effective and ineffective policies. We also investigate other RES-E policies and technology-specific effects. Ultimately, we show that RPS effectiveness is largely explained by a combination of policy design, market context, and inter-state trading effects.

Suggested Citation

  • Gireesh Shrimali & Steffen Jenner & Felix Groba & Gabriel Chan & Joe Indvik, 2012. "Have State Renewable Portfolio Standards Really Worked?: Synthesizing Past Policy Assessments," Discussion Papers of DIW Berlin 1258, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1258
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    References listed on IDEAS

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    Cited by:

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    3. Carfora, A. & Pansini, R.V. & Scandurra, G., 2021. "The role of environmental taxes and public policies in supporting RES investments in EU countries: Barriers and mimicking effects," Energy Policy, Elsevier, vol. 149(C).
    4. Eric Bowen & Donald J. Lacombe, 2017. "Spatial Dependence in State Renewable Policy: Effects of Renewable Portfolio Standards on Renewable Generation within NERC Regions," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    5. Shrimali, Gireesh & Lynes, Melissa & Indvik, Joe, 2015. "Wind energy deployment in the U.S.: An empirical analysis of the role of federal and state policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 796-806.
    6. Rezana Balla, 2020. "Digitalization of Financial Services in Albania Under Restricted Measures Covid-19," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 3, ejme_v3_i.
    7. Go, Roderick S. & Munoz, Francisco D. & Watson, Jean-Paul, 2016. "Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards," Applied Energy, Elsevier, vol. 183(C), pages 902-913.
    8. Guillaume Bourgeois & Sandrine Mathy & Philippe Menanteau, 2017. "The effect of climate policies on renewable energies : a review of econometric studies [L’effet des politiques climatiques sur les énergies renouvelables : une revue des études économétriques]," Post-Print hal-01585906, HAL.
    9. Consolación Quintana-Rojo & Fernando-Evaristo Callejas-Albiñana & Miguel-Ángel Tarancón & Isabel Martínez-Rodríguez, 2020. "Econometric Studies on the Development of Renewable Energy Sources to Support the European Union 2020–2030 Climate and Energy Framework: A Critical Appraisal," Sustainability, MDPI, vol. 12(12), pages 1-26, June.
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    11. Fell, Harrison & Linn, Joshua & Munnings, Clayton, 2012. "Designing Renewable Electricity Policies to Reduce Emissions," RFF Working Paper Series dp-12-54, Resources for the Future.
    12. Eric Bowen & Donald J. Lacombe, 2015. "Spatial interaction of Renewable Portfolio Standards and their effect on renewable generation within NERC regions," Working Papers 15-03, Department of Economics, West Virginia University.

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    More about this item

    Keywords

    Renewable energy; Renewable portfolio standards; Panel data models; Matching analysis;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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