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Climate policy volatility hinders renewable energy consumption: Evidence from yardstick competition theory

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  • Addey, Kwame Asiam
  • Nganje, William

Abstract

Researchers have a high affirmative agreement on climate change and the need for policies to stimulate renewable energy consumption. However, there is little consensus among policymakers who are supposed to implement the solutions researchers propose. This phenomenon has led to extensive research on the effects of climate policy uncertainty (CPU) on energy consumption. We develop a yardstick competition model to examine the effect of climate policy volatility (CPV) on primary energy consumption in the U.S. residential sector. Our approach employs a Dirichlet process mixture linear model (DPM-LM) to account for unobserved heterogeneity and excess kurtosis. The results revealed that all three CPV estimators (Garman-Klass, Parkinson, and Rogers-Satchell) adversely affected the demand for hydrocarbon gas liquids (HGL) and biomass energy. In contrast, they positively affected the consumption of natural gas. The control variables (price and GDP) were consistent with the a priori expectations. This paper concludes that CPV creates a disincentive to increasing renewable energy demand. It is, therefore, critical for policymakers to consider ways of stabilizing climate policy and arriving at specific, measurable, achievable, realistic and definite time-bound goals and policies. It is equally important for policymakers to use science-based ex-post measures to inform the climate policy debate.

Suggested Citation

  • Addey, Kwame Asiam & Nganje, William, 2024. "Climate policy volatility hinders renewable energy consumption: Evidence from yardstick competition theory," Energy Economics, Elsevier, vol. 130(C).
  • Handle: RePEc:eee:eneeco:v:130:y:2024:i:c:s0140988323007636
    DOI: 10.1016/j.eneco.2023.107265
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    as
    1. Tol, Richard S.J., 2017. "The structure of the climate debate," Energy Policy, Elsevier, vol. 104(C), pages 431-438.
    2. Bonev, Petyo & Glachant, Matthieu & Söderberg, Magnus, 2022. "Implicit yardstick competition between heating monopolies in urban areas: Theory and evidence from Sweden," Energy Economics, Elsevier, vol. 109(C).
    3. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    4. Bistline, John E., 2014. "Natural gas, uncertainty, and climate policy in the US electric power sector," Energy Policy, Elsevier, vol. 74(C), pages 433-442.
    5. Apergis, Nicholas & Pinar, Mehmet, 2021. "The role of party polarization in renewable energy consumption: Fresh evidence across the EU countries," Energy Policy, Elsevier, vol. 157(C).
    6. Kostakis, Ioannis & Lolos, Sarantis & Sardianou, Eleni, 2021. "Residential natural gas demand: Assessing the evidence from Greece using pseudo-panels, 2012–2019," Energy Economics, Elsevier, vol. 99(C).
    7. Dijkstra, Peter T. & Haan, Marco A. & Mulder, Machiel, 2017. "Design of yardstick competition and consumer prices: Experimental evidence," Energy Economics, Elsevier, vol. 66(C), pages 261-271.
    8. Apergis, Nicholas & Lau, Marco Chi Keung, 2015. "Structural breaks and electricity prices: Further evidence on the role of climate policy uncertainties in the Australian electricity market," Energy Economics, Elsevier, vol. 52(PA), pages 176-182.
    9. Barros, Carlos Pestana & Gil-Alana, Luis A. & Payne, James E., 2013. "U.S. Disaggregated renewable energy consumption: Persistence and long memory behavior," Energy Economics, Elsevier, vol. 40(C), pages 425-432.
    10. Zhou, Deheng & Siddik, Abu Bakkar & Guo, Lili & Li, Houjian, 2023. "Dynamic relationship among climate policy uncertainty, oil price and renewable energy consumption—findings from TVP-SV-VAR approach," Renewable Energy, Elsevier, vol. 204(C), pages 722-732.
    11. Luísa Novais & Susana Faria, 2021. "Comparison of the EM, CEM and SEM algorithms in the estimation of finite mixtures of linear mixed models: a simulation study," Computational Statistics, Springer, vol. 36(4), pages 2507-2533, December.
    12. Guesmi, Khaled & Makrychoriti, Panagiota & Spyrou, Spyros, 2023. "The relationship between climate risk, climate policy uncertainty, and CO2 emissions: Empirical evidence from the US," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 610-628.
    13. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    14. Andrei Shleifer, 1985. "A Theory of Yardstick Competition," RAND Journal of Economics, The RAND Corporation, vol. 16(3), pages 319-327, Autumn.
    15. Savvanidou, Electra & Zervas, Efthimios & Tsagarakis, Konstantinos P., 2010. "Public acceptance of biofuels," Energy Policy, Elsevier, vol. 38(7), pages 3482-3488, July.
    16. Kwame Asiam Addey & William Nganje, 2023. "The role of the U.S. exchange‐rate equity market volatility on agricultural exports and forecasts," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 71(1), pages 25-47, March.
    17. Aitor Marcos & José M. Barrutia & Patrick Hartmann, 2023. "Carbon tax acceptance in a polarized society: bridging the partisan divide over climate policy in the US," Climate Policy, Taylor & Francis Journals, vol. 23(7), pages 885-900, August.
    18. Arnerić, Josip & Matković, Mario & Sorić, Petar, 2019. "Comparison of range-based volatility estimators against integrated volatility in European emerging markets," Finance Research Letters, Elsevier, vol. 28(C), pages 118-124.
    19. Ben-Salha, Ousama & Hkiri, Besma & Aloui, Chaker, 2018. "Sectoral energy consumption by source and output in the U.S.: New evidence from wavelet-based approach," Energy Economics, Elsevier, vol. 72(C), pages 75-96.
    20. Botta, Enrico, 2019. "An experimental approach to climate finance: the impact of auction design and policy uncertainty on renewable energy equity costs in Europe," Energy Policy, Elsevier, vol. 133(C).
    21. Estiri, Hossein, 2015. "The indirect role of households in shaping US residential energy demand patterns," Energy Policy, Elsevier, vol. 86(C), pages 585-594.
    22. Abbasi, Kashif Raza & Abbas, Jaffar & Tufail, Muhammad, 2021. "Revisiting electricity consumption, price, and real GDP: A modified sectoral level analysis from Pakistan," Energy Policy, Elsevier, vol. 149(C).
    23. Resende, Marcelo, 2002. "Relative efficiency measurement and prospects for yardstick competition in Brazilian electricity distribution," Energy Policy, Elsevier, vol. 30(8), pages 637-647, June.
    24. Syed, Qasim Raza & Apergis, Nicholas & Goh, Soo Khoon, 2023. "The dynamic relationship between climate policy uncertainty and renewable energy in the US: Applying the novel Fourier augmented autoregressive distributed lags approach," Energy, Elsevier, vol. 275(C).
    25. Goldfarb, Jillian L. & Kriner, Douglas L., 2021. "U.S. public support for biofuels tax credits: Cost frames, local fuel prices, and the moderating influence of partisanship," Energy Policy, Elsevier, vol. 149(C).
    26. Allers, Maarten A., 2012. "Yardstick competition, fiscal disparities, and equalization," Economics Letters, Elsevier, vol. 117(1), pages 4-6.
    27. Adams, Samuel & Adedoyin, Festus & Olaniran, Eniola & Bekun, Festus Victor, 2020. "Energy consumption, economic policy uncertainty and carbon emissions; causality evidence from resource rich economies," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 179-190.
    28. Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
    29. Shang, Yunfeng & Han, Ding & Gozgor, Giray & Mahalik, Mantu Kumar & Sahoo, Bimal Kishore, 2022. "The impact of climate policy uncertainty on renewable and non-renewable energy demand in the United States," Renewable Energy, Elsevier, vol. 197(C), pages 654-667.
    30. Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    31. Blyth, William & Bradley, Richard & Bunn, Derek & Clarke, Charlie & Wilson, Tom & Yang, Ming, 2007. "Investment risks under uncertain climate change policy," Energy Policy, Elsevier, vol. 35(11), pages 5766-5773, November.
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    More about this item

    Keywords

    Climate policy volatility; Garman-Klass; Parkinson; Rogers-Satchell; Yardstick competition;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • D7 - Microeconomics - - Analysis of Collective Decision-Making
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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