IDEAS home Printed from https://ideas.repec.org/p/gme/wpaper/202312012.html
   My bibliography  Save this paper

Climate Policy Uncertainty and the Demand for Renewable Energy in the United States of America: Evidence from a Non-Linear Threshold Autoregressive Model

Author

Listed:
  • Mohammad Arief Rajendra

    (Department of Economics, Faculty of Economics & Business, Universitas Gadjah Mada)

  • Sekar Utami Setiastuti

    (Department of Economics, Faculty of Economics and Business, Universitas Gadjah Mada)

Abstract

This study examines the relationship between climate policy uncertainty and the demand for renewable energy in the United States. The primary findings suggest that there is a nonlinear threshold effect resulting from climate policy uncertainty, as measured by the Climate Policy Uncertainty Index (CPU) and the Environmental Policy Uncertainty Index (ENVPU), on renewable energy demand (REC). The findings indicate a negative relationship between the CPU and the REC when the CPU is beyond a specific threshold. This suggests that economic agents adopt a cautious approach, sometimes referred to as the "wait and see" policy, in their renewable energy allocation. In essence, customers may opt to reduce their utilization of renewable energy in favor of alternate sources as a means to circumvent the investment risks associated with renewable alternatives.

Suggested Citation

  • Mohammad Arief Rajendra & Sekar Utami Setiastuti, 2023. "Climate Policy Uncertainty and the Demand for Renewable Energy in the United States of America: Evidence from a Non-Linear Threshold Autoregressive Model," Gadjah Mada Economics Working Paper Series 202312012, Department of Economics, Faculty of Economics and Business, Universitas Gadjah Mada.
    • Handle: RePEc:gme:wpaper:202312012
    as

    Download full text from publisher

    File URL: https://econworkingpaper.feb.ugm.ac.id/download/working_paper/202312012.pdf
    File Function: First version, 2023
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
    2. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
    3. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    4. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    5. Cao, Chunyan & Chen, Wei & Aslam, Misbah, 2023. "COP26 perspective of natural resources extraction: Oil and mineral resources perspective of developed economies," Resources Policy, Elsevier, vol. 82(C).
    6. 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).
    7. Ahn, Eun S. & Lee, Jin Man, 2012. "The Performance Of Nonlinearity Tests On Asymmetric Nonlinear Time Series," The Journal of Economic Asymmetries, Elsevier, vol. 9(2), pages 11-44.
    8. Sadorsky, Perry, 2009. "Renewable energy consumption, CO2 emissions and oil prices in the G7 countries," Energy Economics, Elsevier, vol. 31(3), pages 456-462, May.
    9. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    10. Nicholas Apergis & Dan Constantin Danuletiu, 2014. "Renewable Energy and Economic Growth: Evidence from the Sign of Panel Long-Run Causality," International Journal of Energy Economics and Policy, Econjournals, vol. 4(4), pages 578-587.
    11. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    12. Clara Berestycki & Stefano Carattini & Antoine Dechezleprêtre & Tobias Kruse, 2022. "Measuring and assessing the effects of climate policy uncertainty," OECD Economics Department Working Papers 1724, OECD Publishing.
    13. 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).
    14. Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
    15. Li, Zheng Zheng & Su, Chi-Wei & Moldovan, Nicoleta-Claudia & Umar, Muhammad, 2023. "Energy consumption within policy uncertainty: Considering the climate and economic factors," Renewable Energy, Elsevier, vol. 208(C), pages 567-576.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sekar Utami Setiastuti & Mohammad Arief Rajendra, 2024. "The Nonlinear Nexus of Climate Policy Uncertainty and Renewable Energy Consumption in the United States of America: A Markov-Switching Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 321-334, September.
    2. Behiye Cavusoglu & Saifullahi Sani Ibrahim & Huseyin Ozdeser, 2019. "Testing the relationship between financial sector output, employment and economic growth in North Cyprus," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-11, December.
    3. Travaglini, Guido, 2007. "The U.S. Dynamic Taylor Rule With Multiple Breaks, 1984-2001," MPRA Paper 3419, University Library of Munich, Germany, revised 15 Jun 2007.
    4. Antonio E. Noriega & Daniel Ventosa‐Santaulària, 2006. "Spurious Regression Under Broken‐Trend Stationarity," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 671-684, September.
    5. Wang, Yu & Cheung, Adrian (Wai Kong) & Yan, Wanlin & Wang, Bin, 2024. "Green bond and green stock in China: The role of economic and climate policy uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    6. D. Ventosa-Santaulària, 2009. "Spurious Regression," Journal of Probability and Statistics, Hindawi, vol. 2009, pages 1-27, August.
    7. Romero-Ávila, Diego, 2009. "Are OECD consumption-income ratios stationary after all?," Economic Modelling, Elsevier, vol. 26(1), pages 107-117, January.
    8. Tolga Omay & Furkan Emirmahmutoğlu, 2017. "The Comparison of Power and Optimization Algorithms on Unit Root Testing with Smooth Transition," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 623-651, April.
    9. Tian, Lihui & Li, Xin & Lee, Cheng-Wen & Spulbăr, Cristi, 2024. "Investigating the asymmetric impact of artificial intelligence on renewable energy under climate policy uncertainty," Energy Economics, Elsevier, vol. 137(C).
    10. Yousaf, Imran & Bejaoui, Azza & Ali, Shoaib & Li, Yanshuang, 2024. "Demystifying the dynamic relationship between news sentiment index and ESG stocks: Evidence from time-frequency wavelet analysis," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    11. Zhang, Jiahao & Zhang, Yifeng & Wei, Yu & Wang, Zhuo, 2024. "Normal and extreme impact and connectedness between fossil energy futures markets and uncertainties: Does El Niño-Southern Oscillation matter?," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 188-215.
    12. Uctum, Remzi, 2007. "Économétrie des modèles à changement de régimes : un essai de synthèse," L'Actualité Economique, Société Canadienne de Science Economique, vol. 83(4), pages 447-482, décembre.
    13. Sobreira, Nuno & Nunesz, Luis C. & Rodriguesz, Paulo M. M., 2012. "Neoclassical, semi-endogenous or endogenous growth theory? Evidence based on new structural change tests," Insper Working Papers wpe_291, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    14. Nuno Sobreira & Luis C. Nunes & Paulo M. M. Rodrigues, 2014. "Characterizing Economic Growth Paths Based On New Structural Change Tests," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 845-861, April.
    15. Bogdan Dima & Ștefana Maria Dima, 2024. "The non-linear impact of monetary policy on shifts in economic policy uncertainty: evidence from the United States of America," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(3), pages 755-781, August.
    16. Getu Hailu & Alex Maynard & Alfons Weersink, 2015. "Empirical analysis of corn and soybean basis in Canada," Applied Economics, Taylor & Francis Journals, vol. 47(51), pages 5491-5509, November.
    17. Robert Dixon & David Shepherd & James Thomson, 2001. "Regional Unemployment Disparities in Australia," Regional Studies, Taylor & Francis Journals, vol. 35(2), pages 93-102.
    18. Zhou, Bingjun & Huang, Yan & Gao, Ke & Luo, Chunyang, 2024. "How geopolitical risk and economic policy uncertainty impact coal, natural gas, and oil rent? Evidence from China," Resources Policy, Elsevier, vol. 88(C).
    19. Saadaoui, Haifa & Omri, Emna & Chtourou, Nouri, 2024. "The transition to renewable energies in Tunisia: The asymmetric impacts of technological innovation, government stability, and democracy," Energy, Elsevier, vol. 293(C).
    20. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, January.

    More about this item

    Keywords

    Climate policy uncertainty; Renewable energy demand; Crude oil price;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gme:wpaper:202312012. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dwi Rahmadi Nur Fathoni (email available below). General contact details of provider: https://edirc.repec.org/data/deugmid.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.