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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

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    File URL: https://econworkingpaper.feb.ugm.ac.id/download/working_paper/202312012.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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).
    12. 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.
    13. 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).
    14. 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.
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    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

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