ABD İklim Politikası Belirsizliği Endeksi, Yenilenebilir Enerji Tüketimi ve Petrol Fiyatları için Doğrusal Olmayan Sınır Testi Yaklaşımı

Yıl 2022, , 757 - 776, 27.08.2022

Ozge Dinc Cavlak

https://doi.org/10.26745/ahbvuibfd.1055390

Cited By: 5

Öz

Bu çalışma, Ocak 2000-Mart 2021 dönemi için ABD iklim politikası belirsizliği, yenilenebilir enerji tüketimi ve petrol fiyatları arasındaki asimetrik ilişkiyi ortaya koymayı amaçlamaktadır. Petrol fiyatlarının ve yenilenebilir enerji tüketiminin iklim politikası belirsizliği üzerindeki uzun vadeli ve kısa vadeli dinamik etkileri, Doğrusal Olmayan Sınır Testi (NARDL) yaklaşımı kullanılarak incelenmektedir. Bulgular, uzun vadede iklim politikası belirsizliği, yenilenebilir enerji tüketimi ve ham petrol fiyatları arasında bir asimetrik eşbütünleşme ilişkisi olduğunu göstermektedir. İklim politikası belirsizliği, uzun vadede yenilenebilir enerji tüketimi ve petrol fiyatlarındaki hem olumsuz hem de olumlu değişikliklerden etkilenmektedir. Asimetrik ilişkilerin varlığı, verilerin NARDL modeline uygun olduğunu göstermektedir. NARDL tahmin sonuçları, yenilenebilir enerji tüketimindeki bir artışın iklim politikası belirsizliğini artırırken, yenilenebilir enerji tüketimindeki bir düşüşün de iklim politikası belirsizliğinde uzun vadede bir artışa yol açtığını göstermektedir. Ayrıca, petrol fiyatlarındaki bir artış iklim politikası belirsizliğinde bir artışa yol açarken, petrol fiyatlarındaki düşüş iklim politikası belirsizliğinde uzun vadede bir azalmaya yol açmaktadır.

Anahtar Kelimeler

NARDL, ABD iklim politikası belirsizliği, yenilenebilir enerji tüketimi, petrol fiyatları

A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices

Öz

This study aims to reveal the asymmetric relationship among climate policy uncertainty, oil prices, and renewable energy consumption for January 2000-March 2021 in the U.S. The long- and short-run dynamic impacts of oil prices and renewable energy consumption on climate policy uncertainty are mainly examined utilizing a nonlinear autoregressive distributed lag (NARDL) approach. The findings of the study depict that there exists an asymmetric cointegrating relationship between climate policy uncertainty, renewable energy consumption, and crude oil prices in the long run. Climate policy uncertainty is affected by both negative and positive variations in renewable energy consumption and oil prices in the long-run period. The presence of asymmetric relations is an indicator of the data is suitable for the NARDL model. The NARDL estimation results reveal that an increment in renewable energy consumption causes an increase in climate policy uncertainty while a decrease in renewable energy consumption also causes an increase in climate policy uncertainty in the long-run period. Further, an increase in oil prices causes an increase in climate policy uncertainty while a reduction in oil prices results in a decrease in the climate policy uncertainty for a long-run period.

Anahtar Kelimeler

NARDL, U.S. climate policy uncertainty, renewable energy consumption, oil prices

Kaynakça

• Apergis, N., & Payne, J. E. (2014a). The causal dynamics between renewable energy, real GDP, emissions and oil prices: evidence from OECD countries. Applied Economics, 46(36), 4519-4525.
• Apergis, N., & Payne, J. E. (2014b). Renewable energy, output, CO2 emissions, and fossil fuel prices in Central America: Evidence from a nonlinear panel smooth transition vector error correction model. Energy Economics, 42, 226-232.
• Baker, S.R., Bloom, N. & Davis, S.J. (2016). Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics, 131(4),1593-1636.
• Barnett, J., Dessai, S., & Webber, M. (2004). Will OPEC lose from the Kyoto Protocol?. Energy Policy, 32(18), 2077-2088.
• Caldara, D. & Iacoviello, M. (2018). Measuring Geopolitical Risk. International Finance Discussion Papers 1222.
• Crimmins, A., J. Balbus, J.L. Gamble, C.B. Beard, J.E. Bell, D. Dodgen, R.J. Eisen, N. Fann, M.D. Hawkins, S.C. Herring, L. Jantarasami, D.M. Mills, S. Saha, M.C. Sarofim, J. Trtanj, and L. Ziska, 2016: Executive Summary. The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment. U.S. Global Change Research Program, Washington, DC, page 1–24. http://dx.doi.org/10.7930/J00P0WXS.
• Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
• Dike, J. C. (2014). Does climate change mitigation activity affect crude oil prices? Evidence from dynamic panel model. Journal of Energy, 2014.
• Federation of American Scientists (2021). Countering Climate Change With Renewable Energy Technologies, By Lindsay Milliken, Tricia White and Michael A. Fisher, Science Policy, https://fas.org/blogs/sciencepolicy/countering-climate-change-with-renewable-energy-technologies/.
• Ferrer, R., Shahzad, S. J. H., López, R., & Jareño, F. (2018). Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 76, 1-20.
• Gavriilidis, K. (2021). Measuring Climate Policy Uncertainty. Available at SSRN: https://ssrn.com/abstract=3847388.
• Ghatak, S., & Siddiki, J. U. (2001). The use of the ARDL approach in estimating virtual exchange rates in India. Journal of Applied statistics, 28(5), 573-583. Intergovernmental Panel on Climate Change (IPCC), Contribution of Working Group III to the Fourth Assessment Report, Cambridge University Press, Cambridge, UK, 2007.
• Karacan, R., Mukhtarov, S., Barış, İ., İşleyen, A., & Yardımcı, M. E. (2021). The Impact of Oil Price on Transition toward Renewable Energy Consumption? Evidence from Russia. Energies, 14(10), 2947.
• Katrakilidis, C. & E. Trachanas (2012). What Drives Housing Price Dynamics in Greece: New Evidence from Asymmetric ARDL Cointegration, Economic Modelling, 29, 1064-1069.
• Kurov, A., & Stan, R. (2018). Monetary policy uncertainty and the market reaction to macroeconomic news. Journal of Banking & Finance, 86, 127-142.
• Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of econometrics, 54(1-3), 159-178.
• IEA (2007). Climate Policy Uncertainty and Investment Risk. https://iea.blob.core.windows.net/assets/8d005005-7eb7-4eb4-8449-1da241b116a8/ClimatePolicyUncertaintyandInvestmentRisk.pdf.
• Liang, C.C. & C. Troy & E. Rouyer (2020). U.S. Uncertainty and Asian Stock Prices: Evidence from the Asymmetric NARDL Model. The North American Journal of Economics and Finance, 51, 101046.
• Lopez, J. M. R., Sakhel, A., & Busch, T. (2017). Corporate investments and environmental regulation: The role of regulatory uncertainty, regulation-induced uncertainty, and investment history. European Management Journal, 35(1), 91-101.
• Murshed, M., & Tanha, M. M. (2021). Oil price shocks and renewable energy transition: Empirical evidence from net oil-importing South Asian economies. Energy, Ecology and Environment, 6, 183-203.
• Newell, R. G. (2021). Federal Climate Policy 101: Reducing Emissions. 207th issue of Resources magazine. https://www.rff.org/publications/explainers/federal-climate-policy 101/?gclid=Cj0KCQiA7oyNBhDiARIsADtGRZazhWf58_BoXJfoRsjY4gNbg2NNqq2IgkVhF_FmUT0L-ZBjb50C4M8aAi4QEALw_wcB. (accessed on 28.11.2021).
• Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519-1554. Omri, A., & Nguyen, D. K. (2014). On the determinants of renewable energy consumption: International evidence. Energy, 72, 554-560.
• Pesaran, M. H., & Shin, Y. (1998). An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis. Econometric Society Monographs, 31, 371-413.
• Pesaran, M. H., & Shin, Y. (2002). Long-run structural modelling. Econometric reviews, 21(1), 49-87.
• Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326.
• Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
• Sadorsky, P. (2009). Renewable energy consumption, CO2 emissions and oil prices in the G7 countries. Energy Economics, 31(3), 456-462.
• Shin, Y. & B. Yu & M. Greenwood-Nimmo (2014), “Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework”, in: Festschrift in Honor of Peter Schmidt, Springer, New York, NY, 281-314.
• Torvanger, A., Kallbekken, S., & Tollefsen, P. (2012). Oil price scenarios and climate policy: welfare effects of including transportation in the E.U. emissions trading system. Mitigation and Adaptation Strategies for Global Change, 17(7), 753-768.
• United Nations Framework Convention on Climate Change, V. (2015). Adoption of the Paris agreement. Proposal by the President. https://unfccc.int/sites/default/files/english_paris_agreement.pdf.
• United Nations (2021). What is Climate Change? https://www.un.org/en/climatechange/what-is-climate-change
• United Nations (1998). Kyoto protocol to the united nations framework convention on climate change. https://unfccc.int/resource/docs/convkp/kpeng.pdf.
• U.S. Environmental Protection Agency (2017). Future of Climate Change. https://19january2017snapshot.epa.gov/climate-change-science/future-climate-change_.html
• Vielle, M., & Viguier, L. (2007). On the climate change effects of high oil prices. Energy Policy, 35(2), 844-849.
• Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.