Araştırma Makalesi

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

Cilt: 24 Sayı: 2 27 Ağustos 2022
PDF İndir
TR EN

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

Kaynakça

  1. 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.
  2. 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.
  3. Baker, S.R., Bloom, N. & Davis, S.J. (2016). Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics, 131(4),1593-1636.
  4. Barnett, J., Dessai, S., & Webber, M. (2004). Will OPEC lose from the Kyoto Protocol?. Energy Policy, 32(18), 2077-2088.
  5. Caldara, D. & Iacoviello, M. (2018). Measuring Geopolitical Risk. International Finance Discussion Papers 1222.
  6. 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.
  7. 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.
  8. Dike, J. C. (2014). Does climate change mitigation activity affect crude oil prices? Evidence from dynamic panel model. Journal of Energy, 2014.

Ayrıntılar

Birincil Dil

İngilizce

Konular

-

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

27 Ağustos 2022

Gönderilme Tarihi

9 Ocak 2022

Kabul Tarihi

6 Mayıs 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 24 Sayı: 2

Kaynak Göster

APA
Dinc Cavlak, O. (2022). A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(2), 757-776. https://doi.org/10.26745/ahbvuibfd.1055390
AMA
1.Dinc Cavlak O. A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices. AHBVÜ İİBF Dergisi. 2022;24(2):757-776. doi:10.26745/ahbvuibfd.1055390
Chicago
Dinc Cavlak, Ozge. 2022. “A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices”. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 24 (2): 757-76. https://doi.org/10.26745/ahbvuibfd.1055390.
EndNote
Dinc Cavlak O (01 Ağustos 2022) A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 24 2 757–776.
IEEE
[1]O. Dinc Cavlak, “A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices”, AHBVÜ İİBF Dergisi, c. 24, sy 2, ss. 757–776, Ağu. 2022, doi: 10.26745/ahbvuibfd.1055390.
ISNAD
Dinc Cavlak, Ozge. “A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices”. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 24/2 (01 Ağustos 2022): 757-776. https://doi.org/10.26745/ahbvuibfd.1055390.
JAMA
1.Dinc Cavlak O. A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices. AHBVÜ İİBF Dergisi. 2022;24:757–776.
MLA
Dinc Cavlak, Ozge. “A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices”. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, c. 24, sy 2, Ağustos 2022, ss. 757-76, doi:10.26745/ahbvuibfd.1055390.
Vancouver
1.Ozge Dinc Cavlak. A Nonlinear Autoregressive Distributed Lag (NARDL) Approach for U.S. Climate Policy Uncertainty Index, Renewable Energy Consumption, and Oil Prices. AHBVÜ İİBF Dergisi. 01 Ağustos 2022;24(2):757-76. doi:10.26745/ahbvuibfd.1055390

Cited By