The wind energy-greenhouse gas nexus: The wavelet-partial wavelet coherence model approach


Kuşkaya S., Bilgili F.

Journal Of Cleaner Production, cilt.245, sa.3, ss.1-14, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 245 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.jclepro.2019.118872
  • Dergi Adı: Journal Of Cleaner Production
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Chimica, Communication Abstracts, Compendex, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-14
  • Erciyes Üniversitesi Adresli: Evet

Özet

It is clear that an increase in gases, due to fossil energy consumption, in the atmosphere has been causing greenhouse effect influencing the world’s temperature. The effect of renewable usage, on the other hand, on global warming is not very clear. For instance, the influence of wind energy usage on Greenhouse Gas (GHG) has been a controversial research topic for the last two-three decades. Throughout some empirical analyses, some researches reached no evidence that wind can reduce global warming, as some other researches observed that the wind has proven to have a positive impact on environmental quality by lowering CO2 emissions. This paper, on the other hand, reveals that wind usage can influence GHG positively or negatively at different time periods. By observing the monthly period 1989:1–2017:8, this paper examines the effect of wind energy usage on GHG emissions for the variables of wind energy consumption, coal consumption, natural gas consumption, other renewable energy consumption, and total energy consumption interrelated carbon dioxide (CO2) emissions. This research launches the methodology of Morlet wavelet coherence analysis allowing a researcher to conduct the analyses of variables in both time and frequency dimensions. The research also performed Monte Carlo simulations with 1000, 2500, 5000 and 10000 replications to reach significant outputs. The paper, therefore, (a) observes whether there exists causality between the variables within the whole sample and all sub-samples together with other interrelated control variables, (b) monitors the significant causalities (co-movements) at different time frequencies, (c) hence, determines the potential influence of wind energy usage on CO2 emissions at different time periods corresponding to different time frequencies. This paper eventually detects three significant emergent outcomes. The first outcome is that the increase in CO2 led to enlarged use of wind energy for the periods 1999, 2001–2002, and 2015–2017. The second outcome is that wind energy consumption diminished the CO2 emissions for the period 2015–2017. The 1st and 2nd outputs are obtained at 5% significance level. The third outcome is that the wind power turbines intensified the emissions during the periods 1991–2004 (at10% significance) and 1999–2002 (at 5% significance) in the US. This research later suggests therefore that policy makers consider the consumption of wind energy to reduce CO2 emissions.