The Impact of Renewable Energy Types on CO2 Emissions in the United States: A Spectral Causality Analysis

Abstract

Climate change and global warming necessitate reducing dependence on fossil fuels and increasing the share of renewable energy sources. This study examines the relationship between renewable energy consumption, specifically biofuels, hydroelectric, waste, wood, and solar energy, and CO2 emissions in the United States over the period 1984:01–2025:09 using a spectral causality approach. The findings reveal that the relationship is not homogeneous across frequency bands and varies significantly depending on the time horizon. The results indicate that causality running from CO2 emissions to renewable energy consumption is stronger and observed over a wider range of frequencies across all energy types. In contrast, the causal relationship between renewable energy consumption and CO2 emissions is less clear and varies by energy source. In particular, the effects of biofuels and solar energy are more pronounced in the long run, whereas hydroelectric and waste-based energy are more sensitive to short-term fluctuations. Wood-based energy exhibits a more irregular and frequency-dependent pattern. Overall, the findings suggest that environmental pressures play a decisive role in shaping energy preferences and highlight the importance of analyzing the energy-environment nexus within a multidimensional framework. Accordingly, energy and climate policies should be designed with sensitivity to different time horizons and tailored to specific energy sources.

Keywords

• Climate Change
• Renewable Energy Consumption
• CO2 Emissions
• Spectral Causality Analysis

Supporting Institution

None

Project Number

--

Ethical Statement

An ethical report is not required.

Thanks

None

ABD’de Yenilenebilir Enerji Türlerinin CO2 Emisyonları Üzerindeki Etkisi: Spektral Nedensellik Analizi

Abstract

İklim değişikliği ve küresel ısınma, fosil yakıtlara bağımlılığın azaltılmasını ve yenilenebilir enerji kaynaklarının payının artırılmasını zorunlu kılmaktadır. Bu çalışma, ABD’de biyoyakıt, hidroelektrik, atık, odun ve güneş enerjisi temelli yenilenebilir enerji tüketimi ile CO2 emisyonları arasındaki ilişkiyi 1984:01-2025:09 dönemi için spektral nedensellik yaklaşımıyla incelemektedir. Spektral nedensellik bulguları, ilişkinin frekans bantları boyunca homojen olmadığını ve zaman ufkuna bağlı olarak önemli ölçüde değiştiğini göstermektedir. Elde edilen sonuçlara göre, CO2 emisyonlarından yenilenebilir enerji tüketimine doğru nedensellik tüm enerji türlerinde daha güçlü ve geniş frekans bantlarında ortaya çıkmaktadır. Buna karşılık, yenilenebilir enerji tüketiminden CO2 emisyonlarına doğru nedensellik daha sınırlı ve enerji türüne bağlı olarak farklılaşmaktadır. Özellikle biyoyakıt ve güneş enerjisinin etkisinin uzun dönemli olduğu; hidroelektrik ve atık temelli enerjinin kısa dönemli dalgalanmalara daha duyarlı olduğu; odun temelli enerjinin ise düzensiz bir yapı sergilediği belirlenmiştir. Bulgular, çevresel baskıların enerji tercihlerini yönlendirdiğini ve enerji-çevre ilişkisinin çok boyutlu bir çerçevede ele alınması gerektiğini ortaya koymaktadır. Bu çerçevede, enerji ve iklim politikalarının zaman ufkuna duyarlı ve kaynak türüne özgü olarak tasarlanması önerilmektedir.

Keywords

• İklim değişikliği
• Yenilenebilir Enerji Tüketimi
• CO2 Emisyonları
• Spektral Nedensellik Analizi

Supporting Institution

Bulunmamaktadır.

Project Number

--

Ethical Statement

Etik kurul raporu gerekmemektedir.

Thanks

Bulunmamaktadır.

References

1. Acemoglu, D., Aghion, P., Bursztyn, L., & Hemous, D. (2012). The environment and directed technical change. American Economic Review, 102(1), 131–166. https://doi.org/10.1257/aer.102.1.131
2. Apergis, N., & Payne, J. (2014). 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. http://dx.doi.org/10.1016/j.eneco.2014.01.003
3. Aydın, M., Pata, U. K., & Inal, V. (2022). Economic policy uncertainty and stock prices in BRIC countries: evidence from asymmetric frequency domain causality approach. Applied Economic Analysis, 30(89), 114-129. https://doi.org/10.1108/AEA-12-2020-0172
4. Bilgili, F. (2012). The impact of biomass consumption on CO2 emissions: Cointegration analyses with regime shifts. Renewable and Sustainable Energy Reviews, 16(7), 5349–5354. https://doi.org/10.1016/j.rser.2012.04.021
5. Bilgili, F., Koçak, E., & Bulut, Ü. (2016). The dynamic impact of renewable energy consumption on CO₂ emissions: A revisited Environmental Kuznets Curve approach. Renewable and Sustainable Energy Reviews 54, 838–845. http://dx.doi.org/10.1016/j.rser.2015.10.080
6. BP. (2023). Statistical review of world energy 2023. BP p.l.c. https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html
7. Breitung, J., & Candelon, B. (2006). Testing for short- and long-run causality: A frequency-domain approach. Journal of Econometrics, 132(2), 363–378. https://doi.org/10.1016/j.jeconom.2005.02.004
8. Burnett, J., Bergstrom, J., & Wetzstein, M. (2013). Carbon dioxide emissions and economic growth in the U.S. Journal of Policy Modeling, 35(6), 1014-1028. https://doi.org/10.1016/j.jpolmod.2013.08.001

9. DeCicco, J., Liu, D., Heo, J., Krishnan, R., Kurthen, A., & Wang, L. (2016). Carbon balance effects of U.S. biofuel production and use. Climatic Change, 138, 667–680. http://dx.doi.org/10.1007/s10584-016-1764-4
10. Destek, M., & Aslan, A. (2017). Renewable and non-renewable energy consumption and economic growth in emerging economies: Evidence from bootstrap panel causality. Renewable Energy, 111, 757–763. http://dx.doi.org/10.1016/j.renene.2017.05.008
11. Destek, M., & Sinha, A. (2020). Munich Personal RePEc Archive Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic Co-operation and development countries. Munich Personal RePEc Archive, MPRA Paper No. 104246, 1-36. https://mpra.ub.uni-muenchen.de/104246/
12. Dickey, D., & Fuller, W. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74(366), 427-431. https://doi.org/10.2307/2286348
13. Dogan, E., & Ozturk, I. (2017). The influence of renewable and non-renewable energy consumption and real income on CO2 emissions in the USA: evidence from structural break tests. Environ Sci Pollut Res 24, 10846–10854. https://doi.org/10.1007/s11356-017-8786-y.
14. EIA. (2026). Renewable Energy. https://www.eia.gov/totalenergy/data/monthly/
15. Energy Institute. (2026). 2024 Key highlights. Insights by Source: https://www.energyinst.org/statistical-review/insights-by-source
16. Fromentin, V., & Tadjeddine, Y. (2020). Cross-border workers and financial instability: a frequency domain causality analysis applied to the Luxembourg financial centre. Applied Economics Letters, 27:4, 280-285. https://doi.org/10.1080/13504851.2019.1613496
17. Grafton, R., Kompas, T., Long, N., & To, H. (2014). US biofuels subsidies and CO2 emissions: An empirical test for a weak and a strong green paradox. Energy Policy 68, 550–555. http://dx.doi.org/10.1016/j.enpol.2013.11.006
18. Greenstone, M., McDowell, R., & Nath, I. (2019). Do Renewable Portfolio Standards Deliver? Energy Policy Institute at the University of Chicago, Working Paper, No: 2019-62, https://bfi.uchicago.edu/wp-content/uploads/BFIEPIC_WP_201962_v3.pdf
19. Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a North American Free Trade Agreement. NBER Working Paper No. 3914
20. Holland, S., Kotchen, M., Mansur, E., & Yates, A. (2022). Why marginal CO2 emissions are not decreasing for US electricity: Estimates and implications for climate policy. Proceedings of the National Academy of Sciences (PNAS) Economic Sciences 119 (8) e2116632119, https://doi.org/10.1073/pnas.2116632119
21. IEA. (2022). World Energy Outlook 2022. France: International Energy Agency. https://iea.blob.core.windows.net/assets/830fe099-5530-48f2-a7c1-11f35d510983/WorldEnergyOutlook2022.pdf
22. IPCC. (2023). Climate change 2023: Synthesis report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change. https://doi.org/10.59327/IPCC/AR6-9789291691647
23. IRENA ve CPI. (2025). Global landscape of energy transition finance 2025. Abu Dhabi: International Renewable Energy Agency. https://www.irena.org/publications
24. Jeon, H. (2022). CO2 emissions, renewable energy and economic growth in the US. The Electricity Journal, 35(7) 107170, https://doi.org/10.1016/j.tej.2022.107170
25. Kim, G., Choi, S.-K., & Seok, J. (2020). Does biomass energy consumption reduce total energy CO2 emissions in the US? Journal of Policy Modeling 42, 953–967. https://doi.org/10.1016/j.jpolmod.2020.02.009
26. Mai, T., Jadun, P., Logan, J., McMillan, C., Muratori, M., Steinberg, D., . . . Nelson, B. (2018). Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. Golden CO: NREL/TP-6A20-71500. National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy18osti/71500.pdf
27. Nordhaus, W. (2017). Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences, 114(7), 1518–1523. https://doi.org/10.1073/pnas.1609244114
28. Pata, U. (2018). Renewable energy consumption, urbanization, financial development, income and CO 2 emissions in Turkey: Testing EKC hypothesis with structural breaks. Journal of Cleaner Production 187, 770-779. https://doi.org/10.1016/j.jclepro.2018.03.236
29. Payne, J., Saunoris, J., Nazlioglu, S., & Smyth, R. (2024). Renewable energy production across U.S. states: Convergence or divergence? Energy Economics, 140, 108015, https://doi.org/10.1016/j.eneco.2024.108015
30. Perron, P. (1989). The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis. Econometrica, 57(6), 1361-1401. https://doi.org/10.2307/1913712
31. Perron, P. (1997). Further evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics, 80(2), 355-385. https://doi.org/10.1016/S0304-4076(97)00049-3
32. Phillips, P., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335-346. https://doi.org/10.2307/2336182
33. Popp, D. (2002). Induced innovation and energy prices. American Economic Review, 92(1), 160–180. https://doi.org/10.1257/000282802760015658
34. Sadorsky, P. (2009). Renewable energy consumption and income in emerging economies. Energy Policy, 37(10), 4021-4028. https://doi.org/10.1016/j.enpol.2009.05.003
35. Salari, M., Javid , R., & Noghanibehambari, H. (2021). The nexus between CO2 emissions, energy consumption, and economic growth in the U.S. Economic Analysis and Policy, 69, 182-194. https://doi.org/10.1016/j.eap.2020.12.007
36. Sovacool, B. K. (2016). How long will it take? Conceptualizing the temporal dynamics of energy transitions. Energy Research & Social Science, 13, 202–215. https://doi.org/10.1016/j.erss.2015.12.020
37. Stern, D. I. (2004). The rise and fall of the Environmental Kuznets Curve. World Development, 32(8), 1419–1439. https://doi.org/10.1016/j.worlddev.2004.03.004
38. Stern, N. (2007). The Economics of Climate Change: The Stern Review. Cambridge University Press. https://assets.cambridge.org/97805217/00801/frontmatter/9780521700801_frontmatter.pdf
39. Taştan, H. (2010). Türkiye’de ihracat, ithalat ve ekonomik büyüme arasındaki nedensellik ilişkilerinin spektral analizi. Ekonomi Bilimleri Dergisi, 2(1), 87-98. https://izlik.org/JA52TP66BG
40. Taştan, H. (2015). Testing for spectral Granger causality. The Stata Journal 15, Number 4,, 1157–1166. https://doi.org/10.1177/1536867X1501500411
41. UNDP. (2015). Transforming our world: the 2030 Agenda for Sustainable Development, A/RES/70/1. UNDP. https://files.acquia.undp.org/public/migration/ua/Agenda2030-eng.pdf
42. Upton Jr. , G., & Snyder , B. (2017). Funding renewable energy: An analysis of renewable portfolio standards. Energy Economics, 66, 205-216. https://doi.org/10.1016/j.eneco.2017.06.003
43. Wiser, R., Mai, T., Millstein, D., Barbose, G., Bird, L., Heeter, J., . . . Macknick, J. (2017). Assessing the costs and benefits of US renewable portfolio standards. Environmental Research Letters, 12(9) 094023, https://doi.org/10.1088/1748-9326/aa87bd
44. Zivot, E., & Andrews, D. (1992). Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis. Journal of Business & Economic Statistics, 10(3), 251-270. https://doi.org/10.2307/1391541
45. Zuhal, M., & Göcen, S. (2025). The relationship between CO2 emissions, renewable energy and economic growth in the US: evidence from symmetric and asymmetric spectral granger causality analysis. Environment, Development and Sustainability, 27, 28019–28040. https://doi.org/10.1007/s10668-024-05002-9