R&D Expenditures in Fossil Fuels vs. Renewable Energy: Insights on Energy Transition through Cross-Country Analysis

Abstract

This study examines the impact of public R&D expenditures in the fossil fuel and renewable energy sectors on energy transition through the carbon intensity variable. The analysis uses Pedroni's Panel Cointegration Test and the Group Mean Panel Dynamic Ordinary Least Squares, utilizing data from 16 IEA countries between 1993 and 2022. Unlike previous studies that primarily focus on either general R&D expenditures or aggregate energy sector R&D, this study provides a comparative analysis of fossil and renewable sectors, addressing a significant gap in the literature. The findings reveal that public R&D expenditures in the renewable energy sector may significantly reduce carbon intensity, whereas public R&D expenditures in the fossil fuel sector increase carbon intensity. These results suggest that, contrary to the common assumption in the literature, the heterogeneous effects of R&D spending across subsectors of energy industry should be taken into account. Therefore, Redirecting R&D expenditures toward renewable energy technologies, rather than fossil energy sector, may accelerate the energy transition process.

Keywords

• Energy Transition
• carbon intensity
• R&D

Fosil Yakıtlar ve Yenilenebilir Enerjide Ar-Ge Harcamaları: Enerji Geçişine Dair Panel Veri Analizinden Bulgular

Abstract

Bu çalışma, fosil yakıt ve yenilenebilir enerji sektörlerinde kamu Ar-Ge harcamalarının, karbon yoğunluğu değişkeni aracılığıyla, enerji geçişi üzerindeki etkisi incelenmektedir. Analiz, 1993 ile 2022 yılları arasında 16 IEA ülkesinden alınan verilerle Pedroni'nin Panel Eşbütünleşme Testi ve Grup Ortalama Panel Dinamik En Küçük Kareler Yöntemi'ni kullanmaktadır. Önceki çalışmalardan farklı olarak, bu çalışma, genel Ar-Ge harcamaları ya da toplam enerji sektörü Ar-Ge'si üzerine odaklanmak yerine, fosil ve yenilenebilir enerji sektörlerini karşılaştırmalı bir şekilde analiz ederek literatürdeki önemli bir boşluğu doldurmaktadır. Bulgular, yenilenebilir enerji sektöründeki kamu Ar-Ge harcamalarının karbon yoğunluğunu önemli ölçüde azaltabileceğini, oysa fosil yakıtlar sektöründeki kamu Ar-Ge harcamalarının karbon yoğunluğunu artırabileceğini göstermektedir. Bu sonuçlar, literatürdeki yaygın varsayımın aksine, enerji endüstrisinin alt sektörlerinde Ar-Ge harcamalarının heterojen etkilerinin dikkate alınması gerekliliğini ortaya koymaktadır. Bu bağlamda, Ar-Ge harcamalarının fosil enerji sektöründen ziyade, yenilenebilir enerji teknolojilerine yoğunlaştırılması, enerji geçiş sürecini hızlandırabilir.

Keywords

• Enerji Geçişi
• karbon yoğunluğu
• Ar-Ge

References

1. Antimiani, A., Costantini, V., & Paglialunga, E. (2023). Fossil fuels subsidy removal and the EU carbon neutrality policy. Energy Economics, 119, 106524. https://doi.org/10.1016/j.eneco.2023.106524
2. Arzaghi, M., & Squalli, J. (2023). The environmental impact of fossil fuel subsidy policies. Energy Economics, 126, 106980. https://doi.org/10.1016/j.eneco.2023.106980
3. Bashir, M. F., Shahbaz, M., Ma, B., & Alam, K. (2024). Evaluating the roles of energy innovation, fossil fuel costs and environmental compliance towards energy transition in advanced industrial economies. Journal of Environmental Management, 351, 119709. https://doi.org/10.1016/j.jenvman.2023.119709
4. Bektaş, V., Ursavaş, N. (2023). Revisiting the environmental Kuznets curve hypothesis with globalization for OECD countries: the role of convergence clubs. Environ Sci Pollut Res 30, 47090–47105 https://doi.org/10.1007/s11356-023-25577-6
5. Böhringer, C., Cantner, U., Costard, J., Kramkowski, L. V., Gatzen, C., & Pietsch, S. (2020). Innovation for the German energy transition-Insights from an expert survey. Energy Policy, 144, 111611. https://doi.org/10.1016/j.enpol.2020.111611
6. Bosah, C. P., Li, S., Ampofo, G. K. M., & Sangare, I. (2023). A continental and global assessment of the role of energy consumption, total natural resource rent, and economic growth as determinants of carbon emissions. Science of The Total Environment, 892, 164592. https://doi.org/10.1016/j.scitotenv.2023.164592
7. Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The review of economic studies, 47(1), 239-253. https://www.jstor.org/stable/2297111
8. Caglar, A. E., & Ulug, M. (2022). The role of government spending on energy efficiency R&D budgets in the green transformation process: insight from the top-five countries. Environmental Science and Pollution Research, 29(50), 76472-76484. https://doi.org/10.1007/s11356-022-21133-w

9. Celik, A., Kostekci, A., & Alola, A. A. (2024). Carbon neutrality implication of material productivity, total factor productivity and renewable energy uptake in the Nordics. Ecological Indicators, 160, 111813. https://doi.org/10.1016/j.ecolind.2024.111813
10. Chen, W., Zou, W., Zhong, K., & Aliyeva, A. (2023). Machine learning assessment under the development of green technology innovation: A perspective of energy transition. Renewable Energy, 214, 65-73. https://doi.org/10.1016/j.renene.2023.05.108
11. Cheng, Y., & Yao, X. (2021). Carbon intensity reduction assessment of renewable energy technology innovation in China: A panel data model with cross-section dependence and slope heterogeneity. Renewable and Sustainable Energy Reviews, 135, 110157. https://doi.org/10.1016/j.rser.2020.110157
12. Danish, Ulucak, R. (2022). Analyzing energy innovation-emissions nexus in China: a novel dynamic simulation method. Energy, 244, 123010. https://doi.org/10.1016/j.energy.2021.123010
13. Dong, F., Long, R., Li, Z., & Dai, Y. (2016). Analysis of carbon emission intensity, urbanization and energy mix: evidence from China. Natural Hazards, 82, 1375-1391. https://doi.org/10.1007/s11069-016-2248-6
14. Garrone, P., & Grilli, L. (2010). Is there a relationship between public expenditures in energy R&D and carbon emissions per GDP? An empirical investigation. Energy policy, 38(10), 5600-5613.
15. Gu, W., Chu, Z., & Wang, C. (2020). How do different types of energy technological progress affect regional carbon intensity? A spatial panel approach. Environmental Science and Pollution Research, 27, 44494-44509. https://doi.org/10.1007/s11356-020-10327-9
16. Guo, P., Kong, J., Guo, Y., & Liu, X. (2019). Identifying the influencing factors of the sustainable energy transitions in China. Journal of Cleaner Production, 215, 757-766. https://doi.org/10.1016/j.jclepro.2019.01.107
17. Huang, J., Liu, Q., Cai, X., Hao, Y., & Lei, H. (2018). The effect of technological factors on China's carbon intensity: new evidence from a panel threshold model. Energy Policy, 115, 32-42. https://doi.org/10.1016/j.enpol.2017.12.008
18. Huang, J., Luan, B., Cai, X., & Zou, H. (2020a). The role of domestic R&D activities played in carbon intensity: evidence from China. Science of The Total Environment, 708, 135033. https://doi.org/10.1016/j.scitotenv.2019.135033
19. Huang, J., Wang, L., Wang, D., & Lei, H. (2020b). Decreasing China's carbon intensity through research and development activities. Environmental Research, 190, 109947. https://doi.org/10.1016/j.envres.2020.109947
20. Huang, J., Xiang, S., Wang, Y., & Chen, X. (2021). Energy-saving R&D and carbon intensity in China. Energy Economics, 98, 105240. https://doi.org/10.1016/j.eneco.2021.105240
21. IEA. International Energy Agency. (2023). Total R&D Budgets in IEA Countries (2023 prices, Million $). https://www.iea.org/data-and-statistics/data-tools/energy-technology-rdd-budgets-data-explorer
22. IEA (2011). International Energy Agency. IEA Guide to Reporting Energy RD&D Budget/ Expenditure Statistics. https://iea.blob.core.windows.net/assets/751c1fce-72ca-4e01-9528-ab48e561c7c4/RDDManual.pdf
23. Jayanthakumaran, K., & Liu, Y. (2012). Openness and the environmental Kuznets curve: evidence from China. Economic Modelling, 29(3), 566-576. https://doi.org/10.1016/j.econmod.2011.12.011
24. Kartal, M. T., Shahbaz, M., Taşkın, D., Depren, S. K., & Ayhan, F. (2024a). How are energy transition and energy-related R&D investments effective in enabling decarbonization? Evidence from Nordic Countries by novel WLMC model. Journal of Environmental Management, 365, 121664. https://doi.org/10.1016/j.jenvman.2024.121664
25. Kartal, M. T., Taşkın, D., Shahbaz, M., Kirikkaleli, D., & Depren, S. K. (2024b). Role of energy transition in easing energy security risk and decreasing CO2 emissions: Disaggregated level evidence from the USA by quantile-based models. Journal of Environmental Management, 359, 120971.
26. Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of econometrics, 108(1), 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7
27. Li, L., McMurray, A., Li, X., Gao, Y., & Xue, J. (2021). The diminishing marginal effect of R&D input and carbon emission mitigation. Journal of Cleaner Production, 282, 124423. https://doi.org/10.1016/j.jclepro.2020.124423
28. Linnenluecke, M. K., Han, J., Pan, Z., & Smith, T. (2019). How markets will drive the transition to a low carbon economy. Economic Modelling, 77, 42-54. https://doi.org/10.1016/j.econmod.2018.07.010
29. Luo, S., & Zhang, S. (2022). How R&D expenditure intermediate as a new determinants for low carbon energy transition in Belt and Road Initiative economies. Renewable Energy, 197, 101-109. https://doi.org/10.1016/j.renene.2022.06.152
30. Mahmood, H., Irshad, A. U. R., & Tanveer, M. (2024). Impact of Energy Intensity, R&D, Trade Openness, and Financial Market Development on Carbon Productivity in MENA: A Spatial Analysis. International Journal of Energy Research, 2024(1), 3072594. https://doi.org/10.1155/2024/3072594
31. Neal, T. (2014). Panel cointegration analysis with xtpedroni. The Stata Journal, 14(3), 684-692. https://doi.org/10.1177/1536867X1401400312
32. NOAA (2022). National Oceanic and Atmospheric Administration. Carbon dioxide now more than 50% higher than pre-industrial levels.
33. Nwani, C., Bekun, F. V., Gyamfi, B. A., Effiong, E. L., & Alola, A. A. (2023). Toward sustainable use of natural resources: Nexus between resource rents, affluence, energy intensity and carbon emissions in developing and transition economies. In Natural resources forum (Vol. 47, No. 2, pp. 155-176). Oxford, UK: Blackwell Publishing Ltd. https://doi.org/10.1111/1477-8947.12275
34. Opoku, E. E. O., Acheampong, A. O., Dogah, K. E., & Koomson, I. (2024). Energy innovation investment and renewable energy in OECD countries. Energy Strategy Reviews, 54, 101462. https://doi.org/10.1016/j.esr.2024.101462
35. Ouyang, X., & Lin, B. (2014). Impacts of increasing renewable energy subsidies and phasing out fossil fuel subsidies in China. Renewable and sustainable energy reviews, 37, 933-942. https://doi.org/10.1016/j.rser.2014.05.013
36. Pata, U. K., Karlilar, S., & Kartal, M. T. (2024). On the road to sustainable development: the role of ICT and R&D investments in renewable and nuclear energy on energy transition in Germany. Clean Technologies and Environmental Policy, 26(7), 2323-2335. https://doi.org/10.1007/s10098-023-02677-y
37. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and statistics, 61(S1), 653-670.
38. Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Review of Economics and statistics, 83(4), 727-731. https://www.jstor.org/stable/3211767
39. Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric theory, 20(3), 597-625. https://www.jstor.org/stable/3533533
40. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. SSRN. 572504. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=572504
41. Pesaran, M. H., and Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of econometrics, 142(1), 50-93. https://doi.org/10.1016/j.jeconom.2007.05.010
42. Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias‐adjusted LM test of error cross-section independence. The econometrics journal, 11(1), 105-127 https://www.jstor.org/stable/23116064
43. Ren, S., Yuan, B., Ma, X., & Chen, X. (2014). The impact of international trade on China׳ s industrial carbon emissions since its entry into WTO. Energy policy, 69, 624-634. https://doi.org/10.1016/j.enpol.2014.02.032
44. Rentschler, J., & Bazilian, M. (2017). Reforming fossil fuel subsidies: drivers, barriers and the state of progress. Climate Policy, 17(7), 891-914. https://doi.org/10.1080/14693062.2016.1169393
45. Shahbaz, M., Hye, Q. M. A., Tiwari, A. K., & Leitão, N. C. (2013a). Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia. Renewable and sustainable energy reviews, 25, 109-121. https://doi.org/10.1016/j.rser.2013.04.009
46. Shahbaz, M., Tiwari, A. K., & Nasir, M. (2013b). The effects of financial development, economic growth, coal consumption and trade openness on CO2 emissions in South Africa. Energy policy, 61, 1452-1459. https://doi.org/10.1016/j.enpol.2013.07.006
47. Skovgaard, J., & van Asselt, H. (2019). The politics of fossil fuel subsidies and their reform: Implications for climate change mitigation. Wiley Interdisciplinary Reviews: Climate Change, 10(4), e581. https://doi.org/10.1002/wcc.581
48. Su, T., Chen, Y., & Lin, B. (2023). Uncovering the role of renewable energy innovation in China's low carbon transition: evidence from total-factor carbon productivity. Environmental Impact Assessment Review, 101, 107128. https://doi.org/10.1016/j.eiar.2023.107128
49. Tanil, E., Karakaş, M., & Kalabak, A. Y. (2023). Effectiveness of Carbon Pricing Policy: The Case of Nordic Countries. https://doi.org/10.21203/rs.3.rs-3131634/v1
50. Taylor, M. P., & Sarno, L. (1998). The behavior of real exchange rates during the post-Bretton Woods period. Journal of international Economics, 46(2), 281-312. https://doi.org/10.1016/S0022-1996(97)00054-8
51. Wan, X., Jahanger, A., Usman, M., Radulescu, M., Balsalobre-Lorente, D., & Yu, Y. (2022). Exploring the effects of economic complexity and the transition to a clean energy pattern on ecological footprint from the Indian perspective. Frontiers in Environmental Science, 9, 816519. https://doi.org/10.3389/fenvs.2021.816519
52. Xin, L., Sun, H., Xia, X., Wang, H., Xiao, H., & Yan, X. (2022). How does renewable energy technology innovation affect manufacturing carbon intensity in China?. Environmental Science and Pollution Research, 29(39), 59784-59801. https://doi.org/10.1007/s11356-022-20012-8
53. Yanzhe, F., & Ullah, S. (2023). Energy regulation, energy innovation, and carbon intensity nexus in China: A nonlinear perspective. Energy & Environment, 0958305X231188745. https://doi.org/10.1177/0958305X231188745
54. Zhang, X., Shi, X., Khan, Y., Khan, M., Naz, S., Hassan, T., ... & Rahman, T. (2023). The impact of energy intensity, energy productivity and natural resource rents on carbon emissions in Morocco. Sustainability, 15(8), 6720. https://doi.org/10.3390/su15086720
55. Zhang, Y., & Zhang, S. (2018). The impacts of GDP, trade structure, exchange rate and FDI inflows on China's carbon emissions. Energy policy, 120, 347-353. https://doi.org/10.1016/j.enpol.2018.05.056
56. Zhao, Y., Sun, H., Xia, X., & Ma, D. (2023). Can R&D Intensity Reduce Carbon Emissions Intensity? Evidence from China. Sustainability, 15(2), 1619. https://doi.org/10.3390/su15021619
57. Zhu, Z., Liao, H., & Liu, L. (2021). The role of public energy R&D in energy conservation and transition: Experiences from IEA countries. Renewable and Sustainable Energy Reviews, 143, 110978. https://doi.org/10.1016/j.rser.2021.110978