Abstract
Teknolojik gelişme, sanayileşme ve küreselleşmedeki artış dijital dönüşümün hızlı yaşanmasına sebep olmaktadır. Bu dijital dönüşümle birlikte kripto para kullanımı da artmaktadır. Kripto para kullanımında oldukça yüksek düzeyde enerji tüketimi gerçekleşmektedir. Enerji tüketimindeki bu yoğun artış da beraberinde karbon salınımındaki (CO2) artışı getirmektedir. Küresel ısınmanın ve çevre kirliliğinin her geçen gün insan yaşamı için tehdit oluşturduğu günümüzde, kripto paraların tükettiği ciddi enerji miktarı da akademik yazında önemli bir eleştiri konusu olmuştur. Bu çalışmada kripto paraların ticaretinde kullanılan enerjinin miktarı ve bunun sebep olduğu çevre kirliliğine dikkat çekilmek istenmiştir. Çalışmada kripto para işlem akışının en çok olduğu Amerika Birleşik Devletleri, Seyşeller, Güney Kore, Japonya ve Birleşik Krallık ülkelerinin 2013-2021 yılları arasındaki verileri kullanılmıştır. Bu ülkeler için ekonomik büyüme, enerji tüketimi ve kripto para ticareti ile çevre kirliliğinin göstergesi olan CO2 arasındaki ilişki panel veri analizi ile test edilmiştir. FGLS testi kullanılarak yapılan analiz sonucunda ilgili ülkeler için enerji tüketiminin ve kripto para işlemlerinin CO2’yi arttırdığı tespit edilirken GDP ile CO2arasında anlamlı bir ilişki bulunamamıştır.
Keywords
Çevresel sürdürülebilirlik Enerji tüketimi Kripto para ticareti FGLS (Uygun Genelleştirilmiş En Küçük Kareler) Panel veri
References
- Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., McCallum, P., & Peacock, A. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and sustainable energy reviews, 100, 143-174. doi:10.1016/j.rser.2018.10.014
- Badea, L., & Mungiu-Pupӑzan, M. C. (2021). The economic and environmental impact of Bitcoin. IEEE access, 9, 48091-48104.
- 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. doi:10.2307/2297111
- Brosens, T. (2017). Why bitcoin transactions are more expensive than you think, ING. Economic and Financial Analysis, Global Economics.
- Crystal Blockchain (2023). https://crystalblockchain.com/geography-of-international-blockchain-transactions/ (Erişim 01.10.2023)
- de Vries, A. (2018). Bitcoin's growing energy problem. Joule, 2(5), 801-805. doi:10.1016/j.resconrec.2021.105901 Dilek, Ş., & Furuncu, Y. (2019). Bitcoin mining and its environmental effects. Atatürk üniversitesi iktisadi ve idari bilimler dergisi, 33(1), 91-106.
- Ekim, N., Acar, M., & Uçan, O. (2019). Entelektüel sermayenin finans sektöründe değer yaratmadaki rolü: Türk bankacılık sektöründe bir araştırma. Verimlilik dergisi, (4), 37-63.
- Khan, H., Weili, L., Khan, I., & Khamphengxay, S. (2021). Renewable energy consumption, trade openness, and environmental degradation: a panel data analysis of developing and developed countries. Mathematical problems in engineering, 2021, 1-13. doi:10.1155/2021/6691046
- Korkmaz, T., Yıldız, B., & Gökbulut, R. (2010). FVFM’nin İMKB ulusal 100 endeksindeki geçerliliğinin panel veri analizi ile test edilmesi. İstanbul üniversitesi işletme fakültesi dergisi, 39(1), 95-105.
- Küfeoğlu, S. & Özkuran, M. (2019). Bitcoin mining: A global review of energy and power demand. Energy research & social science, 58, 101273. doi:10.1016/j.erss.2019.101273
- Malmo, C, (2017), One bitcoin transaction consumes as much energy as your house uses in a week, Vice (blog), 2017 (November, 1).
- Mengelkamp, E., Gärttner, J., Rock, K., Kessler, S., Orsini, L., & Weinhardt, C. (2018). Designing microgrid energy markets: A case study: The Brooklyn Microgrid. Applied energy, 210, 870-880. doi:10.1016/j.apenergy.2017.06.054
- Miśkiewicz, R., Matan, K., & Karnowski, J. (2022). The role of crypto trading in the economy, renewable energy consumption and ecological degradation. Energies, 15(10), 3805. doi:10.3390/en15103805
- Mora, C., Rollins, R. L., Taladay, K., Kantar, M. B., Chock, M. K., Shimada, M., & Franklin, E. C. (2018). Bitcoin emissions alone could push global warming above 2 C. Nature Climate Change, 8(11), 931-933.
- Oğuz, S., & Sökmen, A.G. (2020). Araştırma geliştirme harcamalarının yüksek teknolojili ürün ihracatına etkisi: OECD ülkeleri üzerine bir panel veri analizi. International journal of economic & administrative studies, 2020(27), 209-221.
- Parks, R.W. (1967). Efficient estimation of a system of regression equations when disturbances are both serially and contemporaneously correlated. Journal of the american statistical association, 62(318), 500-509. doi:10.2307/2283977
- Pesaran, M.H. (2004). General diagnostic tests for cross section dependence in panels. https://www.econstor.eu/bitstream/10419/18868/1/cesifo1_wp1229.pdf (Erişim 01.10.2023)
- 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. doi:10.1111/j.1368-423X.2007.00227.x
- Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of econometrics, 142(1), 50-93. doi:10.1016/j.jeconom.2007.05.010
- Schinckus, C., Canh, N.P., & Ling, C. H. (2020). Crypto-currencies trading and energy consumption. International Journal of energy economics and policy, 10(3), 355-364. doi:10.32479/ijeep.9258
- Stoll, C., Klaaßen, L., & Gallersdörfer, U. (2019). The carbon footprint of bitcoin. Joule, 3(7), 1647-1661. doi:j.joule.2019.05.012
- Swamy, P.A. (1970). Efficient inference in a random coefficient regression model. Econometrica: journal of the econometric society, 311-323. doi:10.2307/1913012
- Truby, J. (2018). Decarbonizing Bitcoin: Law and policy choices for reducing the energy consumption of Blockchain technologies and digital currencies. Energy research & social science, 44, 399-410. doi:10.1016/j.erss.2018.06.009
- Poi, B., & Wiggins, V. (2001). Testing for panel-level heteroskedasticity and autocorrelation. StataCorp LP. https://www.stata.com/support/faqs/statistics/panel-level-heteroskedasticity-and-autocorrelation/ (Erişim 01.10.2023)
- Wooldridge, J.M. (2002). Econometric analysis of cross section and panel data. Cambridge MA: MIT Press.
Abstract
The increase in technological development, industrialization and globalization causes rapid digital transformation. With this digital transformation, the use of cryptocurrency is also increasing. There is a very high level of energy consumption in the use of cryptocurrency. This intense increase in energy consumption brings with it an increase in carbon emissions (CO2). In today's world where global warming and environmental pollution pose a threat to human life every day, the significant amount of energy consumed by cryptocurrencies has been an important criticism in the academic literature. This study aims to draw attention to the amount of energy used in the trading of cryptocurrencies and the environmental pollution caused by this. In the study, the data of the United States, Seychelles, South Korea, Japan and the United Kingdom, which have the highest cryptocurrency transaction flows, between 2013-2021 are used. For these countries, the relationship between economic growth, energy consumption and cryptocurrency trade and CO2, which is an indicator of environmental pollution, was tested with panel data analysis. As a result of the analysis using the FGLS test, it was found that energy consumption and cryptocurrency transactions increase CO2 for the relevant countries, while no significant relationship was found between GDP and CO2.
Keywords
Environmental sustainability Energy consumption Cryptocurrency trading FGLS (Feasible Generalized Least Square) Panel data
References
- Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., McCallum, P., & Peacock, A. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and sustainable energy reviews, 100, 143-174. doi:10.1016/j.rser.2018.10.014
- Badea, L., & Mungiu-Pupӑzan, M. C. (2021). The economic and environmental impact of Bitcoin. IEEE access, 9, 48091-48104.
- 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. doi:10.2307/2297111
- Brosens, T. (2017). Why bitcoin transactions are more expensive than you think, ING. Economic and Financial Analysis, Global Economics.
- Crystal Blockchain (2023). https://crystalblockchain.com/geography-of-international-blockchain-transactions/ (Erişim 01.10.2023)
- de Vries, A. (2018). Bitcoin's growing energy problem. Joule, 2(5), 801-805. doi:10.1016/j.resconrec.2021.105901 Dilek, Ş., & Furuncu, Y. (2019). Bitcoin mining and its environmental effects. Atatürk üniversitesi iktisadi ve idari bilimler dergisi, 33(1), 91-106.
- Ekim, N., Acar, M., & Uçan, O. (2019). Entelektüel sermayenin finans sektöründe değer yaratmadaki rolü: Türk bankacılık sektöründe bir araştırma. Verimlilik dergisi, (4), 37-63.
- Khan, H., Weili, L., Khan, I., & Khamphengxay, S. (2021). Renewable energy consumption, trade openness, and environmental degradation: a panel data analysis of developing and developed countries. Mathematical problems in engineering, 2021, 1-13. doi:10.1155/2021/6691046
- Korkmaz, T., Yıldız, B., & Gökbulut, R. (2010). FVFM’nin İMKB ulusal 100 endeksindeki geçerliliğinin panel veri analizi ile test edilmesi. İstanbul üniversitesi işletme fakültesi dergisi, 39(1), 95-105.
- Küfeoğlu, S. & Özkuran, M. (2019). Bitcoin mining: A global review of energy and power demand. Energy research & social science, 58, 101273. doi:10.1016/j.erss.2019.101273
- Malmo, C, (2017), One bitcoin transaction consumes as much energy as your house uses in a week, Vice (blog), 2017 (November, 1).
- Mengelkamp, E., Gärttner, J., Rock, K., Kessler, S., Orsini, L., & Weinhardt, C. (2018). Designing microgrid energy markets: A case study: The Brooklyn Microgrid. Applied energy, 210, 870-880. doi:10.1016/j.apenergy.2017.06.054
- Miśkiewicz, R., Matan, K., & Karnowski, J. (2022). The role of crypto trading in the economy, renewable energy consumption and ecological degradation. Energies, 15(10), 3805. doi:10.3390/en15103805
- Mora, C., Rollins, R. L., Taladay, K., Kantar, M. B., Chock, M. K., Shimada, M., & Franklin, E. C. (2018). Bitcoin emissions alone could push global warming above 2 C. Nature Climate Change, 8(11), 931-933.
- Oğuz, S., & Sökmen, A.G. (2020). Araştırma geliştirme harcamalarının yüksek teknolojili ürün ihracatına etkisi: OECD ülkeleri üzerine bir panel veri analizi. International journal of economic & administrative studies, 2020(27), 209-221.
- Parks, R.W. (1967). Efficient estimation of a system of regression equations when disturbances are both serially and contemporaneously correlated. Journal of the american statistical association, 62(318), 500-509. doi:10.2307/2283977
- Pesaran, M.H. (2004). General diagnostic tests for cross section dependence in panels. https://www.econstor.eu/bitstream/10419/18868/1/cesifo1_wp1229.pdf (Erişim 01.10.2023)
- 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. doi:10.1111/j.1368-423X.2007.00227.x
- Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of econometrics, 142(1), 50-93. doi:10.1016/j.jeconom.2007.05.010
- Schinckus, C., Canh, N.P., & Ling, C. H. (2020). Crypto-currencies trading and energy consumption. International Journal of energy economics and policy, 10(3), 355-364. doi:10.32479/ijeep.9258
- Stoll, C., Klaaßen, L., & Gallersdörfer, U. (2019). The carbon footprint of bitcoin. Joule, 3(7), 1647-1661. doi:j.joule.2019.05.012
- Swamy, P.A. (1970). Efficient inference in a random coefficient regression model. Econometrica: journal of the econometric society, 311-323. doi:10.2307/1913012
- Truby, J. (2018). Decarbonizing Bitcoin: Law and policy choices for reducing the energy consumption of Blockchain technologies and digital currencies. Energy research & social science, 44, 399-410. doi:10.1016/j.erss.2018.06.009
- Poi, B., & Wiggins, V. (2001). Testing for panel-level heteroskedasticity and autocorrelation. StataCorp LP. https://www.stata.com/support/faqs/statistics/panel-level-heteroskedasticity-and-autocorrelation/ (Erişim 01.10.2023)
- Wooldridge, J.M. (2002). Econometric analysis of cross section and panel data. Cambridge MA: MIT Press.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Microeconomics (Other) |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 28, 2023 |
| Submission Date | November 3, 2023 |
| Acceptance Date | December 13, 2023 |
| Published in Issue | Year 2023 Volume: 6 Issue: 2 |
The author(s) is (are) the sole responsible for the opinion and views stated in the articles.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.