THE DYNAMIC RELATIONSHIP BETWEEN BITCOIN MINING AND CARBON EMISSIONS: CONNECTEDNESS ANALYSIS USING THE TVP-VAR METHOD

Yıl 2025, Cilt: 13 Sayı: Özel Sayı, 205 - 218, 31.12.2025

Mehmet Çınar , Özge Özbek

https://doi.org/10.52122/nisantasisbd.1808314

Öz

Bitcoin, the cryptocurrency with the highest transaction volume, experiences rising network activity that increases transaction fees and makes mining activities more profitable. The growing profitability of mining encourages additional miners to join the network, which in turn raises overall energy consumption and carbon emissions. This study examines the connectedness between Bitcoin mining and carbon emissions over the period from February 2017 to November 2024. Hash rate, carbon emissions, electricity consumption, and energy consumption are employed as indicators of Bitcoin mining activity. Using both the static Diebold–Yılmaz spillover index and the time-varying parameter TVP-VAR framework, the analysis reveals that Bitcoin mining significantly influences carbon emission levels. In particular, increases in the hash rate require more computational power, leading to higher electricity consumption and consequently greater carbon emissions. These findings highlight the need for policymakers in the fields of energy and environmental economics to develop sustainable strategies, including the transition to clean energy in mining, the implementation of carbon taxation, and the promotion of green blockchain solutions.

Anahtar Kelimeler

Diebold-Yılmaz , TVP-VAR , Bitcoin , Carbon Emission , Connectedness

BİTCOİN MADENCİLİĞİ VE KARBON EMİSYONU ARASINDAKİ DİNAMİK İLİŞKİ: TVP-VAR YÖNTEMİYLE BAĞLANTILILIK ANALİZİ

Öz

En yüksek işlem hacmine sahip kripto varlık olan Bitcoin’deki işlem yoğunluğunun artması, ağ üzerindeki işlem ücretlerini yükselterek madenciliği daha kârlı hâle getirmektedir. Madenciliğin kârlılığındaki bu artış yeni madencilerin ağa katılımını teşvik etmekte ve buna bağlı olarak enerji tüketimi ile karbon emisyonu düzeylerini artırmaktadır. Bu çalışmada, Şubat 2017–Kasım 2024 döneminde Bitcoin madenciliği ile karbon emisyonu arasındaki bağlantılılık incelenmiştir. Bitcoin madenciliğini temsilen hash oranı, karbon emisyonu, elektrik tüketimi ve enerji tüketimi değişkenleri kullanılmıştır. Sabit parametreli Diebold–Yılmaz yayılım endeksi ile zamanla değişen parametreleri dikkate alan TVP-VAR yöntemlerinin birlikte kullanıldığı analizler, Bitcoin madencilik faaliyetlerinin karbon emisyonları üzerinde belirgin bir etkiye sahip olduğunu göstermektedir. Özellikle hash oranındaki artış daha fazla donanım kullanımına ve dolayısıyla elektrik tüketimine yol açmakta; artan enerji talebi de karbon emisyonlarını yükseltmektedir. Bu nedenle politika yapıcıların temiz enerji kullanımının teşvik edilmesi, karbon vergisi uygulanması ve yeşil blok zinciri teknolojilerinin geliştirilmesi gibi sürdürülebilir stratejilere yönelmesi önem arz etmektedir.

Anahtar Kelimeler

Diebold-Yılmaz , TVP-VAR , Bitcoin , Karbon Emisyonu , Bağlantılılık

Kaynakça

• Antonakakis, N. ve Gabauer, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR. MPRA Paper No. 78282. Munich Personal RePEc Archive. https://mpra.ub.uni-muenchen.de/78282/
• Antonakakis, N., Gabauer, D. ve Gupta, R. (2019). International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression. International Review of Financial Analysis, 65, 101382. https://doi.org/10.1016/j.irfa.2019.101382
• Antonokakis, N., Chatziantoniou, I. ve Gabauer, D. (2020) Refined measures of dynamic connectedness based on time-varying parameter vector autoregression. Risk and Financial Managentment, 13(4), 84. httpts://doi.org/10.3390/jrfm13040084
• Antonopoulos, A. M. (2014). Mastering Bitcoin: Unlocking digital cryptocurrencies. O'Reilly Media.
• Attarzadeh, A. ve Balcilar, M. (2022). On the dynamic return and volatility connectedness of cryptocurrency, crude oil, clean energy, and stock markets: A time-varying analysis. Environmental Science and Pollution Research, 29, 65185–65196. https://doi.org/10.1007/s11356-022-20115-2
• Bajra, U. Q., Rogova, E. ve Avdiaj, S. (2024). Cryptocurrency blockchain and its carbon footprint: Anticipating future challenges. Technology in Society, 77, 102571. https://doi.org/10.1016/j.techsoc.2024.102571
• Blockchain.com. (2009, January 12). Bitcoin block 170. Retrieved April 19, 2025, from https://www.blockchain.com/explorer/blocks/btc/170
• Cambridge Centre for Alternative Finance. (2025). CBECI: Comparisons. Cambridge Bitcoin Electricity Consumption Index. Retrieved May 2, 2025, from https://ccaf.io/cbnsi/cbeci/comparisons
• Ciaian, P., Rajcaniova, M. ve Kancs, D. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799–1815. https://doi.org/10.1080/00036846.2015.1109038
• CoinGecko, (2025). Bitcoin Price: BTC Live Price Chart, Market Cap & News Today. CoinGecko. Retrieved May 2, 2025, from https://www.coingecko.com/en/coins/bitcoin
• Dias, R., Alexandre, P., Teixeira, N. ve Chambino, M. (2023). Clean energy stocks: Resilient safe havens in the volatility of dirty cryptocurrencies. Energies, 16(13), 5232. https://doi.org/10.3390/en16135232
• Dogan, E., Majeed, M. T. ve Luni, T. (2022). Are clean energy and carbon emission allowances caused by Bitcoin? A novel time-varying method. Journal of Cleaner Production, 347, 131089. https://doi.org/10.1016/j.jclepro.2022.131089
• Engle, R. F. (2020). The econometrics of ultra-high-frequency data. Econometrica, 68(1), 1-22.
• Fauzi, M. A., Paiman, N. ve Othman, Z. (2020). Bitcoin and cryptocurrency: Challenges, opportunities and future works. Journal of Asian Finance, Economics and Business, 7(8), 695–704. https://doi.org/10.13106/jafeb.2020.vol7.no8.695
• Forbes. (2025). Cryptocurrency Prices, Market Cap and Charts. Forbes. Retrieved May 2, 2025, from https://www.forbes.com/digital-assets/crypto-prices/
• Gabauer, D. ve Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 171, 63–71. https://doi.org/10.1016/j.econlet.2018.07.007
• Gabauer, D. ve Gupta, R. (2020). Spillovers across macroeconomic, financial and real estate uncertainties: A time-varying approach. Structural Change and Economic Dynamics, 52, 167–173. https://doi.org/10.1016/j.strueco.2019.09.009
• Gatabazi, P., Kabera, G., Mba, J. C., Pindza, E. ve Melesse, S. F. (2022). Cryptocurrencies and tokens lifetime analysis from 2009 to 2021. Economies, 10(3), 60. https://doi.org/10.3390/economies10030060
• Ghosh, B. ve Bouri, E. (2022). Is Bitcoin’s carbon footprint persistent? Multifractal evidence and policy implications. Entropy, 24(5), 647. https://doi.org/10.3390/e24050647
• Howson, P. (2019). Tackling climate change with blockchain. Nature Climate Change, 9(9), 644–645. https://doi.org/10.1038/s41558-019-0567-9
• Jirou, I., Jebabli, I. ve Lahiani, A. (2025). A hybrid deep learning model for cryptocurrency returns forecasting: Comparison of the performance of financial markets and impact of external variables. Research in International Business and Finance, 73, 102575. https://doi.org/10.1016/j.ribaf.2024.102575
• Khosravi, A. ve Säämäki, F. (2023). Beyond Bitcoin: Evaluating energy consumption and environmental impact across cryptocurrency projects. Energies, 16(18), 6610. https://doi.org/10.3390/en16186610
• Kohli, V., Chakravarty, S., Chamola, V., Sangwan, K. S. ve Zeadally, S. (2023). An analysis of energy consumption and carbon footprints of cryptocurrencies and possible solutions. Digital Communications and Networks, 9(1), 79–89. https://doi.org/10.1016/j.dcan.2022.06.017
• Koop, G., Pesaran, M. H. ve Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4
• Long, S. (C.), Lucey, B., Zhang, D. ve Zhang, Z. (2023). Negative elements of cryptocurrencies: Exploring the drivers of Bitcoin carbon footprints. Finance Research Letters, 58, 104031. https://doi.org/10.1016/j.frl.2023.104031
• Mora, C., Rollins, R. L., Taladay, K., Kantar, M. B., Chock, M. K., Shimada, M. ve Franklin, E. C. (2019). Response to critiques of ‘Bitcoin emissions alone could push global warming above 2 °C’. Nature Climate Change, 9(9), 658–659. https://doi.org/10.1038/s41558-019-0538-1
• Nakamoto, S. (2009). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf
• Nerurkar, P., Patel, D., Busnel, Y., Ludinard, R., Kumari, S. ve Khan, M. K. (2021). Dissecting bitcoin blockchain: Empirical analysis of bitcoin network (2009–2020). Journal of Network and Computer Applications, 177, 102940. https://doi.org/10.1016/j.jnca.2020.102940
• Papp, A., Almond, D. ve Zhang, S. (2023). Bitcoin and carbon dioxide emissions: Evidence from daily production decisions. Journal of Public Economics, 227, 105003. https://doi.org/10.1016/j.jpubeco.2023.105003
• Parino, F., Beiró, M. G. ve Gauvin, L. (2018). Analysis of the Bitcoin blockchain: Socio-economic factors behind the adoption. EPJ Data Science, 7(1), 38. https://doi.org/10.1140/epjds/s13688-018-0170-8
• Pesaran, M. H. ve Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
• Pham, L., Huynh, T. L. D. ve Hanif, W. (2023). Time-varying asymmetric spillovers among cryptocurrency, green and fossil-fuel investments. Global Finance Journal, 58, 100891. https://doi.org/10.1016/j.gfj.2023.100891
• Ren, B. ve Lucey, B. (2022). A clean, green haven? Examining the relationship between clean energy, clean and dirty cryptocurrencies. Energy Economics, 109, 105951. https://doi.org/10.1016/j.eneco.2022.105951
• Sapra, N., Shaikh, I., Roubaud, D., Asadi, M. ve Grebinevych, O. (2024). Uncovering Bitcoin’s electricity consumption relationships with volatility and price: Environmental repercussions. Journal of Environmental Management, 356, 120528. https://doi.org/10.1016/j.jenvman.2024.120528
• Yang, B., Sun, Y. ve Wang, S. (2020). A novel two-stage approach for cryptocurrency analysis. International Review of Financial Analysis, 72, 101567. https://doi.org/10.1016/j.irfa.2020.101567
• Yousaf, I., Riaz, Y. ve Goodell, J. W. (2023). Energy cryptocurrencies: Assessing connectedness with other asset classes. Finance Research Letters, 52, 103389. https://doi.org/10.1016/j.frl.2022.103389
• Yuan, X., Su, C.-W. ve Dumitrescu Peculea, A. (2022). Dynamic linkage of the bitcoin market and energy consumption: An analysis across time. Energy Strategy Reviews, 44, 100976. https://doi.org/10.1016/j.esr.2022.100976
• Zhang, D., Chen, X. H., Lau, C. K. M. ve Xu, B. (2023). Implications of cryptocurrency energy usage on climate change. Technological Forecasting and Social Change, 187, 122219. https://doi.org/10.1016/j.techfore.2022.122219
• Zheng, P., Luo, X. ve Zheng, Z. (2023). BSHUNTER: Detecting and tracing defects of Bitcoin scripts. In Proceedings of the 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) (pp. 307–316). IEEE. https://doi.org/10.1109/ICSE48619.2023.00037