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Keywords = Bitcoin carbon footprint

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23 pages, 8711 KiB  
Review
Beyond Bitcoin: Evaluating Energy Consumption and Environmental Impact across Cryptocurrency Projects
by Ali Khosravi and Fanni Säämäki
Energies 2023, 16(18), 6610; https://doi.org/10.3390/en16186610 - 14 Sep 2023
Cited by 12 | Viewed by 6604
Abstract
Since their inception with Bitcoin in the late 2000s, cryptocurrencies have grown exponentially, reshaping traditional financial paradigms. This transformative journey, while innovative, brings forth pressing concerns about their energy consumption and carbon footprint. While many studies tend to zoom in on Bitcoin, this [...] Read more.
Since their inception with Bitcoin in the late 2000s, cryptocurrencies have grown exponentially, reshaping traditional financial paradigms. This transformative journey, while innovative, brings forth pressing concerns about their energy consumption and carbon footprint. While many studies tend to zoom in on Bitcoin, this paper broadens the perspective by evaluating energy consumption across various cryptocurrencies. We analyze nine cryptocurrency projects, chosen for their market value, technology, and data availability. These span a spectrum from pioneering to emerging digital coins, offering a holistic view of the crypto realm. To contextualize, we juxtapose the energy usage of these digital currencies with traditional payment means like Visa and Mastercard. Our analysis shows vast differences in energy use among cryptocurrencies, largely tied to their consensus algorithms. Notably, while Bitcoin stands out as highly energy-intensive, several newer digital currencies have energy footprints mirroring those of conventional payment methods. Additionally, CO2 emissions estimation presents challenges due to variances in miner locations and regional energy sources, with potential higher emissions if concentrated in carbon-intensive regions like China. Nonetheless, the silver lining emerges as many cryptocurrencies, especially those beyond Bitcoin, register considerably lower CO2 emissions. By moving the lens beyond Bitcoin, this paper paints a more nuanced picture of the environmental ramifications of the crypto world. Full article
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21 pages, 1585 KiB  
Review
Bitcoin’s Carbon Footprint Revisited: Proof of Work Mining for Renewable Energy Expansion
by Juan Ignacio Ibañez and Alexander Freier
Challenges 2023, 14(3), 35; https://doi.org/10.3390/challe14030035 - 8 Aug 2023
Cited by 9 | Viewed by 26990
Abstract
While blockchain and distributed ledger technology offer immense potential for applications in transparency, security, efficiency, censorship resistance, and more, they have been criticized due to the energy-intensive nature of the proof of work consensus algorithm, particularly in the context of Bitcoin mining. We [...] Read more.
While blockchain and distributed ledger technology offer immense potential for applications in transparency, security, efficiency, censorship resistance, and more, they have been criticized due to the energy-intensive nature of the proof of work consensus algorithm, particularly in the context of Bitcoin mining. We systematically explore the state-of-the-art regarding the relationship between Bitcoin mining and grid decarbonization. We specifically focus on the role of flexible load response through proof of work mining as a potential contributor to renewable energy penetration and net decarbonization of the energy grid. The existing literature has not comprehensively examined this area, leading to conflicting views. We address the gap, analyzing the capabilities and limitations of Bitcoin mining in providing flexible load response services. Our findings show that renewable-based mining could potentially drive a net-decarbonizing effect on energy grids, although key adaptations in mining practices are needed to fully realize this potential. Overall, the paper suggests a re-evaluation of the environmental impact of Bitcoin mining, highlighting its potential role as a facilitator for renewable energy expansion, and decarbonization more broadly. Full article
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12 pages, 1175 KiB  
Article
Is Bitcoin’s Carbon Footprint Persistent? Multifractal Evidence and Policy Implications
by Bikramaditya Ghosh and Elie Bouri
Entropy 2022, 24(5), 647; https://doi.org/10.3390/e24050647 - 5 May 2022
Cited by 24 | Viewed by 2858
Abstract
The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of [...] Read more.
The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of ‘proof-of-work’ with ‘proof-of-space’ or ‘proof-of-stake’, although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage. Full article
(This article belongs to the Special Issue Signatures of Maturity in Cryptocurrency Market)
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18 pages, 7721 KiB  
Article
Based on the Time-Spatial Power-Based Cryptocurrency Miner Driving Force Model, Establish a Global CO2 Emission Prediction Framework after China Bans Cryptocurrency
by Xuejia Sang, Xiaopeng Leng, Linfu Xue and Xiangjin Ran
Sustainability 2022, 14(9), 5332; https://doi.org/10.3390/su14095332 - 28 Apr 2022
Cited by 9 | Viewed by 4685
Abstract
The energy consumption and carbon footprint of cryptocurrencies have always been a popular topic. However, most of the existing studies only focus on one cryptocurrency, Bitcoin, and there is a lack of long-term monitoring studies that summarize all cryptocurrencies. By constructing a time [...] Read more.
The energy consumption and carbon footprint of cryptocurrencies have always been a popular topic. However, most of the existing studies only focus on one cryptocurrency, Bitcoin, and there is a lack of long-term monitoring studies that summarize all cryptocurrencies. By constructing a time series hash rate/power model, this research obtained the 10-year time series data on energy consumption dataset of global top-25 cryptocurrencies for the first time. Both the temporal coverage and the spatiotemporal resolution of the data exceed previous studies. The results show that Bitcoin’s power consumption only accounts for 58% of the top-25 cryptocurrencies. After China bans cryptocurrencies, the conservative change in global CO2 emissions from 2020 will be between −0.4% and 4.4%, and Central Asian countries such as Kazakhstan are likely to become areas of rapid growth in carbon emissions from cryptocurrencies. Full article
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30 pages, 10926 KiB  
Article
Machine Learning the Carbon Footprint of Bitcoin Mining
by Hector F. Calvo-Pardo, Tullio Mancini and Jose Olmo
J. Risk Financial Manag. 2022, 15(2), 71; https://doi.org/10.3390/jrfm15020071 - 5 Feb 2022
Cited by 9 | Viewed by 8144
Abstract
Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on [...] Read more.
Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on a novel bottom-up approach, which (i) conform with recent estimates, (ii) lie within the economic model bounds while (iii) delivering much narrower prediction intervals and yet (iv) raise alarming concerns, given recent evidence (e.g., from climate–weather integrated models). We demonstrate how machine learning methods can contribute to not-for-profit pressing societal issues, such as global warming, where data complexity and availability can be overcome. Full article
(This article belongs to the Section Sustainability and Finance)
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