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比特币的碳足迹是否持续存在?多重分形证据及政策含义。

Is Bitcoin's Carbon Footprint Persistent? Multifractal Evidence and Policy Implications.

作者信息

Ghosh Bikramaditya, Bouri Elie

机构信息

Symbiosis Institute of Business Management (SIBM), Symbiosis International (Deemed University) (SIU), Bengaluru 560017, India.

School of Business, Lebanese American University, Beirut P.O. Box 13-5053, Lebanon.

出版信息

Entropy (Basel). 2022 May 5;24(5):647. doi: 10.3390/e24050647.

DOI:10.3390/e24050647
PMID:35626532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9141479/
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 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.

摘要

比特币挖矿过程能源消耗巨大,这可能会破坏人们所期望的生态平衡。鉴于比特币持续的高能耗可能会产生永久性的政策影响,我们研究了2017年2月25日至2022年1月25日期间比特币能源消耗指数(BECI)(BECI上限、BECI下限和BECI平均值)的日数据中是否存在长期记忆性。本研究采用分数积分广义自回归条件异方差(FIGARCH)模型和多重分形去趋势波动分析(MFDFA)模型来估计分数积分参数的阶数并计算赫斯特指数(用于衡量长期记忆性),结果表明,尽管在数据序列的一阶差分中观察到均值回复,但遥远的序列观测值存在强烈的自相关性,且在大多数情况下存在长期记忆性。这种长期记忆性的深度存在的证据表明,应用关于使用替代能源进行挖矿的永久性政策是合适的;否则,临时性政策将很快过时。我们还建议用“空间证明”或“权益证明”取代“工作量证明”,尽管这会有一个权衡(可能存在安全漏洞),以减少碳足迹、对比特币挖矿量实施直接税或强制使用碳信用额度来限制对环境的破坏。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9eab/9141479/e36f25cf5301/entropy-24-00647-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9eab/9141479/47f6b675704f/entropy-24-00647-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9eab/9141479/ae43bef44951/entropy-24-00647-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9eab/9141479/e36f25cf5301/entropy-24-00647-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9eab/9141479/47f6b675704f/entropy-24-00647-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9eab/9141479/ae43bef44951/entropy-24-00647-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9eab/9141479/e36f25cf5301/entropy-24-00647-g003.jpg

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