Department of Economics & School of Business, University of Ibadan, Nigeria.
School of World Economy, National Research University Higher School of Economics (HSE), Moscow, Russian Federation, Russia.
J Environ Manage. 2024 Sep;367:121958. doi: 10.1016/j.jenvman.2024.121958. Epub 2024 Aug 1.
One of the main current focuses of global economies and decision-makers is the efficiency of energy utilization in cryptocurrency mining and trading, along with the reduction of associated carbon emissions. Understanding the pattern of Bitcoin's energy consumption and its bubble frequency can greatly enhance policy analysis and decision-making for energy efficiency and carbon emission reduction. This research aims to assess the validity of the random walk hypothesis for Bitcoin's electricity consumption and carbon footprint. We employed both traditional methods (ADF and KPSS) and recently proposed unit root techniques that account for structural breaks and non-linearity in the data series. Our analysis covers daily data from July 2010 to December 2021. The empirical results revealed that traditional unit root techniques did not confirm the stationarity of both bitcoin's electricity consumption and carbon footprint. However, novel structural break (SB) and linearity tests conducted enabled us to discover five SB episodes between 2012 and 2020 and non-linearity of the variables, which informed our application of the newly developed non-linear unit root tests with structural breaks. With the new methods, the results indicated stationarity after accommodating the SB and non-linearity. Furthermore, based on Phillips and Shi (2019)'s test, we identified certain bubble episodes in the bitcoin energy and carbon variables between 2013 and 2021. The major drivers of the bubbles in bitcoin energy consumption and carbon footprint are variables relating to the bitcoin and financial markets activities and risks, including the global economic and political risks. The study's conclusion based on the above findings informs several policy implications drawn for energy and environmental management including the encouragement of green investments in cryptocurrency mining and trading.
当前，全球经济和决策者的主要关注点之一是加密货币挖矿和交易中能源利用的效率以及相关碳排放的减少。了解比特币能源消耗的模式及其泡沫频率可以极大地增强政策分析和能源效率及减排决策。本研究旨在评估比特币耗电量和碳足迹的随机游走假说的有效性。我们采用了传统方法（ADF 和 KPSS）和最近提出的单位根技术，这些技术考虑了数据序列中的结构断裂和非线性。我们的分析涵盖了 2010 年 7 月至 2021 年 12 月的每日数据。实证结果表明，传统的单位根技术并未证实比特币耗电量和碳足迹的平稳性。然而，我们进行的新的结构断裂（SB）和线性检验使我们能够发现 2012 年至 2020 年期间的五个 SB 阶段以及变量的非线性，这为我们应用新开发的带有结构断裂的非线性单位根检验提供了信息。使用新方法，在适应了 SB 和非线性之后，结果表明平稳性。此外，根据 Phillips 和 Shi（2019）的检验，我们在 2013 年至 2021 年期间发现了比特币能源和碳变量中的某些泡沫阶段。比特币能源消耗和碳足迹泡沫的主要驱动因素是与比特币和金融市场活动和风险相关的变量，包括全球经济和政治风险。基于上述发现的研究结论为能源和环境管理提供了一些政策启示，包括鼓励在加密货币挖矿和交易中进行绿色投资。
