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    Rights statement: This is the peer reviewed version of the following article:Polemis, ML, Tsionas, MG. The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin. Int J Fin Econ. 2023; 28: 1602– 1621. doi.org/10.1002/ijfe.2496 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/ijfe.2496. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

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The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin

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The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin. / Polemis, Michael L.; Tsionas, Mike G.
In: International Journal of Finance and Economics, Vol. 28, No. 2, 30.04.2023, p. 1602-1621.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Polemis ML, Tsionas MG. The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin. International Journal of Finance and Economics. 2023 Apr 30;28(2):1602-1621. Epub 2021 Feb 2. doi: 10.1002/ijfe.2496

Author

Polemis, Michael L. ; Tsionas, Mike G. / The environmental consequences of blockchain technology : A Bayesian quantile cointegration analysis for Bitcoin. In: International Journal of Finance and Economics. 2023 ; Vol. 28, No. 2. pp. 1602-1621.

Bibtex

@article{19a91aefa39a4e8da789bb0cbc502ea6,
title = "The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin",
abstract = "In recent years, there is a widespread belief among researchers and academicians that Bitcoin usage is imposing an additional burden on the environment inducing climate change. Although several studies have focussed on issues related to the energy consumption of the basic cryptocurrencies, an open question remains regarding the environmental depiction of Bitcoin. By resorting to Bayesian analysis and quantile cointegrated vector autoregression (CQVAR), this study seeks to disentangle the driving forces that shape the carbon footprint of Bitcoin. The sample used in the empirical analysis consists of a daily panel dataset covering 50 countries over the period 2016–2018. The empirical findings corroborate a causal effect between the use of Bitcoin and its underlying carbon dioxide emissions generated by the increasing energy load. The CQVAR is associated with positive marginal posterior means for most of the covariates of the model across all the estimated quantiles. In contrast, there is a negative and statistically significant relationship between Bitcoin miner's revenue and carbon emissions, uncovering a multimodal distribution pattern of the marginal posterior densities which is stronger at higher than in lower quantiles. This finding suggests that the lower (higher) miner's Bitcoin revenues, the more abrupt (gradual) the effect on environmental degradation. Therefore, a sustainable energy strategy focussing on the penetration of renewable energy sources along with the use of energy-efficient mining hardware will alleviate the carbon footprint of Bitcoin.",
keywords = "Economics and Econometrics, Finance, Accounting",
author = "Polemis, {Michael L.} and Tsionas, {Mike G.}",
note = "This is the peer reviewed version of the following article:Polemis, ML, Tsionas, MG. The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin. Int J Fin Econ. 2023; 28: 1602– 1621. doi.org/10.1002/ijfe.2496 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/ijfe.2496. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. ",
year = "2023",
month = apr,
day = "30",
doi = "10.1002/ijfe.2496",
language = "English",
volume = "28",
pages = "1602--1621",
journal = "International Journal of Finance and Economics",
issn = "1076-9307",
publisher = "John Wiley and Sons Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - The environmental consequences of blockchain technology

T2 - A Bayesian quantile cointegration analysis for Bitcoin

AU - Polemis, Michael L.

AU - Tsionas, Mike G.

N1 - This is the peer reviewed version of the following article:Polemis, ML, Tsionas, MG. The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin. Int J Fin Econ. 2023; 28: 1602– 1621. doi.org/10.1002/ijfe.2496 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/ijfe.2496. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2023/4/30

Y1 - 2023/4/30

N2 - In recent years, there is a widespread belief among researchers and academicians that Bitcoin usage is imposing an additional burden on the environment inducing climate change. Although several studies have focussed on issues related to the energy consumption of the basic cryptocurrencies, an open question remains regarding the environmental depiction of Bitcoin. By resorting to Bayesian analysis and quantile cointegrated vector autoregression (CQVAR), this study seeks to disentangle the driving forces that shape the carbon footprint of Bitcoin. The sample used in the empirical analysis consists of a daily panel dataset covering 50 countries over the period 2016–2018. The empirical findings corroborate a causal effect between the use of Bitcoin and its underlying carbon dioxide emissions generated by the increasing energy load. The CQVAR is associated with positive marginal posterior means for most of the covariates of the model across all the estimated quantiles. In contrast, there is a negative and statistically significant relationship between Bitcoin miner's revenue and carbon emissions, uncovering a multimodal distribution pattern of the marginal posterior densities which is stronger at higher than in lower quantiles. This finding suggests that the lower (higher) miner's Bitcoin revenues, the more abrupt (gradual) the effect on environmental degradation. Therefore, a sustainable energy strategy focussing on the penetration of renewable energy sources along with the use of energy-efficient mining hardware will alleviate the carbon footprint of Bitcoin.

AB - In recent years, there is a widespread belief among researchers and academicians that Bitcoin usage is imposing an additional burden on the environment inducing climate change. Although several studies have focussed on issues related to the energy consumption of the basic cryptocurrencies, an open question remains regarding the environmental depiction of Bitcoin. By resorting to Bayesian analysis and quantile cointegrated vector autoregression (CQVAR), this study seeks to disentangle the driving forces that shape the carbon footprint of Bitcoin. The sample used in the empirical analysis consists of a daily panel dataset covering 50 countries over the period 2016–2018. The empirical findings corroborate a causal effect between the use of Bitcoin and its underlying carbon dioxide emissions generated by the increasing energy load. The CQVAR is associated with positive marginal posterior means for most of the covariates of the model across all the estimated quantiles. In contrast, there is a negative and statistically significant relationship between Bitcoin miner's revenue and carbon emissions, uncovering a multimodal distribution pattern of the marginal posterior densities which is stronger at higher than in lower quantiles. This finding suggests that the lower (higher) miner's Bitcoin revenues, the more abrupt (gradual) the effect on environmental degradation. Therefore, a sustainable energy strategy focussing on the penetration of renewable energy sources along with the use of energy-efficient mining hardware will alleviate the carbon footprint of Bitcoin.

KW - Economics and Econometrics

KW - Finance

KW - Accounting

U2 - 10.1002/ijfe.2496

DO - 10.1002/ijfe.2496

M3 - Journal article

VL - 28

SP - 1602

EP - 1621

JO - International Journal of Finance and Economics

JF - International Journal of Finance and Economics

SN - 1076-9307

IS - 2

ER -