Identifying the key drivers of Bitcoin's emissions

Abstract

This study examines the environmental impact of blockchain technology operating under the Proof-of-Work (PoW) algorithm, with a focus on Bitcoin's (BTC) carbon footprint. Utilizing a comprehensive dataset comprising 2895 daily observations from 2014 to 2021, we analyze key mining-related variables—miner efficiency, miner revenues, total BTC mined, mining difficulty, and hash rate—through the application of a Bayesian Vector Autoregression (BVAR) model to evaluate their effects on CO2 emissions over time. The primary objective is to identify the main determinants influencing BTC's carbon footprint within the current mining landscape. Our results indicate that BTC CO2 emissions and mining difficulty are the most significant factors affecting carbon emissions. As mining difficulty increases—typically due to the entry of more miners and the deployment of more powerful hardware—profit margins decrease. High-cost, energy-intensive rigs may temporarily cease operations, leading to a reduction in output and a shift towards more efficient equipment. These findings reinforce and expand upon previous research by elucidating both the causal and time-varying dynamics of mining in relation to environmental outcomes. The results underscore the necessity for policies and industry practices that promote the adoption of more energy-efficient mining hardware and encourage the use of renewable energy sources in cryptocurrency mining. Supporting technological innovation and sustainable energy integration is essential for mitigating the environmental footprint associated with PoW-based blockchain systems such as BTC.

Graphical abstract: Identifying the key drivers of Bitcoin's emissions

Supplementary files

Article information

Article type
Paper
Submitted
21 May 2025
Accepted
22 Oct 2025
First published
24 Oct 2025
This article is Open Access
Creative Commons BY license

Environ. Sci.: Adv., 2026, Advance Article

Identifying the key drivers of Bitcoin's emissions

G. Alkan and H. Özekicioğlu, Environ. Sci.: Adv., 2026, Advance Article , DOI: 10.1039/D5VA00143A

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