An Analysis of Energy Consumption and Carbon Footprints of Cryptocurrencies and Possible Solutions
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Abstract (arXiv)
There is an urgent need to control global warming caused by humans to achieve a sustainable future. CO2 levels are rising steadily and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015, we are still a long way to go from achieving a sustainable mode of global operation. The increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy consumption. Popular energy tracking studies (e.g., Digiconomist and the Cambridge Bitcoin Energy Consumption Index (CBECI)) have estimated energy consumption ranges of 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh respectively for Bitcoin as of July 2021, which are equivalent to the energy consumption of countries such as Sweden and Thailand. The latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO2 emission by Bitcoin as of July 2021, close to the emissions by Greece and Oman. This review compiles estimates made by various studies from 2018 to 2021. We compare with the energy consumption and carbon footprints of these cryptocurrencies with countries around the world, and centralized transaction methods such as Visa. We identify the problems associated with cryptocurrencies, and propose solutions that can help reduce their energy usage and carbon footprints. Finally, we present case studies on cryptocurrency networks namely, Ethereum 2.0 and Pi Network, with a discussion on how they solve some of the challenges we have identified.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Feb 2022.
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Citing papers per year: 2023: 3; 2024: 0; 2025: 1.
4 papers.
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- 2025Universal pre-training by iterated random computation24 Jun 2025
- 2023A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.022 Nov 2023
- 2023Neural Network Methods for Radiation Detectors and Imaging9 Nov 2023
- 2023Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature23 Aug 2023