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Best Practices for Analyzing the Direct Energy Use of Blockchain Technology Systems

Project Leads

Professor and Head of Energy and Resource Systems Analysis Laboratory at the McCormick School of Engineering, Northwestern University


Special Advisor to the Chief Scientist, Rocky Mountain Institute



Project Description

A great deal of uncertainty exists around blockchain’s required energy use and its potential role in energy transitions. This project investigated the direct energy use of blockchain technologies in order to address this knowledge gap. The research included an analysis of the energy use of computational servers that solve blockchain algorithms, their cooling and power provisioning requirements, and the communication systems that provide data transfer between them. It was important to take an application-specific approach given that algorithmic complexity can vary greatly by transaction type. Algorithmic complexity has a direct influence on the configurations and energy demands of the technologies comprising the underlying blockchain systems.

The research team developed a robust framework for quantifying the direct energy use of major blockchain applications, and outlined a research agenda for data collection and knowledge integration to apply the framework. The project provides blockchain energy researchers with a much-needed, analytical structure based on best practices, which enables greater data sharing and inter-study comparability. This will lead to more transparent and replicable results that better inform policymakers and those thinking about blockchain technology adoption.

The project consisted of four key tasks: (1) a review of major blockchain applications (including, but not limited to cryptocurrencies) and the equipment types comprising the associated blockchain technology systems, (2) establishment of a best-practice analysis framework for estimating the direct energy use of blockchain technology system components, (3) development of a future research agenda for applying the proposed analytical framework, and (4) direct energy use measurements of a cryptocurrency mining computer to begin filling empirical data gaps on the energy intensity of algorithm solving.

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