Seminar paper from the year 2020 in the subject Economy - Environment economics, grade: 1,0, Humboldt-University of Berlin (Institut für Wirtschaftstheorie II), course: New Models for the Digital Economy, language: English, abstract: The underlying excel model can be downloaded here: https://box.hu-berlin.de/d/66d69097a3fb4e2bb826/ The paper describes the methods deployed and findings of a bottom-up estimation of the electricity demand and associated CO2 emissions from cryptocurrency mining. The model developed establishes best-practices of prior work and refines their approaches to provide more sound and accurate estimations. The comprehensive and interactive Excel worksheet is provided via download link in the paper. Future researchers in this field are encouraged to leverage my work and use it as a starting point for further research. Since Bitcoin's start in 2009, thousands of cryptocurrencies have emerged and become a growing field of both academic and economic interest. Key of most digital currencies is their underlying technology, the Blockchain, allowing for decentralized temper-proof recording of transactions in a currency's network. Transactions are approved by miners solving complex mathematical tasks set by an algorithm, for which it takes computation capacity of mining hardware to solve them. This raises questions about how much energy the networks require and the associated environmental impact. Addressing these questions, Krause and Tolaymat quantified energy demand and carbon dioxide (CO2) emission of cryptocurrencies. Section 3 of this work reviews prior research, including Krause and Tolaymat's, which served as a basis for this paper. To address identified shortcomings and refine past estimations, a standalone Excel model was developed to re-estimate both energy demand of cryptocurrency networks and resulting CO2 emissions, based on current data and bottom-up methodologies. The model's functionality and results are presented in section 4. The reader is encouraged to make use of the model, providing both an interactive, hands-on Control Cockpit and all data and interim results. It allows for adjustments following one's own assumptions, understanding of the field or interest in the sensitivity of the model to input parameters.