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Commentary 12. Lazard. (2018). Lazard’s Levelized Cost of Energy Analysis – Version 12.0. https://www. lazard.com/media/450784/lazards- levelized-cost-of-energy-version-120-vfinal. pdf. 13. Korkovelos, A., Zerriffi, H., Howells, M., Bazilian, M., Rogner, H., and Nerini, F.F. (2020). A Retrospective Analysis of Energy Access with a Focus on the Role of Mini- Grids. Sustainability 12, 1793. 14. Sepulveda, N.A., Jenkins, J.D., de Sisternes, F.J., and Lester, R.K. (2018). The Role of Firm Low-Carbon Electricity Resources in Deep Decarbonization of Power Generation. Joule 2, 2403–2420. 1Nuclear Innovation Alliance, Washington, D.C., USA 2Payne Institute for Public Policy, Colorado School of Mines, Golden, CO, USA 3Energy Policy and Climate Program, Kreiger School of Arts & Sciences, Johns Hopkins University, Washington, D.C., USA 4Department of Public Policy, Division of Economics and Business, Colorado School of Mines, Golden, CO, USA *Correspondence: agilbert@nuclearinnovationalliance.org https://doi.org/10.1016/j.joule.2020.08.005 Commentary Energy Consumption of Cryptocurrencies Beyond Bitcoin Ulrich Gallersdo ̈ rfer,1 Lena Klaaßen,2 and Christian Stoll3,4,* Ulrich Gallersdo ̈ rfer is a research associate in the Department of Informatics at the Technical Uni- versity of Munich. His research fo- cuses on identity management in blockchains. His interest extends to further aspects of the technol- ogy, ranging from environmental implications to data analytics ap- plications. Lena Klaaßen is a graduate student at TUM School of Management at the Technical University of Munich. She is specialized in energy mar- kets and accounting. Her research focuses on carbon accounting in the corporate and cryptocurrency space. She has previously analyzed blockchain-related firms for a ven- ture capital fund. Christian Stoll conducts research at the Center for Energy and Envi- ronmental Policy Research at the Massachusetts Institute of Technology and at the Center for Energy Markets of the Tech- nical University of Munich. His research focuses on the implica- tions of climate change from an economic point of view. Bitcoin’s energy hunger has triggered a passionate debate in academic litera- ture as well as in the general public about the energy consumption of cryp- tocurrencies. Bitcoin is a digital cur- rency based on a cryptographically secured distributed ledger and repre- sents the first and best-known block- chain application. Its computationally intensive validation process called ‘‘mining’’ requires specific hardware and vast amounts of electricity to reach consensus about ownership and trans- actions. Depending on the methodol- ogy and assumptions, energy con- sumption estimates chart a wide range of results as depicted in Figure 1. The methodologies of the estimates have become more sophisticated over time, and yet, most studies have focused exclusively on Bitcoin and thereby ignored that more than 500 further mineable coins and tokens exist.1 Beyond Bitcoin To estimate the energy consumption of cryptocurrencies beyond Bitcoin, we resort to a methodology proposed by Krause and Tolaymat2 that employs hash rates of cryptocurrency networks and suitable mining devices. Hash rates measure the processing power; they describe the number of attempts per sec- ond to solve a block in the so-called ‘‘proof-of-work’’ mining process. Table 1 lists the hash-rates of the top 20 mineable cryptocurrencies by market capitalization that account for more than 98% of the total market capitalization. These top 20 use 13 different proof-of-work algo- rithms. Bitcoin, for instance, uses the SHA-256 algorithm that allows for mining with highly specialized, ASIC-based de- vices, which are considerably more en- ergy efficient than conventional graphic processing units (GPUs). GPUs are used, for instance, to mine Monero that prevents ASIC-based devices from its validation process.3 Table 1 lists the effi- ciency of mining devices that suit the respective algorithms. Dividing the network hash rates by efficiencies of min- ing devices yields the rated power of each network. Figure 2 illustrates the cu- mulative market capitalization and rated power of the top 20 cryptocurrencies: #1—Bitcoin—accounts for 2/3 of the total energy demand; #2–20 complement 1/3. It is important to note that currencies with ASIC-resistant algorithms consume an overproportionate amount of energy in relation to their market capitalization. As listed in Table 1, RavenCoin, for instance, accounts for 4.32% of the total rated power, whereas its market cap only accounts for 0.06% of the consid- ered top 20. A second example is Mon- ero, which became ASIC-resistant after an update in March 2018. The update led to an abrupt decrease in the net- work’s computational power of more than 80%. After a few days, the hash rate bounced back to half of the pre-up- date level as miners switched from ASIC to less-energy-efficient GPUs.3 In absolute terms, the total energy con- sumption estimate in Figure 1 appears rather conservative. Alternative estima- tion methods (including, e.g., auxiliary losses in mining facilities) suggest that the actual energy consumption of Bitcoin might be higher: Digiconomist,4 for instance, derives 7.9 gigawatts (GW), and the Cambridge Bitcoin Electricity Consumption Index (CBECI)5 states 6.1 GW, whereas we estimate 4.3 GW (all es- timates with a cutoff date of 03/27/2020; ll Joule 4, 1839–1851, September 16, 2020 a 2020 Elsevier Inc. 1843PDF Image | Energy Consumption of Cryptocurrencies Beyond Bitcoin
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