Abstract
Ethereum’s electricity use fell by about 99.95% after the network moved from proof of work to proof of stake. Service providers still need to report how much energy their operations use, e.g. under the EU Markets in Crypto-Assets Regulation (MiCAR). Existing estimates either apply one typical wattage to every node or start from aggregated monitoring counts. Both approaches ignore information that nodes already advertise on the peer-to-peer network: which client software they run, whether they use ARM or x86 hardware, where they are hosted, and whether they act as validators. We combine crawls of the consensus and execution layers, assign a wattage to each peer from those attributes using published measurements, and estimate the remaining incomplete peers with a Random Forest. On 6,934 peers from two Nebula crawls (19 and 22 June 2026), the nodes that the crawler can reach sum to 415 kW, or 3.63 GWh if that draw were held for a year. Applying the same Lighthouse+Nethermind x86 wattage to every one of those peers instead yields 431 kW. Using the observed attributes therefore lowers the total by 3.9%, mainly because nodes at Hetzner and other non-AWS clouds draw less than that home-desktop figure. AWS then accounts for 15.6% of watts from 12.2% of peers, and validatorflagged nodes account for 31.4% of watts from 25.7% of peers. Our snapshot of 415 kW is about 46% of the Cambridge Centre for Alternative Finance (CCAF) estimate of about 0.90 MW. Both approaches use about 60 W per node, so the gap is mostly how many nodes each estimate includes. The assignment rules cover 3,110 peers and the forest estimates the other 3,824. On held-out labeled peers with client, architecture, and OS hidden, the forest’s mean absolute error against the rule wattage is 4.3 W. Twenty-four-hour measurements on a gaming desktop differ from the predictions. After subtracting a 33 W idle graphics card that Ethereum clients do not need, both differences fall to about 19%.
Citation
@misc{hackspacher2026quantifyingethereumenergyconsumption, title={Quantifying Ethereum Energy Consumption via Network Mapping}, author={Yahn Costa Hackspacher and Cornelius Ihle and Vasundhara Shaw and Dennis Trautwein and Geerd-Dietger Hoffmann and Bela Gipp and Moritz Schubotz}, year={2026}, eprint={2610.03440}, archivePrefix={arXiv}, primaryClass={cs.CY}, url={https://arxiv.org/abs/2610.03440}, }