Ethereum energy use drops sharply with new measurement method
Quantifying Ethereum Energy Consumption via Network Mapping
Computers and SocietyCryptography and Security
Summary
Ethereum switched from one way of verifying transactions that used a lot of electricity to a new method that uses much less. The authors developed a way to estimate how much electricity Ethereum nodes use based on specific characteristics that these nodes share openly. Their method gives a more detailed and slightly lower estimate of total energy use compared to simpler approaches. They also checked their estimates with real measurements and found they were close after adjusting for some components not needed by Ethereum clients.
What this means in practice
- •For cryptocurrency infrastructure teams: Provide accurate energy consumption reports for Ethereum nodes by using node attributes rather than applying a uniform power estimate to all nodes.
- •For regulatory compliance officers: Use refined Ethereum node energy estimates to comply with energy reporting requirements under regulations like the EU MiCAR.
Authors
Yahn Costa Hackspacher, Cornelius Ihle, Vasundhara Shaw, Dennis Trautwein, Geerd-Dietger Hoffmann, Bela Gipp, Moritz Schubotz
Abstract
Ethereum's electricity use fell by about 99.95% after the move from proof of work to proof of stake. Service providers still need to report operational energy 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 ignore attributes that nodes already advertise on the peer-to-peer network: client software, ARM or x86 hardware, hosting location, and validator role. We crawl the consensus and execution layers, assign each peer a wattage 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), reachable nodes sum to 415 kW, or 3.63 GWh if that draw were held for a year. The same Lighthouse+Nethermind x86 wattage on every peer yields 431 kW. Observed attributes lower the total by 3.9%, mainly because nodes at Hetzner and other non-AWS clouds draw less than that home-desktop figure. AWS accounts for 15.6% of watts from 12.2% of peers, and validator-flagged nodes for 31.4% of watts from 25.7% of peers. The 415 kW snapshot is about 46% of the Cambridge Centre for Alternative Finance (CCAF) estimate of about 0.90 MW. Both use about 60 W per node, so the gap is mostly how many nodes each estimate includes. Rules cover 3,110 peers and the forest 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%.