🤖 AI Summary
This study addresses the insufficient accuracy of compliance reporting in Ethereum node energy estimation caused by overlooking hardware attributes and hosting environment heterogeneity. We propose a fine-grained power quantification method leveraging publicly available properties of the peer-to-peer network. By collecting node characteristics via web crawling, this work is the first to utilize such features to differentiate client types, system architectures, and cloud service providers, thereby overcoming the limitations of conventional single typical-power assumptions. Furthermore, we integrate a rule engine with a random forest model to map empirically measured energy consumption data, enabling precise per-node power estimation. Experimental results demonstrate that the estimated total network power consumption is 415 kW, representing a 3.9% reduction compared to traditional methods. This approach effectively bridges the assessment gap in the Cambridge Centre for Alternative Finance (CCAF) framework, significantly enhancing the accuracy of energy consumption measurements.
📝 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%.