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Bitcoin mining energy statistics: the key figures
Cambridge’s Digital Mining Industry Report, published in 2025, combines survey findings and modeled estimates. Its global Bitcoin figures describe 2024 conditions; the report’s publication year does not make them measurements for 2025 or 2026.
| Figure | What it measures and how to read it |
|---|---|
| 138.2 TWh annualised as of 30 June 2024 | Cambridge’s estimate of Bitcoin’s annual electricity consumption, based on a dated snapshot. It is not the amount consumed during the first half of 2024 or a metered calendar-year total. Cambridge reported a 17% year-on-year increase for this estimate. |
| 137.4 TWh annualised | CBECI’s estimate around the June 2024 snapshot, which Cambridge said was closely aligned with its 138.2 TWh estimate. The two figures are estimates, not independent measurements of all mining facilities. |
| 183 TWh annualised at 31 December 2024 | Cambridge’s modeled extension of the CBECI estimate beyond its June survey snapshot. Treat it as a year-end modeled rate, not observed electricity consumption over the whole of 2024. |
| 138 TWh; approximately 0.54% | Cambridge’s rounded summary of the estimated annual electricity use and its share of global electricity use. The underlying estimate is for 2024, not 2026. |
| 558.7 EH/s at 30 June 2024; 795.7 EH/s at 31 December 2024 | Network hashrate levels cited in Cambridge’s 2025 report. The rise helps explain why the report’s modeled electricity-use rate increased even as estimated hardware efficiency improved. |
| 23.7 J/TH by the end of 2024 | Cambridge’s modeled year-end estimate of mining-hardware efficiency. Joules per terahash (J/TH) expresses energy used per unit of hashing work; lower values indicate more efficient hardware. |
What powered Bitcoin mining in Cambridge’s 2024 estimate?
Cambridge’s 2025 report estimated the electricity-source mix for Bitcoin mining. Its use of “sustainable energy” includes nuclear power, so that headline should not be read as the renewables-only share.
| Share | Source or category | Qualification |
|---|---|---|
| 52.4% | Sustainable energy | Cambridge’s combined category: 42.6% renewables and 9.8% nuclear. |
| 42.6% | Renewables | The report’s headline estimate for renewable electricity sources. |
| 23.4% | Hydropower | A reported component of the renewables estimate. |
| 15.4% | Wind | A reported component of the renewables estimate. |
| 3.2% | Solar | A reported component of the renewables estimate. |
| 0.5% | Other renewables | A reported component. The rounded component figures may not sum exactly to Cambridge’s 42.6% renewables headline. |
| 9.8% | Nuclear | Included in Cambridge’s 52.4% “sustainable energy” total, but not in the renewables-only figure. |
| 38.2% | Natural gas | Cambridge’s 2024 estimate; the report summary says natural gas had replaced coal as the largest single source. |
| 8.9% | Coal | Cambridge’s 2024 estimate. |
| 25.0% natural gas; 36.6% coal | Electricity mix comparison for 2022 | Cambridge’s report summary gives these as 2022 estimates, compared with the 2024 estimates above. They are report estimates, not direct metering of every mining operation. |
Emissions and curtailment estimates
| Figure | What it means |
|---|---|
| 39.8 MtCO₂e | Cambridge’s 2025 report estimate of annual greenhouse-gas emissions using its survey-based approach, associated with its estimated mining electricity use and energy mix. |
| 69.6 MtCO₂e | Cambridge’s estimate using an IP-based model. The difference from the survey-based estimate illustrates how results depend on method and assumptions; it is not evidence of a measured emissions range. |
| 888 GWh during 2023 | Electrical load that Cambridge’s report says miners reported curtailing. Curtailment means reducing or interrupting electricity demand; this figure alone does not establish a net environmental benefit or show what generation would otherwise have supplied the power. |
| 70.8% | Share of surveyed miners who reported active climate-mitigation measures, according to Cambridge’s 2025 report. It is a survey response, not an independently audited measure of emissions reductions. |
Hardware efficiency and mining-equipment concentration
| Figure | What it measures |
|---|---|
| 28.2 J/TH by June 2024 | Cambridge’s estimate of Bitcoin mining hardware efficiency. The report describes this as a 24% year-on-year improvement. Because J/TH measures energy per hashing work, an improvement in this metric does not by itself mean the whole network uses less electricity. |
| 82% | Market share attributed to the largest ASIC manufacturer in Cambridge’s report. The figure describes manufacturer concentration within the report’s stated scope and period, not the share of mining activity controlled by that manufacturer. |
| More than 99% | Combined market share attributed to the three largest ASIC manufacturers in Cambridge’s report. It provides context for hardware supply concentration, not a measure of network ownership. |
ASICs, or application-specific integrated circuits, are specialized devices used for Bitcoin’s SHA-256 mining. Their efficiency is one part of electricity demand; the number and total computing capacity of machines operating across the network also matter.
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How representative is Cambridge’s survey?
- 49 mining firms took part in Cambridge’s survey for its 2025 report.
- The firms were headquartered in 16 jurisdictions and operated across 23 countries.
- Cambridge said the data represented 48% of global mining activity.
The sample gives the report a substantial basis for describing the industry, but it is not a census of every miner. Survey coverage and modeled estimates should not be mistaken for complete facility-by-facility measurement.
What U.S. electricity estimates say—and do not say
The U.S. Energy Information Administration (EIA) estimated in a February 2024 analysis that cryptocurrency-mining operations used between 0.6% and 2.3% of U.S. electricity consumption. This is a U.S. estimate for cryptocurrency mining broadly, not a Bitcoin-only figure, and it is historical context rather than a current 2026 measurement.
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EIA described significant limits in identifying and measuring mining loads: facilities vary greatly in size, operators may move to seek lower electricity prices, and reliable power-use information is often unavailable or unclear. The agency used top-down information and a bottom-up compilation of identified facilities and potential electricity use. EIA later discontinued its emergency EIA-862 collection, so the range should not be treated as an ongoing, directly metered series.
Why Bitcoin electricity figures vary
Annualised estimates are rates projected over a year
Cambridge’s CBECI presentation starts with estimated power demand in gigawatts and annualises that rate into terawatt-hours, applying a seven-day moving average. An annualised figure answers a question like “what would a year of use look like at this estimated rate?” It is not necessarily the electricity actually consumed over a completed calendar year.
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CBECI models a range of plausible hardware use
CBECI uses a basket of real-world mining hardware and assumes miners use equipment that is economically profitable. It estimates a theoretical lower bound based on highly efficient machines, an upper bound based on less efficient but still profitable equipment, and a best-guess mix. These are model outputs rather than direct readings from every miner’s electricity meter.
Efficiency and total demand are different measures
J/TH tracks the energy efficiency of hashing hardware. Network electricity demand also depends on how much computing power is operating. Cambridge’s 2024 figures show why the distinction matters: its report describes improving hardware efficiency while also reporting a substantial rise in network hashrate and a higher modeled year-end electricity-use rate.
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Emissions depend on electricity and location assumptions
Electricity use does not translate into one fixed emissions total. Cambridge’s emissions methodology assumes average emissions intensity equals marginal emissions intensity and is subject to limits in the electricity-mix and mining-location inputs. Its survey-based and IP-based results therefore answer the question using different methods and assumptions, not two direct measurements of the same emissions total.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare Bitcoin mining statistics responsibly
- Check the reference date. A June snapshot, a year-end modeled extension, and a completed calendar-year total are not interchangeable.
- Check the geography. Global Bitcoin estimates and U.S. cryptocurrency-wide estimates cover different populations.
- Check what is counted. Confirm whether a statistic covers Bitcoin alone or all cryptocurrency mining.
- Check the method. A survey estimate, a model, and a facility-based estimate have different coverage and uncertainty.
- Check the unit and time basis. TWh is an amount of energy; GW is a rate of power demand; an annualised TWh figure projects a rate over a year.
- For emissions, check the assumptions. The electricity mix, mining locations, and emissions-intensity method affect the result.
The headline takeaway is not a verified 2026 total: the available figures here document estimates for 2024, reported in 2025. Cambridge’s snapshot and model illustrate that efficiency improvements do not guarantee lower network electricity demand, while its energy-mix and emissions results depend on definitions and estimation methods.
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