Skip to content

#7 of 30 · AI lab + cloud

Microsoft (Copilot / Azure)

Best-in-class water engineering, emissions 58% above its own baseline.

Redmond, Washington·microsoft.com ·Verified Oct 16, 2026
BGood confidence
Index score
Good confidence

In plain words

How scores work

Microsoft scores 63 out of 100 — middle of the pack. Some disclosure or clean-energy commitments, offset by rising emissions or blind spots. Confidence in this score is good confidence (B).

What helps

  • Efficiency · Fleet PUE and hardware efficiency are reported at facility level, and the Phi family targets small-model deployment.
  • Carbon · Stopped counting non-additional unbundled renewable certificates, which raised reported Scope 2 from 2% to 13% of the total — a transparency gain that makes the number look worse.
  • Carbon · 20-year PPA to restart Three Mile Island Unit 1 for carbon-free baseload from 2028; 40 GW of renewables contracted, 19 GW online.

What hurts

  • Efficiency · No per-prompt energy disclosure for Copilot; Azure serves OpenAI's models, whose only public figure (0.34 Wh) comes without methodology.
  • Carbon · FY2025 emissions of 20.29 MtCO₂e, up from 16.21 Mt — a 25% rise and roughly 58% above the 2020 baseline; electricity up 24% to 37 TWh.
  • Commitments · The company itself says sustainability solutions 'are not scaling fast enough to meet demand'.

What nobody knows

  • Per-prompt energy disclosed
  • Per-prompt water disclosed
  • Training emissions in model cards

Microsoft hosts OpenAI's models and its own Copilot stack on Azure. Its FY2025 emissions reached 20.3 MtCO₂e — up 25% in a year and about 58% above the 2020 baseline it promised to go carbon-negative against by 2030 — while electricity use hit 37 TWh. On water and hardware it leads: fleet WUE of 0.30 L/kWh, zero-water cooling in every design since August 2024, 90.9% of retired servers reused or recycled, and the first year it replenished more than it withdrew.

Products
Microsoft Copilot · Azure OpenAI Service · Phi models · Azure AI Foundry
Hosting
Own Azure fleet across 60+ regions; Fairwater-class AI sites in Wisconsin, Atlanta, Iowa, Arizona and Texas.

Key figures

Per-prompt and fleet metrics. Hover a title for what it means; open “How we got this number” for the working.

How to read the labels:Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Measured independentlyMeasured independently. Measured or estimated by researchers or analysts with no stake in the result.ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.— hover any label.
Energy per promptElectricity used to answer one typical request. Under 0.3 Wh is good. A 10 W LED bulb uses 0.3 Wh in about two minutes.
0.34
Wh / typical prompt
≈ 28.3 smartphone charges per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Sam Altman (blog)Epoch AI

ecogpt.co estimate; no company figure.

How we got this number

Copilot serves OpenAI's models from Azure. OpenAI's own figure for an average ChatGPT query is 0.34 Wh and Epoch's independent estimate is about 0.3 Wh; Microsoft publishes nothing of its own. Uncertainty roughly ±3×.

Water per promptWater evaporated cooling the servers for one request. Scope matters: on-site cooling only, or also the water used to generate the electricity (which adds ≈1.8 mL per Wh).
0.10
mL / typical prompt
≈ 0.20 500 mL bottles per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Sam Altman (blog)Epoch AIMicrosoft

On-site cooling only, so comparable with Google and OpenAI's figures.

How we got this number

0.34 Wh per prompt × 0.3 L/kWh (Microsoft's disclosed fleet WUE). Counting the water evaporated to generate the electricity adds ≈0.61 mL.

Carbon per promptGreenhouse gas from one request on the grid where it actually runs. Below 0.1 g is clean. A kilometre in a petrol car is about 170 g.
0.133
gCO₂e / typical prompt
≈ 0.78 km driven (petrol car) per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Sam Altman (blog)Epoch AI

Location-based: what the grid actually emitted. Renewable certificates would bring the market-based figure near zero.

How we got this number

0.34 Wh × 390 gCO₂/kWh (US grid, location-based).

Annual emissionsThe company's whole yearly footprint, in million tonnes of CO₂e. Clouds and device makers dwarf pure labs because they also count shops, logistics or products. Compare the trend, not the size.
20.3
MtCO₂e (FY2025)
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.ESG Dive
Emissions trendDirection of travel against last year or the company's own baseline. Negative is good. Positive means the AI build-out is outrunning clean energy.
+25.0
% YoY (FY2025)
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.ESG Dive

≈58% above the 2020 baseline.

Water per kWh (WUE)Litres of water consumed per kilowatt-hour of server electricity, across the fleet. 0.15 is excellent, 0.3 is good, 1.8 is the US data-centre average.
0.30
L/kWh (FY2024, withdrawal basis)
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Microsoft
Clean electricityShare of electricity matched with renewable or carbon-free sources. Annual matching counts certificates bought anywhere; hourly matching on the same grid is far stricter.
100
% renewable matching (market-based)
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Data Center Dynamics

Hourly matching target for 2030.

Hardware & materials

The chips, power, copper and future e-waste behind the fleet — the part sustainability reports leave out

Factors we use
AI chips in service
3.5 million
H100-equivalents (Epoch AI, Q4 2025)
≈ 48,611 racks of 72 GPUs
Measured independentlyMeasured independently. Measured or estimated by researchers or analysts with no stake in the result.Epoch AINetwork World

GPUs, TPUs or custom accelerators the company runs. Analyst figures are in H100-equivalents, company figures in physical chips.

How we got this number

≈3.2M NVIDIA plus ≈0.3M AMD; Fairwater in Wisconsin alone adds hundreds of thousands of GB200s.

Power draw
4,224
MW (average draw)
≈ 3.2 million US homes' worth of power
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.S&P Global EnergyESG Dive

Megawatts the AI fleet pulls. One megawatt runs roughly 750 US homes.

How we got this number

37 TWh a year ÷ 8,760 hours. Average draw implied by annual electricity; peak capacity is higher.

Electricity a year
37.0
TWh (2025)
≈ 3.5 million US homes for a year
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.S&P Global EnergyESG Dive

Terawatt-hours per year. One TWh is a year of electricity for about 95,000 US homes.

How we got this number

Up 24% in a year; more than double 2022.

Carbon baked into the hardware
1,799
ktCO₂e embodied in chips and servers
≈ 391,087 petrol cars driven for a year
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Epoch AINetwork WorldarXiv 2607.05408NVIDIA developer blog

CO₂e emitted making the chips and servers before they answer a single prompt.

How we got this number

3,500,000 accelerators × 514 kg CO₂e each (chip plus share of the host server). Emitted in manufacturing, before the first prompt is served.

Future e-waste
59,500
tonnes of servers to retire within ~5 years
≈ 331 empty Boeing 747s by weight
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Epoch AINetwork WorldNVIDIAThe Register

Tonnes of server hardware that will be retired in three to five years.

How we got this number

3,500,000 accelerators × 17 kg of server hardware each. Tomorrow's e-waste unless reused; generative AI could produce 2.5 Mt of it a year by 2030.

Copper in the build
114,048
tonnes of copper in the build
≈ copper for 1.4 million electric cars
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.S&P Global EnergyESG DiveBHPS&P Global Market Intelligence

Tonnes of copper in cabling, busbars and transformers.

How we got this number

4,224 MW × 27 t of copper per MW (cabling, busbars, transformers).

Fossil generators on site
1,625
backup diesel generators (implied)
≈ 4,225 MW of backup capacity
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.S&P Global EnergyESG DiveLatitude MediaLatitude Media / Floodlight

Diesel backup units or gas turbines at the data centres, permitted or observed.

How we got this number

4,224 MW ÷ 2.6 MW per generator, the average size permitted at US data centres. Permit counts are not published fleet-wide; this is the number a fleet this size normally carries.

Hardware reused or recycled
90.9
% of retired servers and components reused or recycled (2024)
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Microsoft

Share of retired servers and parts given a second life.

How we got this number

3.2 million components handled by six Circular Centers; target met a year early.

Mineral depletion
—
Only Mistral has published a mineral-depletion figure.
Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.

Kilograms of antimony-equivalent, the life-cycle unit for scarce minerals consumed.

Score breakdown

Each pillar is 0–100; weights are fixed across the index

Energy efficiency — How much electricity one answer costs.weight 20% · confidence C
55
  • No per-prompt energy disclosure for Copilot; Azure serves OpenAI's models, whose only public figure (0.34 Wh) comes without methodology. Sam Altman (blog)
  • Fleet PUE and hardware efficiency are reported at facility level, and the Phi family targets small-model deployment. Microsoft
Carbon intensity — How clean that electricity is, and whether total emissions are rising.weight 25% · confidence B
55
  • FY2025 emissions of 20.29 MtCO₂e, up from 16.21 Mt — a 25% rise and roughly 58% above the 2020 baseline; electricity up 24% to 37 TWh. ESG DiveS&P Global Energy
  • Stopped counting non-additional unbundled renewable certificates, which raised reported Scope 2 from 2% to 13% of the total — a transparency gain that makes the number look worse. ESG Dive
  • 20-year PPA to restart Three Mile Island Unit 1 for carbon-free baseload from 2028; 40 GW of renewables contracted, 19 GW online. World Nuclear NewsS&P Global Energy
Water — How much water the data centres consume, and whether it is replaced.weight 15% · confidence B
75
  • Fleet-wide WUE of 0.30 L/kWh in FY2024, down 39% from 0.49 in 2021; every new design since August 2024 uses zero-water cooling, and Fairwater's closed loop is filled once. MicrosoftMicrosoftConverge Digest
  • Replenished more water than it withdrew globally for the first time in FY2025. ESG Dive
Transparency — How much the company actually publishes.weight 20% · confidence B
66
  • Detailed annual report with fleet PUE/WUE, electricity, location-based Scope 2 and a hardware reuse rate; no per-prompt or model-level AI figures. MicrosoftESG DiveMicrosoft
Commitments — Whether it has dated, measurable climate and water targets.weight 10% · confidence A
75
  • Carbon negative, water positive and zero waste by 2030; 100% of electricity matched by zero-carbon purchases 100% of the time by 2030. Data Center Dynamics
  • The company itself says sustainability solutions 'are not scaling fast enough to meet demand'. ESG Dive
Conduct — How it behaves: permits, lawsuits, gas turbines, community water.weight 10% · confidence B
65
  • Backed nuclear restarts and additional clean generation rather than on-site gas for new AI capacity. World Nuclear News

Models

What this company ships, and what each prompt costs

No model-level entries — this company hosts or supplies others rather than serving its own models.

Where the compute lives

Named sites from company disclosures and press reporting

OperationalConstructionPlanned
  • QuincyWashington, USA
  • West Des MoinesIowa, USA · GPT-4 training site.
  • Mount Pleasant (Fairwater)Wisconsin, USA · 315 acres; hundreds of thousands of GB200s; closed-loop water.
  • GoodyearArizona, USA
  • San AntonioTexas, USA
  • DublinIreland