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#26 of 30 · AI lab + cloud

ByteDance (Doubao / Volcano Engine)

China's biggest Nvidia buyer; 50 trillion tokens a day; no emissions data.

Beijing, China·bytedance.com ·Verified Oct 16, 2026
DLittle data
Index score
Little data

In plain words

How scores work

ByteDance scores 34 out of 100 — laggard. Little or no disclosure, fossil-backed capacity, or active disputes with regulators and communities. Confidence in this score is little data (D).

What helps

  • Carbon · Pledged operational carbon neutrality (≥90% real cuts) and 100% renewable electricity by 2030; Norway site to run on hydro.
  • Commitments · Operational carbon neutrality by 2030 with at least 90% real reductions; 100% renewable electricity by 2030.

What hurts

  • Carbon · Chinese grid plus Malaysia's gas-heavy grid; no renewable procurement disclosed.
  • Water · Johor's 5.8 GW data-centre pipeline is straining local water and power enough that officials slowed approvals; ByteDance discloses nothing about its own use.
  • Transparency · No environmental report, emissions, energy or footprint in model cards; Greenpeace ranked it third from bottom among Chinese cloud providers.

What nobody knows

  • Per-prompt energy disclosed
  • Per-prompt water disclosed
  • Training emissions in model cards
  • Annual environmental report
  • Location-based Scope 2 reported
  • +3 more

ByteDance's Doubao is China's most-used AI app (157 million monthly users) and Volcano Engine reported more than 50 trillion tokens a day in late 2025. ByteDance was the largest Chinese buyer of Nvidia chips in 2025 (≈RMB 85 billion) and planned a $14 billion H200 order for 2026, with data centres anchored in Johor, Malaysia — a region now slowing approvals over power and water strain. It pledged operational carbon neutrality and 100% renewable electricity by 2030 but has never published emissions or energy figures.

Products
Doubao · Seed models · Volcano Engine · CapCut AI · TikTok AI features
Hosting
Own and partner data centres in China; Bridge Data Centres MY06 (110 MW) in Johor, Malaysia; a Green Mountain hydro site in Norway.

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.
0.20
Wh / typical prompt
≈ 16.7 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.Analytics InsightEpoch AI

ecogpt.co estimate; no company figure.

How we got this number

Doubao's models are mixture-of-experts designs with modest active parameters, comparable to DeepSeek-V3's class, served at enormous scale (50 trillion tokens a day). 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).
0.20
mL / typical prompt
≈ 0.40 500 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.Analytics InsightEpoch AIKoomey Analytics

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

How we got this number

0.2 Wh per prompt × 1 L/kWh (estimated WUE for Chinese and Malaysian colocation sites). Counting the water evaporated to generate the electricity adds ≈0.36 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.
0.116
gCO₂e / typical prompt
≈ 0.68 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.Analytics InsightEpoch AI

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

How we got this number

0.2 Wh × 580 gCO₂/kWh (Chinese 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.
2.64
MtCO₂e / year (AI compute only)
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.Analytics Insight

Location-based estimate for the AI fleet only; not comparable with a full corporate inventory.

How we got this number

650 MW × 8,760 h × 80% utilisation × 580 gCO₂/kWh (Chinese grid). Compute only — nothing else the company does is counted.

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.
+50.0
% (chip budget rising from RMB 85bn in 2025)
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.Analytics Insight
How we got this number

2026's chip budget exceeds 2025's RMB 85 billion; with no inventory to measure, a 50% rise in AI compute is the conservative reading.

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.
1.00
L/kWh
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.Koomey AnalyticsCO/AI
How we got this number

No disclosure. Johor and mainland Chinese sites mostly use evaporative cooling; 1.0 L/kWh is a mid-range assumption.

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.
30.0
% non-fossil (grid share, no procurement)
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.Data Center DynamicsCO/AI
How we got this number

China's grid is about 35% non-fossil and Malaysia's about 20%; ByteDance discloses no renewable purchases despite its 2030 pledge.

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
500,000
accelerators (order of magnitude)
≈ 6,944 racks of 72 GPUs
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.Analytics Insight

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

How we got this number

ByteDance publishes no fleet size. It spent about RMB 85 billion (≈$12bn) on AI chips in 2025 alone — more than any other Chinese company — which at $25–40k per installed accelerator is 300,000–500,000 chips in a single year. Uncertainty ±2×.

Power draw
650
MW
≈ 487,500 US homes' worth of power
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Analytics InsightSemiAnalysis

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

How we got this number

500,000 accelerators × 1.3 kW each, all-in. Fleet power implied by the chip count.

Electricity a year
4.56
TWh / year
≈ 434,286 US homes for a year
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Analytics InsightSemiAnalysis

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

How we got this number

650 MW × 8,760 h × 80% utilisation. Compute fleet only.

Carbon baked into the hardware
257
ktCO₂e embodied in chips and servers
≈ 55,870 petrol cars driven for a year
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Analytics InsightarXiv 2607.05408NVIDIA developer blog

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

How we got this number

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
8,500
tonnes of servers to retire within ~5 years
≈ 47 empty Boeing 747s by weight
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Analytics InsightNVIDIAThe Register

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

How we got this number

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
17,550
tonnes of copper in the build
≈ copper for 211,446 electric cars
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Analytics InsightSemiAnalysisBHPS&P Global Market Intelligence

Tonnes of copper in cabling, busbars and transformers.

How we got this number

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

Fossil generators on site
250
backup diesel generators (implied)
≈ 650 MW of backup capacity
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Analytics InsightSemiAnalysisLatitude MediaLatitude Media / Floodlight

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

How we got this number

650 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
—
No hardware reuse or e-waste figure published.
Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.

Share of retired servers and parts given a second life.

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
  • Doubao models are MoE with modest active parameters, served at 50 trillion tokens a day; nothing measured or published. Analytics Insight
Carbon intensity — How clean that electricity is, and whether total emissions are rising.weight 25% · confidence C
30
  • Chinese grid plus Malaysia's gas-heavy grid; no renewable procurement disclosed. CO/AIData Center Dynamics
  • Pledged operational carbon neutrality (≥90% real cuts) and 100% renewable electricity by 2030; Norway site to run on hydro. Data Center Dynamics
Water — How much water the data centres consume, and whether it is replaced.weight 15% · confidence D
30
  • Johor's 5.8 GW data-centre pipeline is straining local water and power enough that officials slowed approvals; ByteDance discloses nothing about its own use. CO/AI
Transparency — How much the company actually publishes.weight 20% · confidence D
10
Commitments — Whether it has dated, measurable climate and water targets.weight 10% · confidence B
40
  • Operational carbon neutrality by 2030 with at least 90% real reductions; 100% renewable electricity by 2030. Data Center Dynamics
  • A value-chain decarbonisation plan promised for end-2023 has not appeared. Data Center Dynamics
Conduct — How it behaves: permits, lawsuits, gas turbines, community water.weight 10% · confidence C
45
  • No enforcement actions; expanding fastest in a region where regulators have paused approvals over grid and water strain. CO/AI

Models

What this company ships, and what each prompt costs

ModelWeightsEnergy / promptWaterTraining CO₂e
Doubao 1.5 Pro
MoE (≈20B active, undisclosed total)
Closed0.20 Wh 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.0.200 mL 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.3,000 t 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.

Where the compute lives

Named sites from company disclosures and press reporting

OperationalConstructionPlanned
  • Beijing HQBeijing, China
  • Bridge MY06, SedenakJohor, Malaysia · 110 MW anchor tenant; region slowing approvals.
  • ZhangjiakouHebei, China
  • Green MountainNorway · Hydro-powered.