Comptable, 1 an

Name: Accountant, 1 year
Creator: Projet Celsius
License: https://creativecommons.org/licenses/by/4.0/

Projet Celsius

Database entryUsesLevel 4 of 6 occupation or activityOccupation or activity, level 4 of 6Used to calculate 8 entries, levels 5 and 6Calculated with 11 entries, levels 1 and 3Database structure

Accountant, 1 year

One year of work for an accountant who uses AI: about 84 web searches with AI, 62 spreadsheets analysed, 20 PDFs queried, and 7 other tasks. Projet Celsius estimate.

Text and conversation account for 72% of emissions: "Analyse a spreadsheet" has the same footprint as 9.3 quick questions.
About 860 requests to AI over the year, or 3.9 per working day.
1.6 times lower than the AI footprint of an office employee, for the same use.
An estimate: the frequencies and the share done with AI are our assumptions.

Compare

Calculated with

Used to calculate

Breakdown of emissions by item

380 gCO2e in total: 830 Wh of electricity × 458 gCO2e/kWh (servers worldwide).

Inputs of the calculation 9

PUE of the data centre, Read 1,000 tokens, Write, with reasoning, Write 1,000 tokens, Generate an image, Analyse a spreadsheet, Query a PDF, Web search with an AI, Web search (benchmark)

How the calculation is built

One year of work for an accountant who uses AI, task by task: emails, rule questions, spreadsheets, notes and reports, documents to key in. The frequencies come from the content of the occupation, the use of AI from usage surveys (OpenAI, Microsoft, Crédoc); these are assumptions, adjustable.

Comparison with other occupations and activities

See the full list

Field-based occupation, 1 year220 gCO2e

Teacher, 1 year260 gCO2e

Human resources, 1 year330 gCO2e

Free personal use, 1 year340 gCO2e

Manager or executive, 1 year370 gCO2e

Accountant, 1 yearthis entry380 gCO2e

Student, 1 year490 gCO2e

Lawyer, 1 year520 gCO2e

Office worker, 1 year590 gCO2e

Executive assistant, 1 year630 gCO2e

Logarithmic axis: each tick is 10 times the previous one.

Characteristics

Unit	1 person, 1 year : 220 days
Default location	world, 458 gCO2e/kWh (Ember 2025, life cycle); 11 other locations to choose from
Counted	the tasks done with AI over a working year, including data centre cooling
Not counted	server manufacturing, model training, the person's device and the tasks done without AI
Status	estimated

All the characteristics identifier, version, rule for the value, sources, licence

Identifier	IA-0301, projetcelsius.com/en/ai/database/IA-0301/
Version	0.1 of 2 November 2026
Category	Occupations and activities, one person, one year
Choice of the selected value	database calculation from the entries this one is made of, at the quantities of the unit
Sources	calculation of the database
Licence	CC BY 4.0

Emissions by server location

Emissions associated with electricity (CO2e)

Poland490 gCO2e
World380 gCO2e
United States320 gCO2e
European Union170 gCO2e
France34 gCO2e

× 14 between Poland and France, for the same electricity

Ember 2025, over the life cycle. The same use consumes 830 Wh in each location.

Accountant, 1 year · result per person per year · estimated · typical use · reference year: 220 days · one server location per row.Counted: the tasks done with AI over a working year, including data centre cooling. Out of scope: server manufacturing, model training, the person's device and the tasks done without AIFind this case: projetcelsius.com/en/ai/database/activities/accountant-1-year/Projet Celsius calculation, AI Impact Database, version 0.4 of 13 November 2026 · IA-0301, entry in version 0.1 · engine 0.3.1 · CC BY 4.0.

Usage parameters

AI use

Advanced settings tooling, model class, reasoning, image size, calibration, duration

ToolingModel classReasoningImage sizeCalibrationDuration

Tasks and frequencies

Frequency and share done with AI, task by task · typical use

Adjust each task 12 tasks, frequency and share done with AI

Task	Frequency	Share with AI	Per year	Electricity	Share
Write an email	per day	9 %	238	55 Wh	7 %
Quick question	per day	28 %	308	92 Wh	11 %
Analyse a spreadsheet	per day	14 %	61.6	170 Wh	21 %
Proofread a page	per day	23 %	101	61 Wh	7 %
Write a page	per week	19 %	25.1	11 Wh	1 %
Query a PDF	per week	23 %	20.2	96 Wh	12 %
Summarise 30 pages	per week	28 %	12.3	60 Wh	7 %
Write meeting minutes	per week	28 %	12.3	18 Wh	2 %
Web search with an AI	per day	19 %	83.6	230 Wh	28 %
Presentation of 10 slides	a year	23 %	2.76	31 Wh	4 %
Summarise a meetingdoes not apply with this tooling	per week	0 %
Automated workflowdoes not apply with this tooling	per day	0 %

Result for the chosen case

Location of the servers

Quantity and duration

Days

220 days, servers worldwide: 380 gCO2e, 830 Wh.

For a carbon footprint

The row for a carbon footprint

Item	purchases of services (GHG Protocol scope 3, category 1); regulatory BEGES, category 4
Quantity	1 role over one year
Factor	380 gCO2e per poste sur un an, IA-0301 version 0.1, according to the grid at the location (location-based), servers worldwide
Total	380 gCO2e · not counted: server manufacturing, model training, the person's device and the tasks done without AI

Emissions breakdown by task family

Emissions associated with electricity (CO2e)

Text and conversation270 gCO2e · 72 %
Search110 gCO2e · 28 %

Accountant, 1 year · result per person per year · estimated · typical use · reference year: 220 days · servers worldwide.Counted: the tasks done with AI over a working year, including data centre cooling. Out of scope: server manufacturing, model training, the person's device and the tasks done without AIFind this case: projetcelsius.com/en/ai/database/activities/accountant-1-year/Projet Celsius calculation, AI Impact Database, version 0.4 of 13 November 2026 · IA-0301, entry in version 0.1 · engine 0.3.1 · CC BY 4.0.

Frequency and emissions of each task in the occupation "Accountant"

Reading: "Web search with AI" (84 times per year) weighs 2.5 times more over a year than "Quick question" (310 times). Carbon footprint (CO2e), typical use. Each point is a task: the most frequent are on the right, the heaviest each time at the top; the diagonals link what weighs the same over a year. In grey, the tasks of the 19 other occupations, at their default settings. Servers worldwide. Calculation from Projet Celsius's AI Impact Database.

Typical usage profile

Projet Celsius assumption

The typical accountant gives AI 5 emails, 7 questions on rules or tax, 2 text revisions and a little over one spreadsheet; they query a long document every 2 weeks and run 2 web searches. About 20 requests per week. Over the year, about 830 Wh. Frugal, 270 Wh; intensive, 2.9 kWh. With a workflow that reads their invoices, they put 60 per week through AI and the year rises to 1.5 kWh: for them, the AI that works without their seeing it weighs almost as much as their own.

Breakdown by item

Quantity, unit value, contribution and nature of the data for each item

Item	Quantity	Unit value	Contribution	Share	Nature of the data
Web search with an AI	84 times	2.7 Wh	230 Wh	28 %	assumption · Microsoft Research
Analyse a spreadsheet	62 times	2.8 Wh	170 Wh	21 %	assumption · O*NET
Query a PDF	20 times	4.7 Wh	96 Wh	12 %	assumption · Microsoft Research
Quick question	310 times	0.3 Wh	92 Wh	11 %	assumption · Crédoc
Proofread a page	100 times	0.6 Wh	61 Wh	7 %	assumption · Microsoft Research
Summarise 30 pages	12 times	4.9 Wh	60 Wh	7 %	assumption · Microsoft Research
Write an email	240 times	0.23 Wh	55 Wh	7 %	assumption · Microsoft Research
Presentation of 10 slides	2.8 times	11 Wh	31 Wh	4 %	assumption · Microsoft Research
Write meeting minutes	12 times	1.4 Wh	18 Wh	2 %	assumption · Microsoft Research
Write a page	25 times	0.45 Wh	11 Wh	1 %	assumption · O*NET

Entries that use it (8)

SME of 50 people, Mid-cap company of 500 people, Large company (5,000), France, inhabitants, Office workers, Public employees, Île-de-France, inhabitants, European Union, inhabitants

Sources of the calculation

15 publications, for this entry and those used to calculate it, with the chosen parameters.

The result is calculated by Projet Celsius. The sources below support its uses, its factors and its conversion; they do not all measure this total.

Uses and assumptions

These publications shed light on AI tasks and uses. They do not measure the carbon footprint of the case calculated.

Apec · June 2025
Les cadres et l'IA ↗ (new tab)
Accountant, 1 year
Role and limits : Apec
What the publication covers. frequency of generative AI use at work, 2,000 private-sector managers surveyed in March 2025
What it does not cover. non-managers and the number of requests
Reading by the database. Recent French survey on frequency of use at work; copy read on a specialist press site
- Accountant, 1 year →
  Support for the "Typical use" setting
  Scope of the figure used. The median user among those who use AI, end of 2026: they turn to it every working day for part of their questions, writing and reading, about 5 to 15 requests per day in an office job. In an organisation or a territory, it also sets the share of employees who use it (25%).
  Limit. This setting remains an assumption of the database, not a measurement of the occupation.
Crédoc · 2026
Baromètre du numérique, édition 2026 ↗ (new tab)
Quick question
Role and limits : Crédoc
Author. Crédoc for Arcep, Arcom, the CGE and the ANCT
What the publication covers. use of generative AI by people aged 12 and over in France in 2025: who, for what, how often, paid or not
What it does not cover. the number of requests
Reading by the database. The reference survey in France, 4,145 people
- Accountant, 1 year → : Quick question →
  About 5 questions per day (accounting rules, tax), assumption; share done with AI based on the Crédoc Baromètre du numérique (2026 edition, 2025 data), adjusted to the occupation.
  Scope of the figure used. computation of the response, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
EnronSent
EnronSent Corpus, notice ↗ (new tab)
Write, with reasoning · Read 1,000 tokens
Role and limits : EnronSent
Author. Will Styler
What the publication covers. 96,106 messages sent by Enron employees, 13,810,266 words
What it does not cover. the messages received
Reading by the database. Professional emails from 2000 and 2001: an order of magnitude, not a norm
- Write an email → : Write, with reasoning →
  The email written, 150 words
  Scope of the figure used. server, excluding the data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Write an email → : Read 1,000 tokens →
  Length convention: an email
  Scope of the figure used. A typical email: the sent messages of the Enron corpus average about 144 words; figure read in an excerpt, notice not verified
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
- Write an email → : Write, with reasoning →
  Length convention: an email
  Scope of the figure used. A typical email: the sent messages of the Enron corpus average about 144 words; figure read in an excerpt, notice not verified
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Microsoft Research · July 2025
Working with AI: Measuring the Applicability of Generative AI to Occupations ↗ (new tab)
Accountant, 1 year · Write an email · Proofread a page · and 5 other entries
Role and limits : Microsoft Research
Author. Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri (Microsoft)
What the publication covers. 200,000 conversations with Bing Copilot matched to O*NET activities; applicability score by occupation, in open data (version 7 of October 2026)
What it does not cover. actual adoption by occupation
Reading by the database. Measures what AI is used for and succeeds at, not who uses it
- Accountant, 1 year →
  Published observation on uses
  Scope of the figure used. Generative AI applicability score for accountants and auditors (code 13-2011), 200,000 conversations with Bing Copilot, from 0 to 1
  Limit. Used for the occupation coefficient: square root of the score divided by 0.25, the score of office employees
- Accountant, 1 year →
  Published observation on uses
  Scope of the figure used. Generative AI applicability score for accounting clerks (code 43-3031), 200,000 conversations with Bing Copilot, from 0 to 1
  Limit. Used for the occupation coefficient: square root of the score divided by 0.25, the score of office employees
- Accountant, 1 year → : Write an email →
  About 12 emails per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
  Scope of the figure used. tokens read and written, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Accountant, 1 year → : Proofread a page →
  About 2 texts to rework per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
  Scope of the figure used. tokens read and written, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Accountant, 1 year → : Query a PDF →
  About 2 long documents to query per week (tax file, contract, report), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
  Scope of the figure used. document read, response, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Accountant, 1 year → : Summarise 30 pages →
  About 1 regulatory text or report to summarise per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
  Scope of the figure used. document read, summary written, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Accountant, 1 year → : Write meeting minutes →
  About 1 set of minutes per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
  Scope of the figure used. simple tasks, data centre included
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Accountant, 1 year → : Web search with an AI →
  About 2 web searches per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
  Scope of the figure used. searches, pages read, response, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Accountant, 1 year → : 10-slide presentation →
  About 1 presentation per month (closing, budget), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
  Scope of the figure used. simple tasks, data centre included
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
O*NET · 2025
O*NET 30.1 Database, Task Statements and Task Ratings ↗ (new tab)
Accountant, 1 year · Analyse a spreadsheet · Draft a page
Role and limits : O*NET
Author. National Center for O*NET Development, US Department of Labor
What the publication covers. tasks of each occupation and frequency reported by job holders, in 7 classes from "once a year or less" to "every hour or more"
What it does not cover. occupations without a frequency survey, such as data scientists
Reading by the database. The reference classification of tasks; US job holders
- Accountant, 1 year →
  Published observation on uses
  Scope of the figure used. accountants and auditors: writing detailed assignment reports, share who do so daily or more (most often more than once a year)
  Limit. Support for the frequency of the task in the basket; US job holders
- Accountant, 1 year → : Analyse a spreadsheet →
  About 2 spreadsheets to analyse per day (reconciliations, checks), the core of the occupation in O*NET; share done with AI set low, data analysis carrying little weight in messages (OpenAI, 2025), adjusted to the occupation.
  Scope of the figure used. tokens read and written, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Accountant, 1 year → : Write a page →
  About 3 pages of notes or reports per week, report writing being mostly monthly or annual in O*NET; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
  Scope of the figure used. tokens read and written, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.

The 2 other publications in this section

Projet Celsius · November 2026
Tokens per word, per line of code and per cell, measured with 7 public tokenisers
Read 1,000 tokens · Write, with reasoning
Role and limits
What the publication covers. 479,888 words from 196 blog articles and their English translation, code from the site and the Python library, a table of measurements; tokenisers of GPT-4o, GPT-4, Llama 3, Gemini (via Gemma 3), Mistral, DeepSeek and Qwen
What it does not cover. the Claude tokeniser, not public
Reading by the database. A measurement on technical texts, figures included; to be repeated if publishers change their tokeniser
Projet Celsius measurement, repeated on request with the public tokenisers
- Analyse a spreadsheet → : Read 1,000 tokens →
  The spreadsheet, 200 rows and 10 columns, and the question
  Scope of the figure used. server, excluding the data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Analyse a spreadsheet → : Read 1,000 tokens →
  Length convention: a spreadsheet of 200 rows
  Scope of the figure used. A table of 200 rows and 10 columns of numbers and short labels: 4.7 tokens per cell, measured on a real table
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
- Proofread a page → : Read 1,000 tokens →
  Length convention: one page
  Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
- Proofread a page → : Write, with reasoning →
  Length convention: one page
  Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
- Write a page → : Write, with reasoning →
  Length convention: one page
  Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
- Query a PDF → : Read 1,000 tokens →
  Length convention: one page
  Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
- Summarise 30 pages → : Read 1,000 tokens →
  Length convention: one page
  Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
- Summarise 30 pages → : Write, with reasoning →
  Length convention: one page
  Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Zhang and Nam · June 2026
HANSEL: Extracting Breadcrumbs from Web Agent Trajectories for Interactive Verification ↗ (new tab)
Read 1,000 tokens
Role and limits
Author. Yujin Zhang and Daye Nam
What the publication covers. 45 web agent trajectories: 13.16 steps and 6.02 pages visited per task on average
What it does not cover. electricity
Reading by the database. A small sample, which gives the order of magnitude of an agent's steps
- Web search with an AI → : Read 1,000 tokens →
  The 5 pages found, read by the model, and the question: web agents visit 6 pages per task on average
  Scope of the figure used. server, excluding the data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.

Energy and factors

The references of the selected values and of the entries used in this calculation.

Epoch AI · February 2025
How much energy does ChatGPT use? ↗ (new tab)
Quick question
Role and limits : Epoch AI
What the publication covers. the calculation from the hardware and the assumed size of the model
What it does not cover. actual measurements
Reading by the database. A public, transparent calculation
- Quick question →
  Reference of the selected value
  Scope of the figure used. GPT-4o, 500 tokens written, full scope
  Limit. A public calculation, from the hardware and the assumed size of the model
Google · Aug. 2025
Measuring the environmental impact of delivering AI at Google scale ↗ (new tab)
Quick question
Role and limits : Google
What the publication covers. chips, host, standby machines, data centre, on-site water
What it does not cover. manufacturing, training
Reading by the database. The only production measurement published by a provider
- Quick question →
  Reference of the selected value
  Scope of the figure used. Median of a text prompt; chips, host, standby machines, data centre
  Limit. The only production measurement published by a provider, not audited by a third party
Google · January 2009
Powering a Google search ↗ (new tab)
Web search (benchmark)
Role and limits : Google
What the publication covers. a search, data centre
Reading by the database. Never remeasured
- Web search (reference) →
  Reference of the selected value
  Scope of the figure used. A search, data centre included
  Limit. Never publicly remeasured; Google's AI answers are not published
Luccioni et al. · October 2024
Power Hungry Processing ↗ (new tab)
Generate an image
Role and limits : Luccioni et al.
Author. Luccioni, Jernite and Strubell
What the publication covers. GPU, processor, memory
What it does not cover. data centre
Reading by the database. Open models only
- Create an image → : Generate an image →
  One image, of the chosen size: standard by default
  Scope of the figure used. GPU, processor, memory; excluding the data centre
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Generate an image →
  Reference for the variant "Standard image, from 512 to 1,024 pixels per side"
  Scope of the figure used. Average of the open models tested, 1,000 images; median 1.35 Wh, maximum 11.49 Wh
  Limit. Open models only; closed generators publish nothing
Microsoft Research · 2026
Estimation de l'énergie des requêtes en production (Joule) ↗ (new tab)
Quick question
Role and limits : Microsoft Research
Author. Microsoft Research, Oviedo et al.
What the publication covers. H100 nodes, production
Reading by the database. Calibrated on production
- Quick question →
  Reference of the selected value
  Scope of the figure used. Median, 500 tokens read and 300 written, H100 nodes
  Limit. Not a measurement, but calibrated on production; judges public estimates to be 4 to 20 times too high

The 2 other publications in this section

OpenAI · June 2025
The Gentle Singularity ↗ (new tab)
Quick question
Role and limits
Author. Sam Altman, OpenAI
What the publication covers. an average query
What it does not cover. method, scope
Reading by the database. One sentence, no method
- Quick question →
  Reference of the selected value
  Scope of the figure used. "An average query", with no published method
  Limit. No published scope; consistent with Google
Watershed · Aug. 2026
Estimating GHG Emissions from AI Use: Framework for Corporate-Level Measurement ↗ (new tab)
Read 1,000 tokens · Write 1,000 tokens · Data centre PUE
Role and limits
Author. Bistline, Ulissi, Davis, Glidden, Joyce, Li, Mohsenin and Suh (Watershed)
What the publication covers. factors per 1,000 tokens read and written at the server before PUE (host and standby machines included), PUE separate (1.10 for a large operator, 1.56 for an unknown operator)
What it does not cover. clean measurements, the model and the hardware
Reading by the database. Default factors proposed for review (public data as of June 2026); for a large operator, taken from Google's measurement published in 2025 (Elsworth et al.), which already contains the data centre overhead; constant per token, they underestimate long contexts and agents according to the authors
- Read 1,000 tokens →
  Reference for the variant "Medium model, Watershed default factor"
  Scope of the figure used. Default factor for 1,000 tokens read, medium model, large operator, at the server before PUE (Watershed table 2, public data as of June 2026)
  Limit. A default value that Watershed derives from Google's measurement (0.24 Wh for the median text query of the Gemini app), with about 500 tokens per query and a written token costing 3 times a read token; Watershed does not give the split between read and written tokens. The 0.24 Wh already include the overhead of Google's data centre (0.02 Wh, 8%): with 375 tokens read and 125 written, the simplest reading, the factor contains it, and the PUE of 1.10 applied afterwards counts it a second time (about 9%). Values proposed for review, final at the next version of the framework
- Write 1,000 tokens →
  Reference for the variant "Medium model, Watershed default factor"
  Scope of the figure used. Default factor for 1,000 tokens written, medium model, large operator, at the server before PUE (Watershed table 2, public data as of June 2026)
  Limit. 3 times the reading factor, based on the ratio between writing and reading selected by Watershed (Patel et al., Splitwise, 2024). Same derivation and same caveat as for reading: Google's 0.24 Wh already include about 8% of data centre. Values proposed for review, final at the next version of the framework
- Write an email → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Data centre PUE →
  Reference of the selected value
  Scope of the figure used. Default factor for a large operator, from the server to the data centre; 1.56 for an unknown operator
  Limit. A default value, not a measurement
- Quick question → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Analyse a spreadsheet → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Proofread a page → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Write a page → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Query a PDF → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Summarise 30 pages → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Web search with an AI → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.
- Create an image → : Data centre PUE →
  The data centre, convention C3
  Scope of the figure used. building cooling and losses
  Limit. This figure covers part of the calculation; it does not measure the whole entry.

Conversion into carbon

The source of the electricity factor of the selected location.

Worldwide: 458 gCO2e/kWh, life cycle.

Ember · 2026
Global Electricity Review 2026, and Our World in Data based on Ember ↗ (new tab)
Grid electricity
Role and limits : Ember
Author. Ember, Our World in Data
What the publication covers. electricity generation over the life cycle, by country
What it does not cover. guarantees of origin
Reading by the database. The same source for all countries
- Grid electricity →
  Conversion of electricity into carbon: worldwide
  Scope of the figure used. the production of electricity over the life cycle, including power plant construction and fuels
  Limit. An average for the chosen country, not the electricity contract or the data centre of a specific service.

Nature of the data

Published readings, conventions, assumptions

What this figure rests on

Projet Celsius assumptions 100 %

A share counts as an assumption as soon as a quantity set by Projet Celsius (a frequency, a share done with AI, a headcount) enters its calculation, even if the rest rests on published measurements.

Usage assumptions

Frequency, share done with AI according to adoption, source of each assumption

Write an email : 12 per day, 9% with AI in typical use
About 12 emails per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Share done with AI: 3% in light use, 9% typical, 33% intensive · assumption · Microsoft Research · Write an email
Quick question : 5 per day, 28% with AI in typical use
About 5 questions per day (accounting rules, tax), assumption; share done with AI based on the Crédoc Baromètre du numérique (2026 edition, 2025 data), adjusted to the occupation.
Share done with AI: 9% in light use, 28% typical, 98% intensive · assumption · Crédoc · Quick question
Analyse a spreadsheet : 2 per day, 14% with AI in typical use
About 2 spreadsheets to analyse per day (reconciliations, checks), the core of the occupation in O*NET; share done with AI set low, data analysis carrying little weight in messages (OpenAI, 2025), adjusted to the occupation.
Share done with AI: 5% in light use, 14% typical, 49% intensive · assumption · O*NET · Analyse a spreadsheet
Proofread a page : 2 per day, 23% with AI in typical use
About 2 texts to rework per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Share done with AI: 8% in light use, 23% typical, 82% intensive · assumption · Microsoft Research · Proofread a page
Write a page : 3 per week, 19% with AI in typical use
About 3 pages of notes or reports per week, report writing being mostly monthly or annual in O*NET; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Share done with AI: 6% in light use, 19% typical, 66% intensive · assumption · O*NET · Write a page
Query a PDF : 2 per week, 23% with AI in typical use
About 2 long documents to query per week (tax file, contract, report), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Share done with AI: 8% in light use, 23% typical, 82% intensive · assumption · Microsoft Research · Query a PDF
Summarise 30 pages : 1 per week, 28% with AI in typical use
About 1 regulatory text or report to summarise per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Share done with AI: 9% in light use, 28% typical, 98% intensive · assumption · Microsoft Research · Summarise 30 pages
Write meeting minutes : 1 per week, 28% with AI in typical use
About 1 set of minutes per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Share done with AI: 9% in light use, 28% typical, 98% intensive · assumption · Microsoft Research · Write meeting minutes
Web search with an AI : 2 per day, 19% with AI in typical use
About 2 web searches per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Share done with AI: 6% in light use, 19% typical, 66% intensive · assumption · Microsoft Research · Web search with an AI
Presentation of 10 slides : 12 per year, 23% with AI in typical use
About 1 presentation per month (closing, budget), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Share done with AI: 8% in light use, 23% typical, 82% intensive · assumption · Microsoft Research · Presentation of 10 slides
Summarise a meeting : 0.6 per week, 70% with AI in typical use · AI in the office suite tooling
About 3 meetings per week, assumption; with AI in the office suite, most are summarised without being asked, assumption; one summarised meeting serves its 5 participants, each carrying a fifth of it.
Share done with AI: 30% in light use, 70% typical, 100% intensive · assumption · Summarise a meeting
Automated workflow : 30 per day, 40% with AI in typical use · agents tooling
About 30 items to enter per day (invoices, expense claims), assumption; with agents, a share goes through an automated workflow, assumption with no published survey.
Share done with AI: 10% in light use, 40% typical, 90% intensive · assumption · Automated workflow

Published figures in support

Microsoft Research, Tomlinson et al. (2026): 0.195 applicability score
Generative AI applicability score for accountants and auditors (code 13-2011), 200,000 conversations with Bing Copilot, from 0 to 1
Used for the occupation coefficient: square root of the score divided by 0.25, the score of office employees · computation · the source ↗
Microsoft Research, Tomlinson et al. (2026): 0.244 applicability score
Generative AI applicability score for accounting clerks (code 43-3031), 200,000 conversations with Bing Copilot, from 0 to 1
Used for the occupation coefficient: square root of the score divided by 0.25, the score of office employees · computation · the source ↗
O*NET (2025): 5% of job holders
accountants and auditors: writing detailed assignment reports, share who do so daily or more (most often more than once a year)
Support for the frequency of the task in the basket; US job holders · measured · the source ↗

Methodology conventions

With their alternatives

The full methodology

The working day and year	A working year counts 220 days and 44 weeks, a personal year 365 days; the task lists of occupations give, as a median, about 2 requests to AI per working day at low adoption, 6 at standard adoption, 21 at intensive adoption, excluding code suggestions · alternatives: 365 days for personal use; the profiles of the previous version (5, 20 and 80 requests per day), replaced by task lists
The default location	Without other information, the servers are in the world, at the world average electricity · alternatives: the United States, where most of the large assistants run
Electricity, location-based, on a life cycle basis	The kilowatt-hour is that of the servers' country, on a life cycle basis, from a single source for all countries · alternatives: market-based, with purchase contracts, shown alongside when published
Two significant figures	A calculated value is rounded to two significant figures, a published value is reproduced with the precision of its source; the calculation keeps full precision
The formula	the sum of what makes up the entry, then CO2e: electricity (Wh) multiplied by the carbon intensity of the server location (gCO2e/kWh), divided by 1,000
Calculate a case without an entry	The formula, the default values and a step-by-step example

History

Entry in version 0.1 of 2 November 2026, in the database in version 0.4

The changelog

2 November 2026Version 0.1 of the entry, as of the date of its current value.
31 October 2026Creation of the database, in its first version (0.1).

Citation

Projet Celsius, AI Impact Database, version 0.4 of 13 November 2026, "Comptable, 1 an", IA-0301 (entry in version 0.1 of 2 November 2026), projetcelsius.com/en/ai/database/IA-0301/ · CC BY 4.0.

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