Métier de terrain (technicien, soignant), 1 an

Name: Field occupation (technician, care worker), 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 9 entries, levels 1 and 3Database structure

Field occupation (technician, care worker), 1 year

A year of work for a technician or a healthcare worker who uses AI: about 150 reports, 33 web searches with AI, 16 PDFs queried, and 5 other tasks. Projet Celsius estimate.

Text and conversation account for 78% of emissions: "Write a report" weighs as much as 4.8 quick questions.
About 490 requests to AI over the year, or 2.2 per working day.
2.7 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

220 gCO2e in total: 480 Wh of electricity × 458 gCO2e/kWh (servers worldwide).

Inputs of the calculation 9

PUE of the data centre, Read 1,000 tokens, Write 1,000 tokens, Write, with reasoning, Re-read from cache, Write meeting minutes, Query a PDF, Web search with an AI, Web search (benchmark)

How the calculation is built

A year of work for a technician or a healthcare worker who uses AI, task by task: procedural questions, intervention reports, instructions, dictation. The frequencies come from the content of the job, AI use from usage surveys (OpenAI, Microsoft, Crédoc); these are assumptions, and they can be adjusted.

Comparison with other occupations and activities

See the full list

Secondary school pupil, 1 year150 gCO2e

Field-based occupation, 1 yearthis entry220 gCO2e

Teacher, 1 year260 gCO2e

Human resources, 1 year330 gCO2e

Free personal use, 1 year340 gCO2e

Manager or executive, 1 year370 gCO2e

Accountant, 1 year380 gCO2e

Student, 1 year490 gCO2e

Lawyer, 1 year520 gCO2e

Office worker, 1 year590 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-0315, projetcelsius.com/en/ai/database/IA-0315/
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)

Poland280 gCO2e
World220 gCO2e
United States180 gCO2e
European Union100 gCO2e
France20 gCO2e

× 14 between Poland and France, for the same electricity

Ember 2025, over the life cycle. The same use consumes 480 Wh in each location.

Field occupation (technician, healthcare worker), 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 again: projetcelsius.com/en/ai/database/activities/field-occupation-1-year/Calculation by Projet Celsius, AI Impact Database, version 0.4 of 13 November 2026 · IA-0315, entry in version 0.1 · engine 0.3.1 · CC BY 4.0.

Usage parameters

AI use

Advanced settings model class, reasoning, calibration, duration

Model classReasoningCalibrationDuration

Tasks and frequencies

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

Adjust each task 8 tasks, frequency and share done with AI

Task	Frequency	Share with AI	Per year	Electricity	Share
Quick question	per day	22 %	194	58 Wh	12 %
Write meeting minutes	per day	22 %	145	210 Wh	43 %
Write an email	per day	7 %	46.2	11 Wh	2 %
Voice mode, 1 minute	per week	11 %	24.2	15 Wh	3 %
Query a PDF	per week	18 %	15.8	75 Wh	16 %
Translate a page	per week	44 %	19.4	12 Wh	2 %
Proofread a page	per week	18 %	15.8	9.5 Wh	2 %
Web search with an AI	per day	15 %	33	91 Wh	19 %

Result for the chosen case

Location of the servers

Quantity and duration

Days

220 days, servers worldwide: 220 gCO2e, 480 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	220 gCO2e per position over a year, IA-0315 version 0.1, based on the local grid (location-based), servers worldwide
Total	220 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 conversation170 gCO2e · 78 %
Search42 gCO2e · 19 %
Other tasks6.9 gCO2e · 3.2 %

Field occupation (technician, healthcare worker), 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 again: projetcelsius.com/en/ai/database/activities/field-occupation-1-year/Calculation by Projet Celsius, AI Impact Database, version 0.4 of 13 November 2026 · IA-0315, entry in version 0.1 · engine 0.3.1 · CC BY 4.0.

Frequency and emissions of each task of the "Field occupation" occupation

How to read: "Write a report" (150 times a year) weighs 3.6 times more over a year than "Quick question" (190 times). Carbon footprint (CO2e), typical use. Each point is a task: the most frequent on the right, the heaviest each time at the top; the diagonals join what weighs the same over a year. In grey, the tasks of the 19 other occupations, at their default settings. Servers worldwide. Calculation from the AI Impact Database by Projet Celsius.

Typical usage profile

Projet Celsius assumption

The typical technician or healthcare worker asks AI 4 questions a week, has it format 3 reports, translates an exchange every 2 weeks and queries an instruction manual from time to time. About 11 requests a week. Over the year, about 480 Wh, the lowest of the occupations. Light use, 150 Wh; heavy use, 1.6 kWh.

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
Write meeting minutes	150 times	1.4 Wh	210 Wh	43 %	assumption · O*NET
Web search with an AI	33 times	2.7 Wh	91 Wh	19 %	assumption · Microsoft Research
Query a PDF	16 times	4.7 Wh	75 Wh	16 %	assumption · Microsoft Research
Quick question	190 times	0.3 Wh	58 Wh	12 %	assumption · Microsoft Research
Voice mode, 1 minute	24 times	0.62 Wh	15 Wh	3 %	assumption · Microsoft Research
Translate a page	19 times	0.6 Wh	12 Wh	2 %	assumption · Microsoft Research
Write an email	46 times	0.23 Wh	11 Wh	2 %	assumption · Microsoft Research
Proofread a page	16 times	0.6 Wh	9.5 Wh	2 %	assumption · Microsoft Research

Entries that use it (8)

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

Sources of the calculation

14 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)
Field-based occupation, 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
- Field occupation, 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.
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)
Field occupation, 1 year · Quick question · Write an email · 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
- Field occupation, 1 year →
  Published observation on uses
  Scope of the figure used. Generative AI applicability score for nurses (code 29-1141), 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
- Field occupation, 1 year →
  Published observation on uses
  Scope of the figure used. Generative AI applicability score for electricians (code 47-2111), 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
- Field occupation, 1 year → : Quick question →
  About 4 questions per day (procedure, breakdown, instruction), assumption; share done with AI based on the Crédoc Baromètre du numérique (edition 2026, data 2025), adjusted to the occupation, low for occupations where the work is mainly physical (Microsoft, 2025).
  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.
- Field occupation, 1 year → : Write an email →
  About 3 emails or messages per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from 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.
- Field occupation, 1 year → : Voice mode, 1 minute →
  About 5 minutes of dictation per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
  Scope of the figure used. voice heard, voice spoken, context re-read, data centre
  Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
- Field occupation, 1 year → : Query a PDF →
  About 2 long manuals or protocols to query per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from 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.
- Field occupation, 1 year → : Translate a page →
  About 1 exchange to translate per week (patient, client, manual), assumption; high share done with AI, translation being among the tasks most often handed to AI (Microsoft, 2025).
  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.
- Field occupation, 1 year → : Proofread a page →
  About 2 texts to rework per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from 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.
- Field occupation, 1 year → : Web search with an AI →
  About 1 web search per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from 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.
O*NET · 2025
O*NET 30.1 Database, Task Statements and Task Ratings ↗ (new tab)
Field occupation, 1 year · Write a report
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
- Field occupation, 1 year →
  Published observation on uses
  Scope of the figure used. nurses: keeping accurate records and reports, share who do so daily or more often
  Limit. Support for the frequency of the task in the basket; US job holders
- Field occupation, 1 year → : Write meeting minutes →
  About 3 intervention reports or handovers per day, record keeping being several times a day for nurses in O*NET; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from 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.
OpenAI · 2026
Cost optimization, section Realtime API costs ↗ (new tab)
Read 1,000 tokens · Write 1,000 tokens
Role and limits : OpenAI
What the publication covers. audio tokens: one per 100 milliseconds of the user's speech, one per 50 milliseconds of the assistant's speech
What it does not cover. electricity
Reading by the database. The publisher's billing rule, read in the page excerpt
- Voice mode, 1 minute → : Read 1,000 tokens →
  What the user says, half of the minute, in audio tokens
  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.
- Voice mode, 1 minute → : Write 1,000 tokens →
  What the AI answers, the other half, in audio tokens
  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.
- Voice mode, 1 minute → : Read 1,000 tokens →
  Length convention: one minute of voice heard by an AI
  Scope of the figure used. One audio token per 100 milliseconds of the user's speech at OpenAI; Gemini counts 32 per second, or 1,920 per minute
  Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
- Voice mode, 1 minute → : Write 1,000 tokens →
  Length convention: one minute of voice spoken by an AI
  Scope of the figure used. One audio token per 50 milliseconds of the assistant's speech at OpenAI
  Limit. This publication supports the quantity of text or content processed, not its carbon 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
- 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.
- 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.
- Translate a page → : Write, with reasoning →
  The translated page, counted as a French page
  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.
- Translate 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.
- Translate 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.
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
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
OpenAI · June 2025
The Gentle Singularity ↗ (new tab)
Quick question
Role and limits : OpenAI
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

The other publication in this section

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
- 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.
- 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
- 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 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.
- Voice mode, 1 minute → : 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.
- Translate 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.
- 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.

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

Quick question : 4 per day, 22% with AI in typical use
About 4 questions per day (procedure, breakdown, instruction), assumption; share done with AI based on the Crédoc Baromètre du numérique (edition 2026, data 2025), adjusted to the occupation, low for occupations where the work is mainly physical (Microsoft, 2025).
Share done with AI: 7% in light use, 22% typical, 76% intensive · assumption · Microsoft Research · Quick question
Write meeting minutes : 3 per day, 22% with AI in typical use
About 3 intervention reports or handovers per day, record keeping being several times a day for nurses in O*NET; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Share done with AI: 7% in light use, 22% typical, 76% intensive · assumption · O*NET · Write meeting minutes
Write an email : 3 per day, 7% with AI in typical use
About 3 emails or messages per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Share done with AI: 2% in light use, 7% typical, 25% intensive · assumption · Microsoft Research · Write an email
Voice mode, 1 minute : 5 per week, 11% with AI in typical use
About 5 minutes of dictation per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Share done with AI: 4% in light use, 11% typical, 38% intensive · assumption · Microsoft Research · Voice mode, 1 minute
Query a PDF : 2 per week, 18% with AI in typical use
About 2 long manuals or protocols to query per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Share done with AI: 6% in light use, 18% typical, 64% intensive · assumption · Microsoft Research · Query a PDF
Translate a page : 1 per week, 44% with AI in typical use
About 1 exchange to translate per week (patient, client, manual), assumption; high share done with AI, translation being among the tasks most often handed to AI (Microsoft, 2025).
Share done with AI: 15% in light use, 44% typical, 100% intensive · assumption · Microsoft Research · Translate a page
Proofread a page : 2 per week, 18% with AI in typical use
About 2 texts to rework per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Share done with AI: 6% in light use, 18% typical, 64% intensive · assumption · Microsoft Research · Proofread a page
Web search with an AI : 1 per day, 15% with AI in typical use
About 1 web search per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation from the Microsoft study of 200,000 conversations.
Share done with AI: 5% in light use, 15% typical, 51% intensive · assumption · Microsoft Research · Web search with an AI

Published figures in support

Microsoft Research, Tomlinson et al. (2026): 0.122 applicability score
Generative AI applicability score for nurses (code 29-1141), 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): 0.151 applicability score
Generative AI applicability score for electricians (code 47-2111), 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): 98% of job holders
nurses: keeping accurate records and reports, share who do so daily or more often
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, "Field occupation (technician, healthcare worker), 1 year", IA-0315 (entry added in version 0.1 of 2 November 2026), projetcelsius.com/en/ai/database/IA-0315/ · CC BY 4.0.

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