UsesLevel 4 of 6 occupation or activityOccupation or activity, level 4 of 6Used to calculate 6 entries, levels 5 and 6Calculated with 12 entries, levels 1 and 3Database structure 
6Territories
5Organisations
3Uses
2Machine operations
1Calculation factorsData scientist, 1 year
A year of work for a data scientist who uses AI: about 26,000 code suggestions, 390 functions generated, 630 quick questions, and 8 other tasks. Estimate by Projet Celsius.
- Code and agents account for 72% of emissions.
- About 1,500 requests to AI over the year, or 6.6 per working day, not counting 26,000 code suggestions or 13,000 tasks entrusted to a specialised AI.
- 2.5 times the AI footprint of an office employee, for the same use.
- An estimate: the frequencies and the share done with AI are our assumptions.
Sentence to citeData scientist, 1 year, Projet Celsius estimate: 1.5 kgCO2e per item per year, for 3.2 kWh of electricity, servers worldwide (458 gCO2e/kWh).
Breakdown of emissions by item
1.5 kgCO2e in total: 3.2 kWh of electricity × 458 gCO2e/kWh (servers worldwide).
Inputs of the calculation
PUE of the data centre, Read 1,000 tokens, Write, with reasoning, Write 1,000 tokens, Generate an image, Generate a function, Code suggestion, Quick question
How the calculation is built
A year of work for a data scientist who uses AI, task by task: code, suggestions, data explored, items classified by a model; with agents, agent hours. Frequencies come from the content of the occupation, AI use from usage surveys (OpenAI, Microsoft, Crédoc); these are assumptions, and they can be adjusted.
Comparison with other occupations and activities
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 | IA-0310, projetcelsius.com/en/ai/database/IA-0310/ |
|---|---|
| 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 |
Usage parameters
Advanced settings
Tasks and frequencies
Frequency and share done with AI, task by task · typical use
Adjust each task
| Task | Frequency | Share with AI | Per year | Electricity | Share |
|---|---|---|---|---|---|
| Generate a function | 385 | 450 Wh | 14 % | ||
| Code suggestion | 26,400 | 1.8 kWh | 57 % | ||
| Analyse a spreadsheet | 55 | 150 Wh | 5 % | ||
| Quick question | 634 | 190 Wh | 6 % | ||
| Conversation of 10 exchanges | 52.8 | 190 Wh | 6 % | ||
| Query a PDF | 26.4 | 120 Wh | 4 % | ||
| Summarise 30 pages | 15.8 | 77 Wh | 2 % | ||
| Write a page | 21.1 | 9.6 Wh | 0 % | ||
| Presentation of 10 slides | 6.6 | 75 Wh | 2 % | ||
| Write an email | 264 | 61 Wh | 2 % | ||
| Specialised task | 13,200 | 26 Wh | 1 % | ||
| Coding agent hourdoes not apply with this tooling |
Result for the chosen case
220 days, servers worldwide: 1.5 kgCO2e, 3.2 kWh.
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 | 1.5 kgCO2e per item over one year, IA-0310 version 0.1, according to the local grid (location-based), servers worldwide |
| Total | 1.5 kgCO2e · not counted: server manufacturing, model training, the person's device and the tasks done without AI |
Typical usage profile
Projet Celsius assumption
The typical data scientist has 9 functions or queries written, accepts about 600 suggestions, explores a dataset with AI, asks it 14 questions, opens a methodology discussion and has 300 texts classified by a small model. About 33 requests a week, not counting suggestions or classified texts. Over the year, about 3.2 kWh. Frugal, 1.1 kWh; intensive, 6.1 kWh; with agents, 8 hours a week and about 240 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 |
|---|---|---|---|---|---|
| Code suggestion | 26,000 times | 0.069 Wh | 1.8 kWh | 57 % | assumption |
| Generate a function | 390 times | 1.2 Wh | 450 Wh | 14 % | assumption |
| Quick question | 630 times | 0.3 Wh | 190 Wh | 6 % | assumption |
| Conversation of 10 exchanges | 53 times | 3.5 Wh | 190 Wh | 6 % | assumption |
| Analyse a spreadsheet | 55 times | 2.8 Wh | 150 Wh | 5 % | assumption |
| Query a PDF | 26 times | 4.7 Wh | 120 Wh | 4 % | assumption |
| Summarise 30 pages | 16 times | 4.9 Wh | 77 Wh | 2 % | assumption |
| Presentation of 10 slides | 6.6 times | 11 Wh | 75 Wh | 2 % | assumption |
| Write an email | 260 times | 0.23 Wh | 61 Wh | 2 % | assumption |
| Specialised task | 13,000 times | 0.002 Wh | 26 Wh | 1 % | assumption |
| Write a page | 21 times | 0.45 Wh | 9.6 Wh | 0 % | assumption |
Sources of the calculation
16 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
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
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
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
Data scientist, 1 year → : Quick question →
About 8 questions per day, 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
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.
Epoch AI · February 2025
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
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
Conversation of 10 exchanges → : Re-read from cache →
The context re-read from cache at each turn: the previous exchanges, and the document already in the conversation if there is one
Scope of the figure used. server, convention C4
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Role and limits : GitHub
What the publication covers. about 6,000 characters of code read for a suggestion
What it does not cover. electricity
Reading by the database. A post of 2023, updated in 2024: the current size is not published
Code suggestion → : Read 1,000 tokens →
The code around the cursor, about 6,000 characters or 125 lines, read by a small model
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.
The 4 other publications in this section
Microsoft Research · July 2025
Working with AI: Measuring the Applicability of Generative AI to Occupations ↗ (new tab)
Role and limits
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
Published observation on uses
Scope of the figure used. Generative AI applicability score for data scientists (code 15-2051), 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
Data scientist, 1 year → : Conversation of 10 exchanges →
About 1 methodology discussion 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. messages, context re-read from cache, responses, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Data scientist, 1 year → : Query a PDF →
About 2 articles or documentation sets queried 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, response, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Data scientist, 1 year → : Summarise 30 pages →
About 1 long document 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.
Data scientist, 1 year → : Write a page →
About 2 pages of reports 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. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Data scientist, 1 year → : 10-slide presentation →
About 2 presentations of results per month, 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.
Data scientist, 1 year → : Write an email →
About 10 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.
OpenAI · December 2025
Role and limits
What the publication covers. actual use by ChatGPT enterprise customers and a survey of 9,000 employees
What it does not cover. the consumer plans and any absolute volume per employee
Reading by the database. Publisher report; the gaps between users are the useful data
Published observation on uses
Scope of the figure used. Use of the data analysis tool by the most active users, relative to the median, among employees in the data function
Limit. Supporting evidence for the data scientist's use of data analysis
Data scientist, 1 year → : Analyse a spreadsheet →
About 1 dataset to explore per day, assumption; share done with AI based on the 16-fold gap between the most active users and the median for data analysis (OpenAI, 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.
Projet Celsius · November 2026
Tokens per word, per line of code and per cell, measured with 7 public tokenisers
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
Code suggestion → : Read 1,000 tokens →
Length convention: a line of code
Scope of the figure used. From 10 tokens per line (Python standard library) to 19 (the TypeScript code of this site), measured with GPT-4o tokenisation
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Code suggestion → : Write 1,000 tokens →
Length convention: a line of code
Scope of the figure used. From 10 tokens per line (Python standard library) to 19 (the TypeScript code of this site), measured with GPT-4o tokenisation
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
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.
Conversation of 10 exchanges → : Re-read from cache →
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.
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.
Stack Overflow · 2025
Role and limits
What the publication covers. frequency of use of AI and agents, tasks handed over, about 33,000 respondents
What it does not cover. the number of requests
Reading by the database. Online survey of volunteer developers, more engaged than average
Data scientist, 1 year → : Generate a function →
About 5 functions or queries written per day, assumption for lack of published frequencies for this occupation in O*NET; fallback as for developers (Stack Overflow, 2025); replaced by agent hours when there are agents.
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.
Data scientist, 1 year → : Code suggestion →
About 150 suggestions offered per day when the assistant is active, assumption; fallback as for developers (Stack Overflow, 2025).
Scope of the figure used. code read, suggestion written, small model, 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.
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
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
Luccioni et al. · October 2024
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.
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
Reference for the "Classify a text" variant
Scope of the figure used. Text classification, average of the open models tested: 0.002 kWh for 1,000 inferences
Limit. Models of 2023, measured on a node of 8 A100 GPUs
Microsoft Research · 2026
Estimation de l'énergie des requêtes en production (Joule) ↗ (new tab)
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
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
ML.ENERGY · February 2026
Role and limits : ML.ENERGY
Author. Chung et al., University of Michigan
What the publication covers. GPU energy per token, per response, per image, in steady state, according to the number of requests served at once
What it does not cover. processors, host memory, data centre cooling, manufacturing
Reading by the database. The best open benchmark: it serves to compare model sizes with each other, not as a production value
Support for the "Small model" setting
Scope of the figure used. A model of about 8 billion active parameters, like Llama 3.1 8B: about 6 times less electricity per token than the medium model, according to the ML.ENERGY benchmark.
Limit. The setting is a convention of the database supported by this publication.
Reference of the variant "Small model, about 8 billion parameters"
Scope of the figure used. Llama 3.1 8B, one H100 GPU, 64 requests at a time, GPU only, per 1,000 tokens written, reading of the question included
Limit. GPU only, on a benchmark: the database keeps only the ratio to the 70 billion model, 1 to 6.7
The measurement covers a different scope; it is not a direct measurement of this entry.
OpenAI · June 2025
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
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)
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
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
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
Generate a function → : 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.
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
Reference factor: 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. This measurement concerns the reference variant, not a measurement of "Small model, about 8 billion parameters". 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. The 0.24 Wh already include the overhead of Google's data centre (0.02 Wh, 8%): with 375 tokens read and 125 written, on 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, to be finalised in the next version of the framework
Reference factor: 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. This measurement concerns the reference variant, not a measurement of "Small model, about 8 billion parameters". 3 times the read factor, based on the write-to-read ratio adopted by Watershed (Patel et al., Splitwise, 2024). Same derivation and same caveat as for reading: Google's 0.24 Wh already include about 8% for the data centre. Values proposed for review, to be finalised in the next version of the framework
Code suggestion → : 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.
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.
Conversation of 10 exchanges → : 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.
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.
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.
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.
Conversion into carbon
The source of the electricity factor of the selected location.
Worldwide: 458 gCO2e/kWh, life cycle.
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
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
- Projet Celsius assumptions 100 %
Usage assumptions
Frequency, share done with AI according to adoption, source of each assumption
Generate a function : 5 per day, 35% with AI in typical use
About 5 functions or queries written per day, assumption for lack of published frequencies for this occupation in O*NET; fallback as for developers (Stack Overflow, 2025); replaced by agent hours when there are agents.
Code suggestion : 150 per day, 80% with AI in typical use
About 150 suggestions offered per day when the assistant is active, assumption; fallback as for developers (Stack Overflow, 2025).
Analyse a spreadsheet : 1 per day, 25% with AI in typical use
About 1 dataset to explore per day, assumption; share done with AI based on the 16-fold gap between the most active users and the median for data analysis (OpenAI, 2025).
Quick question : 8 per day, 36% with AI in typical use
About 8 questions per day, assumption; share done with AI based on the Crédoc Baromètre du numérique (2026 edition, 2025 data), adjusted to the occupation.
Conversation of 10 exchanges : 1 per day, 24% with AI in typical use
About 1 methodology discussion 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.
Query a PDF : 2 per week, 30% with AI in typical use
About 2 articles or documentation sets queried 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.
Summarise 30 pages : 1 per week, 36% with AI in typical use
About 1 long document 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.
Write a page : 2 per week, 24% with AI in typical use
About 2 pages of reports 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.
Presentation of 10 slides : 22 per year, 30% with AI in typical use
About 2 presentations of results per month, 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.
Write an email : 10 per day, 12% with AI in typical use
About 10 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.
Specialised task : 1,000 per week, 30% with AI in typical use
About 1,000 items to classify per week (texts, reviews, tickets), assumption; share delegated to a specialised model set without a published survey.
Coding agent hour : 4 per day, 40% with AI in typical use
About 4 hours per day in which an agent can work, assumption; share done with AI based on the usage time reported by developers (Stack Overflow, 2026).
Published figures in support
OpenAI (2025): 16 times the median
Use of the data analysis tool by the most active users, relative to the median, among employees in the data function
Microsoft Research, Tomlinson et al. (2026): 0.357 applicability score
Generative AI applicability score for data scientists (code 15-2051), 200,000 conversations with Bing Copilot, from 0 to 1
Methodology conventions
With their alternatives
| 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 |
|---|---|
| The default location | Without other information, the servers are in the world, at the world average electricity |
| 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 |
| 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
- 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).
