UsesLevel 4 of 6 occupation or activityOccupation or activity, level 4 of 6Calculated with 7 entries, levels 1 and 3Database structure 
6Territories
5Organisations
3Uses
2Machine operations
1Calculation factorsGitHub Copilot licence, 1 year
One year of a GitHub Copilot licence for a developer: about 88 coding agent hours, 37,000 code suggestions, 460 generated functions, and 3 other tasks. Projet Celsius estimate.
- Code and agents account for 99% of emissions: "Coding agent hour" weighs as much as 2,200 quick questions.
- About 1,400 requests to AI over the year, or 6.5 per working day, not counting 37,000 code suggestions.
- An estimate: the frequencies and the share done with AI are our assumptions.
Sentence to citeGitHub Copilot licence, 1 year, Projet Celsius estimate: 29 kgCO2e per subscription per year, for 63 kWh of electricity, servers worldwide (458 gCO2e/kWh).
Breakdown of emissions by item
29 kgCO2e in total: 63 kWh of electricity × 458 gCO2e/kWh (servers worldwide).
Inputs of the calculation
PUE of the data centre, Read 1,000 tokens, Write 1,000 tokens, Write, with reasoning, Re-read from cache, Code suggestion, Generate a function, Coding agent hour
How the calculation is built
One year of a GitHub Copilot licence for a developer: code suggestions, functions, reviews, questions, a little coding agent. Suggestions are unlimited, the business licence gives 1,900 credits a month with no published equivalence in agent hours (GitHub, November 2026); typical use is an assumption.
Comparison with other occupations and activities
Characteristics
| Unit | 1 subscription, 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 subscription price |
| Status | estimated |
All the characteristics
| Identifier | IA-0327, projetcelsius.com/en/ai/database/IA-0327/ |
|---|---|
| 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 |
|---|---|---|---|---|---|
| Code suggestion | 37,400 | 2.6 kWh | 4 % | ||
| Generate a function | 462 | 540 Wh | 1 % | ||
| Have code reviewed | 132 | 170 Wh | 0 % | ||
| Quick question | 704 | 210 Wh | 0 % | ||
| Conversation of 10 exchanges | 44 | 150 Wh | 0 % | ||
| Coding agent hour | 88 | 59 kWh | 94 % |
Result for the chosen case
220 days, servers worldwide: 29 kgCO2e, 63 kWh.
For a carbon footprint
| Item | purchases of services (GHG Protocol scope 3, category 1); regulatory BEGES, category 4 |
|---|---|
| Quantity | 1 subscription over a year |
| Factor | 29 kgCO2e per subscription over one year, IA-0327 version 0.1, using the local grid mix (location-based), servers worldwide |
| Total | 29 kgCO2e · not counted: server manufacturing, model training, the person's device and the subscription price |
Typical usage profile
Projet Celsius assumption
The developer with the licence accepts about 850 suggestions, has 10 functions written, 3 pieces of code reviewed, asks 16 questions and hands 2 hours to the licence's agent. About 33 requests a week, not counting suggestions. Over the year, about 63 kWh, more than 90% of it in agent hours. Low usage, 14 kWh; heavy usage, 300 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 |
|---|---|---|---|---|---|
| Coding agent hour | 88 times | 670 Wh | 59 kWh | 94 % | assumption |
| Code suggestion | 37,000 times | 0.069 Wh | 2.6 kWh | 4 % | assumption |
| Generate a function | 460 times | 1.2 Wh | 540 Wh | 1 % | assumption |
| Quick question | 700 times | 0.3 Wh | 210 Wh | 0 % | assumption |
| Have code reviewed | 130 times | 1.3 Wh | 170 Wh | 0 % | assumption |
| Conversation of 10 exchanges | 44 times | 3.5 Wh | 150 Wh | 0 % | assumption |
Entries that use it
No other entry in the database is calculated from this one.
Sources of the calculation
13 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.
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.
GitHub · November 2026
Role and limits : GitHub
What the publication covers. monthly price and credits of each plan, unlimited suggestions outside credits
What it does not cover. the credit consumption of one agent hour
Reading by the database. Page read on 2 November 2026
Published observation on uses
Scope of the figure used. Copilot Business at $19 per user per month: 1,900 credits, at $0.01 per credit beyond that; unlimited code suggestions outside credits (Pro: $10, 1,500 credits)
Limit. No published equivalence between credits and agent hours
GitHub Copilot, 1 year → : Code suggestion →
About 200 suggestions offered per day; they are unlimited and outside credits in all plans (GitHub, 2026); use is an assumption.
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.
GitHub Copilot, 1 year → : Coding agent hour →
About 4 hours per day during which the licence's agent can work, assumption; the enterprise licence gives 1,900 credits per month, with no published equivalent in hours (GitHub, 2026).
Scope of the figure used. agent steps, context re-read from cache, data centre
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.
Projet Celsius · November 2026
Tokens per word, per line of code and per cell, measured with 7 public tokenisers
Role and limits : Projet Celsius
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.
Get code reviewed → : 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.
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.
The other publication in this section
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
GitHub Copilot, 1 year → : Generate a function →
About 6 functions per day, assumption; use as for developers (Stack Overflow, 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.
GitHub Copilot, 1 year → : Get code reviewed →
About 2 code reviews per day, assumption; share done with AI based on Stack Overflow (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.
GitHub Copilot, 1 year → : Quick question →
About 8 technical questions per day, assumption; share done with AI based on Stack Overflow (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.
GitHub Copilot, 1 year → : Conversation of 10 exchanges →
About 1 debugging session per day, assumption; share done with AI based on Stack Overflow (2025).
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.
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
Hausfather · Aug. 2026
Role and limits : Hausfather
Author. Zeke Hausfather, The Climate Brink
What the publication covers. intensive personal use of Claude Code, tokens recorded and converted into electricity
Reading by the database. A researcher's personal measurements, not a study
Coding agent hour → : Re-read from cache →
The context re-read from cache at each of the 120 steps of the hour: 96,000 tokens, 96% of a call in the median published session
Scope of the figure used. server, convention C4
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Coding agent hour → : Read 1,000 tokens →
The new tokens of each step (files read, command results): 3,600, 3.6% of a call
Scope of the figure used. server, excluding the data centre
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Coding agent hour → : Write 1,000 tokens →
The tokens written at each step, draft included: 900 on average in the published logs
Scope of the figure used. server, excluding the data centre
Limit. This figure covers part of the calculation; it does not measure the whole entry.
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 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.
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 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.
Get code reviewed → : 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.
Coding agent hour → : 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
Code suggestion : 200 per day, 85% with AI in typical use
About 200 suggestions offered per day; they are unlimited and outside credits in all plans (GitHub, 2026); use is an assumption.
Generate a function : 6 per day, 35% with AI in typical use
About 6 functions per day, assumption; use as for developers (Stack Overflow, 2025).
Have code reviewed : 2 per day, 30% with AI in typical use
About 2 code reviews per day, assumption; share done with AI based on Stack Overflow (2025).
Quick question : 8 per day, 40% with AI in typical use
About 8 technical questions per day, assumption; share done with AI based on Stack Overflow (2025).
Conversation of 10 exchanges : 1 per day, 20% with AI in typical use
About 1 debugging session per day, assumption; share done with AI based on Stack Overflow (2025).
Coding agent hour : 4 per day, 10% with AI in typical use
About 4 hours per day during which the licence's agent can work, assumption; the enterprise licence gives 1,900 credits per month, with no published equivalent in hours (GitHub, 2026).
Published figures in support
GitHub (2026): 1,900 credits per month
Copilot Business at $19 per user per month: 1,900 credits, at $0.01 per credit beyond that; unlimited code suggestions outside credits (Pro: $10, 1,500 credits)
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.
- 12 November 2026Calculated value, servers worldwide : 19 kgCO2e becomes 29 kgCO2e.Consequence of the new value for the coding agent hour (from 440 to 670 Wh), decided on 12 November 2026; the entry's own data do not change, and its citation carries version 0.4 of the database.
- 31 October 2026Creation of the database, in its first version (0.1).
