UsesLevel 4 of 6 occupation or activityOccupation or activity, level 4 of 6Used to calculate 7 entries, levels 5 and 6Calculated with 10 entries, levels 1 and 3Database structure 
6Territories
5Organisations
3Uses
2Machine operations
1Calculation factorsVideographer, 1 year
A year of work for a video maker who uses AI: about 79 meetings transcribed and summarised, 6.6 clips of 10 seconds, 22 clips of 5 seconds, and 6 other tasks. Estimate by Projet Celsius.
- Video and voice account for 60% of emissions: "Clip of 10 seconds" weighs as much as 1,100 quick questions.
- About 530 requests to AI over the year, or 2.4 per working day.
- 5.7 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 citeVideo maker, 1 year, Projet Celsius estimate: 3.4 kgCO2e per item per year, for 7.4 kWh of electricity, servers worldwide (458 gCO2e/kWh).
Breakdown of emissions by item
3.4 kgCO2e in total: 7.4 kWh of electricity × 458 gCO2e/kWh (servers worldwide).
Inputs of the calculation
PUE of the data centre, Transcribe one minute, Read 1,000 tokens, Second of video, Generate an image, Summarise a meeting, Clip of 5 seconds, Clip of 10 seconds
How the calculation is built
A year of work for a video maker who uses AI, task by task: rushes transcribed, shots generated, thumbnails, subtitles translated. 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-0312, projetcelsius.com/en/ai/database/IA-0312/ |
|---|---|
| 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 |
|---|---|---|---|---|---|
| Summarise a meeting | 79.2 | 2.3 kWh | 32 % | ||
| Clip of 5 seconds | 22 | 2.2 kWh | 30 % | ||
| Clip of 10 seconds | 6.6 | 2.2 kWh | 30 % | ||
| Visual in 4 attempts | 26.4 | 340 Wh | 5 % | ||
| Retouch a photo | 44 | 150 Wh | 2 % | ||
| Write a page | 14.1 | 6.4 Wh | 0 % | ||
| Translate a page | 42.2 | 25 Wh | 0 % | ||
| Quick question | 158 | 48 Wh | 1 % | ||
| Write an email | 141 | 32 Wh | 0 % |
Result for the chosen case
220 days, 5.4 seconds per video, servers worldwide: 3.4 kgCO2e, 7.4 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 | 3.4 kgCO2e per item over one year, IA-0312 version 0.1, according to the local grid (location-based), servers worldwide |
| Total | 3.4 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 video maker has nearly 2 hours of rushes transcribed, generates a 5-second shot every 2 weeks and a 10-second shot every 6 to 7 weeks, looks for a thumbnail with AI every 2 weeks and asks it 4 questions. Barely 12 requests a week, the fewest of any office occupation. Over the year, however, about 7.4 kWh, of which 60% for around thirty generated shots and 32% for the rushes transcribed. Frugal, 1.4 kWh; intensive, 34 kWh, when 4 shots a week come from AI: the gap between frugal and intensive is the largest of the occupations without an agent.
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 |
|---|---|---|---|---|---|
| Summarise a meeting | 79 times | 30 Wh | 2.3 kWh | 32 % | assumption |
| Clip of 10 seconds | 6.6 times | 340 Wh | 2.2 kWh | 30 % | assumption |
| Clip of 5 seconds | 22 times | 99 Wh | 2.2 kWh | 30 % | assumption |
| Visual in 4 attempts | 26 times | 13 Wh | 340 Wh | 5 % | assumption |
| Retouch a photo | 44 times | 3.5 Wh | 150 Wh | 2 % | assumption |
| Quick question | 160 times | 0.3 Wh | 48 Wh | 1 % | assumption |
| Write an email | 140 times | 0.23 Wh | 32 Wh | 0 % | assumption |
| Translate a page | 42 times | 0.6 Wh | 25 Wh | 0 % | assumption |
| Write a page | 14 times | 0.45 Wh | 6.4 Wh | 0 % | assumption |
Sources of the calculation
17 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.
Anthropic · 2026
Role and limits : Anthropic
What the publication covers. tokens of an image according to its size: one patch of 28 × 28 pixels per token, 1,296 for a megapixel
What it does not cover. electricity
Reading by the database. The publisher's billing rule
Analyse an image → : Read 1,000 tokens →
The photo cut into about 1,100 image tokens, read as text
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 an image → : Read 1,000 tokens →
Length convention: a photo sent to an AI
Scope of the figure used. A one-megapixel photo, cut into image patches: 765 tokens at OpenAI for GPT-4o, 1,032 at Google, 1,229 for recent OpenAI models, 1,296 at Anthropic
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
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
Video maker, 1 year → : Quick question →
About 3 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.
Figma · April 2025
Role and limits : Figma
What the publication covers. 2,500 Figma users, designers and developers
What it does not cover. the frequency of use
Reading by the database. Survey by a software publisher of its users
Video maker, 1 year → : Visual in 4 attempts →
About 3 thumbnails or storyboards per week, assumption; fallback as for graphic designers (Figma, 2025).
Scope of the figure used. images generated, 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
Role and limits
Author. Linguistic Data Consortium and International Computer Science Institute
What the publication covers. 75 meetings, about 72 hours, about 795,000 words transcribed
What it does not cover. meetings in French
Reading by the database. Research meetings in English, with several speakers
Summarise a meeting → : Read 1,000 tokens →
The transcript read by the model, about 11,000 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.
Summarise a meeting → : Read 1,000 tokens →
Length convention: one hour of meeting
Scope of the figure used. The 75 transcribed meetings of the ICSI corpus: 795,000 words for about 72 hours, or 184 words per minute with several speakers
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)
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 video editors (code 27-4032), 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
Published observation on uses
Scope of the figure used. Generative AI applicability score for camera operators (code 27-4031), 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
Video maker, 1 year → : 5-second clip →
About 10 illustration shots per week, editing being done several times an hour in O*NET; share done with AI set low, the Microsoft study finding little use among editors.
Scope of the figure used. server and data centre; 7 models measured
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Video maker, 1 year → : 10-second clip →
About 5 long shots per week, assumption; share done with AI set very low, a clip of 10 seconds remaining expensive and rarely usable, assumption.
Scope of the figure used. server and data centre; one model measured
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Video maker, 1 year → : Write a page →
About 2 pages of script or synopsis 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.
Video maker, 1 year → : Translate a page →
About 2 pages of subtitles to translate per week, assumption; share done with AI set high, translation being among the tasks most often delegated 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.
Video maker, 1 year → : Write an email →
About 8 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.
Role and limits
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
Published observation on uses
Scope of the figure used. video editors: assembling rushes into a continuous whole, share who do so daily or more (most often hourly or more)
Limit. Support for the frequency of the task in the basket; US job holders
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
Summarise a meeting → : Write 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.
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.
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.
Energy and factors
The references of the selected values and of the entries used in this calculation.
Delavande et al. · September 2025
Role and limits : Delavande et al.
Author. Delavande, Pierrard and Luccioni
What the publication covers. GPU, processor, memory
What it does not cover. data centre
Reading by the database. Seven open models
5-second clip → : Second of video →
The seconds of the measured video, 5.4
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.
10-second clip → : Second of video →
A single continuous video costs as much as the square of its duration: 10 seconds is worth 3.4 times the 5.4-second video that was measured
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.
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
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
Reference for the variant "Standard image, from 512 to 1,024 pixels per side"
Scope of the figure used. Average of the open models tested, 1,000 images; median 1.35 Wh, maximum 11.49 Wh
Limit. Open models only; closed generators publish nothing
Microsoft Research · 2026
Estimation de l'énergie des requêtes en production (Joule) ↗ (new tab)
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
The 2 other publications in this section
OpenAI · June 2025
Role and limits
Author. Sam Altman, OpenAI
What the publication covers. an average query
What it does not cover. method, scope
Reading by the database. One sentence, no method
Reference of the selected value
Scope of the figure used. "An average query", with no published method
Limit. No published scope; consistent with Google
Watershed · Aug. 2026
Estimating GHG Emissions from AI Use: Framework for Corporate-Level Measurement ↗ (new tab)
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
Summarise a meeting → : 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
5-second clip → : 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.
10-second clip → : 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.
Visual in 4 attempts → : 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.
Edit a photo → : 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.
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.
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.
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
Summarise a meeting : 3 per week, 60% with AI in typical use
About 3 hours of rushes or interviews to transcribe per week, assumption; share done with AI set high, automatic transcription being built into editing software, assumption.
Clip of 5 seconds : 10 per week, 5% with AI in typical use
About 10 illustration shots per week, editing being done several times an hour in O*NET; share done with AI set low, the Microsoft study finding little use among editors.
Clip of 10 seconds : 5 per week, 3% with AI in typical use
About 5 long shots per week, assumption; share done with AI set very low, a clip of 10 seconds remaining expensive and rarely usable, assumption.
Visual in 4 attempts : 3 per week, 20% with AI in typical use
About 3 thumbnails or storyboards per week, assumption; fallback as for graphic designers (Figma, 2025).
Retouch a photo : 5 per week, 20% with AI in typical use
About 5 images to rework per week, assumption; fallback set as for graphic designers' retouching, a little lower.
Write a page : 2 per week, 16% with AI in typical use
About 2 pages of script or synopsis 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.
Translate a page : 2 per week, 48% with AI in typical use
About 2 pages of subtitles to translate per week, assumption; share done with AI set high, translation being among the tasks most often delegated to AI (Microsoft, 2025).
Quick question : 3 per day, 24% with AI in typical use
About 3 questions per day, assumption; share done with AI based on the Crédoc Baromètre du numérique (2026 edition, 2025 data), adjusted to the occupation.
Write an email : 8 per day, 8% with AI in typical use
About 8 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.
Published figures in support
Microsoft Research, Tomlinson et al. (2026): 0.116 applicability score
Generative AI applicability score for video editors (code 27-4032), 200,000 conversations with Bing Copilot, from 0 to 1
Microsoft Research, Tomlinson et al. (2026): 0.205 applicability score
Generative AI applicability score for camera operators (code 27-4031), 200,000 conversations with Bing Copilot, from 0 to 1
O*NET (2025): 83% of job holders
video editors: assembling rushes into a continuous whole, share who do so daily or more (most often hourly or more)
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).
