Per day — add a row for each kind of thing you ask for
What it writes
“Typical output” is how long each reply is. A “coding / agent session” means one full run of an AI coding assistant that writes and edits code across many steps — it uses EcoLogits’ 100,000-token “assist application development” benchmark (much of it the model’s own reasoning and tool calls, not final code).
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vs. a day for someone in who lives in , drives , eats , and flies
A day of your AI use vs. everyday things
Excludes training, image and video generation, and retries.
In a year, ways you add emissions
In a year, ways you can cut emissions
How these numbers are made
Per-model AI estimates. Energy, carbon and water per prompt are computed with the open-source EcoLogits library (v0.10), the engine behind the EcoLogits calculator; the model list is current as of June 2026. EcoLogits estimates electricity from each model’s (active) parameter count and output length, and adds the embodied impact of building the hardware. The low-to-high range is EcoLogits’ own 95% confidence interval, mostly uncertainty in the parameter counts of closed models. Water is the water-consumption footprint — water actually consumed (evaporated in data-centre cooling and in generating the electricity), not withdrawn and returned, so every water figure on this page compares on the same basis.
Carbon basis. EcoLogits gives each prompt’s electricity use and its embodied (hardware-manufacturing) emissions separately. The embodied figure is kept as-is and the electricity is costed on the grid where you live, using carbon intensities from Ember 2024 and Our World in Data: roughly 380 (US), 215 (EU), 125 (UK), 580 (China), 700 (India), and 480 (world) g CO₂e/kWh. Water is left on EcoLogits’ global basis.
Output, words and code. Each row maps to an EcoLogits output-token count, from a 50-token tweet up to the 500,000-token Lord of the Rings re-write — both EcoLogits benchmark tasks. Cost scales with length, so long generations dominate. Counts are per day; annual figures multiply by 365. Words assume ~0.75 words per token, read at 238 words a minute (Brysbaert 2019). A coding session is counted as lines of code (~10 tokens a line) with only about 15% of its output treated as actual code — the rest is reasoning, tool calls and exploration — so a 100,000-token session is on the order of 1,500 lines. That is a rough estimate with wide uncertainty.
Your footprint. “Where you live” is a regional baseline for goods, services and shared infrastructure (per-capita consumption from Our World in Data); home energy and driving are separate so they do not double-count. Home runs from a small flat to a big house, about 1.5 to 7 t CO₂e a year (EIA RECS; Goldstein et al. 2020). Driving uses EPA’s ~400 g CO₂/mile across roughly 3,000 to 25,000 miles a year (FHWA). Diet uses food footprints from Poore & Nemecek (2018) and Scarborough et al. (2023): about 1.05 t CO₂e a year for a vegan diet up to 3.2 t for a heavy-meat one. Flying is anchored to a transatlantic round trip of about 1.6 t (Wynes & Nicholas 2017); the rarely / sometimes / often options are roughly 0.5, 1.5 and 5 such trips a year.
Water. The water footprint uses blue water only — freshwater actually drawn from rivers, lakes and aquifers. Green water (rain that would evaporate anyway) and grey water (a notional pollution-dilution volume) are deliberately excluded, because neither is a real draw on freshwater supplies. The AI figure is already blue (data-centre cooling and power-plant water), so both sides match. The per-person blue footprint comes from the Water Footprint Network (Mekonnen & Hoekstra). Removing green water changes the picture a lot: most of a food’s water is rain, so a beef burger drops from ~1,700 L total to about 6 L of freshwater, while irrigated crops stay high.
Lifestyle comparisons. The yearly “saved” figures come from the Founders Pledge Climate & Lifestyle report, drawing on Wynes & Nicholas (2017) and Ivanova et al. (2020). The “added” and everyday comparisons draw on product life-cycle studies including Berners-Lee’s How Bad Are Bananas?, Apple’s environmental reports, Levi’s jeans LCA, EPA WaterSense, ENERGY STAR appliance data, and the Water Footprint Network. Appliance carbon figures are each item’s measured electricity times the US grid, so they fall on cleaner grids; where a single authority does not exist, figures are rounded mid-range estimates from the sources cited, not false precision.
Origin. This calculator is adapted, with its data and methodology, from Andy Masley’s AI prompt footprint calculator, which he dedicated to the public domain under CC0 1.0.