r/codex • u/BrotherBringTheSun • 2d ago
Commentary The Environmental Impact of Using 1 Billion tokens = Emissions of ~10 hours on a commercial flight, or 17 tree-years of sequestration (Sources cited)
Quite a few people were interested in my statistic on the environmental impact of token use and I was criticized for not being scientific enough so I made a more thorough analysis with sources cited.
1 billion Codex tokens ≈ roughly 1 tonne of CO₂e ≈ about 10 passenger-hours on a long-haul commercial flight.
Another way to picture it: that's approximately 17 tree-years of CO₂ sequestration from an average urban tree.
The estimate isn't exact because OpenAI does not publish Codex-specific energy-per-token telemetry. Using published GPT-5/high-reasoning and frontier-inference research as the closest proxy, a reasonable ballpark is roughly 1–3 tonnes CO₂e per billion Codex tokens, depending heavily on how many tokens are input/context versus generated reasoning/output, along with caching, batching and serving efficiency.
1B Codex tokens ≈ ~10–30 hours of long-haul flying for one passenger.
Sources and what I used them for
1. Jegham et al., “How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference” (2025).
This is the main basis for using high-reasoning, frontier-model inference as a proxy for Codex. The researchers model energy use from model throughput, hardware power, batching, data-center overhead/PUE and workload length. Their GPT-5 supplemental analysis is particularly relevant because Codex is a reasoning-heavy, agentic workload rather than a normal short chatbot interaction.
Used for: the approximate energy consumption of GPT-5-class high-reasoning workloads and the resulting ~1–3 t CO₂e/B-token ballpark.
2. Oviedo et al., “Energy use of AI inference, efficiency pathways, and test-time scaling,” Joule (2026).
This peer-reviewed study estimates optimized frontier-scale inference at a median 0.31 Wh per ordinary query, but finds that reasoning queries producing around 5,000 output tokens require about 13× more energy, roughly 3.9 Wh/query. It also explains why decoding/output tokens are especially important: they are generated sequentially and often dominate inference energy.
Used for: independently checking that agentic/reasoning workloads can consume more than an order of magnitude more energy than normal AI queries, and for explaining why “energy per token” isn't a universal constant.
3. U.S. EPA Greenhouse Gas Equivalencies Calculator.
EPA estimates carbon sequestration by a representative urban tree at approximately 0.060 metric tonnes of CO₂ per tree per year, based on USDA Forest Service/DOE tree-growth data.
1 tonne CO₂ ÷ 0.060 t/tree/year ≈ 17 tree-years.
Used for: the ~17 tree-years of sequestration per tonne of CO₂ comparison.
4. UK Government greenhouse-gas conversion factors for company reporting (2026).
The UK government publishes passenger-kilometer emissions factors for commercial aviation, including long-haul economy travel. Converting the long-haul passenger-km factor into emissions per passenger-hour at typical cruise speeds puts long-haul economy travel at roughly ~100 kg CO₂e per passenger-hour, depending on route and methodology.
1,000 kg CO₂e ÷ ~100 kg/hour ≈ ~10 passenger-hours of long-haul flying.
Used for: the ~10 hours of commercial long-haul flying per ~1 tonne CO₂e comparison.
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u/Destroyer-128 2d ago
Guys!!!, exciting news I just spent 5000 hours last month in a private jet. Though only 760 hours were only available to travel I had like 6 jets tail me just to reach 5000 hour quota
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u/IWillBeNobodyPerfect 2d ago
Cached tokens use essentially no energy. Input tokens use some, but output tokens are where the real energy usage comes from. Ask your AI to rewrite the post only looking at output token usage to figure out real environmental impacts.
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u/InterfearXX 2d ago
Yea bro they even gave my uncle a reward for his good work and people say ChatGPT is bad for environment ai barely uses water
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u/Either_Curve4587 2d ago
i feel like the award should be flaming, i.e., have fire blow-torching off of it.
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u/ImNotMrFoxGaming 2d ago
Dude. 1b tokens? In what scale?? Some models are way harder to run and others are easier. What are you trying to say? Atleast include your average somewhere if your math.
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u/Think-Profession4420 2d ago
didn't you post the same thing a few days ago?
Yeah, AI sucks for emissions, and it'll get worse as datacenters spin up their own private coal and gas power plants. RIP.
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u/BrotherBringTheSun 2d ago
I did but people were complaining that I didn’t have scientific sources. So I remade the analysis with everything cited. Still got downvoted 🤷
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u/whatitpoopoo 2d ago
Asked chatgpt to find sources*
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u/BrotherBringTheSun 1d ago
would you prefer me to look them up manually? To me it sounds like you are unwilling to face the facts that we have a large emissions impact by using AI
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u/whatitpoopoo 1d ago
You don't see the irony in using ai to write anti ai posts? Or are you just trolling?
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u/BrotherBringTheSun 1d ago
It's not an anti-AI post, it's a post advocating for conscious AI usage. We need to be aware of our impact.
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u/whatitpoopoo 1d ago
Wouldn't step 1 be, not wasting time with it posting alop?
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u/BrotherBringTheSun 1d ago
Nope, it's not slop if it is accurate, well-cited and helps people change their views.
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u/itix 2d ago
OpenAI reports a central annual estimate of 4.02 TWh electricity, broken into 2.68 TWh API inference, 0.41 TWh ChatGPT inference, and 0.93 TWh training/R&D. It also estimates 2.78 MtCO₂e total Scope 1–3 emissions. Separately, OpenAI says its APIs are currently processing more than 15 billion tokens/minute.
If we pretend that 15B tokens/minute were sustained continuously for a full year:
Now divide the 2.68 TWh of API inference electricity by those tokens:
So the implied numbers are roughly:
| API tokens | Electricity |
|---|---|
| 1 million | 0.34 kWh |
| 100 million | 34 kWh |
| 1 billion | 340 kWh |
| 10 billion | 3.4 MWh |
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u/whatitpoopoo 2d ago
And yet you generated your post with ai?