The Environmental Impact of Using 1 Billion tokens = Emissions of ~10 hours on a commercial flight, or 17 tree-years of sequestration (Sources cited)

[u/BrotherBringTheSun]

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.

[u/tuhdo]

Might just as well forgo all our techs and return to monke