Watershed Publishes Comprehensive Open Framework for Measuring Corporate AI Emissions
Standardized measurement methodology reveals more than four-fold range in AI emissions estimates and proposes disclosure standards to close the gap
SAN FRANCISCO, CA, UNITED STATES, July 22, 2026 /EINPresswire.com/ -- Watershed, the sustainability AI platform, today published a comprehensive open framework for estimating greenhouse gas emissions from corporate AI use, designed to produce defensible, auditable estimates across providers and use cases.
The framework, developed in consultation with Dr. Steve Davis from Stanford University and Dr. Sangwon Suh from Tsinghua University; Watershed customers including Block and Okta; and the non-profit Business Council on Climate Change (BC3), addresses a growing gap in corporate emissions reporting: despite rapid growth in AI adoption, no widely accepted methodology exists for measuring AI's carbon footprint and electricity use.
“Since AI emissions accounting is still nascent, it has the opportunity to design for actionability from the outset, so that measurement incentivizes responsible choices during AI’s rapid buildout. This framework gives companies a defensible starting point and a shared roadmap for improving precision as provider data matures, laying the groundwork for emissions reductions,” said Dr. John Bistline, Head of Science at Watershed.
The framework has three elements designed to make AI emissions measurement consistent and actionable: 1) A comprehensive system boundary that includes model training, inference, data center overhead, and embodied hardware; 2) A functional unit (kgCO2e per million tokens) that aligns with the metric companies already track for cost management; 3) A three-tier calculation approach that aligns with companies’ data quality.
Key findings include:
- Data quality drives a roughly four-fold range in estimates. For a company spending $100,000 annually on a frontier AI model, estimates range from 3.2 to 13.4 tCO2e depending on the calculation tier used—illustrating how much accuracy depends on the underlying data.
- Better provider data can materially improve accuracy. Non-production benchmarks can overstate inference energy by 4 to 20 times relative to production deployments. The framework proposes a standardized disclosure table to enable more precise corporate reporting and encourage collaboration across stakeholders.
- Training emissions are the largest source of uncertainty, depending on assumptions about total lifetime usage—a parameter no frontier provider currently discloses.
The framework also identifies practical levers for reducing AI emissions: model selection (reasoning models can use roughly 30 times more energy than smaller variants), region selection (US grid regions vary by more than five-fold in carbon intensity), and prompt optimization.
The full white paper is available here. This is the first in a series of resources Watershed will release to advance AI emissions measurement and support better sustainability data science across the industry.
About Watershed
Watershed is the sustainability AI platform. Companies like Airbnb, Carlyle Group, FedEx, Visa, and Dr. Martens use Watershed to measure, report, and act on their emissions, so they can produce audit-ready carbon footprints that drive real reductions. With embedded sustainability intelligence from the world's leading climate and policy experts, Watershed is the platform of choice for companies seeking to satisfy regulatory requirements, unlock new business opportunities, and address climate-related risks.
Amelia Penniman
Watershed
amelia@watershed.com
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