Watershed releases open framework for measuring corporate AI emissions

Watershed published an open methodology on July 22, 2026 to help companies estimate greenhouse gas emissions from AI use across providers and workloads. The framework aims to close a major reporting gap by standardizing disclosure, improving auditability and guiding emissions cuts as corporate AI adoption accelerates.

Why it matters: - Corporate AI use is growing faster than the industry’s ability to measure its climate impact. - Watershed’s framework is designed to give companies a defensible, auditable way to report AI-related electricity use and emissions. - The methodology could help narrow wide estimation gaps and push providers toward more useful disclosure.

What happened: - Watershed published a comprehensive open framework for estimating greenhouse gas emissions from corporate AI use. - The release came on July 22, 2026, and is meant to work across providers and use cases. - The framework was developed with Dr. Steve Davis of Stanford University, Dr. Sangwon Suh of Tsinghua University, Watershed customers including Block and Okta, and the nonprofit Business Council on Climate Change. - Watershed said the framework is the first in a planned series of resources on AI emissions measurement. - The full white paper is available here: the full white paper.

The details: - The framework has three core parts. - It sets a system boundary that includes model training, inference, data center overhead and embodied hardware. - It uses a functional unit of kgCO2e per million tokens, aligned with the cost metric companies already track. - It applies a three-tier calculation approach based on the quality of available company data. - The methodology is intended to produce estimates that are defensible and auditable. - Watershed said the framework creates a shared roadmap for improving precision as provider data improves. - Dr. John Bistline, Watershed’s head of science, said the framework gives companies a defensible starting point and supports emissions reductions as AI deployment expands. - For a company spending $100,000 a year on a frontier AI model, estimated emissions range from 3.2 to 13.4 tCO2e depending on the calculation tier. - Non-production benchmarks can overstate inference energy use by 4 to 20 times compared with production deployments. - The framework proposes a standardized disclosure table to support more precise corporate reporting. - Training emissions are the biggest source of uncertainty because frontier providers do not disclose assumptions about total lifetime usage. - The framework identifies model selection, region selection and prompt optimization as practical levers for reducing emissions. - Reasoning models can use roughly 30 times more energy than smaller variants. - U.S. grid regions vary by more than five-fold in carbon intensity.

Between the lines: - The biggest reporting problem is not just lack of data. It is the lack of a common method that companies and providers can use consistently. - By tying emissions measurement to tokens and disclosure tiers, Watershed is trying to make AI climate accounting easier to operationalize inside procurement and finance teams. - The standardized disclosure table signals pressure on AI providers to share more production-level data.

What's next: - Watershed plans to release more resources focused on AI emissions measurement. - The company is positioning the framework as a foundation for better sustainability data science across the industry. - Companies using AI at scale may use the framework to compare vendors, choose lower-carbon regions and refine prompts and model selection.

The bottom line: - Watershed is trying to turn AI emissions from an opaque estimate into a measurable, reportable and potentially manageable corporate metric.