Sustainable Open-Source AI Requires Tracking the Cumulative Footprint of Derivatives

TL;DR

Open-source AI sustainability requires tracking the environmental impact of model derivatives through a standardized, transparent system that aggregates data across the ecosystem.

Contribution

The paper introduces Data and Impact Accounting (DIA), a lightweight framework for measuring and aggregating environmental impacts of AI model derivatives.

Findings

DIA standardizes impact reporting metadata.

DIA integrates impact measurement into training pipelines.

DIA enables ecosystem-level impact dashboards.

Abstract

Open-source AI is scaling rapidly, and model hubs now host millions of artifacts. Each foundation model can spawn large numbers of fine-tunes, adapters, quantizations, merges, and forks. We take the position that compute efficiency alone is insufficient for sustainability in open-source AI: lower per-run costs can accelerate experimentation and deployment, increasing aggregate environmental footprint unless impacts are measurable and comparable across derivative lineages. However, the energy use, water consumption, and emissions of these derivative lineages are rarely measured or disclosed in a consistent, comparable manner, leaving ecosystem-level impact largely invisible. We argue that sustainable open-source AI requires coordination infrastructure that tracks impacts across model lineages, not only base models. We propose Data and Impact Accounting (DIA), a lightweight, non-restrictive transparency layer that (i) standardizes carbon and water reporting metadata, (ii) integrates low-friction measurement into common training and inference pipelines, and (iii) aggregates reports through public dashboards to summarize cumulative impacts across releases and derivatives. DIA makes derivative costs visible and supports ecosystem-level accountability while preserving openness. https://vectorinstitute.github.io/ai-impact-accounting/