What the study found
The study found that mandatory, uncertainty-aware emissions disclosure for AI training runs is operationally feasible at publication time when venues use tiered requirements and light-touch verification. The authors report that a minimal disclosure template can achieve high coverage with modest added burden.
Why the authors say this matters
The authors conclude that their framework offers venues and policymakers decision support for comparing transparency policies without relying on proprietary telemetry or speculative large-scale estimates. They also suggest that near-universal disclosure would enable comparable, reproducible emissions reports.
What the researchers tested
The researchers developed a policy-level analytical framework rather than estimating emissions for specific AI models. They modeled disclosure requirements, reviewer and editorial workload, and uncertainty propagation under realistic instrumentation assumptions, and tested tiered venue policies called P0, P1, and P2 using Monte Carlo simulation.
What worked and what didn't
A minimal disclosure template requiring hardware, duration, energy or carbon dioxide equivalent, and an emission-factor source achieved high coverage with modest burden: median completion time was about 10.8 minutes, reviewer checklist time was about 1.6 minutes per paper, and P2 editorial audits were about 24.1 minutes per 100 submissions. Coverage rose from about 25% under P0 to about 80% under P1 and P2, and uncertainty intervals could be reported using lightweight assumptions, with median relative half-widths of about 0.33 for location-based and 0.77 for market-based reporting. Under baseline priors, H1-H3 were met, H4b was met, and H4a was narrowly missed.
What to keep in mind
The abstract does not describe limitations beyond the modeled assumptions and policy framework. The results are based on simulation and a policy-level model, not on direct measurement of emissions from specific training runs.
Key points
- The paper argues that mandatory emissions disclosure for AI research venues is operationally feasible.
- A minimal disclosure template can raise coverage to about 80% with modest review burden.
- The modeled template included hardware, duration, energy or CO2e, and emission-factor source.
- Uncertainty-aware emissions intervals were reported as usable under lightweight assumptions.
- The framework compares disclosure policies without using proprietary telemetry or speculative large-number estimates.
Disclosure
- Research title:
- Mandatory emissions disclosure in AI research is feasible
- Authors:
- Malka N. Halgamuge, Narayan Srinivasa
- Institutions:
- Archangel Systems (United States), RMIT University
- Publication date:
- 2026-02-23
- OpenAlex record:
- View
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