# Criteria for Credible AI-assisted Carbon Footprinting Systems: The Cases of Mapping and Lifecycle Modeling

arXiv: https://arxiv.org/abs/2509.00240 · HTML: https://mlsanity.com/paper/2509.00240/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 46 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [Sustainability assessment using multimodal AI agents](https://mlsanity.com/paper/2507.17012/) (2025)
- [Entity Linking using LLMs for Automated Product Carbon Footprint Estimation](https://mlsanity.com/paper/2502.07418/) (2025)

## Built on by (most cited first, 2 of 2)

- [SpiderGen: Towards Procedure Generation For Carbon Life Cycle Assessments with Generative AI](https://mlsanity.com/paper/2511.10684/) (2025)
- [PCFBench: A Diagnostic Benchmark for Product Carbon Footprint Estimation](https://mlsanity.com/paper/2608.27716/) (2026)

## Abstract (arXiv)

As organizations face increasing pressure to understand their corporate and products' carbon footprints, artificial intelligence (AI)-assisted calculation systems for footprinting are proliferating, but with widely varying levels of rigor and transparency. Standards and guidance have not kept pace with the technology; evaluation datasets are nascent; and statistical approaches to uncertainty analysis are not yet practical to apply to scaled systems. We present a set of criteria to validate AI-assisted systems that calculate greenhouse gas (GHG) emissions for products and materials. We implement a three-step approach: (1) Identification of needs and constraints, (2) Draft criteria development and (3) Refinements through pilots. The process identifies three use cases of AI applications: Case 1 focuses on AI-assisted mapping to existing datasets for corporate GHG accounting and product hotspotting, automating repetitive manual tasks while maintaining mapping quality. Case 2 addresses AI systems that generate complete product models for corporate decision-making, which require comprehensive validation of both component tasks and end-to-end performance. We discuss the outlook for Case 3 applications, systems that generate standards-compliant models. We find that credible AI systems can be built and that they should be validated using system-level evaluations rather than line-item review, with metrics such as benchmark performance, indications of data quality and uncertainty, and transparent documentation. This approach may be used as a foundation for practitioners, auditors, and standards bodies to evaluate AI-assisted environmental assessment tools. By establishing evaluation criteria that balance scalability with credibility requirements, our approach contributes to the field's efforts to develop appropriate standards for AI-assisted carbon footprinting systems.