# Leveraging AI-derived Data for Carbon Accounting: Information Extraction from Alternative Sources

arXiv: https://arxiv.org/abs/2312.03722 · HTML: https://mlsanity.com/paper/2312.03722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 26 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)

## Built on by (most cited first, 1 of 1)

- [GreenIQ: A Deep Search Platform for Comprehensive Carbon Market Analysis and Automated Report Generation](https://mlsanity.com/paper/2503.16041/) (2025)

## Abstract (arXiv)

Carbon accounting is a fundamental building block in our global path to emissions reduction and decarbonization, yet many challenges exist in achieving reliable and trusted carbon accounting measures. We motivate that carbon accounting not only needs to be more data-driven, but also more methodologically sound. We discuss the need for alternative, more diverse data sources that can play a significant role on our path to trusted carbon accounting procedures and elaborate on not only why, but how Artificial Intelligence (AI) in general and Natural Language Processing (NLP) in particular can unlock reasonable access to a treasure trove of alternative data sets in light of the recent advances in the field that better enable the utilization of unstructured data in this process. We present a case study of the recent developments on real-world data via an NLP-powered analysis using OpenAI's GPT API on financial and shipping data. We conclude the paper with a discussion on how these methods and approaches can be integrated into a broader framework for AI-enabled integrative carbon accounting.