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

TL;DR

This paper explores how AI and NLP techniques, especially using GPT, can leverage alternative unstructured data sources like financial and shipping data to improve the reliability and trustworthiness of carbon accounting.

Contribution

It introduces a novel approach to utilize AI-driven NLP methods on diverse data sources for enhanced carbon accounting accuracy and trust.

Findings

NLP methods can extract valuable insights from unstructured data for carbon accounting.

GPT API can analyze financial and shipping data to support emissions tracking.

Proposes a framework for integrating AI methods into broader carbon accounting systems.

Abstract

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.