  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.
