Climate AI for Corporate Decarbonization Metrics Extraction
Corporate Greenhouse Gas (GHG) emission targets are important metrics in sustainable investing [12, 16]. To provide a comprehensive view of company emission objectives, we propose an approach to source these metrics from company public disclosures. Without automation, curating these metrics manually...
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Zusammenfassung: | Corporate Greenhouse Gas (GHG) emission targets are important metrics in
sustainable investing [12, 16]. To provide a comprehensive view of company
emission objectives, we propose an approach to source these metrics from
company public disclosures. Without automation, curating these metrics manually
is a labor-intensive process that requires combing through lengthy corporate
sustainability disclosures that often do not follow a standard format.
Furthermore, the resulting dataset needs to be validated thoroughly by Subject
Matter Experts (SMEs), further lengthening the time-to-market. We introduce the
Climate Artificial Intelligence for Corporate Decarbonization Metrics
Extraction (CAI) model and pipeline, a novel approach utilizing Large Language
Models (LLMs) to extract and validate linked metrics from corporate
disclosures. We demonstrate that the process improves data collection
efficiency and accuracy by automating data curation, validation, and metric
scoring from public corporate disclosures. We further show that our results are
agnostic to the choice of LLMs. This framework can be applied broadly to
information extraction from textual data. |
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DOI: | 10.48550/arxiv.2411.03402 |