用AI从企业报告中自动提取碳减排指标，省时又准确。
Climate AI for Corporate Decarbonization Metrics Extraction
- 用大模型自动从企业披露文本中提取碳目标数据
- 相比人工可提升效率与准确性，且不依赖具体大模型
- 适合做可持续投资分析的机构或研究者使用
企业温室气体（GHG）排放目标是可持续投资中的关键指标。为全面了解企业排放目标，我们提出一种从企业公开披露文件中获取这些指标的方法。传统方式需人工逐篇查阅非标准化的可持续性报告，耗时费力，且需领域专家验证，导致上市周期长。我们引入气候人工智能（CAI）模型与流程，利用大语言模型（LLMs）自动提取并验证企业披露中的关联指标。实证表明，该方法显著提升数据采集效率与准确性，实现自动化数据整理、验证与评分。结果还显示该框架对不同大模型选择具有鲁棒性，适用于各类文本信息抽取任务。
原文摘要 · Abstract (English)
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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