标题： 气候人工智能用于企业去碳化指标提取 显示英文标题

标题： Climate AI for Corporate Decarbonization Metrics Extraction

摘要： 企业温室气体(GHG)排放目标是可持续投资中的重要指标[12, 16]。 为了全面了解公司的排放目标，我们提出了一种从公司公开披露中获取这些指标的方法。 如果没有自动化，手动整理这些指标是一个劳动密集型的过程，需要仔细查阅冗长的公司可持续性披露文件，而这些文件通常没有标准格式。 此外，生成的数据集需要由领域专家(SMEs)进行彻底验证，进一步延长了上市时间。 我们介绍了 用于企业去碳化指标提取的气候人工智能(CAI)模型和流程， 这是一种新方法，利用大型语言模型(LLMs)从企业披露文件中提取和验证相关指标。 我们证明了该过程通过自动化数据整理、验证和指标评分，提高了数据收集的效率和准确性。 我们进一步表明，我们的结果与LLMs的选择无关。 该框架可以广泛应用于文本数据的信息提取。 显示英文摘要

摘要： 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.