用AI自动提取企业气候政策证据，提升评估效率与准确性。
Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach
- 结合布局感知解析与多语言嵌入模型，自动从文本中提取证据。
- 在多语言企业文件中，关键证据提取准确率显著提升。
- 适合气候政策分析、可持续投资等领域的研究者与从业者使用。
InfluenceMap的LobbyMap平台监测超过500家企业和250个行业协会的气候政策参与情况，评估其对实现巴黎协定1.5℃目标的科学政策路径的支持或反对态度。尽管已在分析流程自动化方面取得进展，但评估仍需大量人工操作，耗时且易出错。本文提出一种AI辅助框架，利用检索增强生成（RAG）技术，自动化从大规模文本数据中提取相关证据，以加速企业气候政策参与的监控。评估表明，结合布局感知解析、Nomic嵌入模型和少样本提示策略，在多语言企业文档中提取与分类证据表现最佳。结论指出，尽管自动化RAG系统能有效加速证据提取，但分析的复杂性仍需人类专家介入，以确保准确性，即技术应作为辅助而非替代。
原文摘要 · Abstract (English)
InfluenceMap's LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity's support or opposition to science-based policy pathways for achieving the Paris Agreement's goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human error. We propose an AI-assisted framework to accelerate the monitoring of corporate climate policy engagement by leveraging Retrieval-Augmented Generation to automate the most time-intensive extraction of relevant evidence from large-scale textual data. Our evaluation shows that a combination of layout-aware parsing, the Nomic embedding model, and few-shot prompting strategies yields the best performance in extracting and classifying evidence from multilingual corporate documents. We conclude that while the automated RAG system effectively accelerates evidence extraction, the nuanced nature of the analysis necessitates a human-in-the-loop approach where the technology augments, rather than replaces, expert judgment to ensure accuracy.
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