Automating the Construction of Environmental Policy Knowledge Graph with Large Language Models

Abstract

Enterprises engaged in transnational operations are confronted with increasingly complex environmental policies and compliance challenges. A critical hurdle to achieving sustainable development lies in rapidly and accurately extracting environmental protection requirements from vast volumes of policy. To address this, our study introduces an automated framework for knowledge graph construction, termed iteration–extraction–optimization (IEO), driven by a large language model (LLM). Diverging from conventional linear extraction methods, the IEO framework employs an iterative enhancement process to progressively build and refine a policy knowledge network, capturing the intricate relationships among legislation, institutions, and environmental obligations. As a case study, we applied the framework to Niger’s environmental policy, constructing a large-scale knowledge graph with 61,912 entities and 81,389 relations. Preliminary evaluations demonstrate the framework’s high performance in knowledge capture completeness, achieving a recall of 0.93 and an F1-score of 0.84. This research presents a novel paradigm for the intelligent parsing of environmental policy texts, providing a knowledge graph that serves as a vital decision-support tool for corporate environmental risk management and strategic sustainability planning.