Environmental Law Compliance Assessment in Livestock Enterprises Using Natural Language Processing and AI

Abstract

The increasing environmental impact of livestock production has led to the implementation of stringent environmental regulations aimed at controlling greenhouse gas emissions, manure management, wastewater discharge, and sustainable resource utilization. However, manual assessment of environmental law compliance is labor-intensive, time-consuming, and prone to inconsistencies due to the complexity and frequent updates of legal documents. This study proposes an AI-driven Environmental Law Compliance Assessment Framework that integrates Natural Language Processing (NLP), semantic legal analysis, machine learning, knowledge graphs, and Explainable Artificial Intelligence (XAI) to automate regulatory compliance evaluation in livestock enterprises. The proposed framework acquires environmental legislation from multiple regulatory sources, extracts legal obligations using transformer-based NLP models, converts regulations into semantic knowledge representations, and compares them with enterprise operational data to identify compliance gaps and assess environmental risks. Experimental evaluation was conducted using environmental regulations and simulated livestock enterprise datasets. The proposed framework achieved a compliance detection accuracy of 96.8%, precision of 96.2%, recall of 95.9%, F1-score of 96.0%, regulatory coverage of 96.5%, and risk prediction accuracy of 95.1%, while reducing compliance assessment time from 245 minutes to 24 minutes compared with conventional auditing. The results demonstrate that AI-enabled legal text analysis significantly enhances regulatory interpretation, transparency, and environmental governance. The proposed framework provides a scalable and intelligent solution for continuous environmental compliance monitoring, supporting sustainable livestock management and evidence-based regulatory decision-making.