Multi-Method Natural Language Processing for European Green Deal Policy Documents: An application to FABLE Pathways

Natural Language Processing (NLP) has emerged as a transformative tool for sustainability policy analysis, enabling automated assessment of vast policy corpora at previously impossible scales. This paper presents a production-ready multi-method NLP system for detecting and quantifying six FABLE National Commitments (Biodiversity, Climate Mitigation, Food Security, Economy, Fertiliser Use, and Water Management) in European Green Deal policy documents. Our system employs four complementary NLP techniques-enhanced keyword analysis, spaCy-based phrase detection, TF-IDF semantic similarity, and syntactic pattern matching-with conservative evidence-based scoring to ensure analytical reliability. Through rigorous validation and systematic elimination of false positives, the system achieves over 90% reduction in erroneous classifications compared to baseline approaches. We demonstrate the system's capabilities through analysis of 42 European Green Deal policy documents, including detailed case studies of the REPowerEU Energy Plan and EU Energy Integration Strategy. The system processes documents at 1.4-3.9 seconds per file with 100% success rate, providing transparent evidence trails with keyword counts, context excerpts, and multi-method validation. Our work bridges the gap between sustainability science and computational methods, providing policymakers and researchers with reliable, scalable tools for evidence-based policy analysis aligned with the FABLE Consortium's integrated pathways approach to sustainable land-use and food systems transformation.