Abstract

Climate action plans play a critical role in global efforts to combat climate change. They outline strategies for reducing greenhouse gas emissions and enhancing climate resilience. However, these plans exist in diverse, unstructured formats, making automated extraction, comparison, and analysis challenging.
This thesis presents an AI-powered framework that leverages natural language processing (NLP) and machine learning techniques to process climate action plans systematically. The
framework performs document preprocessing, text chunking, and information extraction to convert unstructured reports into structured, queryable data. The extracted content is stored in two
distinct databases:
(1) An action plan database, which captures detailed policy strategies, governance mechanisms,
and implementation stages.
(2) A progress reports database, which enables tracking of policy evolution over time.
The framework facilitates structured querying and analysis, allowing stakeholders to compare climate strategies across different regions and timeframes. A key feature is the progress report generator, which systematically identifies added, removed, and modified actions between different versions of a plan, providing insights into policy effectiveness. Experimental results demonstrate the framework’s ability to process diverse climate action plans, extract structured data, and generate insights into trends, policy focus areas, and implementation progress. By offering a scalable, data-driven approach to climate policy analysis, this research contributes to the field of environmental data science and supports evidence-based decision-making for climate action.