Aligning and Quantifying Higher Education Institutions’ Climate Actions With Nationally Determined Contributions Through AI-Enabled Data Discovery and Verification

Authors

PMPC Gunathilake

Postgraduate Institute of Science, University of Peradeniya, Peradeniya (Sri Lanka)

Tilak Hewawasam

Department of Geography, University of Peradeniya, Peradeniya (Sri Lanka)

Jagath Gunatilake

Department of Geology, University of Peradeniya, Peradeniya (Sri Lanka)

Article Information

DOI: 10.47772/IJRISS.2025.903SEDU0766

Subject Category: Education

Volume/Issue: 9/26 | Page No: 9984-9997

Publication Timeline

Submitted: 2025-12-18

Accepted: 2025-12-24

Published: 2025-12-30

Abstract

Higher education institutions (HEIs) serve as knowledge producers and innovation ecosystems in climate governance, yet their contributions remain limited in formal accountability frameworks. This research presents a comprehensive framework that systematically discovers, quantifies, and reports HEIs' climate contributions through integrated automated web scraping, Artificial Intelligence, semantic modelling, and standardised data submission protocols. The system extracts climate action data from digital sources and structures it using a Climate Action Data Model (CADM). Each climate action undergoes automated impact quantification using standardised emission calculation methodologies aligned with IPCC protocols and localised emission factors. A standardised data submission mechanism enables systematic transmission of quantified interventions to established national Measurement, Reporting, and Verification (MRV) systems, facilitating integration into provincial and national climate transparency frameworks. Pilot implementation across nineteen Sri Lankan universities validates the framework's technical robustness and policy relevance. The system achieves automated data extraction with the precision of 0.87 and the recall of 0.82, while the RAG-based Large Language Model generates evidence-grounded climate action classifications with the factual grounding of 0.91, effectively minimising hallucinations. This research delivers a scalable, replicable architecture for establishing AI-enabled university climate data pipelines that bridge the gap between institutional sustainability efforts and formal national climate accounting, positioning universities as verifiable implementing nodes in global climate governance. By demonstrating that digitally documented climate actions can be systematically discovered, quantified, and integrated into national MRV systems, the framework establishes a pathway for transforming fragmented institutional initiatives into formally recognised contributions to national climate commitments under the Paris Agreement.

Keywords

Greenhouse Gas Inventories, Climate

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