Automated Mapping of Environmental Higher Education Ranking Systems Indicators to SDGs Indicators using Natural Language Processing and Document Similarity

To evaluate the ESHERSs and determine their efficiency to measure environmental sustainability, we tackle this problem as a classification assignment. This study benchmark three ESHERSs: UI GreenMetric, Times Higher Education Impact ranking, and STARS (Sustainability Tracking, Assessment Rating System) by AASHE (the association for the advancement of sustainability in higher education). Next, we recruited a group of experts who mapped the ESHERS indicators to the SDGs indicators. Then, we use NLP techniques to classify (map) the ESHERS indicators to the SDGs indicators. Since most of the ESHERS indicators and the SDGs indicators are in the form of short text, we use the query expansion technique to make the NLP techniques more effective. Each ESHERS indicator and its expanded text represents a document. And, each SDG indicator and its expanded text represents a document. We took the expanded text from the description of the ESHERS indicators and the description of SDG indicators, forming the corpus for our study. Then, we used document similarity to find the similarity between every pair of the corpus documents. We used different similarity measures to see the similarity between the forms. Then, we used a voting system to map the ESHERSs indicators to the SDGs indicators. The proposed system was able to automatically map the underlying ranking systems indicators to the UN SDGs with 99% accuracy compared to the experts mapping.