Predictive Analytics Model for Optimizing Carbon Footprint From Students’ Learning Activities in Computer Science-Related Majors

Abstract

Global warming poses a significant challenge to environmental sustainability due to the high greenhouse gas emissions originating from human activities. Among the various sectors contributing to these emissions, the education sector, particularly at the university level, plays a crucial role in generating carbon footprints. While several studies have examined carbon footprints within universities, few have focused on understanding the behavior and activities of individuals. Statistics show that students are the primary contributors to carbon emissions in higher education. Despite the availability of various methods for calculating carbon emissions, limited studies have utilized such data to predict future trends. Therefore, this study aimed to develop a predictive analytics model that leveraged students’ learning activities as a significant factor to predict future trends in university carbon emissions. Institut Teknologi Bandung (ITB) was used as a case study, especially computer science-related majors. The carbon emission calculation utilized a formula that incorporated various emission sources, including electricity, transportation, and paper consumption. In the 2022/2023 academic year, ITB generated 612.8 tons of CO $_{2}\text{e}$ . The prediction modeling employed the SVR algorithm and utilized historical data, such as carbon emissions from the last 30 days, and external information, such as weather, event-related data, and university data. The model’s performance was evaluated using metrics, yielding values of 129.41 for MAE, 158.03 for RMSE, 0.98 for R2, and 15.83 for MAPE. The results provided insights for universities to assess their carbon footprint and raised awareness among the academic community, supporting decision-making to optimize carbon emissions at the students’ level.

Keywords

• Carbon footprint
• information technology
• predictive analytics
• students