This study utilizes data mining techniques to enhance the advancement of environmentally sustainable smart campuses, with a particular emphasis on Chinese higher education institutions. Research uses substantial secondary data analysis of academic journals, government databases, and college campus sustainability reports. Trash generation, energy and water use, and green campus infrastructure adoption are quantified. The research tries to disclose and illustrate the complex relationships and correlations between these variables to understand how data mining may drive educational institution sustainability. Data mining can increase energy efficiency, water conservation, and waste reduction on green smart campuses, according to this study. These theoretical and practical ideas help campus managers manage resources and promote sustainability. This research provides real resource efficiency and environmental sustainability solutions. Theory explains how data mining, green technology maturity and integration, and higher education infrastructure growth are linked. The work enhances our theoretical understanding of data mining, green technology maturity, and campus infrastructure integration. The research provides a comprehensive approach for evaluating data mining's sustainability optimisation success in additional campus contexts. In conclusion, this study lays the groundwork for data-driven ecological responsibility and sustainable development plans in Chinese higher education institutions and worldwide. It advances data-driven sustainability management decision-making, making green campus construction worldwide more informed and effective.
Keywords
Green Smart Campus, Data Mining Technology, Sustainability, Higher Education, Optimization.
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Qiangjun Liu
Krirk University International College, Bangkok, 10220, Thailand.
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Cite this article
Qiangjun Liu, “Enhancing Green Smart Campus Development with Data Mining Technology”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505041.
