Authors : Anurag Tiruwa; Shuchi Dikshit

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/55mk3xc7

Scribd : https://tinyurl.com/4br7dsda

DOI : https://doi.org/10.38124/ijisrt/26jan033

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Indian higher education institutions (HEIs) operate as “mini-cities” with substantial electricity, water, mobility, and material footprints. This paper synthesizes how artificial intelligence (AI) can accelerate campus sustainability through (i) resource optimization (especially building energy and water), (ii) monitoring and operational reliability (maintenance, waste, and compliance), and (iii) behavior and mobility nudges (transport and paper reduction). Using a mini-review approach, we consolidate high-impact, campus-relevant AI applications and outline the measurement logic that links interventions to auditable sustainability indicators. We then identify key adoption barriers in Indian HEIs, including limited metering and data interoperability, procurement and skills constraints, governance and privacy concerns, and the environmental footprint of AI systems themselves. To move from isolated pilots to measurable outcomes, we propose the KPI–Data–Duty (KDD) framework, which connects a small set of time-bound sustainability KPIs to minimal viable data architecture, pilot design, and a lightweight Responsible/Green AI duty checklist. The paper contributes an implementation- oriented roadmap and use-case mapping that can support HEI leaders in planning, governing, and scaling AI-enabled sustainability initiatives with accountability.

Keywords : AI for Sustainability; Green Campus; Smart Buildings; Higher Education Institutions; Energy Management; Water Conservation; Responsible AI; Green AI.

References :

1. Aghili, S. A., Haji Mohammad Rezaei, A., Tafazzoli, M., Khanzadi, M., & Rahbar, M. (2025). Artificial intelligence approaches to energy management in HVAC systems: A systematic review. Buildings, 15(7), 1008. https://doi.org/10.3390/buildings15071008
2. Aghili, S. A., Rezaei, A. H. M., Tafazzoli, M., Khanzadi, M., & Rahbar, M. (2025). Artificial intelligence approaches to energy management in HVAC systems: A systematic review. Buildings, 15(7), 1008. https://doi.org/10.3390/buildings15071008
3. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21). https://doi.org/10.1145/3442188.3445922
4. Bin, L., Shahzad, M., Javed, H., Muqeet, H. A., Akhter, M. N., Liaqat, R., & Hussain, M. M. (2022). Scheduling and sizing of campus microgrid considering demand response and economic analysis. Sensors, 22(16), 6150. https://doi.org/10.3390/s22166150
5. Bolón-Canedo, V., & Morán-Fernández, L. (2024). A review of green artificial intelligence: Towards a more sustainable future. Neurocomputing, 599, 128096. https://doi.org/10.1016/j.neucom.2024.128096
6. Botman, L., Lago, J., Fu, X., Chia, K., Wolf, J., Kleissl, J., & De Moor, B. (2024). Building plug load mode detection, forecasting and scheduling. Applied Energy, 369, 123098. https://doi.org/10.1016/j.apenergy.2024.123098
7. Bouquet, P., (et al.). (2024). AI-based forecasting for optimised solar energy management and enhanced smart grid reliability. International Journal of Production Research. https://doi.org/10.1080/00207543.2023.2269565
8. Chen, Y., & Lotti, L. (2025). Evaluating the effectiveness of behavioural nudges in reducing energy consumption in student accommodations: A quasi-experimental approach. UCL Open Environment Preprints. https://doi.org/10.14324/ucloepreprints.286.v2
9. Cheong, P. H., & Nyaupane, P. (2022). Smart campus communication, Internet of Things, and data governance: Understanding student tensions and imaginaries. Big Data & Society, 9(1). https://doi.org/10.1177/20539517221092656
10. de Bakker, C., Aries, M., Kort, H., & Rosemann, A. (2017). Occupancy-based lighting control in open-plan office spaces: A state-of-the-art review. Building and Environment, 112, 308–321. https://doi.org/10.1016/j.buildenv.2016.11.042
11. de Bakker, C., Van de Voort, T., & Rosemann, A. (2018). The energy saving potential of occupancy-based lighting control strategies in open-plan offices: The influence of occupancy patterns. Energies, 11(1), 2. https://doi.org/10.3390/en11010002
12. Domínguez-Bolaño, T., Barral, V., Escudero, C. J., & García-Naya, J. A. (2024). An IoT system for a smart campus: Challenges and solutions illustrated over several real-world use cases. Internet of Things, 25, 101099. https://doi.org/10.1016/j.iot.2024.101099
13. Es-sakali, N., Cherkaoui, M., Mghazli, M. O., & Naimi, Z. (2022). Review of predictive maintenance algorithms applied to HVAC systems. Energy Reports, 8(Suppl. 9), 1003–1012. https://doi.org/10.1016/j.egyr.2022.07.130
14. Ghezzi, R., & Mikkonen, T. (2024). On public procurement of ICT systems: Stakeholder views and emerging tensions. In Software Business (ICSOB 2023) (pp. 61–76). Springer. https://doi.org/10.1007/978-3-031-53227-6_5
15. Gilman, E., Tamminen, S., Yasmin, R., Ristimella, E., Peltonen, E., Harju, M., Lovén, L., Riekki, J., & Pirttikangas, S. (2020). Internet of Things for smart spaces: A university campus case study. Sensors, 20(13), 3716. https://doi.org/10.3390/s20133716
16. Government of India. (2023). The Digital Personal Data Protection Act, 2023 (No. 22 of 2023). Ministry of Electronics and Information Technology.
17. Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
18. GRIHA Council. (n.d.). GRIHA rating (GRIHA V3) – criteria overview.
19. Hashim, M. A. M., Tlemsani, I., & Matthews, R. (2022). Higher education strategy in digital transformation. Education and Information Technologies, 27, 3171–3195. https://doi.org/10.1007/s10639-021-10739-1
20. Indian Green Building Council. (2024). IGBC Green Campus rating system (Version 1.0): Reference guide (July 2024). Indian Green Building Council.
21. International Organization for Standardization. (2018). ISO 50001:2018—Energy management systems—Requirements with guidance for use.
22. Kanyama, M. N., Bhunu Shava, F., Gamundani, A. M., & Hartmann, A. (2024). Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review. Physics and Chemistry of the Earth, Parts A/B/C, 134, 103558. https://doi.org/10.1016/j.pce.2024.103558
23. Kanyama, M. N., Bhunu Shava, F., Gamundani, A. M., & Hartmann, A. (2025). AI-driven anomaly detection in smart water metering systems using ensemble learning. Water, 17(13), 1933. https://doi.org/10.3390/w17131933
24. Kanyama, M. N., Shava, F. B., Gamundani, A. M., & Hartmann, A. (2024). Machine learning applications for anomaly detection in smart water metering networks: A systematic review. Physics and Chemistry of the Earth, Parts A/B/C, 134, 103558. https://doi.org/10.1016/j.pce.2024.103558
25. Kim, I., & Lee, A. J. (2024). “I know what you did last semester”: Understanding privacy expectations and preferences in the smart campus. Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24). https://doi.org/10.1145/3613904.3642174
26. Komljenovic, J., Birch, K., Sellar, S., Bergviken Rensfeldt, A., Deville, J., Eaton, C., Gourlay, L., Hansen, M., Kerssens, N., Kovalainen, A., Nappert, P.-L., Noteboom, J., Parcerisa, L., Pardo-Guerra, J. P., Poutanen, S., Robertson, S., Tyfield, D., & Williamson, B. (2025). Digitalised higher education: Key developments, questions, and concerns. Discourse: Studies in the Cultural Politics of Education, 46(2), 276–292. https://doi.org/10.1080/01596306.2024.2408397
27. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
28. Maciel, R. R., de Souza, A. D., Almeida, R. M. A., & Leite, J. P. R. R. (2025). The impact of IoT-enabled routing optimization on waste collection distance: A systematic review and meta-analysis. Logistics, 9(4), 161. https://doi.org/10.3390/logistics9040161
29. MeitY. (2025). Digital Personal Data Protection Rules, 2025.
30. Melo, S., Gomes, R. J. R., Abbasi, R., & Arantes, A. (2024). Demand-responsive transport for urban mobility: Integrating mobile data analytics to enhance public transportation systems. Sustainability, 16(11), 4367. https://doi.org/10.3390/su16114367
31. Ministry of Electronics and Information Technology (MeitY). (2023). The Digital Personal Data Protection Act, 2023.
32. Murtaza, A. A., Saher, A., Zafar, M. H., Moosavi, S. K. R., Aftab, M. F., & Sanfilippo, F. (2024). Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study. Results in Engineering, 24, 102935. https://doi.org/10.1016/j.rineng.2024.102935
33. Nahiduzzaman, M., Ahamed, M. F., & Naznine, M. (2025). An automated waste classification system using deep learning techniques: Toward efficient waste recycling and environmental sustainability. Knowledge-Based Systems, 310, 113028. https://doi.org/10.1016/j.knosys.2025.113028
34. Nahiduzzaman, M., Ahamed, M. F., Naznine, M., Karim, M. J., Kibria, H. B., Ayari, M. A., Khandakar, A., Ashraf, A., Ahsan, M., & Haider, J. (2025). An automated waste classification system using deep learning techniques: Toward efficient waste recycling and environmental sustainability. Knowledge-Based Systems, 310, 113028. https://doi.org/10.1016/j.knosys.2025.113028
35. National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). https://doi.org/10.6028/NIST.AI.100-1
36. NITI Aayog. (2021a). Principles for Responsible AI.
37. NITI Aayog. (2021b). Operationalizing Principles for Responsible AI (Part 2).
38. Noor, R. M., Rasyidi, M. A., & others. (2020). Campus shuttle bus route optimization using machine learning predictive analysis: A case study. Sustainability, 13(1), 225. https://doi.org/10.3390/su13010225
39. Opara-Martins, J., Sahandi, R., & Tian, F. (2016). Critical analysis of vendor lock-in and its impact on cloud computing migration: A business perspective. Journal of Cloud Computing: Advances, Systems and Applications, 5(1). https://doi.org/10.1186/s13677-016-0054-z
40. Organisation for Economic Co-operation and Development. (2025). Digital transformation of public procurement. OECD
41. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
42. Paunov, Y., & Grüne-Yanoff, T. (2023). Boosting vs. nudging sustainable energy consumption: A long-term comparative field test in a residential context. Behavioural Public Policy. https://doi.org/10.1017/bpp.2023.30
43. Press Information Bureau. (2025, November 17). DPDP Rules, 2025 notified: A citizen-centric framework for privacy protection and responsible data use.
44. Rajbhandari, Y., (et al.). (2024). Enhanced demand side management for solar-based isolated microgrids using forecasting and optimization. IET Smart Grid. https://doi.org/10.1049/stg2.12151
45. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939778
46. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831
47. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
48. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). https://doi.org/10.18653/v1/P19-1355
49. United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1
50. Verdecchia, R., Sallou, J., & others. (2023). A systematic review of Green AI. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1507
51. Wang, Z., Hong, T., & Piette, M. A. (2019). Predicting plug loads with occupant count data through a deep learning approach. Energy, 181, 29–42. https://doi.org/10.1016/j.energy.2019.05.138
52. Zhang, W., Wu, Y., & Calautit, J. K. (2022). A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment. Renewable and Sustainable Energy Reviews, 167, 112704. https://doi.org/10.1016/j.rser.2022.112704