AI and the Future of Sustainability Disclosures: A Systematic Desk Review Preparedness of Public Universities for Mandatory Non-Financial Reporting by 2028
Keywords:
Artificial Intelligence; ESG Reporting; Sustainability Disclosures; Non-Financial Reporting; Higher Education; Kenya; Institutional PreparednessAbstract
The global shift toward sustainability reporting has substantially broadened expectations for organizations to disclose non-financial information pertaining to environmental, social, and governance (ESG) performance. In Kenya, the gradual adoption of international sustainability disclosure standards—like the anticipated application of the IPSASB Sustainability Reporting Standard (SRS1) to public sector entities signals a consequential transformation in institutional accountability. Public universities, which operate large campuses and fulfil essential public service roles through teaching, research, and community engagement, will increasingly be required to report sustainability performance in a transparent, structured, and measurable manner. Yet many of these institutions continue to rely on fragmented administrative systems that render sustainability data collection both difficult and inconsistent. This paper examines how Artificial Intelligence (AI) can support the measurement, verification, and reporting of sustainability indicators in Kenyan public universities as the country prepares for mandatory non-financial reporting requirements. Employing a systematic desk review methodology guided by PRISMA principles, the study synthesizes scholarly and policy literature on sustainability reporting frameworks, ESG indicators relevant to higher education, and emerging AI technologies applied to sustainability analytics. The study proposes an AI-enabled sustainability reporting architecture tailored to the institutional and operational realities of Kenyan public universities and identifies critical readiness gaps that must be addressed. The findings indicate that AI-driven data systems can significantly enhance the accuracy, efficiency, and credibility of sustainability disclosures while simultaneously strengthening institutional governance and accountability. This paper contributes to the growing discourse on responsible AI, digital governance, and sustainability reporting within higher education institutions across Africa.
References
1. Agrawal, A., Gans, J., & Goldfarb, A. (2018). *Prediction machines: The simple economics of artificial intelligence*. Harvard Business Review Press.
2. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. *Qualitative Research in Psychology*, 3(2), 77–101. [https://doi.org/10.1191/1478088706qp063oa](https://doi.org/10.1191/1478088706qp063oa)
3. Brynjolfsson, E., & McAfee, A. (2017). *Machine, platform, crowd: Harnessing our digital future*. W. W. Norton.
4. Cho, C. H., Magnan, M., & Patten, D. M. (2012). On the usefulness of financial statements for predicting future returns: The case of the software industry. *Accounting Forum*, 36(1), 1–15. [https://doi.org/10.1016/j.accfor.2011.06.001](https://doi.org/10.1016/j.accfor.2011.06.001)
5. Cortese, A. D. (2003). The critical role of higher education in creating a sustainable future. *Planning for Higher Education*, 31(3), 15–22.
6. Creswell, J. W., & Poth, C. N. (2018). *Qualitative inquiry and research design: Choosing among five approaches* (4th ed.). SAGE Publications.
7. Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017). A review on time series forecasting techniques for building energy consumption. *Renewable and Sustainable Energy Reviews*, 74, 902–924. [https://doi.org/10.1016/j.rser.2017.02.085](https://doi.org/10.1016/j.rser.2017.02.085)
8. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. *American Sociological Review*, 48(2), 147–160. [https://doi.org/10.2307/2095101](https://doi.org/10.2307/2095101)
9. Disterheft, A., Caeiro, S., Azeiteiro, U. M., & Leal Filho, W. (2015). Sustainable universities—A study of critical success factors for participatory approaches. *Journal of Cleaner Production*, 106, 11–21. [https://doi.org/10.1016/j.jclepro.2014.01.030](https://doi.org/10.1016/j.jclepro.2014.01.030)
10. Eccles, R. G., & Krzus, M. P. (2010). *One report: Integrated reporting for a sustainable strategy*. John Wiley & Sons.
11. Ethics and Anti-Corruption Commission (EACC). (2019). *Ethics and anti-corruption survey in public institutions*. Government of Kenya.
12. Findler, F., Schönherr, N., Lozano, R., Reider, D., & Martinuzzi, A. (2019). The impacts of higher education institutions on sustainable development. *International Journal of Sustainability in Higher Education*, 20(1), 23–38. [https://doi.org/10.1108/IJSHE-07-2018-0114](https://doi.org/10.1108/IJSHE-07-2018-0114)
13. Freeman, R. E. (1984). *Strategic management: A stakeholder approach*. Pitman Publishing.
14. Global Reporting Initiative. (2021). *GRI universal standards 2021*. GRI. [https://www.globalreporting.org](https://www.globalreporting.org)
15. Government of Kenya. (2007). *Kenya Vision 2030: A globally competitive and prosperous Kenya*. Ministry of Planning and National Development.
16. Gray, R., Kouhy, R., & Lavers, S. (1995). Corporate social and environmental reporting: A review of the literature and a longitudinal study of UK disclosure. *Accounting, Auditing & Accountability Journal*, 8(2), 47–77. [https://doi.org/10.1108/09513579510146996](https://doi.org/10.1108/09513579510146996)
17. IFRS Foundation. (2023). *IFRS S1 general requirements for disclosure of sustainability-related financial information*. IFRS Foundation.
18. International Public Sector Accounting Standards Board (IPSASB). (2024). *Exposure draft: SRS ED 1, Climate-related disclosures*. IFAC.
19. International Public Sector Accounting Standards Board (IPSASB). (2026). *IPSASB SRS 1, Climate-related disclosures*. IFAC.
20. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. *Nature Machine Intelligence*, 1(9), 389–399. [https://doi.org/10.1038/s42256-019-0088-2](https://doi.org/10.1038/s42256-019-0088-2)
21. Kmet, L. M., Lee, R. C., & Cook, L. S. (2004). *Standard quality assessment criteria for evaluating primary research papers from a variety of fields*. Alberta Heritage Foundation for Medical Research.
22. Kylili, A., Fokaides, P. A., & Christoforou, E. A. (2016). Key performance indicators (KPIs) approach in buildings renovation for the sustainability of the built environment: A review. *Renewable and Sustainable Energy Reviews*, 56, 906–915. [https://doi.org/10.1016/j.rser.2015.11.096](https://doi.org/10.1016/j.rser.2015.11.096)
23. Leal Filho, W., Vargas, V. R., Salvia, A. L., Brandli, L. L., Pallant, E., Klavins, M., Ray, S., Moggi, S., Price, E., & Lok, S. (2019). The role of higher education institutions in sustainability initiatives at the local level. *Journal of Cleaner Production*, 233, 1004–1015. [https://doi.org/10.1016/j.jclepro.2019.06.059](https://doi.org/10.1016/j.jclepro.2019.06.059)
24. Lozano, R. (2011). The state of sustainability reporting in universities. *International Journal of Sustainability in Higher Education*, 12(1), 67–78. [https://doi.org/10.1108/14676371111098311](https://doi.org/10.1108/14676371111098311)
25. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. *PLOS Medicine*, 6(7), e1000097. [https://doi.org/10.1371/journal.pmed.1000097](https://doi.org/10.1371/journal.pmed.1000097)
26. Munasinghe, M. (2010). *Making development more sustainable: Sustainomics framework and practical applications*. MIND Press.
27. Ndetei, D. M., Khasakhala, L., Mutiso, V., & Mbwayo, A. W. (2016). Knowledge, attitude, and practice of health care workers in government hospitals in Kenya toward mental health patients. *Social Psychiatry and Psychiatric Epidemiology*, 46(10), 1117–1124. [https://doi.org/10.1007/s00127-010-0269-6](https://doi.org/10.1007/s00127-010-0269-6)
28. Nhamo, G., & Mjimba, V. (Eds.). (2020). *Sustainable development goals and institutions of higher education*. Springer.
29. Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. *International Journal of Qualitative Methods*, 16(1), 1–13. [https://doi.org/10.1177/1609406917733847](https://doi.org/10.1177/1609406917733847)
30. Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A., Maharaj, T., Sherwin, E., Mukkavilli, S. K., Krakovna, V., Boulanger-Lapointe, N., Chen, Y., & Bengio, Y. (2019). Tackling climate change with machine learning. *ACM Computing Surveys*, 55(2), 1–96. [https://doi.org/10.1145/3485128](https://doi.org/10.1145/3485128)
31. Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. *Academy of Management Review*, 20(3), 571–610. [https://doi.org/10.5465/amr.1995.9508080331](https://doi.org/10.5465/amr.1995.9508080331)
32. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. *British Journal of Management*, 14(3), 207–222. [https://doi.org/10.1111/1467-8551.00375](https://doi.org/10.1111/1467-8551.00375)
33. Tsai, F. S., & Wang, C. H. (2017). Natural language processing for ESG scoring. *Journal of Financial Data Science*, 3(4), 97–113.
34. UNESCO. (2019). *Women in higher education: Has the female advantage eliminated gender gaps in career progression?* UNESCO Institute for Statistics.
35. UNEP. (2016). *A snapshot of the world's water quality: Advancing a global assessment*. United Nations Environment Programme.
36. Wals, A. E. J. (2014). Sustainability in higher education in the context of the UN DESD: A review of learning and institutionalization processes. *Journal of Cleaner Production*, 62, 8–15. [https://doi.org/10.1016/j.jclepro.2013.06.007](https://doi.org/10.1016/j.jclepro.2013.06.007)
37. Zeng, Y., Chen, G., & Zhang, X. (2021). Predictive analytics for carbon reduction pathway modelling in institutional campuses. *Energy and Buildings*, 248, 111152. [https://doi.org/10.1016/j.enbuild.2021.111152](https://doi.org/10.1016/j.enbuild.2021.111152)
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