Digitizing Talent, Intensifying Emissions: A Review of the Rebound Effect in AI-Driven Human Resource Management Systems

References

[1] M. Akpan and A. Adebayo, “Artificial intelligence and the dual paradoxes: Reporting the interplay of efficiency, resource consumption, and labor dynamics,” Reporting and Accountability Review, vol. 3, no. 1, pp. 37–46, 2026, doi: 10.22495/rarv3i1p3. Crossref

[2] A. S. Al Alaq, A. O. Damayanti, D. Efendi, G. T. Tahara, and N. Setyawati, “Integration of digital technology and Green Human Resource Management (GHRM) in aligning equity principles to drive innovation, employee development, and environmental sustainability,” Jurnal Ilmiah Manajemen dan Bisnis, vol. 21, no. 1, 2025.

[3] Benabou and F. Touhami, “Artificial Intelligence in Human Resource Management: A PRISMA-based Systematic Review,” Acta Informatica Pragensia, vol. 14, pp. 489–505, 2025, doi: 10.18267/j.aip.264. Crossref

[4] Conference Paper, “Optimizing and improving Green HRM strategies in manufacturing with data and AI,” 2026.

[5] H. V. Erkutlu, “Artificial intelligence and human resources management: transformation in the workplace,” Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, vol. 15, no. 1, pp. 346–357, 2025, doi: 10.30783/nevsosbilen.1622590. Nevşehir Hacı Bektaş Veli University

[6] M. Gebhardt, A. Spieske, and H. Birkel, “The future of the circular economy and its effect on supply chain dependencies: empirical evidence from a Delphi study,” Transportation Research Part E: Logistics and Transportation Review, vol. 157, Art. no. 102570, 2022, doi: 10.1016/j.tre.2021.102570. ScienceDirect

[7] T. Gnatzy, J. Warth, H. von der Gracht, and I.-L. Darkow, “Validating an innovative real-time Delphi approach - a methodological comparison between real-time and conventional Delphi studies,” Technological Forecasting and Social Change, vol. 78, no. 9, pp. 1681–1694, 2011, doi: 10.1016/j.techfore.2011.04.006. ScienceDirect

[8] L. Hughes, F. Davies, K. Li, S. M. Gunaratnege, T. Malik, and Y. K. Dwivedi, “Beyond the hype: Organisational adoption of Generative AI through the lens of the TOE framework–A mixed methods perspective,” International Journal of Information Management, vol. 86, Art. no. 102982, 2026, doi: 10.1016/j.ijinfomgt.2025.102982. ScienceDirect

[9] M. Hynes, “Virtual consumption: A review of digitalization's ‘green’ credentials,” Frontiers in Sustainability, vol. 3, Art. no. 969329, 2022, doi: 10.3389/frsus.2022.969329. Frontiers

[10] IBRS, “True or False? AI is intensifying work, not reducing it!,” Webinar and Presentation Kit, 2026. IBRS

[11] Journal of Universitas Airlangga, “Digital Green Human Resource Management and organisational sustainability: A systematic review,” The Indonesian Journal of Accounting and Business, vol. 21, no. 1, 2026.

[12] U. T. Khatoon, M. Babgi, N. T. Hadi, R. N. Mir, and A. Velidandi, “Technology-Driven Change in Human Resource Management: Reshaping Talent Management and Organizational Design,” Administrative Sciences, vol. 15, no. 11, Art. no. 452, 2025, doi: 10.3390/admsci15110452. MDPI

[13] M. Kopyto, S. Lechler, H. A. von der Gracht, and E. Hartmann, “Potentials of blockchain technology in supply chain management: long-term judgments of an international expert panel,” Technological Forecasting and Social Change, vol. 161, Art. no. 120330, 2020, doi: 10.1016/j.techfore.2020.120330. ScienceDirect

[14] D. Laddha, A. G. Ogueda, and P. Seshaiyer, “Modeling and analysis of the impacts of the rise in AI use on data center growth, resulting workforce displacement, and environmental impacts through a coupled system of ordinary differential equations within a feedback framework,” Journal of Student Systems Research, 2025, doi: 10.13021/jssr2025.5317. Crossref

[15] Y. Li, J. Feng, X. Dong, Z. Yuan, L.-L. Zheng, L. Zhang, and L. Wang, “Targeting lysosome-dependent cell death in cancer: towards therapeutic strategies,” Biomarker Research, 2026, doi: 10.1186/s40364-026-00969-1. Springer

[16] A. S. Luccioni, E. Strubell, and K. Crawford, “The 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT '25), June 23--26, 2025, Athens, Greece,” 2025.

[17] B. L. Magee and T. C. Devezas, “A Simple Extension of Dematerialization Theory: Incorporation of Technical Progress and the Rebound Effect,” Technological Forecasting and Social Change, vol. 117, pp. 196–205, 2017.

[18] Missemer, “William Stanley Jevons' The Coal Question (1865), beyond the rebound effect,” Ecological Economics, vol. 82, pp. 97–103, 2012, doi: 10.1016/j.ecolecon.2012.07.010. ScienceDirect

[19] M. H. Khan and S. N. Muktar, “Green employee empowerment: The missing linchpin between green HRM and sustainable organizational performance,” Journal of Cleaner Production, vol. 434, Art. no. 139812, 2024, doi: 10.1016/j.jclepro.2023.139812. ScienceDirect

[20] Muniba, A. Majeed, and A. M. Du, “Greening the supply chain: How digital transformation shapes environmental sustainability in emerging economies,” Energy Economics, vol. 157, Art. no. 109283, 2026, doi: 10.1016/j.eneco.2026.109283. ScienceDirect

[21] R. P. Nar, “Will neural scaling laws activate Jevons' Paradox in AI labor markets? A time-varying elasticity of substitution (VES) analysis,” arXiv preprint arXiv:2503.05816, 2025. arXiv

[22] D. Owen, “The Efficiency Dilemma,” The New Yorker, Dec. 12, 2010.

[23] E. C. Nwokoro, V. C. Ayozie-Samuel, J. E. Osuchukwu, and D. O. Ozuzu, “Emerging technologies: Digital inclusion and skills development for women and youths in Africa,” 2024.

[24] R. M. Pasha, S. A. Salman, and A. Bhaumik, “Impact of Artificial Intelligence on Workforce Engagement and Retention in Digital Human Resource Management,” International Journal of Modern Computation, Information and Communication Technology, vol. 8, no. 2, pp. 20–31, 2025, doi: 10.65000/5jhqd598. International Journal of Modern Computation, Information and Communication Technology

[25] T. Patnaik, R. I. Gnankob, and N. S. Nanda, “Governing algorithms, empowering people: How ethical AI oversight shapes trust, technostress, and employee autonomy in AI-enabled HRM,” Frontiers in Artificial Intelligence, vol. 9, Art. no. 1911545, 2026, doi: 10.3389/frai.2026.1911545. Frontiers

[26] D. K. Prestini, “Algorithmic management in AI-driven recruitment: The AI Recruitment Governance Framework (ARGF) for responsible AI governance,” Science Discovery Artificial Intelligence, vol. 1, no. 2, pp. 69–77, 2026, doi: 10.11648/j.sdai.20260102.12. Science Publishing Group

[27] Psychiatric Times, “Data Centers Are Swallowing Up Our Human Resources,” 2026. Psychiatric Times

[28] S. Pueyo, “Jevons' Paradox and a Tax on Aviation to Prevent the next Pandemic,” SocArXiv, 2020, doi: 10.31235/osf.io/vb5q3. SocArXiv

[29] D. Punitha, S. S. Bhat, S. Singh, and K. S. Kumar, “Smart HR Sustainability System using Green AI for Attrition Prediction and Retention Strategy,” in Proc. ICISD 2025, 2025.

[30] S. Sorrell, “Reducing energy demand: A review of issues, challenges and approaches,” Renewable and Sustainable Energy Reviews, vol. 47, pp. 74–82, 2015, doi: 10.1016/j.rser.2015.03.002. ScienceDirect

[31] SThree, “Hiring for the future: why AI and sustainability are shaping the next generation of STEM talent,” The Times, 2024.

[32] M. Syahrudin, “Digital Green Human Resource Management and Organisational Sustainability,” TIJAB (The International Journal of Applied Business), vol. 10, no. 1, pp. 130–142, 2026, doi: 10.20473/tijab.v10.I1.2026.84654. Crossref

[33] Tamarack Technology, “Jevons Paradox Meets Moore's Law: Why AI Will Drive More Hiring in Equipment Finance,” 2022.

[34] P. Thanapathy, “AI's power surge: Rethinking energy efficiency in the age of data centers,” CIET Canada, 2026. CIET Canada

[35] Universidad Politécnica de Madrid, “Energy efficiency in data centers: 2024-2025 snapshot,” 2026. Universidad Politécnica de Madrid

[36] M. Włodarczyk and R. Wisła, The Digital Economy and the Productivity Paradox. Taylor & Francis, 2025, doi: 10.4324/9781003662181. Taylor & Francis

[37] J. Yang, H. Jin, R. Tang, X. Han, Q. Feng, H. Jiang, B. Yin, and X. Hu, “Harnessing the power of LLMs in practice: A survey on ChatGPT and beyond,” arXiv preprint arXiv:2304.13712, 2023. arXiv

@article{1723430,
      author = {Esther Dominic Peter, Chibueze Uzoma Chibundu, Samuel Iheanyi Ayozieuwa, Onyinyechi Henrietta Usoh, Chidinma Grace Emmanuel; Idara Okokon Asuquo},
      title = {Digitizing Talent, Intensifying Emissions: A Review of the Rebound Effect in AI-Driven Human Resource Management Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2775-2785},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723430.pdf},
      abstract = {The integration of artificial intelligence into human resource management has been widely championed as a pathway to organizational efficiency and, increasingly, as a tool for advancing sustainability objectives through digital transformation. This review challenges the prevailing assumption that AI-enabled HR systems inherently reduce environmental impact by examining the phenomenon through the lens of the Jevons paradox. Synthesizing evidence from engineering informatics, energy economics, and strategic human resource management, we argue that efficiency gains in algorithmic HR processes generate rebound effects that may partially or fully offset anticipated resource savings. The review identifies three mechanisms through which digital HR intensifies resource consumption: the scaling effect, wherein reduced marginal costs drive increased transaction volumes; the complexity effect, wherein ever-expanding datasets and model architectures demand escalating computational resources; and the oversight effect, wherein algorithmic outputs generate new categories of human labor for validation and correction. We propose the Net-HR Carbon Account framework as a methodological response, integrating Life Cycle Assessment principles with HR process analytics. The review concludes that sustainable HR scholarship must move beyond surface-level digitization metrics toward a systemic accounting of the energy, water, and human costs embedded in AI-driven talent management.},
      keywords = {Jevons paradox; artificial intelligence; human resource management; rebound effect; sustainable innovation; green computing.},
      month = {September},
  }