Digital technologies for sustainable operations: a single-reviewer systematic review of artificial intelligence, internet of things, and data analytics applications for resource efficiency and emissions reduction

The need to reduce carbon emissions, manage scarce resources, and improve operational efficiency has increased interest in digital technologies for sustainable operations. This PRISMA 2020-guided, single-reviewer systematic review examines how artificial intelligence (AI), the Internet of Things (IoT), data analytics, digital twins, and related technologies are associated with sustainability outcomes in organizational operations, and under which implementation conditions those outcomes are reported. Four databases (Scopus, Web of Science Core Collection, ScienceDirect, and IEEE Xplore) were searched on 15 March 2026 for peer-reviewed journal articles published between January 2023 and March 2026. The review log records 420 records identified, 110 duplicates removed, 310 records screened, 95 full-text reports assessed, and 42 studies retained. These counts are arithmetically consistent, but screening, eligibility assessment, and data extraction were performed by one reviewer, the protocol was not prospectively registered, and no inter-rater statistic exists; the 42-study set therefore should not be regarded as independently verified. Because the studies use different baselines, system boundaries, and outcome definitions, no pooled estimate and no cross-study percentage range is reported. Quantitative results are presented only where they can be traced to a named primary study, for example a 4.21% reduction in energy consumption from a physics-guided neural network in two industrial case studies [1], a 7.62% reduction in automated-guided-vehicle energy consumption from digital-twin-based scheduling [2], a 51.5% observed reduction in energy consumption at a single manufacturing machine from IIoT energy monitoring [3], and a model-predicted 6.0% energy-consumption reduction from a machine-learning model applied to a pelleting press [4]; each is treated as a context-specific observation rather than a general effect size. The review also examines life-cycle burdens, rebound effects, e-waste, data-center electricity and water demand, workforce implications, digital inequality, and critical-mineral supply risks, drawing on adjacent literature outside the 42-study set. A Digital-Operations-Sustainability (DOS) framework is proposed as a conceptual model whose five propositions are hypotheses for future testing, not validated relationships. Priorities for future work include dual-reviewer, prospectively registered evidence synthesis; standardized sustainability indicators; integration of AI with life cycle assessment; longitudinal validation; and evidence from small and medium-sized enterprises (SMEs) and developing economies.