Global Journal of Engineering and Technology Research (GJETR)

Systematic Review of Predictive Safety Analytics Applications in LNG Projects with ESG Implications

Arumosoye, Oluwakemi Motunrayo, Obriki, Oghenepawon David, Ozobu, Cynthia Obianuju

24 February 2026 · Vol. 2, Issue 2, pp. 61-73

DOI: 10.65150/EP-gjetr/V2E2/2026-05

Abstract

The liquefied natural gas (LNG) industry operates within high-hazard project environments where operational safety, environmental sustainability, and governance considerations are tightly intertwined. This systematic review examines the applications of predictive safety analytics in LNG projects and explores their implications for environmental, social, and governance (ESG) performance. Predictive safety analytics leverage historical data, real-time monitoring, and advanced modeling techniques such as machine learning, statistical forecasting, and hazard trend analysis to anticipate incidents before they occur, thereby enhancing proactive risk management. The review synthesizes findings from peer-reviewed literature, industry reports, and case studies to identify prevailing methodologies, implementation strategies, and outcomes associated with predictive safety applications in LNG operations. Key insights highlight that predictive analytics enable the early identification of latent hazards, unsafe behaviors, and procedural deviations, allowing project teams to implement timely interventions that reduce the likelihood of injuries, operational disruptions, and environmental incidents. The analysis further underscores the importance of integrating predictive insights into decision-making, safety governance, and ESG reporting, illustrating how safety performance metrics can align with environmental compliance, social responsibility, and corporate governance objectives. Challenges associated with implementation, including data quality, multi-contractor integration, workforce adoption, and cultural barriers, are also examined, providing a nuanced understanding of the organizational conditions required for successful adoption. The review identifies research gaps, particularly in longitudinal validation of predictive models, cross-project benchmarking, and quantitative assessment of ESG outcomes linked to safety analytics. Future directions emphasize the development of standardized leading indicators, multi-level predictive frameworks, and industry-specific adaptation strategies to enhance both safety and ESG performance in LNG projects. By systematically evaluating predictive safety analytics in LNG operations, this review contributes to the emerging body of knowledge at the intersection of project safety, risk forecasting, and ESG management, offering actionable insights for practitioners, researchers, and policymakers seeking to leverage data-driven approaches for sustainable and injury-free project performance.

Keywords: Predictive safety analytics, LNG projects, leading safety indicators, ESG performance, risk forecasting, proactive safety management, operational resilience

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Arumosoye, Oluwakemi Motunrayo, Obriki, Oghenepawon David, Ozobu, & Cynthia Obianuju (2026). Systematic Review of Predictive Safety Analytics Applications in LNG Projects with ESG Implications. Global Journal of Engineering and Technology Research, 2(2), 61-73. https://doi.org/10.65150/EP-gjetr/V2E2/2026-05

@article{Arumosoye2026,
  title   = {Systematic Review of Predictive Safety Analytics Applications in LNG Projects with ESG Implications},
  author  = {Arumosoye and Oluwakemi Motunrayo and Obriki and Oghenepawon David and Ozobu and Cynthia Obianuju},
  journal = {Global Journal of Engineering and Technology Research},
  year    = {2026},
  volume  = {2},
  number  = {2},
  pages   = {61-73},
  doi     = {10.65150/EP-gjetr/V2E2/2026-05},
  url     = {https://doi.org/10.65150/EP-gjetr/V2E2/2026-05}
}

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