1. Introduction
The transition to sustainable energy infrastructure represents a critical contemporary challenge requiring sophisticated coordination between technological innovation, project governance, and environmental stewardship (Alka et al., 2025; Elhamahmy et al., 2025).
Established project prediction methodologies exhibit fundamental limitations in their capacity to integrate sustainability considerations with traditional performance metrics (Banihashemi et al., 2017; Silvius & Schipper, 2014; Stanitsas et al., 2021), creating systematic inefficiencies in energy infrastructure delivery. This limitation becomes particularly acute when deploying artificial intelligence solutions (Dacre et al., 2025; Dacre & Kockum, 2022a; Ghafari & Samaei, 2025), which introduce additional processing complexity whilst offering transformative potential for optimising complex energy systems.
Contemporary project governance frameworks predominantly employ retrospective analysis of cost, schedule, and technical risks (Armenia et al., 2019), hitherto fail to incorporate forward-looking sustainability metrics that capture lifecycle environmental impacts, resource circularity, and systemic interdependencies (Kivilä et al., 2017; Rosengart et al., 2023). This methodological limitation stems from the inherent complexity of operationalising sustainability indicators within real-time predictive models (Klug & Kmoch, 2015; Olawumi & Oladapo, 2025), particularly when addressing the temporal and spatial variations characteristic of energy infrastructure projects. The International Energy Agency projects that global energy infrastructure investment must reach $4.5 trillion annually through 2030 to achieve net-zero targets (Abdulla et al., 2024), however established project prediction methodologies largely treat sustainability as an external constraint rather than an integral component of predictive modelling frameworks (Sabini & Alderman, 2021). This approach is compounded by the inherent complexity of communicating sustainability trade-offs to stakeholders, who require both technical accuracy and explanations of AI-driven recommendations (Kunkel et al., 2023).
Furthermore, project management approaches typically treat individual projects as isolated entities (Martinsuo et al., 2022), failing to capture complex interdependencies between project delivery outcomes, broader energy system resilience, and supply chain sustainability (da Silva et al., 2025). This reductionist perspective undermines the systemic benefits that AI could potentially deliver in coordinating multiple infrastructure projects across regional or national scales (Chen et al., 2025). The emergence of digital transformation approaches in energy infrastructure necessitates new theoretical frameworks that can accommodate the complexity of sustainability-aware project prediction (Gong et al., 2022; Zarifis, 2024; Zarifis & Cheng, 2023).
As such this PhD will extend greyfly.ai's Intelligent Project Prediction (IPP) platform to incorporate the sustainability implications of large-scale energy infrastructure projects by addressing three interconnected challenges:
First, the development of methodologies for identifying, modelling, and integrating sustainability indicators alongside traditional project data. This involves creating frameworks that can process lifecycle carbon assessments, energy demand projections, and circularity measures within predictive models without compromising computational efficiency.
Second, the application of explainable AI techniques to ensure that sustainability-related predictions maintain transparency and persuasive power for decision-makers across diverse stakeholder groups.
Third, the implementation of systems thinking approaches to capture interdependencies between project delivery, energy systems, and supply chains, ensuring that AI predictions reflect real-world complexity rather than simplified project-level metrics (Dacre & Kockum, 2022b).
The methodology and associated tasks are described below.
The research methodology will employ a combination of predictive model development, theoretical framework construction, and empirical validation through industry partnership with greyfly.ai (Brookes et al., 2020; Hsu et al., 2021). The investigation will begin with a systematic examination of sustainability metrics applicable to energy infrastructure projects, developing novel multi-dimensional scoring systems that incorporate environmental, economic, and social indicators. This foundation will support the creation of interpretable models specifically optimised for multi-criteria sustainability assessment, with particular attention to stakeholder-specific communication requirements (Dacre & Kockum, 2022a; Kunkel et al., 2023).
Expected academic contributions include novel theoretical frameworks for integrating sustainability science with project management AI systems, peer-reviewed outputs for explainable AI in multi-stakeholder sustainability contexts, and empirical evidence regarding the effectiveness of systems approaches to project prediction in energy infrastructure. Practical contributions will comprise validated prototype implementations demonstrating the operationalisation of sustainability metrics within commercial project prediction platforms, governance frameworks for sustainable project supply chain management, and policy recommendations for regulatory adoption of AI-powered sustainability assessment tools.
Whilst early outputs will align with greyfly.ai's ongoing development initiatives, the scope of the PhD extends significantly further. The doctoral research will address longer-term challenges that reach beyond a typical 9-month development cycle, including the integration of lifecycle sustainability metrics into predictive project models, the application of explainable AI to improve stakeholder trust, and the design of governance approaches for sustainable project supply chains.
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