AI-driven whole-life costing for decision-ready UK public building retrofits

Abstract Public-sector building retrofits in the United Kingdom require decisions that balance whole-life cost, carbon reduction, service continuity and auditability under limited data and constrained capital budgets. This paper develops a decision-ready whole-life costing (WLC) workflow that integrates environmental, social and governance (ESG) priorities with uncertainty-aware artificial intelligence (AI) for public-building retrofit appraisal. The workflow combines national non-domestic Energy Performance Certificate (EPC) and Display Energy Certificate (DEC) data, Public Sector Decarbonisation Scheme (PSDS) delivery evidence and stakeholder inputs to construct a scenario library of public-building archetypes, retrofit actions and appraisal constraints. Stakeholder priorities from interviews, a questionnaire and a co-design workshop are translated into transparent weights using fuzzy analytic hierarchy process (F-AHP). These weights parameterise a reinforcement learning (RL) planner, while a Bayesian convolutional neural network–long short-term memory (BCNN–LSTM) surrogate provides uncertainty-bounded energy, carbon and cost trajectories for repeated policy evaluation. Results show that the workflow supports auditable retrofit sequencing across archetype and budget conditions and improves composite ESG performance relative to conventional WLC-only, £/tCO 2 e and static multi-criteria decision analysis (MCDA) baselines. For 1000 matched archetype–budget cases, the trained deep Q-network (DQN) decision-search stage achieves a 30.03-fold speedup over traditional scalar feasible-sequence enumeration. The contribution is an integrated and explainable WLC–ESG–AI workflow for early-stage public retrofit decision support. The current validation remains based on public datasets and scenario-level evidence so future work should extend the framework using project-specific metered data, digital twins and live portfolio deployment.