From least cost to more just

Abstract

Energy system optimisation models increasingly inform low-carbon energy transition strategies, but conventional least-cost, central-planner formulations raise questions about who bears transition costs, whose perspectives shape scenarios, or which actors remain poorly represented. This paper examines how distributive, procedural and recognition justice enter – or fail to enter – TIMES-based modelling; what enables or constrains their treatment; and how pathway analysis could become more justice-responsive. We conduct a theory-sensitising single-case study at VITO (Belgium) covering two models, TIMES-Be and TIB3R. As the TIMES framework is widely used for whole energy system planning, this policy-facing case exposes broader tensions between aggregate system efficiency and plural justice claims. Eight semi-structured interviews with experienced modellers were analysed using a hybrid deductive–inductive thematic approach. Findings indicate that justice rarely served as an explicit organising principle. Instead, it entered tacitly and unevenly through scenario framing, discounting, constraints, stakeholder engagement and post-processing. Aggregate affordability and acceptability were emphasised over analysis of differentiated costs and spatial burdens; industry actors had greater influence than households or the public; and heterogeneous household capacities and motivations so far remained weakly represented. These patterns arose from interactions among TIMES architecture, organisational conditions and modeller agency. Conceptually, the study relocates justice from a possible model add-on to a concern spanning the modelling lifecycle. For practice, it supports treating least cost as a reference rather than as a definition of fairness, combining TIMES with near-optimal, multi-criteria, behavioural, spatial and financial analyses, and strengthening participation and responsible transparency. Although not representative of the TIMES community, the case identifies testable propositions for comparative research across organisations and modelling traditions.