Interest in linking energy models with energy justice is growing, with a rising number of studies explicitly addressing the three tenets of justice – distributive, procedural, and recognition – and reviews mapping this field. Yet procedural and recognition justice have been treated in limited ways, leaving it unclear how models can meaningfully engage with them. This paper addresses this gap through a structured review of 63 peer-reviewed studies that develop or use models to support local and regional energy transition decisions while incorporating justice considerations. We find that procedural justice is primarily operationalized as stakeholder participation, with less efforts made to explicitly address other principles such as transparency, inclusivity, accountability and to include non-participatory ways of including stakeholder input. Recognition justice is either omitted or conflated with procedural principles, whereas energy justice literature defines it in systemic terms that extend beyond the mere acknowledgement of stakeholder groups. We argue that early-stage decisions such as funding, research design, and stakeholder selection significantly influence whose values are represented in models, whose knowledge is excluded, and which outcomes are prioritized. These influences, despite their justice implications, are rarely acknowledged, with existing efforts biased toward implementations of justice within model logic. We propose expanding the scope of modelling to include these early-stage influences and outline four recommendations for modellers: broaden justice conceptualizations beyond model logic; evaluate early-stage justice implications; adopt reflexive practices; and leverage multi-modelling approaches to capture the multi-dimensionality of energy justice.

It is widely considered that the energy transition should be just, yet achieving this goal is a complex socio-technical process. Models serve as valuable tools to support decision-making in navigating these complexities. However, they are not adequately equipped to address justice considerations that are becoming central to energy transition planning. They are unable to provide support in decision spaces that are rich in normative uncertainties, with stakeholders holding differing interpretations of what a just energy transition is. While the importance of integrating justice into computational models is recognized, a significant gap remains in understanding how justice is and can be defined, interpreted, and implemented within these models or, in short, how justice can be operationalized. This paper addresses the gap by examining studies that use computational models for decision-support through the lens of the three tenets of energy justice: procedural, recognition, and distributive justice. We argue that operationalizing justice in energy transition modelling can take place both in the modeling process and with the enrichment of model logic. This paper emphasizes that discussions of justice in relation to models cannot be separated from the design of effective participatory modelling settings that stem from a careful evaluation of the justice requirements of stakeholders in the decision space. We propose a framework that enables modellers and model users to be more explicit about their normative interpretations of justice and derive modelling processes and model requirements that represent diverse justice perceptions in the decision space. By doing this, models can refrain from propagating only dominant ideas of justice and instead actively incorporate otherwise neglected perceptions, to ensure that the decision-support facilitates a just energy transition.

Governments often use price-based policies such as tax-subsidies and rebates to encourage households to shift to renewable energy sources like rooftop solar photovoltaics (PV). These policies, however, have primarily benefited high-income homeowners, leaving others behind. This paper proposes leveraging social networks’ influence on attitudes and perceptions to design more equitable solar PV adoption programs. Using data from Albany county (New York State, USA) we develop an Agent-based model, integrating a novel implementation of circles of influence into the theory of planned behavior. We test two policy categories (generic and targeted) under two network scenarios (integrated and segregated). Resulting solar PV adoption rates are evaluated using egalitarian, utilitarian and cost metrics to analyze policy impact on different income groups. Our findings indicate that network structure significantly influences adoption rates within income groups. Low-income groups in segregated networks can experience higher adoption driven by positive attitudes towards solar PV, while high-income groups in segregated networks may face poor policy performance despite higher affordability. Seeding policies and information dissemination through influential network members may not necessarily improve adoption rates, as trust can a more important role. The study underscores the importance of trusted information sources in influencing adoption decisions. The insights gained from this research can guide policy design for tailored interventions to improve access to renewable energy for all income groups.