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.

The digital transformation of education has rapidly evolved in recent years, driven by advancements in technology and further accelerated by the COVID-19 pandemic. Digital education Technologies (DETs) have become integral to higher education, reshaping how institutions deliver learning and manage resources. However, despite the widespread adoption of DETs, there has been limited focus on the sustainability of these technologies. This paper explores how sustainability considerations are integrated into DET selection processes at European Higher Education Institutions (HEIs) through semi-structured interviews with key decision-makers. The research focuses on three sustainability dimensions-environmental, social, and technological-and their impact on decision-making. The results indicate that while HEIs are making efforts toward sustainability, economic considerations still dominate the decision-making process. Moreover, the emphasis across sustainability dimensions remains unbalanced: social dimensions, such as privacy, are prioritized over environmental dimensions due to the former being treated as knockout criteria and due to a lack of reliable data on the environmental impacts of DETs. This study also identifies several challenges, including long procurement processes, limited financial resources, and heavy dependence on external service providers for digital infrastructure. The findings offer insights into how HEIs can better align their digital strategies with broader sustainability goals.

During epidemics, decision-making regarding intervention measures faces complex trade-offs. Interventions targeting indoor venues can mitigate disease spread, since they are associated with higher infection risk for respiratory pathogens. However, as experienced during the COVID-19 pandemic, these measures can lead to economic losses, especially in the hospitality sector. In this study, we propose a hybrid modeling and simulation framework to provide decision support for reducing the infection risk in indoor venues while maintaining viable economic activity. Our framework integrates (i) a microscopic pedestrian model for human movement, (ii) a hybrid simulation model for virus spread and transmission, and (iii) a multi-criteria decision-making approach to identify the best service options. The framework is demonstrated for the SARS-CoV-2 infection risk. The restaurant case study results illustrate that maximizing the distance between seating groups can have a limited effect on the infection risk. Service duration and service capacity are key determinants of expected economic activity, but they constitute significant trade-offs: the former has a substantial impact on the infection risk, and the latter drives the probability of infectious introductions. Our analysis demonstrates the need for multi-criteria approaches during an outbreak and consideration of the epidemiological context for operational decision-making, even at an individual venue.

Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the particle filter (PF) method is often a good option for stochastic simulation models of discrete systems. The probability distributions of model runs, however, make it computationally intensive. The experimental conditions therein are understudied. This paper studied three critical conditions of PF-based DA in a discrete event model: (1) the time interval between two consecutive DA iterations, (2) the number of particles, and (3) the actual level and perceived level of measurement errors (or noises). The study conducted identical-twin experiments of an M/M/1 single server queuing system. The ground truth is imitated in a stand-alone simulation model. The measurement errors are superimposed so that the effect of the three conditions can be quantitatively evaluated in a controlled manner. The results show that the estimation accuracy of such a system using PF is more constrained by the choice of time intervals than the number of particles. An under estimation of measurement errors produces worse state estimates than an over estimation of errors. A correct perception of the measurement errors does not guarantee better state estimates. Moreover, a slight over estimation of errors results in better state estimates, and it is more responsive to abrupt system changes than an accurate perception of measurement errors.

New developments in the automotive world have the power to change mobility, but because of high uncertainties, municipalities are adopting a wait-and-see attitude. Nonetheless, autonomous, connected and shared vehicle technologies are in a far stage of development and it is only a matter of time before shared autonomous vehicles (SAVs) enter urban traffic. This research aims to provide insights into the congestion effects of SAVs on urban traffic, focusing on the differences in microscopic behaviour from conventional cars, and to investigate which easy-to-implement solutions a municipality could apply to facilitate the new mix of traffic. This was researched by performing a simulation study, using the traffic simulation package Vissim and a case study of a network in the city of The Hague during the morning peak in 2040. Several SAV market penetration scenarios were tested: 0%, 3%, 25%, 50% and 100% SAV usage by travellers. Additionally, two network designs were implemented: dedicated lanes for SAVs and kiss & ride (K&R)-facilities. From the results, it was clear that while the autonomous driving capabilities of SAVs help reduce traffic congestion, they also have a negative effect by stopping on the curbside to drop off passengers, forming bottlenecks for other road users, and by circulating on the network using low capacity links. Below the line, this adds up to an overall negative effect on urban traffic congestion according to our results. The dedicated lanes design was unsuccessful at reducing this congestion caused by SAVs. The K&R design, however, was successful at reducing delays, but only for SAV penetration rates higher than 25%. These exact effects are not generalizable due to limitations in network size and simulation software. However, the results can be seen as indicative for planning purposes. Similar effects could be expected in cities where transport network companies (TNCs) such as Uber become exceptionally popular with non-autonomous cars. The advice for municipalities is to closely monitor the situation and to account for SAVs (and TNCs) in each new infrastructural project.

The attitude-behaviour gap in energy consumption refers to the imparity between people's environmental values (and attitudes) and their actual behavior in consumption. This paper calls for the facilitation of the behaviour change process that is implementable in the context of one's everyday life to address the attitude-behaviour gap in household energy consumption. Two interrelated intervention design constructs are proposed based on the results of literature review, namely (1) providing consumers accurate information about actionable suggestions in the specific context of their everyday life, and (2) fostering consumers' motivation to engage in the behavior change process towards energy conservation. Smart Grid technologies are instrumental in both intervention types.