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 petrochemical industry must transition its material and energy sources from fossil-based sources to more sustainable alternatives. While decarbonizing the energy source is challenging, defossilization of the material feedstock is significantly more difficult. In this work, we present a superstructure-based, multi-period, multi-objective optimization framework to address this problem. This framework focuses on minimizing the use of fossil carbon and modifications to petrochemical clusters while explicitly controlling the order of appearance of new processes. The combination of process options becoming available to the solution space over time and the cluster being locked in a path-dependent transition allows the framework to capture realistic transformation pathways. We demonstrate the framework with a small-scale case study of 10 fossil-based and 6 alternative processes. The results demonstrate the ability of the framework to select optimal defossillization pathways while simultaneously considering the impacts on mass and energy flows across the cluster.

Wat hebben rekenkundige computermodellen te maken met een rechtvaardige energietransitie? Meer dan je denkt: modellen laten zien welke opties als ‘haalbaar’ of ‘optimaal’ gelden, terwijl participatie laat zien welke keuzes legitiem en eerlijk zijn. In dit essay laten Eefje Cuppen, Shivant Jhagroe, Sander ten Caat, Igor Nikolic en Aarthi Sundaram zien hoe modellen en rechtvaardigheid toch met elkaar samenhangen. En hoe belangrijk het is om de wereld van modelleren en de wereld van participatie bij elkaar te brengen om het democratische proces van een rechtvaardige energietransitie te versterken. Zij reflecteren hierop en doen een aantal voorstellen die daarbij kunnen helpen.

Climate change impacts the power system globally. It also creates a challenge for Indonesia's energy transition, which aims for net-zero emissions by 2060. Aside from decarbonization efforts, planning for this transition adds a challenge due to the deeply uncertain nature of climate change. This refers to a condition where planners cannot agree on models, probabilities, or even which variables to prioritize. That degree of climate uncertainty has not yet been addressed in Indonesia's current power systems planning approach. Failure to address these uncertainties could bring significant vulnerabilities to Indonesia's future power system. Furthermore, only a small number of studies on power systems planning in Indonesia have addressed these climate uncertainties, and even then, only in a limited way. This paper offers a conceptual recommendation of an adaptive planning approach as one potential method to address these uncertainties. The approach is based on Dynamic Adaptive Pathways Planning (DAPP), which comes from the decision-making under deep uncertainty (DMDU) taxonomy. It supports planners in exploring a range of possible futures, considering policies and uncertainties, and enabling more robust decision-making.

The petrochemical industry is composed of several interconnected processes that use fossil-based feedstock for producing chemicals. These processes are typically geographically clustered and often belong to different parties. Reducing the environmental impacts of the petrochemical industry is not straightforward due to, on the one hand, their reliance on fossil fuels for energy and as a feedstock and, on the other hand, the significant level of interconnected energy and material flows among processes. Current methods for analyzing changes to existing processes cannot capture the multitude and level of interactions. The goal of this paper is to create a model of a petrochemical cluster and analyze its physical characteristics and performance. This paper addresses this goal by developing an assessment method that combines process simulations, multiplex graph analysis, and key performance indicators. The method is applied to a case study based on the petrochemical cluster in the Port of Rotterdam, resulting in a uniquely highly detailed model of a petrochemical cluster. The network analysis results show that only some of the processes are very interconnected. From the performance analysis, it can be observed that the olefins process is the most carbon-intense and has high CO2 emissions. Additionally, the results showed the importance of considering existing interconnections when assessing the current performance of existing petrochemical clusters or the performance due to future changes to chemical processes. For instance, some changes would occur to an industrial cluster by introducing alternative carbon sources, such as biomass or CO2.

Agent-based modeling proliferates across applications and scientific disciplines. The downsides of this success are the plurality of code implementations and redundant solutions to recurring modeling tasks. It is especially critical for simulations concerned with modeling human behavior and social institutions. Reusable building blocks (RBBs) are seen as a solution due to their potential to foster standardization grounded in best practices, integration of domain knowledge (including qualitative social sciences) in code, and efficient model design. RBBs are compact code components representing mechanisms or processes useful across models and applications. RBBs have been extensively discussed in the agent-based community, with little progress in implementation. Here, we present an open-access online community platform – AGENTBLOCKS – designed to facilitate the sharing, comparison, review, reuse, and improvement of RBBs. As an international community effort, AGENTBLOCKS leverages lessons from past RBBs discussions and principles from other modeling communities that successfully apply modular, reusable code practices. The paper introduces the interface and structure of this repository, presents templates for RBBs documentation, provides tips to support aspiring users, and first examples. We highlight the need for alternative RBB implementations that share the same generic description. We also acknowledge that RBBs might represent different levels of interactions, starting from decisions concerning a single agent to interactions between multiple agents or agents and their environment. While initially designed to assist agent-based community, the platform can be utilized by other modelers (e.g. system dynamics, integrated assessment, equilibrium) who seek to improve the representation of human behavior, micro-level processes, heterogeneity, interactions, learning, and other complex dynamics. Naturally, the platform is only one element in the chain towards a successful adoption of best software development practices like RBBs. Future work should focus on populating the repository, refining review processes, and systematizing the variety of RBBs’ implementations including engagement with domain experts. Following this initial phase, we hope to further support technical improvements of the platform and widen its impact in and beyond the agent-based community.

Computational simulation models serve as powerful instruments for analysing complex systems, but individually they are often limited in representing systems of an interdisciplinary nature. This paper presents a multi-modelling project that takes the first steps towards an open generic solution of multi-model infrastructure (MMI). It aims to loosely couple individual independent (often already existing) models, facilitate model reuse and meaningful model interoperation in order to support integral decision-making in the Dutch energy transition.
The MMI is a minimal viable product collaboratively designed and developed by a diverse group of modellers and energy experts. It includes facilitating services such as software and methods that enable multi-modelling but not the individual independent models themselves. We share the vision, approach and initial outcomes of the project, in particular, give an overview of the multi-model platform architecture design and the three use cases (and multi-models) of marco, meso and micro scales developed to demonstrate the potential of MMI. We also discuss the lessons learnt and future work, with the intension to invite more research, debate and collaboration on topics of multi-modelling, in particular simulation model reuse and interoperation, and to form an even stronger and broader interdisciplinary community of multi-modellers.

Human interstellar exploration involves navigating through a realm of significant uncertainty. Assessing the exact impact and consequences of moving at high velocities through the interstellar medium is challenging. Interstellar space is home to considerable amounts of cosmic dust, comprising microscopic particles with a wide range of sizes and compositions. At high speeds, spacecraft face significant risks from accumulating collisions with these particles. However, the expansive nature of interstellar space currently makes it impossible to accurately measure and chart the spread of this dust along specific trajectories. Interstellar space is also filled with high-energy cosmic rays, emitted by distant stars and other cosmic bodies. Dominated by protons and atomic nuclei, these cosmic rays travel nearly at the speed of light. The enduring effects of exposure to such radiation on the spacecraft, its crew, and the life support systems that sustain them remain unknown. The question then arises how to design an interstellar spacecraft capable of withstanding such inherent uncertainties. The solution requires a system robust enough to remain functional across diverse conditions. To try to cover for all possibilities in a top-down approach quickly becomes unfeasible. A promising direction is a bio-inspired adaptative approach. The Evolving Asteroid Starships (E|A|S) project integrates the utilization and recycling of local resources, self-organization, and bioregenerative principles to create a resilient spacecraft design. This aligns with the top priorities from NASEM's 2023 decadal survey, emphasizing space research on circular materials and bioregenerative life support. Within the framework of the E|A|S project, two distinct computer models have been developed, aiming for their eventual integration into a unified multi-model system. The inspiration for these models came in part from ESA's MELiSSA program and a visionary 1982 NASA study on a self-replicating lunar factory. Once living artificial ecosystems and self-organizing architectures are deployed, one is confronted with potential chaotic behaviour characteristic of complex systems. Sets of critical conditions that can push an otherwise stable self-sustaining system into collapse and failure were identified. It's crucial to gain a deeper understanding of how these systems function over extended periods, both under ideal environmental conditions and within the unpredictable exacting context of the interstellar medium. To address these challenges, the key drivers of systemic resilience (or lack thereof) were identified through an exploration of the characteristics of the individual components of each system. Moreover, potential mitigation strategies were also explored. These include enlarging buffer capacities, integrating redundancy, and enhancing system adaptability.

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.

The petrochemical industry needs to reduce the use of fossil fuel as carbon feedstock to reduce its CO2 emissions. Several alternative carbon sources (ACSs), such as biomass, CO2 and plastic waste are being proposed to replace fossil carbon. As each of these ACS process routes has its tradeoffs, it is essential to identify the defossilization pathways that will have the most significant impact. In this work, a superstructure-based optimization approach is presented that can be used to assess defossilization pathways in existing petrochemical clusters. The small case study shows that CO₂ is a promising ACS to replace fossil fuel as the main carbon source but requires a large amount of green hydrogen and significant modifications to the existing cluster.

The reliance of the petrochemical industry on fossil-based sources will need to be reduced by the introduction of Alternative carbon sources (ACS). Introducing ACS in a petrochemical cluster will require existing processes to be modified or replaced, potentially affecting other chemical processes within the cluster due to existing material and energy interconnections. Therefore, it is important to understand the current level of interconnections, functioning, and performance of the petrochemical cluster before introducing ACS. In this work, a representative cluster model based on the petrochemical cluster of the Port of Rotterdam was developed and considered as a case study. This model was analyzed using complex network analysis and environmental and technical key performance indicators. The selected key performance indicators (KPIs) provide insight into the performance of a petrochemical cluster, while the network properties give an understanding of the exchange of material and energy in an industrial cluster.

Bioregenerative life support systems (BLSS) are vital for long-duration and remote space missions to increase mission sustainability. These systems break down human waste materials into nutrients and CO2 for plants and other edible organisms, which in turn provide food, fresh water, and oxygen for astronauts. The central idea is to create a materially closed loop, which can significantly reduce mission mass and volume by cutting down or even eliminating disposable waste. In most BLSS studies only a fraction of the resources, such as food, are provided by the system itself, with the rest taken on board at departure or provided through resupply missions. However, for autonomous long-duration space missions without any possibility of resupply, a BLSS that generates all resources with minimal or no material loss, is essential. The goal of this study is to develop a stoichiometric model of a conceptually fully closed BLSS that provides all the metabolic needs of the crew and organisms. The MELiSSA concept of the European Space Agency is used as reference system, consisting of five interconnected compartments, each inhabited by different types of organisms. A detailed review of publicly available MELiSSA literature from 1989 to 2022 revealed that no existing stoichiometric model met the study’s requirements. Therefore, a new stoichiometric model was developed to describe the cycling of the elements C, H, O, and N through all five MELiSSA compartments and one auxiliary compartment. A compact set of chemical equations with fixed coefficients was established for this purpose. A spreadsheet model simulates the flow of all relevant compounds for a crew of six. By balancing the dimensions of the different compartments, a high degree of closure is attained at steady state, with 12 out of 14 compounds exhibiting zero loss, and oxygen and CO2 displaying only minor losses between iterations. This is the first stoichiometric model of a MELiSSA-inspired BLSS that describes a continuous provision of 100% of the food and oxygen needs of the crew. The stoichiometry serves as the foundation of an agent-based model of the MELiSSA loop, as part of the Evolving Asteroid Starships (E|A|S) research project.

Climate change intensifies the likelihood of extreme flood events worldwide, amplifying the potential for compound flooding. This evolving scenario represents an escalating risk, emphasizing the urgent need for comprehensive climate change adaptation strategies across society. Vital to effective response are models that evaluate damages, costs, and benefits of adaptation strategies, encompassing non-linearities and feedback between anthropogenic and natural systems. While flood risk modeling has progressed, limitations endure, including inadequate stakeholder representation and indirect risks such as business interruption and diminished tax revenues. To address these gaps, we propose an innovative version of the Climate-economy Regional Agent-Based model that integrates a dynamic, rapidly expanding agglomeration economy populated by interacting households and firms with extreme flood events. Through this approach, feedback loops and cascading effects generated by flood shocks are delineated within a socio-economic system of boundedly-rational agents. By leveraging extensive behavioral data, our model incorporates a risk layering strategy encompassing bottom-up and top-down adaptation, spanning individual risk reduction to insurance. Calibrated to resemble a research-rich coastal megacity in China, our model demonstrates how synergistic adaptation actions at all levels effectively combat the mounting climate threat. Crucially, the integration of localized risk management with top-down approaches offers explicit avenues to address both direct and indirect risks, providing significant insights for constructing climate-resilient societies.

Fast growth of cities decreases the quality of life in these places. In response, Municipalities install policies aiming to improve local livability. While literature suggests social structures to have a defining impact on policy effectiveness, current evaluation metrics are not able to take this into account. This paper presents the Social Neighbourhood model, an agent-based model used to simulate and explore how livability changes in a neighbourhood given various social structures and policies. The model is applied to a neighbourhood in The Hague, Netherlands. The main result of the modelling experiments is that social structures have a very strong influence on whether or not a policy to improve livability is effective. Three hypotheses, concerning this relationship between social structures, livability, and policy interventions are drawn up as a starting point for future research.

Agent-based modelling and simulation (ABMS), whether simple toy models or complex data-driven ones, is regularly applied in various domains to study the system-level patterns arising from individual behaviour and interactions. However, ABMS still faces diverse challenges such as modelling more representative agents or improving computational efficiency. Research shows that machine learning (ML) techniques, when used in ABMS can address such challenges. Yet, the ABMS literature is still marginally leveraging the benefits of ML. One reason is the vastness of the ML domain, which makes it difficult to choose the appropriate ML technique to overcome a specific modelling challenge. This paper aims to bring ML more within reach of the ABMS community. We first conduct a structured literature review to investigate how the ABMS process uses ML techniques. We focus specifically on articles where ML is applied for the structural specifications of models such as agent decision-making and behaviour, rather than just for analysing output data. Given that modelling challenges are mainly linked to the purpose a model aims to serve (e.g., behavioural accuracy is required for predictive models), we frame our analysis within different modelling purposes. Our results show that Reinforcement Learning algorithms may increase the accuracy of behavioural modelling. Moreover, Decision Trees, and Bayesian Networks are common techniques for data pre-processing of agent behaviour. Based on the literature review results, we propose guidelines for purposefully integrating ML in ABMS. We conclude that ML techniques are specifically fit for currently underrepresented modelling purposes of social learning and illustration; they can be used in a transparent and interpretable manner.

Energy communities are gaining momentum in the context of the energy transition. Given the distributed and collective action nature of energy communities, energy security of these local energy systems is more than just security of supply and related to issues such as affordability and acceptability of energy to members of the community. We build an agent-based model of energy communities to explore their security challenges. The security dimensions we consider are availability, affordability, accessibility and acceptability, which are referred to as the 4As. The results confirmed that there is always a trade-off between all four dimensions and that although it is difficult to achieve a high energy security performance, it is feasible. Results also showed that among factors influencing energy security, the investment of the community plays the biggest role.

This paper presents a theoretical framework for a new concept of organizational control, that stimulates organizational change and adaptation. It introduces Participatory Control Systems (PCS) as a distinct type of control based on the complex adaptive system literature. These control systems are fundamentally different from the traditional notion of (management) control. Building on the notion of complex systems and the concept of social learning, PCS increase organizational adaptivity by enabling and facilitating social learning processes that may emerge to transformational change over time. To illustrate the PCS concept in practice, three examples are given in this paper. Moreover, some key implications for internal auditors and suggestions for future research are provided.

Cities consume almost 80 percent of world’s energy and account for 60 percent of all the emissions of carbon dioxide and significant amounts of other greenhouse gases (GHG). The ongoing rapid urbanization will further increase GHG emissions of cities. The quantification of the environmental impact generated in cities is an important step to curb the impact. In fact, quantifying the consumption activities taking place inside a city, if differentiated by socioeconomic and demographic groups, can provide important insights for sustainable-consumption policies. However, the lack of high-resolution data related to these activities makes it difficult to quantify urban GHG emissions (as well as other impacts). This paper presents a methodology that can quantify the carbon footprint of households in cities using consumption data from a national or European level, where the resource consumption is linked to socioeconomic attributes of a population. The methodology is applied to analyzing the environmental impact by household resource consumption in the city of The Hague in the Netherlands. The key insights reveal potential intervention areas regarding resource consumption categories and demographic groups that can be targeted to reduce GHG emissions due to consumption-driven activities in the city.

Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.

The viability of novel network-level circular business models (CBMs) is debated heavily. Many companies are hesitant to implement CBMs in their daily practice, because of the various roles, stakes and opinions and the resulting uncertainties. Testing novel CBMs prior to implementation is needed. Some scholars have used digital simulation models to test elements of business models, but this this has not yet been done systematically for CBMs. To address this knowledge gap, this paper presents a systematic iterative method to explore and improve CBMs prior to actual implementation by means of agent-based modelling and simulation. An agent-based model (ABM) was co-created with case study participants in three Industrial Symbiosis networks. The ABM was used to simulate and explore the viability effects of two CBMs in different scenarios. The simulation results show which CBM in combination with which scenario led to the highest network survival rate and highest value captured. In addition, we were able to explore the influence of design options and establish a design that is correlated to the highest CBM viability. Based on these findings, concrete proposals were made to further improve the CBM design, from company level to network level. This study thus contributes to the development of systematic CBM experimentation methods. The novel approach provided in this work shows that agent-based modelling and simulation is a powerful method to study and improve circular business models prior to implementation.