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.

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.

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.

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 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.

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.

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.