Integrating hydrogen into energy systems presents challenges involving social dynamics among stakeholders beyond technical considerations. A gap exists in understanding how these dynamics influence the deployment of hydrogen technologies and infrastructure, particularly in infrastructure development and market demand for widespread adoption. In the Netherlands, despite ambitious strategies and investments, comprehensive explanations of social dynamics' impact on integration processes and market development are lacking. This study addresses this gap by analyzing the hydrogen value chain and stakeholder interactions in the Dutch hydrogen sector. A literature review highlights system integration challenges and the need for decentralized coordination and cross-sector collaboration. Using the Dutch energy grid and its hydrogen initiatives as a case study, social network analysis and semi-structured interviews are applied to analyze over 60 hydrogen initiatives involving more than 340 stakeholders. Initiatives are categorized into large-scale centralized and decentralized local types based on scale and stakeholder involvement, allowing targeted analysis of stakeholder interactions in different contexts. Findings reveal that centralized networks may limit innovation due to concentrated influence, while decentralized networks encourage innovation but require better coordination. These insights guide strategic planning and policymaking in hydrogen energy initiatives, aiming to enhance scalability and efficiency of hydrogen technologies for sustainable energy solutions.

This editorial paper for the special section on “Modelling Values in Socio/Technical/Ecological Systems” introduces interdisciplinary perspectives on values and reflects on growing appeals for modelling values. In public and academic discourses, values typically relate to matters of importance (e.g., beliefs, priorities) and principles about what is considered to be good (e.g., moral values) and are often seen as shaping individual and collective behaviour. As shown by eight contributions to this special section, it is relevant for social simulation modelling to dive deeper into embedding values in models in order to explore behavioural change on different levels and across contexts. Our goal with this special section is to stimulate interest in developing various approaches that study and operationalise values in agent-based models to investigate the complex problems raised in social, socio-technical and socio-ecological systems. We conclude with a call for future research to be explicit in their modelling assumptions, thus fostering a vigorous foundation for scientific discourse.

“De woningcrisis biedt kansen. De kans om een duurzaam en langdurig gezond woonklimaat te realiseren.”

Woningnood is geen nieuw verschijnsel in ons land. Al in de negentiende eeuw leidde snelle bevolkingsgroei en verstedelijking tot een huisvestingstekort; de woningwet van 1901 moest daar een eind aan maken. In de wederopbouwjaren na de tweede wereldoorlog kwam de woningbouw maar langzaam op gang. En in de jaren 80 van de vorige eeuw was ‘geen woning, geen kroning’ de leus van demonstranten die aandacht vroegen voor woningnood.

Solid waste management in low- and middle-income countries like India faces significant challenges due to the increasing waste generation that surpasses the current capacity. Therefore, the informal waste sector (IWS) is more vital than ever in handling consumer waste alongside municipal solid waste management (SWM) systems. However, the integration of the IWS into formal waste management systems remains unresolved due to adverse social and economic conditions. This study focuses on identifying the root causes that hinder the integration of the IWS in India's waste management system, using the city of Chennai as a case study. Adopting an institutional perspective, we analyse the institutional landscape of the waste management system, considering both formal rules (in policy documents) and informal rules (i.e., social norms and routines). The institutional network analysis reveals a significant misalignment in perceptions among governance levels concerning the integration of the IWS. The study shows a considerable gap between rules-in-form and rules-in-use, leading to 1) Preclusion of waste pickers in collecting door-to-door source-segregated waste (i.e., recyclables). 2) Unfair pricing in transactions with small aggregators. 3) Lack of ID cards for waste pickers. These barriers are ultimately rooted in caste discrimination, misalignment between governance levels, and the exclusion of waste pickers in the policymaking process. In conclusion, understanding and rectifying the institutional gaps and discriminatory practices are essential steps towards effectively integrating the IWS in India's waste management system, promoting a more inclusive and sustainable approach to waste management.

Governments worldwide are intensifying efforts to address escalating flood risks exacerbated by climate change. Central to this endeavor is the implementation of institutional frameworks, such as public policies, aimed at mitigating, planning for, responding to, and recovering from flood events. However, the effectiveness of these institutions relies heavily on their practical application. This study delves into the institutional landscape of flood risk management (FRM) through a comprehensive case study in Sint Maarten, a Caribbean island. Specifically, we scrutinize the degree of institutional compliance, focusing on the alignment between formally advised policies (institutions-in-form) and their informal adoption in practice (institutions-in-use). Employing Institutional Network Analysis (INA), we explore discrepancies between these two dimensions across the various phases of FRM (response, recovery, mitigation, and preparation). Our findings reveal that institutional compliance varies significantly across the FRM phases, with mitigation presenting the most pronounced challenges. Notably, the discrepancies are more prevalent among those tasked with implementing the policies rather than the targeted property owners. Generally speaking, the transition of institutions from mere forms to actionable rules is often hindered by established or emerging practices diverging from prescribed directives.

Energy security is one of the most important topics in energy-related literature. As such, various concepts and dimensions are introduced to contribute to energy security assessments. However, the literature lacks an approach to prioritise these dimensions, as they cannot always be addressed simultaneously. This study is the first step in investigating the importance and prioritisation of energy security dimensions by focusing on the context of community energy systems. Such collective and decentralised energy systems are gaining momentum in the energy transition context; however, they have received minimal attention on their energy security aspects. First, a literature review is conducted to gain an overview of the studied energy security dimensions, highlighting dimensions such as energy availability and infrastructure that are studied the most. In contrast, environment and societal effect dimensions have received minimal attention. Next, an existing agent-based model is used to assess the importance of energy security dimensions and their priority in community energy systems. The results revealed that infrastructure and governance are the most impactful dimensions for the energy security assessment of community energy systems. Energy prices were one of the least influential dimensions in energy security assessments. The study also explored various existing energy security concepts and proposed the most suitable one in the energy communities' context. A research agenda emphasising the need to study governance, societal effects and environmental dimensions is also presented. Lastly, infrastructure, governance, environment and societal effects are concluded to be the most crucial energy security dimensions for community energy systems.

Social disruptions caused by the COVID-19 pandemic challenged existing institutional arrangements that govern the society. During that time, nation-states had to prevent the collapse of society and rapidly establish new institutions and adapt existing ones to address public health, job security, and freedom-of-movement concerns. At the same time, institutional developments are explicitly or implicitly related to the cultural and moral values relevant to societal well-being. Values hold a significant role in governing society during crises, guiding states' institutional response to unforeseen challenges. However, values themselves are not static: research has shown that values may change rapidly during crises. This paper studies the relationship between value change and institutional change in times of crisis using agent-based modelling and machine learning techniques. In our model, we represent countries as agents who define institutional strategies to control disease spread and subsequently protect the well-being of their citizens. Institutional change and value change are modelled as two independent processes. Yet, the model confirms the seemingly trivial inverse correlation between them: when the value of openness-to-change increases in a society, the institutional strategies also become less strict. Conversely, when conservatism increases, the strategies become stricter on average. However, there is no direct causal relationship between the two changes: being open to change does not necessarily make a government select more relaxed rules, but this correlation is rather an emergent consequence of being more flexible in changing rules, whether the new ones are stricter or more relaxed.

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.

Foremost in dealing effectively with water conflicts is comprehensive analysis. The global surge in water conflicts, coupled with the imperfect success of solutions, initiatives, and policies, calls for a critical reevaluation of the paradigms that guide approaches to water conflicts. The approach taken—conflict management, resolution, or transformation—has a decisive impact on addressing water conflicts, from the initial analysis to the crafting of policies and their practical implementation. Iran is a prime example of this, with water conflicts increasing at various scales despite concerted mitigation efforts. This paper, through a qualitative content analysis of 159 peer-reviewed papers collected via a systematic review, aims to delineate the prevailing approach to water conflict analysis within Iran's academic discourse, thereby partially shedding light on the shortcomings in both policy and practice. Water conflict resolution, and its respective models such as game theory and optimization–simulation, is the predominant approach in Iran's literature on water conflict analysis, thereby marginalizing attention dedicated to conflict management and transformation. This reflects an overarching focus on techno-economic functions to deal with water conflicts, often overlooking the myriads of managerial and societal factors. The adoption of water conflict transformation can be vital to rectify these deficits in conflict analysis, potentially with subsequent impacts on policy and practice.

In recent decades, distinct national approaches to engineering ethics have evolved, each tailored to its unique contextual factors. These contextual disparities make it unfeasible to transfer one country's engineering ethics approach directly into another. This calls for a compelling need to enhance our comprehension of engineering ethics within specific national contexts. This paper introduces a novel conceptual framework for national engineering ethics (NEE), inspired by Elinor Ostrom's Institutional Analysis and Development (IAD) framework. The NEE framework categorises engineering ethics activities into three core pillars: research, education, and professional behaviour. This framework facilitates a comprehensive analysis of these activities across three levels—operational, organisational, and governmental. The proposed framework offers a valuable resource for scholars seeking a deeper understanding of engineering ethics within specific national boundaries, enabling structured reporting and analysis. It serves as a critical step towards achieving mutual understanding, allowing for cross-national comparisons and the exchange of best practices. Additionally, it provides a structured platform for policymakers and developers to devise strategies for implementing engineering ethics at the national level.

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.

In the European Union, energy democracy (ED) is considered a socially desirable policy goal. One way to achieve ED is through empowering local communities to become agents of value change who can pursue more sustainable and equitable energy provision with community energy initiatives (CEI). However, such people-driven value change is complex in nature. CEIs are multifaceted sociotechnical systems that bring together sets of values and are composed of agents (i.e., people), technologies (e.g., solar panels), and institutions (e.g., renewable energy policies). Yet not much research is conducted into how values relate and overlap within this complex nexus of people - technology - institutions on a pathway to democratizing the energy sector through civic participation. In this paper, we spotlight value relationality to capture the diversity in the value landscape of people-driven energy transitions. We claim that each sociotechnical system has “climate” of its own, or value dynamics, induced by interrelating values. We propose an account that captures value dynamics, explores value sets brought together by the different sociotechnical components of CEIs and investigates various ways in which these value sets interrelate. To elucidate value dynamics in the context of CEIs, we have conducted a literature review, a content analysis of regional, national (i.e., Dutch), and EU policies, and expert interviews in two illustrative case studies. We finalize the paper with recommendations for further research on value dynamics in CEIs across various sociotechnical contexts.

Based on analyzing the composing elements of the water governance regime in the Hirmand River Basin, Iran, this paper examines the factors that facilitate the emergence of Adaptive Governance in a Global South context. Although the literature provides valuable insights into the characteristics of a well-established Adaptive Governance regime in the context of the Global North, relatively little research has been conducted on Adaptive Governance’s fostering factors in the states in the Global South. To address this gap, this study utilizes an analytical framework upon which the features of water governance regimes are assessed. A combination of primary and secondary qualitative data (survey research and document analysis) is used to evaluate the assessment framework, which aims to analyze the characteristics that enhance resilience to the imposed changes and disturbances in complex environmental and water systems. The analysis suggests that addressing scalar and sectoral tensions, well-functioning reflecting mechanisms, adaptable policies, and flexible financial mechanisms are vital requisites for the transition towards more adaptive forms of water governance. The results also propose that the formal water governance system in the region has felt the urgency to adapt to new circumstances; however, unlike cases from the Global North, it lacks the required agility to escape from the rigidity trap it finds itself in.

The importance of decreasing industrial CO2 footprints has become evident, as also highlighted in COP26. As such, the transition to renewable energy in the industrial sector is essential to meet the targets. To this aim, establishing industrial community energy systems (InCES) where industries collectively invest in a shared energy system is an economically and environmentally attractive option. Yet, the emergence and continuity of such collective initiatives among industrial companies has neither received considerable attention in the scientific literature nor in practice. This research, as the first of its kind, aims to investigate institutional design options that allow for such collaboration to take place for the establishment and continuity of an InCES. Given the bottom-up and collaborative nature of such initiatives, we take an agent-based modeling and simulation approach, for the first time in this area, that incorporates the institutional and societal attributes that influence the formation and continuation of an InCES. We take data from an industrial cluster in Arak, one of the most prominent industrial cities in Iran. The results of this study confirm the economic feasibility of an InCES as compared to individual renewable energy investment in the cluster. The results also highlight the importance of flexible membership in increasing the number of investors (i.e., industrial companies) in such initiatives. Other important recommendations are: considering the installation of at least 15% extra capacity for the powerplant, restricting electricity consumption and enforcing on-time payment of monthly premium fees.

Although good water governance (GWG) is a widely accepted concept, mistaking its principles for silver bullet approaches has hindered its successful applications. This highlights the idea that one-size-fits-all thinking does not satisfy the need to navigate toward sustainable outcomes in ever-changing complex water systems. This also indicates that endeavors toward governing water systems must be tailored to the specific context that these systems are nested within. Scholars have pointed out the importance of residents' input in contextualizing water governance practices. With that being said, this paper proposes an innovative approach to tailoring principles of GWG to the context by building an analytical framework upon which survey research was conducted. The survey, it took input from three categories of residents, namely experts, authorities, and experienced locals. Analyzing the data led to a group decision-making problem that was approached using fuzzy risk-based multiple-attribute decision-making methods, including technique for order of preference by similarity to ideal solution and ordered weighted averaging, while considering the amount of consensus among mentioned groups. Furthermore, to validate the results of the decision-making problem analysis, additional interviews were conducted to get a more pragmatic picture of the situation. Sistan Delta in Iran was selected as a case study mainly due to the current undesirable situation and also the international social, political, and environmental significance of the area. This study aims to take the first step of rethinking water governance in the area. The results indicated that to operationalize good governance, the principles of collaboration, legitimacy, adaptability, and trust and engagement must be prior considerations to redefine the water governance structure in the Sistan region.

Institutional modelling is a branch of agent-based modelling and simulation (ABMS) that pays special attention to the social structure by incorporating institutions into these models. Institutions, in this regard, are the rules of the system that shape individual behaviour and interaction. Institutional modelling can make use of the Institutional Grammar (IG), which has 6 pre-specified components to conceptualize institutions. The IG can be used for collecting institutional data for modelling purposes, for coding institutions, and for integrating them into different parts of an agent-based model. This modelling approach helps capture the structural complexities of social systems in agent-based models. It also supports the explanation of the emergence and dynamics of institutions, not only for better understanding institutions but also for studying social systems, especially for policy analysis. • Institutional modelling is a branch of agent-based modelling that focuses on and explicitly models the social aspects of socio-ecological-technical systems. • Institutional modelling supports theory development by enabling modellers to study institutions, and institutional change within, the systems they are embedded in. • While agent-based modelling is a bottom-up (individual-based) simulation approach, institutional modelling also incorporates top-down institutional structures and aims at studying interactions between bottom-up processes and top-down structural patterns.

Like many other sectors, climate change strategies have put various restrictions on industry, the most prominent one being caps on CO₂ and other energy-related emissions. At the same time, and especially in many developing economies, the industry struggles with an increasing gap between the fast development of the sector and lagging energy supply capacity. Collective generation of renewable energy is seen as a promising means of transition, next to other forms of renewable energy generation (centralised, individual). The aim of this research is to investigate factors influencing willingness to participate in Industrial Community Energy Systems (InCES). Using existing literature on Industrial Symbiosis and Community Energy Systems, we formulate plausible hypotheses on the most relevant factors for the willingness of industries to join such initiatives. As one of the largest and most diversified industrial clusters in Iran, Arak industrial park is selected as the case study. Data were collected from the CEOs of 96 companies through survey research. Our results highlight the crucial role of awareness about the benefits of renewable power generation in an InCES. Social identity among industries and trust between them are also determining factors for their willingness to join InCES. Finally, proper institutional design for overcoming the partnership complexities (e.g., conflict resolution) was highlighted as a crucial factor for industries. It can be concluded from the results of this study that policymakers should avoid one-size-fits-all incentive design approaches and reach out to larger companies with targeted incentives, introduce specially designed bank loans for different target groups, and make use of consulting companies as intermediaries to increase the awareness of the industries regarding the benefits of investing in an InCES.

In community energy systems, the energy demand of a group of households is met by collectively generated electricity and heat from renewable energy sources. What makes these systems unique is their collective and collaborative form of organization and their distributed energy generation. While these features are crucial to the resilience of these systems and are beneficial for the sustainable energy transition in general, they may at the same time undermine the security of energy within these systems. This paper takes a comprehensive view of the energy security of community energy systems by considering dimensions such as energy price, environment and availability, which are all impacted by decentralized and collective means of energy generation and distribution. The study analyses community energy systems' technical and institutional characteristics that influence their energy security. An agent-based modelling approach is used for the first time to study energy security, focusing on thermal energy communities given the considerable share of thermal energy applications such as heating, cooling, and hot tap water. The simulation results articulate that energy communities are capable of contributing to the energy security of individual households. Results demonstrated the substantial potential of energy communities in CO2 emissions reduction (60% on average) while being affordable in the long run. In addition, the results show the importance of project leadership (particularly regarding the municipality) concerning energy security performances. Finally, the results reveal that the amount of available subsidy and natural gas prices are relatively more effective for ensuring high energy security levels than CO2 taxes.

Since its first appearance in Wuhan (China), countries have been employing, to varying degrees of success, a series of non-pharmaceutical interventions aimed at limiting the spread of SARS-CoV-2 within their populations. In this article, we build on scientific work that demonstrates that culture is part of the explanation for the observed variability between countries in their ability to effectively control the transmission of SARS-CoV-2. We present a theoretical framework of how culture influences decision-making at the level of the individual. This conceptualization is formalized in an agent-based model that simulates how cultural factors can combine to produce differences across populations in terms of the behavioral responses of individuals to the COVID-19 crisis. We illustrate that, within our simulated environment, the culturally-dependent willingness of people to comply with public health related measures might constitute an important determinant of differences in infection dynamics across populations. Our model generates the highest rates of non-compliance within cultures marked as individualist, progressive and egalitarian. Our model illustrates the potential role of culture as a population-level predictor of infections associated with COVID-19. In doing so, the model, and theoretical framework on which it is based, may inform future studies aimed at incorporating the effect of culture on individual decision-making processes during a pandemic within social simulation models.