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.

Energy system optimization models are widely used to aid long-term investment decision-making for energy systems. From a socio-technical system viewpoint, existing models focus on the cost modeling of the technical subsystem, while the indirect costs of the social subsystem are not often modeled. This paper incorporates indirect costs into such a model, including those associated with generation capacity, energy production, and bilateral trades, respectively. As a proof-of-concept, the model has been applied to a case study for the Dutch power system, reflecting the Dutch national program Regional Energy Strategies, where regions collectively plan wind and solar energy capacities. We conclude that incorporating indirect costs significantly changed the optimal investment capacities and the associated costs for the regions compared to benchmark results from the conventional models. Furthermore, in this case study, a potential free-rider problem with regard to the national climate target occurs. Our model is used as a negotiation simulator to inform the regions about the hypothetical free-riding behaviors and thus helps to achieve a socially acceptable investment plan. The proposed energy system optimization model with indirect costs goes beyond the prevalent cost-minimization paradigm, and can be used to study transaction costs, trading barriers, and willingness to pay.

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.

Long term uncertainties in combination with long lifetime of assets of drinking water infrastructures (DWIs) and changing expectations of stakeholders make strategic decisions in drinking water infrastructures (DWIs) complex. A framework with building blocks and design spaces was developed to support these decisions. Building blocks, divided in governance and system properties, were generic resilience enhancing design principles found in literature. The design spaces were defined by characteristics (water quantity, water quality and environmental impact), and the scale dimension. The DWI design principles framework was operationalised in a case study. The case showed that the DWI design principles framework was useful for strategic issues and the results were recognised and accepted by a diverse group of stakeholders. It may also be possible to apply the framework for other water infrastructures with comparable characteristics and dimensions.

Energy communities are decentralized socio-technical systems where energy is jointly generated and distributed among a community of households locally. As the energy that is shared among the community is commonly electricity, the energy community's literature is dominated by electricity-systems and mostly neglects collective thermal energy as an alternative energy carrier for heating and cooling. Our goal in this article is to organise the existing research on “community-based initiatives for heating and cooling ” by using the Institutional Analysis and Development (IAD) framework, and based on this analysis, identify a future research agenda. Our analysis reveals that the number of publications in this area has been growing fast recently, focusing on technological challenges. Fewer papers take an institutional point of view, in which they cover policies, price reforms and values. The institutionally oriented papers focus on solar thermal energy and bio-based thermal energy. Other thermal technologies, such as geothermal wells, are largely neglected in the literature, but are known to have different institutional constraints. Informal rules and values are mainly researched from a consumer perspective. Since energy communities often consist of consumers and prosumers, additional research is warranted into this area. Evaluative criteria for such communities are limited to economic aspects and greenhouse gas emissions, while indicators such as soil pollution and spatial planning that may play an equally important role are neglected. We recommend studying thermal energy communities as distinctive entities with their own unique characteristics, and we develop a research agenda for this purpose.

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.

This study furthers game-based learning for circular business model innovation (CBMI), the complex, dynamic process of designing business models according to the circular economy principles. The study explores how game-play in an educational setting affects learning progress on the level of business model elements and from the perspective of six learning categories. We experimented with two student groups using our game education package Re-Organise. All students first studied a reader and a game role description and then filled out a circular business model canvas and a learning reflection. The first group, i.e., the game group, updated the canvas and the reflection in an interactive tutorial after gameplay. The control group submitted their updated canvas and reflection directly after the interactive tutorial without playing the game. The results were analyzed using text-mining and qualitative methods such as word co-occurrence and sentiment polarity. The game group created richer business models (using more waste processing technologies) and reflections with stronger sentiments toward the learning experience. Our detailed study results (i.e., per business model element and learning category) enhance understanding of game-based learning for circular business model innovation while providing directions for improving serious games and accompanying educational packages.

This paper demonstrates an approach to assess, ex ante, the social acceptance of sustainable heating systems in city districts. More sustainable heating systems are required in city districts to reduce greenhouse gas emissions. However, these systems may lack social acceptance as they often require significant adjustments to homes and may lead to a noticeable loss of in-home thermal comfort. Predicting social acceptance is often difficult due to the long-term planning horizon for energy systems. It is therefore unclear which design requirements and policy guidelines need to be specified ex ante. We suggest an approach to anticipate social acceptance by identifying value conflicts embedded in sustainable heating systems in specific social settings. These value conflicts might cause a lack of social acceptance over time due to value change. We demonstrate this approach using a case of community-driven heating initiative in The Hague, the Netherlands. We identify value conflicts embedded in various sustainable heating systems using an agent-based model. We formulate scenarios of value change to understand the severity of resulting social acceptance issues and discuss suitable heating systems for the city district. The approach can be used to support the decision-making process of policymakers at the local level, even in situations of limited local expertise.

This study is a comprehensive literature review about the field of the urban commons and its diversity, which we investigate through the lens of the new commons. Acknowledging a potential for adaptive capacity in the urban commons, we classify its traits into ecosystem, socio-economic and institutional factors. To make our work more practical, we further arrange them as benefits, challenges or supports. Our literature review highlights the need to further study the institutions which have an impact on the urban commons, as well as the individual and collective behaviour mechanisms at stake in the emergence and management of this commons. In addition, more light needs to be shed on the property-regimes relevant to the urban commons, with a focus on the access or use rules, rather than on ownership.

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.

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.

This paper presents an agent-based model that explores the conditions for ongoing participation in community gardening projects. We test the effects of Ostrom’s well-known Design Principles for collective action and use an extensive database collected in 123 cases in Germany and two case studies in the Netherlands to validate it. The model uses the Institutional Analysis and Development (IAD) framework and integrates decision mechanisms derived from the Theory of Reasoned Action (TRA). This allows the analysis of volunteer participation in urban community gardens over time, based on the garden’s institutions (Design Principles) and the volunteer’s intention to join gardening. This intention is influenced by the volunteer’s expectations and past experiences in the garden (TRA). We find that not all Design Principles lead to higher levels of participation but rather, participation depends on specific combinations of the Design Principles. We highlight the need to update the assumption about sanctioning in such systems: sanctioning is not always beneficial, and may be counter-productive in certain contexts.

In this paper, we investigate the potential of urban commons for building community resilience. We focus on the issue of adaptability to socio-ecological issues, which depends on the social capital built by the local community of practice. We measure this capital through the variables of volunteer involvement, perceived trust, and social cohesion in an agent-based model, which simulates the dynamics of participation in collective activities. We anchor our model with the case of KasKantine in Amsterdam, a cooperative and restaurant run by volunteers. Our model shows that both trust and social cohesion emerge from the interactions in the cooperative, especially when group sizes are kept small. This contributes to the adaptability of such social-ecological systems, helping their communities build social resilience.

A significant challenge for managers of drinking water infrastructures is to make effective strategic decisions for assets with a long lifetime in an uncertain and changing environment. Water resources, which are part of the drinking water infrastructures, have a special position in this decision making process as they operate at the interface of the socio-technical and the socio-ecological subsystems: water resources are the input for the technical system, consisting of pipes and pumps that interact with different actors; and water resources can be seen as output from the ecological system which is influenced by environmental and political issues like climate change, drought and competing and changing uses of water and space. This paper discusses how to design the water resources in such a way that they function in the desired way at present, but that the design also fits an uncertain future as good as possible. To this extent, a framework was developed based upon the notion of resilience. Existing general design principles for socio-technical systems from literature were used as building blocks in the development of our water resources design principles (WR-DP) framework. Three key characteristics of water resources were distinguished in this framework: water quantity, water quality and the environmental impact of the water resources. In addition, we distinguished two dimensions that allowed us to explicitly describe all possible design principles: system scale and class. The first dimension, system scale, ranges from one water resource to a network of water resources. The second dimension, class, makes a distinction between social aspects and technical aspects. Application of the WR-DP framework in a case of a drinking water company in the Netherlands showed the usability of the framework.

According to the extant literature on standards wars, when a regulator imposes a standard it automatically achieves market dominance. In the case of smart metering in the Netherlands, the regulator enforced a smart metering standard but it did not achieve dominance initially. Later, when changes were incorporated in the standard it did achieve dominance. One of the reasons for the fact that the standard did not achieve dominance at first lies in the fact that it was initially not accepted by consumers because important values were not taken into account during its development. Later, when those values were taken into account, the smart meter did achieve acceptance. For example, privacy and informed consent were initially not taken into account and, as a result, people did not accept the standard. Later, when changes were incorporated, people did accept the standard. The case of the development and rollout of the smart meter in the Netherlands which is presented in this chapter provides us with an illustration of where responsible innovation emerged in the standardisation process after an initial attempt at innovation failed due to a lack of value incorporation. Also, the chapter will present areas for further research.

This paper explores capability conflicts in the deployment of decentralised energy systems and identifies the affected population. These systems have positive societal impacts in terms of sustainability and consumer empowerment, but they are not accessible to all and their deployment may increase socio-economic inequalities. The societal impacts of decentralised energy systems can be understood in terms of conflicting capabilities; for some citizens capabilities may increase, whereas for others they may decrease. While problematic, capability conflicts may not be inherent. They may only occur in certain neighbourhoods, for example, where both affluent and less affluent populations coexist. By understanding why these capability conflicts occur, we may be able to anticipate whether these decentralised energy projects could result in societal problems. We use agent-based modelling and the scenario discovery technique to identify capability conflicts and the populations that may be affected. We distinguish five classes of conflicts, which can be used to anticipate social acceptance issues. Affected populations can be involved in the decision-making process to foster acceptance of decentralised energy systems. This work contributes to the growing political and scientific debate on issues of energy justice and inclusiveness related to the energy transition. Additionally, we contribute to the operationalisation of such capabilities, as this is one of the first papers to formalise the Capability Approach using an agent-based model.

Energy communities are key elements in the energy transition at the local level as they aim to generate and distribute energy based on renewable energy technologies locally. The literature on community energy systems is dominated by the study of electricity systems. Yet, thermal energy applications cover 75% of the total energy consumption in households and small businesses. Community-driven initiatives for local generation and distribution of thermal energy, however, remain largely unaddressed in the literature. Since thermal energy communities are relatively new in the energy transition discussions, it is important to have a better understanding of thermal energy community systems and how these systems function. The starting point of this understanding is to study factors that influence the formation and continuation of thermal energy communities. To work towards this aim, an abstract agent-based model has been developed that explores four seemingly trivial factors, namely: neighborhood size, minimum member requirement, satisfaction factor and drop-out factor. Our preliminary modelling results indicate correlations between thermal community formation and the 'formation capability' (the percentage of households that joined) and with the satisfaction of households. No relation was found with the size of the community (in terms of number of households) or with the 'drop-out factor' (individual households that quit after the contract time).

Variable Renewable Energy Sources (VRES) are characterized by intensive land-use and variable production. In existing optimization models that minimize the total cost of the energy system, location-specific VRES production profiles are often used to estimate VRES potential, but land-use and land cover aspects have been largely ignored. In this study, we therefore connect the literature in land cover assessment, VRES potential estimation and energy system optimization modelling by proposing a spatially explicit planning approach. This approach was applied to a case of the Netherlands to showcase its applicability and strength and to give results towards various RES targets. A baseline land-use scenario, a scenario with stricter constraints on land-use that reflects social resistance and spatial policy on wind energy and, thirdly, a scenario assuming unlimited land availability were analyzed. The baseline scenario results show the optimal geographical distribution of the generation capacities over the Netherlands. Wind energy dominates the generation mix and storage is only present at the 100% RES target. Under the strict constraints on land-use, 92% of the suitable land in the country will be deployed to place wind turbines in order to reach 100% RES share compared to 37% in the baseline case. However, the cost of electricity only increases by no more than 5 €/MWh. The unlimited land scenario highlights that the regional optimized capacities are infeasible. Apart from the useful results from the case study, the proposed approach is a first-of-a-kind contribution to the literature and provides a data-driven way to operationalize the location-specific land-use of VRES such that the role of the constraints on the land-use of VRES can be revealed and that policy-relevant results can be obtained.

This paper reviews the literature on the modeling approaches on decentralized energy investment and operation in the prosumer era. The study has several contributions. Firstly, it adds investment models into the review which have not been previously reviewed for decentralized energy modeling. Secondly, a modeling framework consisting of four building blocks is proposed that covers model conceptualization and model operationalization. Thirdly, the relationship between trading mechanisms and model methods is revealed using four evaluation criteria. Furthermore, by reviewing the papers, several trends in the literature are found. Operational models and local markets have been extensively studied, while wholesale market integration and investment models lack scientific attention. Among different trading mechanisms, the usage of bilateral contracts is most commonly seen. Lastly, optimization models significantly outnumber other model methods, and then it follows that their pitfalls such as the scalability of the model and the existence of stable outcomes need to be further addressed in future research.