Societal inequities and barriers to participation in societal decisions mean that some people and groups have difficulty in accessing sufficient resources to meet their essential needs. This increases the vulnerability of those who lack social and political capital in times of crises, such as climate change impact events. Collective action institutions can redress this inequity by facilitating the redistribution of essential resources from the wealthy to the vulnerable. In this article, we use a stylised agent-based model of a community subject to a climate change impact that affects the availability of an essential resource. We use it to explore the types of societal conditions and policies that contribute to the emergence of a collective action institution that effectively redistributes resources to ensure that people are able to maintain a sufficient level of welfare. We find that removing barriers to participation in societal decisions for vulnerable people, and increasing the sensitivity and urgency in the decision-making process to impacts in the community, help to realise effective collective action institutions. The key insight that our model helps to uncover is that participatory justice promotes timely distributive justice.

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Despite the increasing use of standards for documenting and testing agent-based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to ad hoc and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social-ecological systems (SES). We argue that reusable building blocks (RBBs) known from professional software development can mitigate these issues. An RBB is a submodel that represents a particular mechanism or process that is relevant across many ABMs in an application domain, such as plant competition in vegetation models, or reinforcement learning in a behavioural model. RBBs need to be distinguished from modules, which represent entire subsystems and include more than one mechanism and process. While linking modules faces the same challenges as integrating different models in general, RBBs are “atomic” enough to be more easily re-used in different contexts. We describe and provide examples from different domains for how and why building blocks are used in software development, and the benefits of doing so for the ABM community and to individual modellers. We propose a template to guide the development and publication of RBBs and provide example RBBs that use this template. Most importantly, we propose and initiate a strategy for community-based development, sharing and use of RBBs. Individual modellers can have a much greater impact in their field with an RBB than with a single paper, while the community will benefit from increased coherence, facilitating the development of theory for both the behaviour of agents and the systems they form. We invite peers to upload and share their RBBs via our website - preferably referenced by a DOI (digital object identifier obtained e.g. via Zenodo). After a critical mass of candidate RBBs has accumulated, feedback and discussion can take place and both the template and the scope of the envisioned platform can be improved.

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Electric vehicle (EV) users who aim to become flexibility providers face a tradeoff between staying in control of charging and minimizing their electricity costs. The common practice is to charge immediately after plugging in and use more electricity than necessary. Changing this can increase the EV’s flexibility potential and reduce electricity costs. Our extended electricity cost optimization model systematically examines how different changes to this practice influence electricity costs. Based on the Prospect Theory and substantiated by empirical data, it captures EV users’ tradeoff between relinquishing control and reducing charging costs. Lowering the need to control charging results in disproportionally large savings in electricity costs. This finding incentivizes EV-users to relinquish even more control of charging. We analyzed changes to two charging settings that express the need for control. We found that changing only one setting offsets the other and reduces its positive effect on cost savings. Behavioral aspects, such as rebound effects and inertia that are widely documented in the literature, support this finding and underline the fit of our model extension to capture different charging behaviors. Our findings suggest that service providers should convince EV-users to relinquish control of both settings.

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This paper systematically reviews the literature on sociotechnical multi-system innovation frameworks that broaden the usual focus on one sociotechnical system to encompass influences from multiple systems. The review includes 75 peer-reviewed papers that span a broad range of energy-demanding systems and mainly build upon the core frameworks of the Multi-level Perspective (MLP) and Technological Innovation Systems (TIS). The analysis identifies three key aspects to consider in multi-system frameworks. The first aspect is the importance of considering the overarching directionality of multiple sociotechnical systems and how they influence each other. The second is to explicitly analyse the phase of each transitioning system. The third aspect is a need for explicit system configuration analysis. This includes analysing the value chain and the number and types of sectors linked to it, typifying the distinct characteristics of sectors internally and how they interact, and analysing complementary or competitive technologies. The paper concludes by providing recommendations for future research, with a particular focus on the further development of new multi-system frameworks that include one or more of the prior-mentioned three key takeaways. Firstly, focusing on dynamics within multi-system niches. Secondly, performing actor-level analysis, including demand-side analysis. Finally, applying quantitative methods, such as computer simulation modelling.

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Households equipped with flexible technologies, such as electric vehicles, can support the energy transition by shifting electricity consumption to times of high renewable supply and by preventing consumption peaks that cannot be covered by existing grid and generation infrastructure. Demand response services support households in performing these consumption shifts. Households ask for specifications of services that stand partly in contrast to each other. For instance, while electric vehicle owners tend to insist on retaining control over their charging, others prefer data-driven automation to minimize their active involvement. Recent studies exploring the acceptance of demand response services focused either solely on specific household groups (e.g. electric vehicle users) or on a broad representative sample without further differentiation. Complementarily to fill this gap, we examine differences in preferences for contrasting service designs between household groups. Specifically, we consider: (i) the type of flexible technology to which demand response is applied, and (ii) the adoption level, i.e., whether the households plan to, or currently own, a flexible technology. In a vignette survey, we examine the preferences towards four contrasting service designs with German households that either own or have expressed interest in acquiring a flexible technology (n = 962). Our results show that the preferences do not fundamentally differ between the kind of flexible technology and adoption level. Generally, participants prefer automated demand response services with data sharing. The added value of realizing energy cost savings effectively and efficiently stands out as the main driver for the diffusion of demand response services, outweighing data privacy concerns. Contrary to our expectations, electric vehicle owners did not show a special need for control and households not yet owning flexible technologies did not express a need for little effort. We discuss the implications of our findings for demand response service providers and outline pathways of future research in this domain.

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CO₂ emissions need to be reduced drastically to fight climate change and minimise the further increase of average global temperatures. The decarbonisation of the energy system aims at reducing CO₂ emissions and is thus urgently needed. This transition is facilitated by inter alia switching to renewable energy sources and more efficient technologies. In the past, the energy transition has mostly focused on supply-side measures. However, at least since the publication of the 6th IPCC assessment report, demand-side measures have gained attention. Thereby, the roles individuals play in achieving this transition is recognised as important. This Special Feature is dedicated to exploring the roles of individuals within the energy transition. The nine thematically featured articles provide insights on this topic using different foci and angles, such as the information to guide individuals' behaviour, the influence of media in framing roles, and technology acceptance. To contextualise and synthesise these diverse contributions, this editorial introduction outlines three different, complementary clusters of roles: technology adoption, lifestyle choices, and political action. By theorising users as participants in transitions through diverse practices, we widen the basis for future research to address and incorporate the roles users play in engaging with and shaping these transitions.

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Aligning prosumers' electricity consumption to the availability of self-generated electricity decreases CO₂ emissions and costs. Nudges are proposed as one behavioral intervention to orchestrate such changes. At the same time, fragmented findings in the literature make it challenging to identify suitable behavioral interventions for specific households and contexts - specifically for optimizing self-consumption. We test three sequentially applied interventions (feedback, benchmark, and default) delivered by digital tools in a field experiment with 111 German households with rooftop-photovoltaics. The experiment design with a control-group, baseline measurements, and high-frequency smart-meter-data allows us to examine the causal effects of each intervention for increasing self-consumption. While feedback and benchmark deliver small self-consumption increases (3–4 percent), the smart changing default leads to a 16 percent increase for active participants. In general, households with controllable electric vehicles show stronger effects than those without. For upscaling behavioral interventions for other prosumers, we recommend interventions that require little interaction and energy literacy because even the self-selected, motivated sample rarely interacted with the digital tools.

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Energy production and consumption are major contributors to greenhouse gas (GHG) emissions, exacerbating one of the greatest challenges faced by modern societies: climate change. Thus, societies must switch to more sustainable energy sources. Green hydrogen has emerged as a promising alternative energy carrier, facilitating storage and utilization across various industries. However, amidst different production processes, solely sustainable electrolysis stands out as an environmentally benign production method. Hydrogen producers must prove provenance and sustainable production to regulatory bodies and hydrogen buyers to comply with the regulations for sustainable development. Blockchain provides a viable solution encompassing trustworthy and secure information sharing between untrusted partners. In this article, we employ a design science research approach to develop a blockchain-based certification system (BLC-CS) for green hydrogen. Through collaboration with experts to gather requirements and conduct evaluations, we design an artifact that streamlines the certification process for producers, regulators, and consumers. Our proposed solution facilitates information gathering, verification, and reporting, contributing to the advancement of sustainable energy practices. We provide a comprehensive discussion of the BLC-CS’s feasibility for green hydrogen certification, including technical extensions, recommendations for practitioners, and directions for future research.@en

Electric vehicles have penetrated the Dutch market, which increases the potential for decreased local emissions, the use and storage of sustainable energy, and the roll-out and use of electric car-sharing business models. This development also raises new potential issues such as increased electricity demand, a lack of social acceptance, and infrastructural challenges in the built environment. Relevant stakeholders, such as policymakers and service providers, need to align their values and prioritize these aspects. Our study investigates the prioritization of 11 Dutch decision-makers in the field of public electric vehicle charging. These decision-makers prioritized different indicators related to measurements (e.g., EV adoption rates or charge point profitability), organization (such as fast- or smart-charging), and developments (e.g., the development of mobility-service markets) using the best-worst method. The indicators within these categories were prioritized for three different scenario's in time. The results reveal that priorities will shift from EV adoption and roll-out of infrastructure to managing peak demand, using more sustainable charging techniques (such as V2G), and using sustainable energy towards 2030. Technological advancements and autonomous charging techniques will become more relevant in a later time period, around 2040. Environmental indicators (e.g., local emissions) were consistently valued low, whereas mobility indicators were valued differently across participants, indicating a lack of consensus. Smart charging was consistently valued higher than other charging techniques, independent of time period. The results also revealed that there are some distinct differences between the priorities of policymakers and service providers. Having a systematic overview of what aspects matter supports the policy discussion around EVs in the built environment.@en

Prosumers with photovoltaic systems can reduce their electricity expenses by increasing their consumption of self-generated electricity. This makes them more resilient to price shocks, like the 2022 European energy crisis. We evaluate how prosumers adapt their consumption behavior in response to such political uncertainty and increasing electricity prices. The collected survey and smart meter data allow us to evaluate the perceived self-reported and measured impact on self-consumption.Saving intentions due to the energy crisis are more clearly displayed by the survey than by the measured self-consumption. While solar radiation predominantly explains self-consumption changes, Google searches on electricity-related topics have limited explanatory power. However, considering time lags and the interaction with solar radiation leads to more nuanced insights on the effect of Google searches. Depending on the level of solar radiation, the effect of Google searches ranges from decreasing the daily self-consumption by 26.45 Wh to increasing it by 69.45 Wh. @en

Simulating collective decision-making and behaviour is at the heart of many agent-based models (ABMs). However, the representation of social context and its influence on an agent’s behaviour remains challenging. Here, the Social Identity Approach (SIA) from social psychology, offers a promising explanation, as it describes how people behave while being part of a group, how groups interact and how these interactions and ingroup norms can change over time. SIA is valuable for various application domains while also being challenging to formalise. To address this challenge and enable modellers to learn from existing work, we took stock of ABM formalisations of SIA and present a systematic review of SIA in ABMs. Our results show a diversity of application areas and formalisations of (parts of) SIA without any converging practice towards a default formalisation. Models range from simple to (cognitively) rich, with a group of abstract models in the tradition of opinion dynamics employing SIA to specify group-based social influence. We also found some complex cognitive SIA formalisations incorporating contextual behaviour. When considering the function of SIA in the models, representing collectives, modelling group-based social influence and unpacking contextual behaviour all stood out. Our review was also an inventory of the formalisation challenge attached to using a very promising socialpsychological theory in ABMs, revealing a tendency for reference to domain-specific theories to remain vague.@en

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. @en

This paper studies factors for the adoption of quality standards. The identified factors are applied to a typical example of such a standard; a new standardized measurement and calculation methodology for corporate greenhouse gas inventories. Standardization of these methodologies fosters innovation, as it will provide innovators and regulators in this field with qualitatively superior and more homogeneous emissions data. This will allow for the creation of better substantiated and more focussed innovations and regulations. A framework of 31 factors that determine the adoption of quality standards was first established from extant literature. The framework consists of tangible and intangible standard characteristics, standard supporting alliance, standard creating process, standard support strategy, and stakeholders. Factor weights were determined by applying the Best worst method, and interviews with experts in the field of greenhouse gas accounting were conducted. The existing literature on success in standardization is mainly concerned with compatibility standards; this paper contributes to the existing standardization literature by focusing on quality standard adoption factors. Counterintuitively, the most important factors for adopting quality standards are not related to strategic considerations or the standard's tangible technical characteristics but to pressure from customers and support from governmental bodies.@en

Agent-based models are particularly suitable to reflect the dynamics of humans, nature, and their interactions, making them a crucial approach for understanding social-ecological systems. The formalisations of human decision-making are central to resulting model behaviours. Despite awareness of the complexity of human behaviour in social-ecological systems research, scholars tend to represent human decision-makers as simplified, perfectly informed rational optimisers, without explicitly considering the fit with decision context. Key reasons are a lacking uptake of social theories and insights. To advance, we need a practice of reflecting, sharing, and inquiring on the justification of the decision model fit with its context. This paper stimulates this practice by 1) supporting the justification of decision model (DM) fit by describing the DM landscape and providing guiding questions; and 2) by supporting researchers in considering alternative DMs through a survey-based impression of modeller practices, and through highlighting DM frontiers as inspiration for future research.@en

Charging infrastructure in neighborhoods is essential for inhabitants who use electric vehicles. The development of public charging infrastructure can be complex because of its dependency on local grid conditions, the responsibility to prepare for anticipated fleet growth policies, and the implicit biases that may occur with the allocation of charging resources. How can accessible EV charging be ensured in the future, regardless of energy infrastructure and socio-economic status of the neighborhood? This study aims to represent the decision-making in the allocation of public charging infrastructure and ensure that various key issues are accounted for in the short-term and long-term decision making. The paper first identifies these issues, then describes the decision-making process, and all of these are summarized in a visual overview describing the short-term and long-term decision loop considering various key indicators. A case study area is identified by comparing locally available data sources in the City of Amsterdam for future simulation.

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Technological innovations are a key part of the energy transition. However, the social sphere as a force to facilitate the energy transition must not be neglected. While the socio-technical transition theory provides a framework to connect the technological with the social sphere, the role of the individuals within the transition process remains limited (Upham et al. 2020). Individuals are reduced to the role of consumers and technology users (Avelino and Wittmayer 2016; Nijhof et al. 2022), and the social sphere is subject to change rather than an instigator of it (Kivimaa et al. 2021).

Potentially alternative concepts need to be developed and employed to understand the role of the individual within the energy transition. With a focus on the energy transition, this special feature is dedicated to compiling scientific contributions that provide insights into how the individual is embedded within the larger system and how individuals are facilitating the energy transition. Thus, the role of the individual is not limited to the consumer or the user of technology. Rather special feature contributions identify how individuals are agents of change within the energy transition. Furthermore, the seed of change is not limited to technological or market innovations but includes, for example, social innovations contributing to the energy transition. Scientific articles will also provide insights into how individuals are embedded within the larger system. To shed fresh light on interconnections and the role of individuals in the energy transition, alternative approaches to socio-technical or socio-economic transition theory are welcomed.@en

In this paper, we link findings from a demographically representative discrete choice experiment (DCE) in eight European countries on the adoption of smart thermostats with an agent-based model (ABM) in a methodologically consistent way. We employ the ABM to simulate the diffusion pattern of smart thermostats until 2030 and to examine the effects of subsidies and recommendations by specific agents. Our findings highlight the importance of allowing for within- and across country heterogeneity in preferences for these policies and for technology attributes such as heating cost savings. Further, social interactions reinforce country differences in technology stock in the starting year of the simulations. We find that subsidies moderately accelerate the diffusion of smart thermostats, but they are less effective in countries with a large stock of smart thermostats in the starting year, strong preferences for heating cost savings, and when smart thermostats lead to a strong reduction in heating costs. For some countries, targeting subsidies at particular socio-economic groups (in our case low-income households) slightly mitigates free-riding effects. Our policy simulations further imply that recommendations by energy providers or by energy experts accelerate the diffusion of smart thermostats compared to recommendations by peers.@en

Mitigating the adverse effects of climate change requires us, globally, to speed up the energy transition: to decarbonise energy supply while maintaining the security of supply and the affordability of energy services. This is an unprecedented challenge. The energy transition seems to have moved into a phase of scaling up: technologies are becoming competitive; more and more of the public will have to be involved in the energy transition in the coming years. In this report, we outline the opportunities that come from studying so-called ‘emergent behaviour’ in the energy transition. Emergent behaviour are trends or patterns observed on the aggregated/system level that are not a simple sum of the individual actions. What drives behaviour is hard to anticipate, but understanding it is crucial for a successful energy transition – to avoid lock-in and delays. As such, this report outlines what the concepts emergence and complexity theory can entail for the energy transition, what approaches there are to study this, and how to translate this into action. We focus on how we may be able to anticipate how such behaviour (i.e., opinions, choices and using) of larger societal groups may promote or hamper progress in the energy transition. And, at the end of the day, how to affect those processes to scale up a fair, inclusive energy transition robustly. Based on literature and interviews, we provide five perspectives: 1) behaviour in the energy transition, 2) the transformation of large systems, 3) emergent behaviour and marketing, 4), modelling emergent behaviour, and 5) intervening to shape complex systems. There are substantial opportunities to better develop, study and integrate concepts from complex systems and energy systems research and modelling into decision-making processes for the energy transition. We present a research agenda including a concrete action plan for the following research challenges: behavioural theory and modelling of the energy transition, 2) anticipating emergent behaviour to scale up the energy transition, 3) developing transition narratives, and 4) embracing key/change agents and emergent leadership. The action items vary between quick wins and fundamental research ideas that may, together, help us to better shape the energy transition.@en