This systematic literature review synthesises the literature on socio-psychological drivers and barriers to heat pump adoption and efficient use in households, drawing from the 16 research articles available. The review reveals mixed findings: variables were found influential in some studies but not in others. In addition to financial considerations, negative expectations regarding comfort and performance also hinder adoption. The literature on user behaviours suggests that comfort, knowledge, and home characteristics influence how heat pumps are operated, including temperature settings, heating area, and ventilation behaviour. A key research gap is the insufficient study of variables relating to the individual, such as psychological and socio-demographic factors. Based on the findings, we recommend public awareness campaigns to emphasise non-financial benefits of heat pumps, particularly comfort, which users often experience as an advantage. To optimise user behaviour, we recommend offering technical support services, simplifying system interfaces, and providing actionable feedback information on energy consumption.

The vehicle-to-grid (V2G) innovation—which enables electric vehicles to return stored electricity to the grid—holds significant potential to support renewable energy integration and electric vehicle adoption. Despite growing interest in V2G, there is still limited understanding of user preferences and the factors influencing decision-making. To explore this, we conducted a stated intention study with 1018 participants, examining their likelihood of participating, and their primary drivers and barriers. Our analysis—using a random parameters order probit model and mixed logit models—revealed that most respondents were likely to participate (42%) or remained neutral (32%). Financial incentives were the primary driver (49%), followed by electricity grid-stability (26%) and environmental (25%) factors. The main barrier for most was loss of flexibility (55%), followed by battery degradation (27%) and data concerns (18%). The study highlights how user characteristics—including socio-demographic, household, car use, and attitude factors—influence these preferences. Finally, we provide policy recommendations, including targeted education and communication, income-based incentives, accessible charging infrastructure, and a regulatory framework supportive to technology development and user protections.

The vehicle-to-grid (V2G) innovation—which enables electric vehicles to return stored electricity to the grid—holds significant potential to facilitate the integration of intermittent renewable energy and support climate goals. However, user preferences and how they vary across different user groups remain poorly understood, even though V2G’s success depends on driver participation. This study addresses this gap by conducting a stated choice experiment with 1,018 participants in the Netherlands. Participants chose between hypothetical V2G contracts based on four key attributes: financial compensation, guaranteed driving range, minimum plug-in time, and battery degradation—each varied at three levels. Using a latent class choice model, the analysis identified four distinct user preference profiles (or classes). Overall, guaranteed range and plug-in time were found to outweigh financial incentives for most users. The largest class (43% of users) prioritizes guaranteed range and shows the lowest sensitivity to financial incentives. The second-largest class (29%) also prioritizes guaranteed range, while assigning the least importance to plug-in time. The third class (18%) places the greatest importance on reducing plug-in time, followed by increasing guaranteed range. The smallest class (10%) is primarily motivated by financial compensation. The study further examines how user characteristics—such as socio-demographic, household, car use, and attitude factors—relate to class membership. The analysis provides a comprehensive overview of how these characteristics influence user preferences. These findings offer valuable insights into the diversity of V2G user preferences and inform targeted policy recommendations.

When dealing with Agent-Based Models (ABMs), calibration, sensitivity analysis, and robustness testing are often limited to parameter space and seeding, while structural calibration is omitted. However, we know that model structure necessarily also influences model outcome. Omitting structural calibration would thus pose a significant hurdle to robust model-based decision support, policy evaluation, and behavioural insights. Inverse modelling is an explorative modelling approach newly introduced for ABMs, aimed at directly inferring the generative mechanisms underlying observed outcomes by iteratively posing forward problems to match the ABM output with the desired patterns. We propose a method that leverages the inverse method on an ABM's building blocks to calibrate the model for generative insights structurally. We exemplify this through a case study using a solar panel diffusion model with Dutch province-level data, for which we operationalise "structure" through the order and presence or absence of procedures called in the model iteration. Our method shows that it is possible to vary and evaluate model structures automatically via inverse modelling. We find structures that fit each province’s solar panel adoption curve well and others poorly, and that variations, structural or in seed, significantly influence model outcome. We find multiple alternative well-performing model structures that exhibit large deviations concerning order and even the presence of functions. We exemplify how these structures can be made sense of and point directions for further real-life investigations and theory-building, such as the effect of hassle factors or complexity perceptions on adoption rates. With this, we present not an automated replacement of the participatory modelling process but an add-on to systematically reflect on the structure, implementation, and validity of the ABM and the theory utilised.

Households are crucial in the energy transition, accounting for over 25% of the European Union's energy consumption. To design effective policy measures that motivate households to change their behavior in favor of the energy transition, agent-based models (ABMs) are vital. For ABMs to reach their full potential in policy design, they must appropriately represent behavioral dynamics. One way to accomplish this is by strengthening the fit in ABMs between behavioral determinants (e.g., trust in energy companies) and the behavior of interest (e.g., adopting tariff structures). This study investigates whether a structured behavioral analysis improves this “determinants-behavior-fit.” A systematic review of 71 ABMs addressing household energy decisions reveals that models incorporating a behavioral analysis formalize nearly twice as many behavioral determinants, indicating a more systematic uptake. Subsequently, we find a difference between models focusing on investment-related behaviors (e.g., households buying solar panels) and those examining daily energy practices (e.g., households adjusting charging habits). Models in the first category integrate more social factors when incorporating behavioral analyses, corresponding with the influence of networks and peer effects on investment behaviors. Models in the second category emphasize individual and external factors in response to behavioral analyses, corresponding with the energy practices' habitual and contextual nature. Despite the benefits of a behavioral analysis for improving the determinants-behavior fit in ABMs, only one-third of the studies apply it partially. On top of that, almost half of the studies do not report a rationale for their choice of behavioral determinants. This suggests that many models may not fully capture the behavioral mechanisms underlying household energy decisions, limiting ABMs' potential to inform policymakers. Our findings highlight the need for systematic behavioral assessments in model development. We conclude that collaboration between behavioral scientists and modelers is crucial to accomplish such integration, and we emphasize the importance of allowing sufficient time and resources for meaningful exchange. Future research could further investigate empirical validation of behavioral insights in ABMs and explore how ABM results improve with a better determinants-behavior fit. By bridging behavioral science with computational modeling, ABMs' decision-support power to policymakers can be improved, ultimately accelerating the energy transition.

Despite the need for methodologies that support early-phase decision-making in the transition to a circular economy, current sustainability assessments often lack a prospective method that dynamically accounts for consumer decision-making based on empirical evidence. This study addresses this need by evaluating the circularity and environmental impacts of circular business models over a 30-year period, using an empirically grounded agent-based model coupled with life cycle assessment and material flow analysis. We developed a methodology to parameterize agents’ decision-making using data from demographically representative surveys and to prospectively assess the sustainability impacts of circular strategies. The case study examines the reuse, refurbishment, and subscription models of refrigerators and laptops in Japan. Results from Morris Elementary effects method and scenario analyses revealed that manufacturer-led refurbishment could reduce emissions of the whole society by 10%–12% and extend product lifetimes by 30%–33%. In contrast, the subscription model shows minimal benefits, with improvements of only 0%–3%, primarily due to consumer preferences for new products. Our consequential approach extends beyond technical strategies to evaluate the effectiveness of strategies targeting consumer behavior, including pricing, advertisements, and improvements in repair and collection services. The findings highlight the need for combining synergistic circular and diffusion strategies and suggest the need for a reorientation of policy efforts from end-of-life material recovery to refurbishment, reuse, and repair, supported by intensive campaigns and substantial price reductions in circular offerings. The methodology presented here facilitates prospective, dynamic, and consequential assessments of circular economy strategies to enhance consumer acceptance and ensure sustainability gains.

This study presents Adaptive Dual-OPtimization with Tree learning Genetic Programming (ADOPT-GP), a dual-loop evolutionary framework that simultaneously discovers symbolic rule structures and calibrates parameters. ADOPT-GP couples adaptive genetic programming with a two-stage parameter tuning process: rapid logistic-regression initialization followed by evolutionary calibration. Across runs, fitness improves by 20%–40% on average. Against a bilevel sequential baseline, ADOPT-GP delivers similar or better accuracy while reducing runtime by over 85%, demonstrating scalability. In a university library evacuation case, it yields diverse, interpretable rules that expose tensions between group cohesion and spatial constraints, supporting context-sensitive behaviors. The approach can advance inverse generative social science (IGSS) by linking behavioral theory with computation and offers practical tools for emergency planning.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.