Private households account for 25.8% of the EU's final energy consumption and have a rooftop photovoltaic (PV) potential of 680 TWh/year, making them key players in climate change mitigation. Unlike institutional actors, household decisions are influenced by non-rational factors such as opinion dynamics, societal and peer pressure, perceptions, preferences, advertisement and numerous cognitive factors. Understanding their decision-process and their influence on each other and the system overall is paramount for policy making and successful product launch planning, making it informative for strategies of policy makers and companies. To model sustainable product diffusion in a flexible manner, we developed the agent-based innovation diffusion framework IRPact and implemented the granular rooftop PV diffusion model PVact as a case study for the diffusion of PV systems in private households. Most models focus on explanation or prediction; however, this requires low uncertainty and a detailed understanding of the phenomena under study. In contrast, explorative modeling is suited for studying the dynamics of systems of high uncertainty. As the influence and relative strength of different decision factors on the adoption decision of private households in municipal context is not well understood, explorative modeling suggests to be a promising approach to their investigation. Through evaluating extensive simulations with a focus on the interplay of monetary evaluation, normative pressure, agent attitudes, opinion dynamics and social network parameters, we found a high sensitivity of the system to normative pressure and opinion dynamics. Variation of normative pressure showed strong phase transitions, where opinion dynamics and financial evaluation exhibited more gradual behavior. Contrary to most existing literature, the system showed no sensitivity to the variation of network parameters.

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed-a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive-deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement.

How are policies affecting the social behaviour of actors and thus the dynamics of the corresponding socio-technical systems? This question is of central relevance to understand what policies might be effective for sustainable transitions and transformation where social changes are immanent. Exploratory modelling approaches such as agent-based modelling (ABM) are suitable and established to analyse how individual, especially heterogeneous behaviour emerges to affect social systems. In contrast to this micro-level perspective of understanding how social behaviour affects the system behaviour, System Dynamics models (SDM) focus on the macro-level perspective by analysing how system interactions drive complex system behaviour. Qualitative modelling approaches such as Causal Loop Diagrams belonging to the Systems Thinking Modelling (STM) toolbox remain descriptive but need much less effort for model development. In this article, we compare how the different approaches ABM, SDM and STM can model social behaviour in socio-technical systems and what different kind of insights can be gained to assess the efficacy of policies. It discusses the advantages and limitations of the different approaches for their application in behavioural public policy modelling for sustainability transition. Therefore, three exemplary models (STM, SDM, ABM) are created for the same topic of implementation dynamics of adaptation measures to foster rainwater retention (Sponge City concept) in a multi-residential quarter. As a result, the risk of green gentrification is identified. Three hypothetical policies are implemented to illustrate the differences between the three modelling approaches for their use in policy assessment. Based on that, we discuss how the three modelling approaches conceptualise behaviour in general.

Policy assessment is often limited to the evaluation of the physical effectiveness or economic efficiency. To analyse how policies intervene in the complex reality, qualitative and quantitative complex system modelling approaches from the field of Systems Thinking (STM), System Dynamics (SDM) or Agent-based modelling (ABM) can provide valuable insights. This article aims to illuminate the opportunities and limitations of these three exploratory modelling approaches for policy assessment. It addresses stakeholders in policy advices and decision makers designing policies and gives an overview of modelling approaches to evaluate the complex and social impact of policies including side-effects and non-linear system behaviour. After a short review of policy modelling in general, we compare STM, SDM and ABM regarding their methodology, modelling process and applicability in practical policy advice. We suggest that STM as a qualitative modelling approach is applicable to foster short-term policy design processes. In contrast, SDM and ABM as a time-resolved quantitative technique enable deeper system insights but requires more effort regarding model quantification. They are ideal to analyse how policies affect long-term societal problems like sustainability transformations. Finally, we propose first measures to establish such complex system modelling approaches, which today represent only a niche in policy evaluation.

Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages ‘knowledge’, ‘decision’, and ‘reevaluation’. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it.

Global initiatives on climate protection and national sustainability policies are accelerating the replacement of fossil fuels with renewable energy sources. Many electricity suppliers are engaged in efforts to monetize this transition with 'green' services and products, such as Green Electricity Tariffs. These promise customers that their supply includes a specific share of green electricity, yet since electricity suppliers often fail to deliver on those promises, many customers have lost trust in their suppliers. Further information asymmetries may not only exacerbate this loss of trust, but also spark distrust and lead to an overall feeling of ambivalence. Eventually, ambivalent customers may feel inclined to switch suppliers. To prevent this domino effect, electricity suppliers must eliminate ambivalence by increasing customer trust and reducing customer distrust. Here, we discuss how these challenges can be met with a customer loyalty program built on blockchain technology. We developed the program following a Design Science Research approach that facilitated refinement in four iteration and evaluation cycles. Our results indicate that the developed customer loyalty program restores trust, reduces distrust, and resolves customer ambivalence by providing four features: improved customer agency, sufficient and verifiable information, appropriate levels of usability, and unobstructed data access.

The uptake of residential photovoltaic systems is essential for energy system transformation towards carbon neutrality and decentralization. However, despite numerous campaigns to incentivize their uptake, adoption by residential homeowners is lacking behind. While countless drivers and barriers have been identified, the decision process is not fully understood. To address this gap, we developed an agent-based residential rooftop photovoltaic adoption model called PVact. Our model analyzes the interactions of potential household adopters based on their utility functions and social network, with a focus on the role of monetary evaluation and social pressure in adoption behavior. In this paper, we aim to assess the influence of monetary evaluation and social pressure in an abstract case study based on real-world data from the municipality of Leipzig, Germany. We consider stochastic dynamics through scenario analysis to investigate the influence of these factors on adoption behavior. Our results show that monetary evaluation and social pressure have a significant impact on adoption behavior. Specifically, we find shifting adoption patterns with an increased requirement for monetary returns and higher level of normative pressure required for households to act. Higher resistance against these pressure shows more stochastic variations, more pronounced tipping points and stronger run-away effects.

This paper explores the role of social interactions in residential photovoltaic (PV) adoption. Our survey data from Germany indicate that residential PV decision makers are influenced primarily by stakeholders to whom they ascribe beneficial attributes. The data further show that key attributes vary along the decision making process: integrity and likeability demonstrate the strongest association with influence strength at the awareness stage, while availability and trustworthiness have the strongest association at the planning stage. The perception of the competence of the stakeholder is associated with greater influence across all stages.

Logos Verlag Berlin, Germany, Simon Johanning, Fabian Scheller, Stefan Kühne, Thomas Bruckner (Hrsg.) Agentenbasierte Modellierung urbaner Transformationsprozesse. Smart Utilities And Sustainable Infrastructure Change, Reihe: Studien zu Infrastruktur und Ressourcenmanagement, Bd. 12

Agent-based simulation models are an important tool to study the effectiveness of policy interventions on the uptake of residential photovoltaic systems by households, a cornerstone of sustainable energy system transition. In order for these models to be trustworthy, they require rigorous validation.However, the canonical approach of validating emulation models through calibration with parameters that minimize the difference of model results and reference data fails when the model is subject to many stochastic influences. The residential photovoltaic diffusion model PVact features numerous stochastic influences that prevent straightforward optimization-driven calibration.From the analysis of the results of a case-study on the cities Dresden and Leipzig (Germany) based on three error metrics (mean average error, root mean square error and cumulative average error), this research identifies a parameter range where stochastic fluctuations exceed differences between results of different parameterization and a minimization-based calibration approach fails.Based on this observation, an approach is developed that aggregates model behavior across multiple simulation runs and parameter combinations to compare results between scenarios representing different future developments or policy interventions of interest.

Logos Verlag Berlin, Germany, Simon Johanning, Fabian Scheller, Stefan Kühne, Thomas Bruckner (Hrsg.) Agentenbasierte Modellierung urbaner Transformationsprozesse. Smart Utilities And Sustainable Infrastructure Change, Reihe: Studien zu Infrastruktur und Ressourcenmanagement, Bd. 12

Logos Verlag Berlin, Simon Johanning, Fabian Scheller, Stefan Kühne, Thomas Bruckner (Hrsg.) Agentenbasierte Modellierung urbaner Transformationsprozesse. Smart Utilities And Sustainable Infrastructure Change, Reihe: Studien zu Infrastruktur und Ressourcenmanagement, Bd. 12

Although there is a clear indication that stages of residential decision making are characterized by their own stakeholders, activities, and outcomes, many studies on residential low-carbon technology adoption only implicitly address stage-specific dynamics. This paper explores stakeholder influences on residential photovoltaic adoption from a procedural perspective, so-called stakeholder dynamics. The major objective is the understanding of underlying mechanisms to better exploit the potential for residential photovoltaic uptake. Four focus groups have been conducted in close collaboration with the independent institute for social science research SINUS Markt- und Sozialforschung in East Germany. By applying a qualitative content analysis, major influence dynamics within three decision stages are synthesized with the help of egocentric network maps from the perspective of residential decision-makers. Results indicate that actors closest in terms of emotional and spatial proximity such as members of the social network represent the major influence on residential PV decision-making throughout the stages. Furthermore, decision-makers with a higher level of knowledge are more likely to move on to the subsequent stage. A shift from passive exposure to proactive search takes place through the process, but this shift is less pronounced among risk-averse decision-makers who continuously request proactive influences. The discussions revealed largely unexploited potential regarding the stakeholders local utilities and local governments who are perceived as independent, trustworthy and credible stakeholders. Public stakeholders must fulfill their responsibility in achieving climate goals by advising, assisting, and financing services for low-carbon technology adoption at the local level. Supporting community initiatives through political frameworks appears to be another promising step.

Distributed load-management is seen as a means to enhance the integration of fluctuating renewable energy sources into the energy system and address the increasing strain on the German electricity grid. However, the advantages and disadvantages of different service provision approaches for this application, in particular blockchain-based solutions in contrast to aggregators, have not yet been investigated, a research avenue important to inform policymakers in the early stages of market and technology development. The sustainability and feasibility of three approaches, public blockchain, consortium blockchain and an aggregator using a relational database, were evaluated for their suitability to provide grid redispatch services through prosumer engagement, according to seven criteria by applying a multi-criteria analysis (MCA). The three feasibility criteria considered were scalability, regulatory acceptance and implementation network capacity, while the four sustainability criteria considered were privacy, security, costs and energy consumption. The analysis showed that public blockchain is least suitable for this application, whilst consortium blockchain and an aggregator using a relational database perform similarly on sustainability criteria, with the consortium blockchain performing better on security and the aggregator model on energy consumption. From a feasibility perspective, an aggregator using a relational database clearly outperformed blockchain solutions, suggesting better suitability at this point in time, with a need for regulatory innovation to allow for the future adoption of blockchain models.

Blockchain-based peer-to-peer (P2P) electricity markets received considerable attention in the past years, leading to a rich variety of proposed market designs. Yet, little comparability and consensus exists on optimal market design, also due to a lack of common evaluation and benchmarking infrastructure.This article describes LabChain, an interactive prototype as research infrastructure for conducting experiments in (simulated) P2P electricity markets involving real human actors. The software stack comprises: (i) an (open) data layer for experiment configuration, (ii) a blockchain layer to reliably document bids and transactions, (iii) an experiment coordination layer and (iv) a user interface layer for participant interactions.As evaluation environment for human interactions within a laboratory setting, researchers can investigate patterns based on energy system and market setup and can compare and evaluate designs under real human behavior allowing alignment of intentions and outcomes. This contributes to the evaluation and benchmarking infrastructure discourse.

Insights into the diffusion process can help decision makers to detect weak points of potential business models. Yet, due to the multitude of factors to consider, modeling the diffusion of innovations is a very challenging task. In the literature, various models and methodologies to address this problem can be found. Among these, empirically grounded agent-based modeling turns out to be one of the most promising approaches. However, the current culture is dominated by papers that fail to document critical methodological details. Thus, existing agent-based models for real-world analysis differ extensively in their design and grounding and therefore also in their predictions and conclusions. Being aware of this, this research paper seeks to identify requirements as building blocks in order to design and develop a versatile, but robust model to assess innovation diffusion processes. Subsequently, a formal approach is developed based on the derived model entities, dynamics and foundations. The main objective of this modeling approach is to achieve modularity and flexibility, as well as clarity through an explicit description of the concepts used. This is achieved by a three-layer approach, with a super agent layer, an agent layer and a sub-agent layer. Building on this, an object-oriented code base, organized in 24 packages and 273 classes is created. This empirically grounded agent-based modeling framework can be utilized by innovation diffusion researchers in order to build upon existing frameworks and concepts and to model a diverse range of domains in innovation diffusion.