Introduction

Energy communities are increasingly presented as a key vehicle for a more democratic, local and resilient energy transition in buildings and neighbourhoods. In practice, there is no single formula to make them. Behind the shared label are very different constellations of actors, interests, governance arrangements and technical set-ups. Energy communities and related energy-sharing initiatives are better understood as socio-technical systems: outcomes depend on more than a technology stack or a balance sheet, they are shaped by people, their interests, behaviour, interpersonal relationships, and the way they live, think, and work together. 

This is particularly relevant when considering the replication of successful models. If an energy community is treated as a template, important differences in local context can be overlooked. Even when initiatives involve some form of energy cooperation, they can be driven by very different goals and mechanisms: solidarity and self-governance, public readiness for a positive energy district, local renewable electricity generation, or industrial heat recovery, for example. Behind the simple idea of cooperation in energy can lie different stakeholder dynamics, organisational models, technology choices and overall strategies. 

The key question is not how to copy one energy community model, but what is needed to understand both the energy community and its target context in order to assess replicability. How to recognise which type of energy community can work in each place, for whom, and under what conditions? System mapping helps make this visible by identifying the actors who matter, the relationships that shape outcomes, and the leverage points for building durable forms of participation and partnership. This article presents the experience from the ENERGY4ALL project, where system mapping was used across four pilot cases in Hungary (Megyeri and Kazán, as two cases), Austria (Lebring and GU Süd, as one case) and Norway (Stavanger) to derive insights into replicability.
 

_{Figure 1.  Energy4All project case studies. Image credit: ABUD}

Why a standard template does not work

When people talk about energy communities, they often present the main challenges as technical: how to organise generation, storage, sharing, billing or investment. However, when stakeholder networks are constructed and analysed, the true source of complexity becomes clear: people. Before any of these systems can work, someone must bring the right people together, manage expectations, build trust, negotiate roles, and keep different interests aligned long enough to make cooperation viable. In that sense, the first challenge of an energy community is not purely technical. It is organisational, political and relational. 

The four cases presented make this very clear. They differ not only in the type of energy cooperation they pursue, but also in who is involved, how large the network is, who mediates between actors, and where tensions arise. Stavanger, in Norway, is the smallest and most minimalistic network, focused on the key actors needed to enable a transaction around secondary energy between an industrial plant and the utility. The Megyeri case in Budapest, Hungary, is at the opposite end of the spectrum: it involves two levels of urban governance, multiple municipal departments, external project partners, asset owners, utilities and peripheral supporters around a positive energy district. The Austrian cases sit somewhere in between, with municipalities, households, public buildings and SMEs brought together into local energy communities. Communication, coordination and conflict resolution in the Megyeri case are particularly challenging, because adding even a few nodes or a few relationships in a stakeholder network disproportionately increases communication risks, the distance between stakeholders, and the transaction costs of management. 
 

_{Figure 2: The contrast of the complex Megyeri stakeholder network (left) and the minimalistic Stavanger one (right). Image credit: ABUD}

The cases also show that there is no single community actor. In Kazán, the core actor is ACRED, a coalition that both incubates and manages community real estate and energy initiatives. Around it are tenants, formal community members, external stakeholders with commenting rights, the building owner, and Ganz Holding as the utility provider. Internal communication is continuous and bidirectional, and the internal network is marked by strong cohesion and solidarity. However, this does not mean the model is conflict-free. The more successful the community becomes in reducing dependence on external energy, the more it threatens the business model of the incumbent provider. 

Megyeri looks very different. Here, the network is anchored in metropolitan and district-level public administration, with the project team and civil servants trying to build a Positive Energy Districts (PED) in an existing urban fabric. Asset owners such as schools, churches, shops or homeowners are not yet fully embedded insiders. They are closer to potential future members, and some remain neutral because they are waiting to see whether the project produces convincing, tangible value. In other words, this is not yet a settled community but a coalition under construction. 
The Austrian and Norwegian cases offer further contrasts. In Lebring and GU Süd, municipalities are core members and SO-Strom acts as a practical intermediary, providing administration, billing, visualisation, contracts and a platform for interaction. This intermediary role gives the Austrian networks a clearer centre of coordination than in the Hungarian cases. At the same time, the municipalities’ goals of autonomy, low and stable energy prices and community cohesion put them in direct tension with incumbent providers, who see energy communities as competitors. Stavanger reverses that logic. There, the utility is not an opponent but a potential customer in a waste-heat arrangement, while the municipality and university play enabling roles through political support, research and regulatory alignment. 

What emerges from these comparisons is that stakeholder diversity is not just a background condition. It determines the organisational investment and risks for the prospective energy community. Some cases depend on municipal coordination or an intermediary platform; others rely on contractual relationships, preexisting partnerships or community cohesion. Some must manage open conflicts with incumbents; others can build partnerships with them. This is why treating energy communities as a standard template is misleading. What can be replicated is not a fixed form, but the ability to understand which actors matter, how they relate to one another, and what kind of participation and partnership structure fits the local system. 

The role of system dynamics

Beyond the people, the systems are also different: they have different core problems, outcomes, drivers, and ways in which social, technical and economic factors reinforce or constrain one another. By constructing causal loop diagrams with people from different backgrounds, we can visualise and understand the known forces that shape the energy community. What we look for here are the main drivers, outcomes, pathways between them and feedback loops that either accelerate or dampen the system. 

Kazán is the most feedback-rich case. Although it started from the issue of energy expenditure, that variable does not even emerge as a clear outcome in the final map. Instead, the system splits into a social and a technical cluster, linked by variables such as energy consciousness, participation in energy-related decisions, energy demand and investment in efficiency. The map contains 17 independent loops. Some are reinforcing: savings and revenues can feed a rolling fund, which supports new investments, asset value and participation. Others are balancing: greater affordability can reduce interest in further action, or flexibility can come at the expense of feed-in revenues. This is a system that grows, learns and sometimes stalls through its own internal feedback. 

Megyeri is different in both structure and ambition. Its map is centred on a single main outcome: public readiness for energy communities. Around that outcome, the diagram organises a set of pathways driven by economic barriers, legal barriers, government support, education and financial wellbeing. Funding availability, technological accessibility and social trust act as the main funnel variables through which other factors shape readiness. The result is a mostly cascading model, with one reinforcing loop around engagement, awareness and stakeholder involvement. This is less a self-reinforcing community ecology than a mobilisation system: its central question is how to create the conditions under which people are ready to participate. 

The Austrian case has a stronger growth logic. Its main outcome is local renewable electricity generation, which also becomes a driver of wider co-benefits such as resilience, lower costs, reduced energy poverty, community image and climate performance. Here, more production can attract more members, and a higher share of renewables can help stabilise prices and unlock further investment. The system still points towards a clear policy objective, but, unlike Megyeri, it also contains virtuous cycles that can accelerate scale once the process is underway. 
 

_{Figure 3. Causal loop diagram of the Lebring/GU Süd case study. Image credit: ABUD}

Stavanger takes yet another path. Its map focuses on the use of industrial waste heat, with two main outcomes: energy security and winter energy demand met through recovered heat. The strongest drivers are policy support and stakeholder collaboration. The pathways toward energy sharing are therefore more transactional and infrastructural than community-building in the classic sense: participation in demand-side response, co-financing of heat recovery infrastructure, and market design all matter. Notably, the workshop did not identify a feedback loop. That makes this the most linear of the cases, built around aligning actors and conditions for a specific exchange rather than cultivating an internally reproducing community dynamic. 

Taken together, the diagrams show that energy cooperation can follow several distinct systemic pathways. Cascading systems can clarify intermediate steps between actions and the assumed benefits, helping to temper expectations and identify supporting actions for the success of energy communites. Clustered systems help understand how technological, economic, and social domains influence one another. Finally, feedback loops show how barriers are holding citizens back, and how exactly enablers surge them forward.  

Conclusion: what this means for practitioners and policymakers?

What these cases show is simple, even if the systems themselves are not: energy communities are not standard products that can be copied from one place to another. They are socio-technical arrangements that emerge from specific combinations of people, institutions, infrastructures, incentives and conflicts. In practice, this points to three priorities. The right question for policymakers and practitioners is not how to reproduce a single successful model, but how to identify the core functions that make an energy community work in each context. More specifically:

• Start with system diagnosis, not model transfer. Before designing an energy community, map who matters, what they want, where influence sits, and what the main drivers, bottlenecks and risks are. Systems mapping is useful precisely because it helps identify leverage points, unintended consequences and context-specific barriers before implementation.
• Design for multiple pathways of participation and partnership. Different actors rarely join for the same reason. Some seek affordability, some autonomy, some political credibility, some technical learning, and some business opportunities. Durable initiatives therefore need different entry points, different roles, and often trusted intermediary actors to coordinate interests and mediate conflicts.
• Support virtuous loops but manage resistance and balancing effects. Successful initiatives do not only need funding and technology. They also need trust, stable expectations and governance structures that can sustain engagement over time. Where participation, investment and social learning reinforce one another, policy should help accelerate them; where incumbent resistance, uncertainty, or declining motivation create balancing effects, policy should actively address them.