3.3.7. Network Drivers and Criteria

Networks shape knowledge diffusion, resource, procedural flows among EC actors, and may provide platforms for social cohesion and grassroots empowerment [42,49,105] (Table 8). In terms of the internal network of the community itself, there must be a clarity of the objectives and purposes of the community: whether it serves a public task or brings profit or provides community service, all needs to be specified [43] and be consistent among members [3]. It is a common barrier to adequately communicate the project scope, conditions and benefits to prospective members [2]. This hinges on the bridging, or linking capital in EC actor networks—the efficiency of interactions cascading through them [12]. Mirroring self-identity, it is also beneficial to develop a group-identity, or to base the EC on an existing social group, with which members associate with [42]. However, it is necessary for EC mobilization to go beyond social networks to exploit multiple benefits [2]. Broad coalition of stakeholders are required to make many projects feasible, and it is difficult to identify and engage all of them [44]. Connecting to external, established interests may also prompt cross-fertilization and support, such as relationships to social movements or similar projects [43], and favourable network conditions improve access to relevant competences, resources, implementers, change-agents [44]. This does not undervalue social networks. Frequency and emergent saturation of community energy in social network clusters accelerates total saturation in said cluster due to peer effects [42]. However, there are certain actors who cannot be neglected in the decentralization of the energy market: providers of technical infrastructure. The partnership, or lack of partnership from local utilities provider can make or break community microgrid projects [49], while ICT providers of services, platforms and infrastructure are crucial for the operation of energy markets [106].


**Table 8.** Network-bound progression factors.

#### 3.3.8. Classification of Progression Factors

There is no consistent classification of progression factors in the literature, although many take an attempt to classify by relevant discipline [2,42,55,105]. The identified factors display an interdisciplinary scope of energy communities, with a slight skew towards social sciences and humanities (Figure 14). However, a sizeable proportion of factors (18 out of 49) had implications from multiple perspectives—one notable example is the optimal size of energy communities, which influences social acceptance and cohesion [42], economies of scale [2] and the complexity of computations [53].

**Figure 14.** Progression factor distribution by discipline.

Factors were also classified by relative importance: conditions, which are necessary requirements for EC progression, barriers/challenges, which are definitive for successful progression, and enablers, which accelerate/hinder project progression. For example, the existence of a microgrid for peer-to-peer markets [53], or the compliance of the grid operator in case of renewable energy communities [49], are preconditions, with no possibilities of initiating the project without them in place. Internal incentives are challenges, as financially unsustainable ECs might still exist through subsidization [42]. Finally, access to other, community-based networks is an enabler for recruitment of support and diffusion of knowledge, for example [43]. The distribution of factors in the three categories is even, with slight skew towards barriers (Figure 15).

**Figure 15.** Progression factor distribution by role.

Based on relevant use-case, the distribution of factors reflects the distribution of literature (Figure 16). As the largest, single-use-case group of articles focus on renewable energy communities, most of the progression factors describe them. This does not mean that these factors are not applicable to other use-cases, however, there is insufficient evidence to confirm that they do or do not (see discussion).

**Figure 16.** Progression factor distribution by use-case.

Finally, the classification by lifecycle phase heavily skews towards the earlier stages of energy communities. However, this does not mean that most barriers are overcome by the time operational phase kicks in, due to overlaps among the categories. A total of 19 factors are relevant in more than one phase, and 7 factors are relevant for all phases. The largest overlap (12) is between initiation and both technical and social upscaling phases, while the overlap between operation and initiation is only 1 (Figure 17). This displays a polarization of progression factors between operating the community and setting up the community—whether this "setting up" is the one that launches the project, or one that develops it further either technically or socially.

If all classification rationales are taken into consideration, with equal weight, hierarchical clustering returns the dendrogram shown on Figure 18. It is notable that none of the clusters are tight: even the most distant clusters can be reached with less than 3 steps, and over two-thirds of the factors would not be paired with any other, when setting cut-off for clustering to the average distance.

**Figure 17.** Progression factor distribution by lifecycle phase.

**Figure 18.** Agglomerative hierarchical clustering of progression factors by discipline, role, use-case, lifecycle phase.

**Figure 19.** Pairwise correlation of metadata. Colder colour indicates a category consistently appearing together for progression factors.

The clusters themselves are heterogeneous both in terms of discipline and in terms of lifecycle, meaning it would be difficult to bundle interventions and responses even if the use-case is known. This is because there are very few classes that share the same factors, as shown on the correlation matrix of features (Figure 19). The top five positive correlates are shown on Table 9.


**Table 9.** Top 5 pairwise correlates of metadata.

#### *3.4. The Analysis of UBEM Tools*

In this section, the results are presented as follows: first the choice of UBEM capabilities from the features listed in the four reviews are justified, then the individual affordances are constructed in the context of EC lifecycle phases, for goals of meeting progression factors, for EC planners as users and from UBEM capabilities as the affording agents. Due to inconclusive matching of progression factors to use-cases, the use-case as a context was not used (see Section 3.3). The section concludes with the disaggregation of results to EC phases.

The choice of UBEM capabilities is justified by the sparseness of agglomerative hierarchical clustering and the correlation matrix of capabilities. Clustering shows that the tools are generally distinct, as clusters only start to form at around 2.0 average distance, while all tools can be covered at 4.0 average distance (Figure 20). If four groups were to be generated—as shown in the figure—the threshold average distance would have to be set for 3.5, and this would still yield 6 unique tools (UrbanOPT [107], COFFEE [108], UrbanFootprint [109], CoBAM [110], SEMANCO [111] and OpenIDEAS [112]). The most similar tools at 2.0 average distance are UMEM [113] to MESCOS [114] and Georgia University [115] to Simstadt [79].

**Figure 20.** Agglomerative hierarchical clustering of UBEM tools by capabilities.

This result is also supported by the pairwise correlation of capabilities that show how often two capabilities share the same tool (Figure 21). It is notable that the correlation matrix is sparse and the only strongly correlated (coefficient higher than 0.7) pair is target groups: urban planner, and target groups: policymaker. They appear together in 81.67% of the tools.

In this framework, the capabilities for the 22 tools which remained after the data extraction were filled in (Appendix B), and affordances were constructed accordingly. A total of 5 affordances were generated, responding to 45 of the 49 progression factors (Table 10). The individual affordances are described below.

**Figure 21.** Pairwise correlation of UBEM capabilities. Colder colour indicates a capability consistently appearing together for tools.




**Table 10.** *Cont*.
