A list of 14 seeds was used for a snowball sampling of Dutch actors engaged in the energy transition hyperlink-sphere. After a multistep cleaning and preprocessing procedure—both hyperlink network and text content—the topological characteristics of the networks and the main topics emerging in this relational space were analyzed.
3.1. The Hyperlink Network
Our results reveal the existence of a sparse network consisting of 88 nodes (actors) and 79 edges (hyperlinks). Table 3
below shows the descriptive statistics of this network, calculated by using the igraph package for R [87
Both the average path length and the clustering coefficient scores are quite small (2.482 and 0.043, respectively) suggesting that the network does not exhibit an heterogenous distribution of attributes over a wide spectrum [90
].Thus, unlike many real networks, the nodes of the Dutch energy transition hyperlink network are rather isolated.
The network consists of 39 private companies, 25 associations, 10 NGOs, 8 research institutions and 6 public institutions. Cumulatively private companies and associations are the main creators of content about the energy transition, with 1700 and 1192 documents, respectively. Conversely, public institutions as RVO, Rijksoverheid and PBL and research institutions, as NVO and CE are the single major contributors (Figure 5
). Topologically, the web-based communication structure of the energy transition revolves around few organizations acting as hubs of the network.
Associations and private organizations are the most central players in the hyperlink network. Specifically, three associations (NVDE, NWEA and Netherlands Platform Warmtepompen) and two private companies (Itho Daalderop and Stadsverwarming Purmerend) have the highest scores of outdegree centrality (Figure 6
), emerging as gatekeepers [92
], i.e., actors that facilitate connections promoting links in the Dutch energy transition hyperlink network. In terms of indegree centrality, the most authoritative actors [24
] are associations (NVDE, NWEA, Holland Solar, Dutch Heat Pump Association, BodemenergieNL), with the exception of RVO and Itho Daalderop, a public institution and a private company, respectively. Indeed, among the organization of the network, the indegree distribution is much more heterogenous, while the outdegree distribution shows a few highly connected nodes.
These nodes, namely NWEA, NVDE and to a lesser extent Itho Daalderop and Stadsverwarming Purmerend, are also hubs for incoming hyperlinks, suggesting that being active in linking other nodes pays off in terms of reciprocal connections. Nevertheless, some associations such as RVO, BodemenergieNL, Daalderop, Hollandsolar, despite their low or inexistent linking activity (low outdegree), still receive much attention (high indegree) from the other nodes of the network.
3.2. The Structural Topic Model (STM)
Based on the most recurring terms and looking at the most representative documents, two coders assigned a label summarizing the main argument of each of topic (Table 4
“The Employment Implications of the Green” and “The Natural Gas Alternative” are the most relevant topic with 11.26% and 11.12% of frequency in the corpus (topical prevalence), respectively (Figure 7
); the other most represented topics are “Policy Making for Climate“ and “The Heating Market” with a topical prevalence 10.49% and 9.12%, respectively. On the other side of the spectrum, “The Solar Energy Sector”, “Academy and Research Progress” and “Smart Cities and Energy Grid”, are the least addressed topics in the corpus, with a prevalence of 5.68%, 5.60% and 4.05%, respectively.
To investigate the overlapping relationships between the topics in terms of topic co-existence in the corpus documents, a partial correlation matrix of the prevalence scores was calculated. Using the technique described in Epskamp & Fried [96
], we built a sparse graph where each topic represents a node and each link is a significant correlation (ρ < 0.01) within a pair of nodes (Figure 8
The strongest association emerges between the topics “The Natural Gas Alternative” and “The Heating Market” with a positive correlation of 0.26. Topic 11, “Policy Making for Climate” and “The local economic strategy”, have the second strongest association, with a correlation of r = 0.18. Other weaker correlations occur between Topic 11 and, respectively, Topic 5 (“Global level debate”, r = 0.13) and Topic 10 (“Employment Implications of Green”, r = 0.13). Topic 10, “Employment Implications of the Green”, is, in turn, weakly correlated with Topic 6 (“The local economic strategy”, r = 0.14) and Topic 7 (“The Heating Market”, r = 0.13), while Topic 5 shows a weak positive correlation with Topic 4: “The Natural Gas Alternative”. Topologically, “ Policy Making for Climate” and “Global level debate” are the most connected topics, while, remarkably, the “Technical challenges in R&D” together with the “Wind Energy Sector” and the “Solar Energy” are loosely connected.
Connections (edges) in topic-analysis highlight the extent of document contents overlap. Most connected topics are thus able to inbreed information through documents (websites) belonging to actors of different backgrounds. To understand the hierarchical structure of such interbreeding and how topics group around shared issues, a hierarchical clustering analysis was carried out. To do so, we used the R package stmCorrViz [82
]. At the first level of clustering, STM topics tend to be grouped in 5 groups as shown in Table 5
Topic 1 and Topic 10, referring to “The Wind Energy Sector” and “Employment Implications of Green” group together around the terms “wind”, “cooperate”, “build”, “people”, “project”, “work”, “municipality”, representative terms of cluster Ia. The thematic link between Topic 6 and Topic 11, previously highlighted by the correlation graph, is reflected in the hierarchical correlation analysis, where the relationship between climate politics and local economic strategies is made clear in cluster Ib, through the terms “policy”, “agreement”, “region”, “climate”, “achieve”, “government”, “invest”. As expected, the intuitive association between “The Natural Gas Alternative” (Topic 4) and “The Heating Market” (Topic 7) is confirmed by the analysis and represented by the cluster IIa through the terms, “transport”, “nature”, “supply”, “source”, “heat”, “network”, “gas”. Another intuitive association, the one between Topic 8 and Topic 9, respectively “Academy and Research Progress” and “Technical challenges in R&D”, is confirmed and summarized by cluster IIb through the terms “technology”, “vision”, “develop”, “solution”, “environment”, “innovation”, “challenge”. Finally, the cluster IIc (“electricity”, “city”, “smart”, “storage”, “grid”, “environment”, “solar”) groups together the themes concerning “The Solar Energy Sector” and “Smart Cities and Energy Grid”.
Moreover, these first-level clusters can be aggregated into 2 macrogroups, cluster I and cluster II strongly characterized at a thematic level; cluster I, which comprises Topics 1, 2, 6, 10, 11, appears to focus on aspects of local, national and global policies and governance, as suggested by its characteristic terms: “government”, “work”, “project”, “agreement”, “municipality”, “policy”, “climate”. In contrast, cluster II, which comprises Topics 3, 4, 5, 7, 8, 9, 12, 13, focuses on technology development, research, industry and emphasizes terms such as “renew”, “electricity”, “gas”, “network”, “develop”, “technology”, “heat”.
Hoping to gain a better understanding of the relation between the type of organization and the topics discussed in the Dutch energy transition hyperlink network, we calculated the estimated differences in topic proportion between the 5 types of organizations. Figure 9
shows the results of the effect of the type of organization on the topical corpus prevalence. The 8 topics with higher estimated differences per organization-type level are shown.
The agenda of public institutions seems to be set on topics related to “The National Regulation” (Topic 2), “Global level debate” (Topic 5), “The local economic strategy” (Topic 6), “Policy Making for Climate” (Topic11). Indeed, our results show that some issues of paramount social relevance regarding the impact of energy transition on employment and research—represented by Topic 10, “Employment Implications of the Green” and Topic 8, “Academy and Research Progress”—are under-represented in the discussions of the public institutions.
Dutch associations contribute extensively to the communication on the aspects of local economic strategy adjustment (Topic 6). In addition, they seem to extensively discuss issues related to employment and climate policy (Topic 10 and Topic 11). Similarly, NGOs contribute to the communication on Topic 10, but their contributions seem to revolve around natural energy resources, “The Natural Gas Alternative” (Topic 4). As for the communication agenda of the research institutions, they seem to be limited to aspects of scientific progress (Topic 8) and its global impact (Topic 5). Moreover, the core topics discussed by private organizations concerns “The Heating Market” and the energy supply (Topic 4), although the contribution made by private companies to the debate on “Employment Implications of the Green” is also significant (Topic 10). For details on the marginal topic proportion of each of the covariate level on each Topic see the Appendix A