Abstract
The chapter investigates actor collaboration in the context of water quality management. It asks whether the intensity and structure of such collaboration informs the public policies actors elaborate and implement to address water quality issues. To do so, the authors conceptualize collaboration as a feature of the actor policy network within a water quality management context and assess actor collaboration through Social Network Analysis (SNA). They study actor collaboration in the context of micro-pollutant regulation in three sub-catchments of the River Rhine. Findings show that actor collaboration in all three cases is similarly intense and only differs with regard to the networks’ composition of factions. Sectoral fragmentation within collaboration thus may have an influence on the way actors handle the water quality problem.
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Notes
- 1.
Switzerland is not part of the European Union, and is thus not obliged to follow the EU WFD. However, the country calibrates its actions to the WFD, in part because the ICPR adapted its monitoring program according to the WFD, and Switzerland is an ICPR member state.
- 2.
There is an exception regarding NGOs and scientific actors: In the Basel case study, five actors are NGOs (accounting for 9.8% of the case’s actor sample), while, in the Ruhr case study, only one actor is an NGO (constituting 2.6% of the actor sample). Scientific actors diverge even more, accounting for 17.6% (nine actors) in the Basel case study, to 23.1% (nine actors) in the Ruhr case study, and 9.1% (four actors) in the Moselle case study.
- 3.
To assure actors understand the same by collaboration, a brief description of which actions can potentially fall under the term collaboration was stated below the question: discussing new findings on the issue, working out possible courses of action regarding the management of micro-pollutants, exchanging viewpoints on the topic, and accomplishing joint projects regarding micro-pollutants.
- 4.
The benchmark, by how many actors an actor has (?? Do you mean how many connections an actor has?) to be judged as important to regard him/her important for this analysis, was set at 40% for each case study’s actors.
- 5.
Connectedness equals 1 minus fragmentation (cf. Borgatti et al. 2013, p. 154).
- 6.
A path is a sequence of connections in which both—edges and vertices—are only included once (Scott 2000, p. 68).
- 7.
Isolates are nodes that have no connections (Borgatti et al. 2013, p. 14).
- 8.
In the case that a node entertains paths or is adjacent to one or more nodes in several factions, the algorithm still forces it into one faction only (cf. Herzog 2018, p. 109).
- 9.
- 10.
- 11.
The Ruhr case study’s network also has two weak components, with one representing an actor that is completely disconnected from the rest of the network. These components are not shown in Table 8.4.
- 12.
The component ratio is the number of components, c, minus 1 divided by the number of nodes in the network, n, minus 1: c − 1/n − 1 (Borgatti et al. 2013, p. 153).
- 13.
The component ratio values for the three networks are 0.067 for the Moselle case study, 0.083 for the Basel case study, and 0.12 for the Ruhr case study.
- 14.
To ensure the validity of the faction analysis’ results, the algorithm was run several times. This procedure assured that actors were always assigned to the same faction. The factions’ number, that needs to be determined prior to the analysis, was decided for each case study based on a series of different numbers of faction partitions that were run beforehand. Based on their validity in actor assignment, their value in meaningfulness, and their final proportion correctness, the number of factions for each case study was chosen (cf. Herzog 2018, p. 134f.).
- 15.
Centrality measures were calculated in UCINET, using Freeman Betweenness Centrality.
- 16.
Faction 4 has a density of 3%, its members thus entertaining loose connections.
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Herzog, L.M.J., Ingold, K. (2020). Collaboration in Water Quality Management: Differences in Micro-Pollutant Management Along the River Rhine. In: Fischer, M., Ingold, K. (eds) Networks in Water Governance. Palgrave Studies in Water Governance: Policy and Practice. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-46769-2_8
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