CLINF stakeholder analysis - Böhme, Sepp (2017)
With climate change, habitats suitable for organisms transmitting southerly infectious diseases are expected to migrate towards the North, and tackling them will require joint action and awareness shared across national borders. In the present study, experts representing different scientific backgrounds supplied contacts and information regarding societal groups (stakeholders) potentially vulnerable to climate sensitive infections (CSI), and their associations with each other. From standardized questionnaires and open-ended interviews, the study infers a “stakeholder network” which identifies not only potential stakeholders, but also the underlying network implied by administrative stakeholder relations. The administrative and social depths of such relations were estimated with associative correlations whereupon a cluster analysis was performed with results depicted on a geographic map that covers the entire project-area from Greenland to Eastern Siberia (combining multivariate statistical methods with geographic information systems). As a result, stakeholder patterns across the geographic expanses from Nuuk to Yakutsk seem to be clustered into five relatively independent groups, covering topics from health sciences and governmental health authorities to organisations dealing with reindeer herding and indigenous cultures. The two latter topics of reindeer herding and indigenous interest are strongly correlated across national borders, and particularly provide a rather rare bilateral connection across northern Russia and western Europe. In contrast with associations across national borders, institutions, companies, and authorities related to reindeer meat/food production, land-use, and tourism seem to be relatively confined within national borders. If and when a pannorthern organisation from Greenland to Eastern Siberia is constituted to tackle CSI threats, it should encompass member organisations representing each of the five identified CSI stakeholder clusters, where the most central organisations of each cluster may be identified by means of maximum associative depth.