Managing Climate Change Adaptation in the Pacific Region by Walter Leal Filho

Author:Walter Leal Filho
Language: eng
Format: epub
ISBN: 9783030405526
Publisher: Springer International Publishing

Results

The responses to the participant SNA surveys (N = 31| CSO = 7, Buariki = 8, Gov = 9, Abaiang = 7) were used to create directional symmetric matrixes for the three scenarios, thus creating 6 networks (Low Rainfall Get, Low Rainfall Share, Storm Surge Get, Storm Surge Share, Poor Water Quality Get, Poor Water Quality Share). Every nomination from a participant became an individual node in the network. For example, if a participant listed “radio” as an information source, “radio” would be a node within the network. As there are limited radio stations in the region, it was possible to qualitatively estimate and aggregate all participant answers of “radio”. In order to maintain anonymity of both the participant and the information source and/or share point, each response was aggregated into a category being Community (e.g., family member, neighbour, local meetings, local wise people “Tani Bouru”), Local Civil Society (e.g., Church, School, work colleagues, Village leaders), Government (Department of Health, OB, etc.), Health (Clinic, Doctor, Nurse), High Tech Media (internet, facebook), Low tech media (Newspaper, Radio, CB radio, telephone),CSO (other CSO events, or individuals), CSO International, Observations (personal observations of weather or health symptoms), Office Bearers (Council, Mayor, Village Water Committee, Policeman, Island leaders), and Water (Water Technicians). To ensure the anonymity of the participants the respondents were aggregated and grouped into Abaiang, Government, CSO and Buariki. Within the analysis, Buariki self-nominated to be North Tarawa so is coded as NT in this analysis.

Figure 1 outlines the network nodes types and the number of each node type per network. For example, during scenario 1 (Low Rainfall) participants reported obtaining information from 12 discrete government sources, and shared with 2 government sources. Within scenario 2 (Storm Surge), participants reported obtaining information from an equal number of sources including community, government and low tech media. However, as Fig. 3 shows (storm scenario top), the low tech media (Radio), received far more nominations than the government node (MET office). Table 1 further demonstrates that participants had larger Access/Get networks in low rain and storm surge scenarios (Low Rainfall Get N = 70/Share N = 56); Storm Surge Get N = 60/Share = 50) whereas in the poor water quality scenario, participants reported to have larger access than share networks (Poor Water Quality Get N = 55/Share N = 58).Table 1Network centrality and whole network measures for each scenario

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