Lunenburg Part 2 - Section 5 - Social Vulnerability - August 30.pdf

While Cutter made no attempt to determine weights for the variables considered, Dwyer et al.
employed a method – known as decision tree analysis – for weighting indicators, based on the
results of a risk perception questionnaire distributed to both experts in the field of natural
hazards and non-experts. They also used the statistical procedure of synthetic estimation to
approximate the degree of cross-correlation amongst indicators – the degree to which members
of the population experience multiple factors of social vulnerability. Like Cutter’s SOVI, Dwyer et
al.’s method for assessing social vulnerability is for use in conjunction with natural hazards
data. 5
All of the aforementioned studies also employ nationally available statistical information about
factors of vulnerability, so that the resulting index can be applied throughout a country. The
advantage of this approach is that it allows for the comparison of social vulnerability amongst
various locations, facilitating targeted funding for the most vulnerable areas.
However, some researchers argue that this type of approach is too large in scale, so that locally
relevant factors and important details are masked. 6 These researchers advocate a qualitative,
bottom-up approach to assessing social vulnerability. These methods tend to evaluate social
vulnerability on a community level, based on features and experiences of an entire community.
This contrasts the methods discussed above, which are all concerned with the social
vulnerability of individuals in households: they measure the prevalence of factors of social
vulnerability that are experienced by individuals, such as age, income, family composition, or the
ability to speak the dominant local language. This information is aggregated at various levels,
depending on the geographic units used by the relevant national statistical agency.
Drs. Ellen Wall and Katia Marzall of the University of Guelph studied the adaptive capacity for
climate change of Canadian rural communities in 2006. They chose to document current
adaptive responses as the basis for understanding future adaptive capacity, rather than applying
a theoretical model. Their approach was to identify community resources – social, human,
institutional, natural and economic – and select appropriate indicators for measuring them. They
used a variety of data sources and types, including statistical data, contemporary research on
rural Canada, and key informant interviews. 7
In 2003, Drs. John Dolan and Ian Walker, then of the University of Victoria Department of
Geography, integrated an assessment of individual and household vulnerability, based on
demographic features, with community-scale determinants such as income distribution, reliance
on natural resources and critical infrastructure susceptible to sea-level rise impacts, access to
technology, and institutional frameworks. Similar to Wall and Marzall, they emphasized the use
of qualitative, community-based research methods involving institutions, local decision-makers,
resource users and residents, arguing that scientific knowledge must be grounded in local
experience to obtain the most meaningful results. 8
In another similar study, researchers from McGill University, Carleton University, and the
University of Toronto cooperated in a study of community vulnerability in the Canadian arctic.
The approach was a case study of a small Inuit community. This research used a process of
retrospective analysis to examine how community members have responded to anomalous
conditions; identify adaptive responses; characterize processes and conditions shaping
vulnerability; and suggest the potential implications of future climate change. These researchers
5 Dwyer et al., 2004.
6 Tapsell et al., 2010. p. 26.
7 Wall and Marzall, 2006.
8 Dolan and Walker, 2003.
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