Environmental Engineering Reference
In-Depth Information
Fig. 5.3 Taking a multi-pronged approach to understanding and assessing adaptive capacity in
order to triangulate towards a more nuanced and empirically based set of adaptive capacity
indicators
behaviour within the context of extreme events. Interview and archival data were
then used to categorise adaptive mechanisms, correlate them with associated gover-
nance mechanisms and identify set of bridges and barriers. These analytical steps
then allowed for the operationalisation and measurement of more nuanced and
empirically grounded indicators of adaptive capacity according to three broad cat-
egories ( Regime, Knowledge, Networks ) that are presented and discussed in Part III.
Criteria (or sub-indicators) were qualitatively operationalised to allow for factors
that could be monitored and assessed as being indicative of positive manifestations
of adaptive capacity. Intersections of codes were extracted from MaxQDA to iden-
tify combinations of input variables (independent) and their associated output vari-
ables (dependent) to establish trends in correlations between governance mechanisms
and adaptive outcomes. Different relationships were explored across the study cases,
variables and scales or sectors. Intersections were extracted between coded seg-
ments relating to both adaptive outcomes and associated governance related deter-
minants in order to identify the governance mechanisms associated with the different
categories of adaptation. Descriptive codes (event impacts and water resource man-
agement issues) were extracted and analysed in order to better characterise the case
events and the broader challenges within which adaptive capacity is mobilised.
Finally, drawing on these initial analytical steps, the adaptive capacity determinants
(according to the refined categories of Regime, Knowledge, Networks ) were more
deeply operationalised within the context of the emergent codes, i.e. core tensions
in adaptive capacity, that of balancing flexibility with predictability.
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