ateam - Potsdam Institute for Climate Impact Research
ateam - Potsdam Institute for Climate Impact Research
ateam - Potsdam Institute for Climate Impact Research
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ATEAM final report Section 5 and 6 (2001-2004) 37<br />
the indicators and population and GDP data were developed. Indicator scenarios were then<br />
extrapolated using the historical functional relationships between the respective indicator and population<br />
and GDP. Finally, we developed a conceptual framework to aggregate indicators to a generic index of<br />
adaptive capacity in three steps using a fuzzy logic approach. For future scenarios of the index of<br />
adaptive capacity, the projected indicator data were aggregated per scenario and time slice.<br />
To guide our choice of determinants of adaptive capacity, we used a set of questions that would provide<br />
insight into different aspects of adaptive capacity (Table 10). We then looked at the availability of<br />
indicators of these determinants (Figure 32). Neither the list of determinants or the list of indicators are<br />
exhaustive but rather a compromise between rational justification and availability of data.<br />
Table 10. Guiding questions to the conceptual framework of adaptive capacity and related determinants. The list of<br />
determinants is restricted by the availability of indicators. The List of indicators can be found in Figure 32.<br />
Guiding question<br />
Is awareness building encouraged<br />
in society?<br />
Is society aware of the issue and<br />
does it perceive it as a problem?<br />
Is society equipped to address the<br />
problem?<br />
Is society constrained to take<br />
action?<br />
Related components of<br />
adaptive capacity<br />
Awareness<br />
Awareness<br />
Ability<br />
Action<br />
Related determinants of adaptive<br />
capacity<br />
Equality<br />
Knowledge<br />
Technology, Infrastructure<br />
Flexibility, Economic power<br />
The aggregation is per<strong>for</strong>med acknowledging that adaptive capacity is not a binary concept. Literature<br />
and expert knowledge helps to classify a particular indicator value as low, medium or high, which then<br />
defines membership functions <strong>for</strong> each indicator. Inference rules are then applied <strong>for</strong> stepwise<br />
aggregation of indicators (e.g., if literacy rate is high and enrolment ratio is medium, then knowledge is<br />
high). Fuzzy set theory then produces a value between 0 and 1 <strong>for</strong> the adaptive capacity index. The<br />
aggregation is per<strong>for</strong>med in three steps: from the list of indicators to the six determinants, which are<br />
then aggregated into the three components, which are then aggregated into an adaptive capacity index<br />
(Figure 32).<br />
Using this methodology, maps of the generic adaptive capacity index <strong>for</strong> the four SRES scenarios and<br />
four time slices <strong>for</strong> each of the scenarios were produced (Figure 33). The top row shows an initial step<br />
of ‘calibration’, i.e. comparing the adaptive capacity index based on observed data from 1995 with the<br />
maps of extrapolated data <strong>for</strong> the baseline year 2000. Both maps show about the same patterns of<br />
adaptive capacity, with improvements in adaptive capacity in most areas in the year 2000.<br />
Areas in the Iberian Peninsula and Greece tend to have low macro-scale adaptive capacity relative to<br />
Northern European regions. Comparing the different SRES scenarios over time, macro-scale adaptive<br />
capacity generally increases over time. In areas where it slightly decreases again towards 2080, e.g.<br />
France in A1 2020 compared to 2080, this leads to a more homogeneous pattern within Europe.<br />
The maps of macroscale adaptive capacity index allow comparison between regions and scenarios. On<br />
first sight the spatial and temporal patterns of the preliminary maps of adaptive capacity make sense.<br />
Nevertheless, further analysis of the underlying data is needed. The sensitivity of the index to changes<br />
in these data needs to be further explored. One next step is to carry out a sensitivity analysis of the<br />
membership functions in the fuzzy model and of the indicators.<br />
The preliminary results shown here seem reasonable, but are work in progress. Whilst this approach<br />
has produced the first spatially explicit projections of future adaptive capacity, the preliminary maps can<br />
be improved in a number of ways, including increasing the number of independent variables <strong>for</strong> the<br />
indicator scenario development or using more variables <strong>for</strong> the regression analysis (multivariate