Abstract
Composite indices are used to assess and prioritize mitigation and adaptation strategies for addressing the impacts of global environmental change. We evaluate different aggregation tools for creating these indices and their potential effects on mitigation and adaptation efforts. We assess the association of each aggregation tool with different types of trade-offs, risk strategies, and the resulting spatial and statistical distribution of their composite scores. Four aggregation tools are investigated (Weighted Linear Combination, WLC; Ordered Weighted Average, OWA; Data Envelopment Analysis, DEA; Compromise Programming, CP) using an example of vulnerability to flooding in the eastern United States. The choice of aggregation tool affects vulnerability outcomes, decision risk strategies, and the prioritization of vulnerability reduction strategies. DEA produces the highest vulnerability scores, representing a risk averse strategy associated with pessimistic outcomes. WLC implies a neutral and fixed risk strategy. CP produces a range of outcomes from neutral (equivalent to the WLC) to pessimistic, depending on its parameters. OWA offers the highest flexibility to adjust the levels of trade-off and risk strategy, producing a range of vulnerability outcomes, from optimistic to pessimistic. The units of analysis, when prioritized across the different aggregation tools, are more consistent for the top ranked units. However, the differences in rank become substantial as the selection threshold score decreases. To obtain better informed vulnerability reduction strategies, we recommend to (i) address how trade-off and decision risk are embedded in the aggregation tool chosen, and (ii) evaluate their effect in the prioritization of mitigation and adaptation strategies being considered.
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Notes
For exposure, refer to indicators numbered 130 (Population in 500 year floodplain), and 441 (Area in 500 year floodplain). See indicator 568C (Flood magnification factor, Cumulative) for sensitivity, and indicators 450 (Number of communities with flood insurance, which we reversed) and 443 (Number of people below the poverty Line) for coping capacity.
“Dispersion” is defined as the weight distribution at a particular level of ORness, calculated as the normalized sum of products of each order weight with its natural logarithm (Yager 1988; Rinner and Malczewski 2002). It is zero when ORness is 0 or 1 and maximum when the order weights are equally distributed across the order ranks (Yager 1988; Sadiq and Tesfamariam 2007).
A related frontier method, Pareto ranking, that has been applied in VAAs (Rygel et al. 2006) relies on a relative ranking of the HUCs based on their constituent indicator values and provides an ordinal measure of relative composite vulnerability.
CP can produce variable results depending on the p and q parameters used (and equal WLC when p and q = 1).
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Acknowledgements
We thank Ron Eastman (Clark University), Colin Polsky (Florida Atlantic University), Julie Blue (ERG), and Kathleen White (IWR, USACE) for their insights on the methodologies presented in the paper. This manuscript has also benefited greatly from the constructive criticisms and comments of the anonymous reviewers and the journal editor. Any errors or misinterpretations are our own.
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Machado, E.A., Ratick, S. Implications of indicator aggregation methods for global change vulnerability reduction efforts. Mitig Adapt Strateg Glob Change 23, 1109–1141 (2018). https://doi.org/10.1007/s11027-017-9775-7
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DOI: https://doi.org/10.1007/s11027-017-9775-7