A common tool in appraisal when there are multiple objectives is Multi-Criteria Analysis (MCA). MCA uses the judgements of decision makers or experts on the importance of the various criteria, which are then used to assess options. In MCA, weights are given to each criterion, ideally reflecting the preferences of the decision makers. The weighted sum of the different criteria is taken in order to get an overall score for option, which can be used to rank options.
MCA can prioritise alternative policy options. Based on a thorough analysis of the most suitable criteria that decision makers can adopt in their decision making, a multi- level MCA can categorize and rank promising and feasible adaptation options. The steps include a clear problem definition, which includes the identification of all alternatives, selection of a set of criteria and assessment of scores. Then the scores are standardized and the weight of each criteria is determined.
Multi criteria analysis is a potentially elegant method to assess alternative policy options, on the basis of a set of alternatives and an explicit set of criteria. The main problem is that such an approach is inevitably subjective, and/or requires very large stakeholder input, in relation to the scoring and weighting assessments. When choosing the weights, a natural candidate is equal weights; this mirrors an unweighted summation of the scores. Another relevant weighting is to give a higher weight to urgency, thereby indicating that this is the most important criterion. There is a scope for the use of MCA in those areas where monetary benefits are only a part of the criteria used.
This toolbox entry has been labelled with the following tags:
| Sector: | independent | |
| Spatial scale: | independent | |
| Temporal focus: | independent | |
| Onset: | independent | |
| Role in decision process: | prescriptive | |
| Level of skills required: | ||
| Data requirements: | ||
| Adaptation tasks: | Expected outcomes: utility, welfare, effect etc. (e.g. CEA, CBA) |
It is important to notice the differences among these three different methods (MCA, CBA and CEA). Particularly, CBA can handle optimisation, it can also provide an absolute measure of desirability, albeit judged by only one criterion: economic efficiency. CBA has comparatively heavy data requirements. MCA is suitable when quantification and valuation in monetary terms is not possible. MCA is normally used for the ranking of options, or prioritisation. Subjective judgement plays an important role in this method, making outcomes more arbitrary than CBA. CEA is a method that falls between CBA and MCA. As is the case with MCA, CEA only produces relative rankings. Given the CBA is the more objective method and can handle optimisation, it may be the most desirable option (OECD, 2009). However, this depends on the analysis. In cases where important criteria cannot be accommodated in CBA (such as sociological and cultural barriers), or when benefits cannot be quantified and valued (e.g. the benefits of preserving biodiversity), MCA may be preferred. If desired, the outcomes of CBA can be incorporated into MCA, making the overall analysis a hybrid one.
Compared to CEA, MCA involves multiple indicators of effectiveness. Technically, CEA can work with multiple indicators but is primarily used for single common goals (e.g. reductions in emissions of greenhouse gas emissions, achievement of levels of acceptable risks). Like CEA, policy or scheme cost in an MCA is always (or should always be) one of the indicators chosen. As with CEA, when effectiveness is compared to cost in ratio form MCA cannot say anything about whether or not it is worth undertaking any project or policy. Its domain is to restricted to choices between alternatives in a portfolio of options or to the choice of doing nothing. Both MCA and CEA are therefore "efficient" in the sense of seeking to secure maximum effectiveness for a given unit of cost, but may be "inefficient" in the sense of economic efficiency (depending on the original goal or target).
Criteria in MCA may or may not be measured in monetary terms. MCA differs from CBA in that not all criteria will be monetised. MCA tends to be more transparent than CBA since objectives and criteria are usually clearly stated, rather than assumed. Because of its adoption of multiple objectives, MCA tends to be less transparent than CEA with a single objective, although also more comprehensive, with the ability to tackle multiple attributes many of which it is not possible to monetise.
An adaptation option would represent a good investment if the aggregate benefits exceed the aggregate costs. Although CBA is important, other criteria are also considered when making a decision because CBA does not cover all aspects: it ignores the distribution of the costs and benefits of adaptation options and it fails to account for those costs and benefits that cannot be reflected in monetary terns, such as ecological impacts, as well as concerns that influence welfare, such as peace and security. Therefore, CBA is only one input into the decision-making process, and other approaches (CEA, MCA and others) are often used as a complement or a substitute.
This approach has for instance been used in de Bruin et al (2009) in the context of the Dutch Routeplanner project. In this project, a multi-level MCA was carried out to categorize and rank promising and feasible adaptation options in the Netherlands. The weights used in the MCA was based on expert judgement because experts are capable to compare options across various sectors with a broad multi-sectoral perspective (De Bruin et al, 2009). Another example of an adaptation decision matrix, in a form of MCA, is the water resource planning case study in South Africa (USAID, 2007).
MCA is not a proprietary tool or software package, and is discussed in the toolbox as the operationalization of the method. Thus, only limitation to access is the knowledge required to perform MCA.
Further information on MCA can be found via the in-depth
OECD, 2009. Policy guidance on integrating climate change adaptation its development cooperation.
De Bruin, K., R. B. Dellink, A. Ruijs, L. Bolwidt, A. van Buuren, J. Graveland, R.S. de Groot, P.J. Kuikman, S. Reinhard, R.P. Roetter, V.C. Tassone, A. Verhagen, E.C. van Ierland. 2009. Adapting to climate change in The Netherlands: an inventory of climate adaptation options and ranking of alternatives. Climatic Change 95: 23-45.
De Bruin, K.C., R. B. Dellink, R. S. J. Tol. 2009 AD-DICE: an implementation of adaptation in the DICE model. Climatic Change 95: 63-81.
USAID (United States Agency for International Development) 2007. Adapting to climate variability and change: a guidance manual for development planning. USAID and Stratus Consultancy, Washington.
The case study pool contains the following steps that were performed applying the described entry:
| SE3 - Guadiana basin | |
| Appraising options: Which options are preferred by stakeholders? | |
weADAPT case studies identified for this toolbox entry:
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Building Capacity in Vulnerable Mountain Regions
Impacts of climate change on the ecosystems of semi-arid mountainous regions can have serious consequences on local livelihoods. Climate change effects can also provoke an emergence and expansion of vector transmissible diseases such as Malaria and Chagas. To evaluate this situation and find possible preventive measures, two vulnerable semi-arid mountainous regions in Bolivia were selected as case studies... | |
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Linking Water Scarcity with Climate Change
The main goal of the Yemen NCAP project was to mainstream priority adaptation activities that would build resilience against increasing water scarcity among vulnerable communities, sectors, and ecosystems in three representative areas. Within this overarching goal, there were four specific objectives for each case study area... |
Available related training material
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MCA
Training material for MCA |
