Multi-Criteria Analysis
What is Multi-criteria Analysis?
Multi-criteria Analysis is a methodology by which the relative merit of different proposals can be compared using a range of quantitative & qualitative criteria.
Why do we need Multi-criteria Analysis?
For most proposals, and waste management proposals are no exception, there are many, many considerations which must be factored in by decision makers. Community awareness of social and environmental impacts is increasing while general expectations of financial and technical efficiency remain strong. However, these considerations are reflected in different ways. Some are measured in dollars or tonnes while some can only be measured in a relative way, for example Option A is somewhat better than Option B. Now if you’re a decision maker, you need to give attention to all these kinds of considerations. Multi-criteria Analysis is means of doing just that. Moreover, because it’s a systematic methodology, you can replicate the analysis and open it up to public scrutiny.
How Does Multi-criteria Analysis work?
The Multi-criteria Analysis framework used by the Municipal Waste Advisory Council is very simple. Briefly, the steps are as follows:
- Identify the alternatives to be compared;
- Identify a set of criteria for comparing the alternatives;
- Identify the relative importance of each criterion (weighting);
- Score the alternatives against each criterion;
- Multiply the score by the weighting for the criterion;
- Add all the scores for a given alternative and rank the alternatives
by their total score.
How do we score alternatives against qualitative criteria?
The methodology used by the Municipal Waste Advisory Council for scoring alternatives against qualitative criteria is again quite simple. Consider the example of the criterion of Residential Amenity as used by iris in its social assessment. iris supplies five descriptions of the possible impacts upon residential amenity that a piece of waste management infrastructure might have. These five scenarios fit loosely along a continuum with very good outcome at one end and very bad outcome at the other end. When the user selects the best qualitative description of the system in question, iris associates a score with the selected description.
Qualitative Description | Score |
| No or limited discernible impact; negligible consequences. High level of user-convenience to participate in resource recovery and waste collection programs. | 100 |
Impacts localised to a specific area; impacts can be mitigated and/or managed; low consequences. Medium level of user convenience to participate in resource recovery and waste collection programs. | 75 |
Impacts across several residential areas; impacts can be mitigated | 50 |
Impacts localised to a specific area; impacts difficult to mitigate | 25 |
Impacts across several residential areas; impacts difficult to mitigate and/or manage; extensive consequences. Participation in resource recovery and waste collection programs is inconvenient. | 0 |
It should be noted that this procedure presents some risk of distorting the data. For instance, it might be argued that the decision to equally space the alternatives along the continuum of 0 - 100 is an arbitrary one. It might also be argued that limiting the user to choosing only integer scores from 1 to 5 limits the ability of the system to make fine distinctions between IRR systems. These are valid arguments, but it is submitted that the final effect of such distortions is limited.
How can we combine scores from different types of criteria?
The biggest challenge for Multi-criteria Analysis is how to collate the performance of the alternatives across the different criteria types. This is the old problem of how to compare apples with oranges. iris overcomes this problem using a methodology called Concordance Analysis, which is sometimes called ELECTRE.
Concordance Analysis
Concordance Analysis is a pair-wise comparison technique which plays off the alternatives against each other, one criterion at a time. In other words, option A is compared against option B first against criterion 1, then against criterion 2, etc. For each pair of alternatives this comparison results in one option ‘winning’ and the other ‘losing’ for each criterion. A cumulative total of wins is established across all criteria and one alternative will emerge as ‘best’ out of the two alternatives being compared. This procedure is carried out for all combinations of alternatives. The method also incorporates the criteria weightings into the comparison, but we will not explore this in detail here. For more information on concordance analysis, the following references are recommended:
Annandale D and Lantzke R (2000), Making Good Decisions: A Guide to Using
Decision-Aiding Techniques in Waste Facility Siting, Institute for Environmental
Science, Murdoch University.
Resource Assessment Commission (1992), Multi-criteria Analysis as a Resource
Assessment Tool, RAC Research Paper No. 6, Commonwealth of Australia