Comparing SMART and ELECTRE methods for multi-criteria decision analysis: A case study evaluating conservation strategies

Tradeoffs are part of the inherent challenge of making decisions. Defaulting to a prevalent method can mask methodological variation and potential improvement in decision quality. We applied and compared methods for multi-criteria decision analysis (MCDA) in real-world environmental planning. Decision makers at a global conservation organization formed teams to review and prioritize the allocation of resources across a set of existing strategies across seven teams. Each team evaluated and rated strategies based on a common scale composed of weighted objectives and criteria. Sensitivity analysis included implementing two different MCDA methods, varying strategy ratings, and using two different criteria weighting techniques. The MCDA methods were SMART (Simple Multi-Attribute Rating Technique) and ELECTRE (ELimination and Choice Expressing The REality). This study tested user satisfaction between methods, overall rankings between methods, and whether MCDA improved the decision-making process. The methods did not differ in most comparisons, although participants were more likely to use the SMART method in future tradeoff decisions. We found evidence of rank correlation between methods. Lastly, the teams’ final recommendations were consistent with MCDA results. This study highlights the advantages and disadvantages of MCDA in real-world applications. • Multiple MCDA methods rarely compared in decision-making • Conservation strategies evaluated in real-world case study • SMART and ELECTRE methods used • Methods did not differ in most comparisons

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Publication Details

Journal
Environmental and Sustainability Indicators
Published
2025-06-19
DOI
https://doi.org/10.1016/j.indic.2025.100764
Citations
2
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
4.21
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article

Comparing SMART and ELECTRE methods for multi-criteria decision analysis: A case study evaluating conservation strategies

David R. Smith, David M. Martin
2 citations
Environmental and Sustainability Indicators
Multi-Criteria Decision Making
4.21
article

Comparing SMART and ELECTRE methods for multi-criteria decision analysis: A case study evaluating conservation strategies

David R. Smith, David M. Martin
article en
2 citations

Abstract

Tradeoffs are part of the inherent challenge of making decisions. Defaulting to a prevalent method can mask methodological variation and potential improvement in decision quality. We applied and compared methods for multi-criteria decision analysis (MCDA) in real-world environmental planning. Decision makers at a global conservation organization formed teams to review and prioritize the allocation of resources across a set of existing strategies across seven teams. Each team evaluated and rated strategies based on a common scale composed of weighted objectives and criteria. Sensitivity analysis included implementing two different MCDA methods, varying strategy ratings, and using two different criteria weighting techniques. The MCDA methods were SMART (Simple Multi-Attribute Rating Technique) and ELECTRE (ELimination and Choice Expressing The REality). This study tested user satisfaction between methods, overall rankings between methods, and whether MCDA improved the decision-making process. The methods did not differ in most comparisons, although participants were more likely to use the SMART method in future tradeoff decisions. We found evidence of rank correlation between methods. Lastly, the teams’ final recommendations were consistent with MCDA results. This study highlights the advantages and disadvantages of MCDA in real-world applications. • Multiple MCDA methods rarely compared in decision-making • Conservation strategies evaluated in real-world case study • SMART and ELECTRE methods used • Methods did not differ in most comparisons

Environmental and Sustainability IndicatorsVol. 27
The Nature Conservancy (US), Hood College (US)
Life in Land
Openalex Percentile: Top 6%
Multi-Criteria Decision Making
4.21
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