Multicriteria decision making for sustainable development: A systematic review
Bibliographic record
Abstract
Abstract Making decisions by integrating social and environmental concerns beyond the financial dimension involves complex decision‐making processes in which innovative approaches and best practices need to be implemented. Recently, the literature on decision‐making related to sustainable development has grown rapidly, and multiple criteria decision making or analysis (MCDM/A) methods appear to be the most widely used approaches. The general objective of this paper is to provide a systematic review of the literature on the use of MCDM/A in a sustainable development context. We carefully analysed 343 papers dealing with decision‐making in sustainable development contexts published in the last 7 years (2010‐2017) using MCDM/A methods. Descriptive statistics were provided to highlight the main trends and gaps in the literature, and future research avenues were presented. The results show that although sustainable development strives to achieve a balance between the short and the long term, most articles surveyed did not investigate either the long‐term perspective related to sustainable development or the unforeseen events that could impact future project evaluations. Indeed, the need for temporal MCDM/A methods under uncertainty emerges as an important research challenge. In addition, results show that the social dimension was the most frequently ignored dimension. In future research, decision‐making processes should closely investigate social well‐being and encourage the participation of stakeholders (including the communities affected). Finally, the recent research on sustainability is relatively easy to implement but may not lead to the desired outcome. Future research needs to develop methods that promote sustainability without being overly difficult to implement in practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".