A DEMATEL approach for evaluating barriers for sustainable end-of-life practices
Bibliographic record
Abstract
Purpose Sustainable end-of-life (Sus-EoL) practices can be achieved through manufacturing of sustainable products, and recovery and recycling after the use phase. To achieve Sus-EoL, the manufacturing organizations should handle their products after their EoL. The recovery of used products is achieved through the design of the collection location. However, the first step is to understand and identify the barriers (e.g. lack of awareness among people, lack of technology, etc.) which prevent the implementation of Sus-EoL practices. The paper aims to discuss these issues. Design/methodology/approach This paper is about the 18 barriers responsible for the poor success of Sus-EoL practices of used plastic parts. By applying the DEMATEL method and by incorporating experts’ knowledge, a prominence and causal relationship diagram was developed through which the influential strength among barriers was studied. Findings The α value is computed as 0.068, and the values lower than α were eliminated to obtain the digraph. Poor curbside pick is identified as the most dominant barrier in implementation of Sus-EoL practices in plastic parts with an influential score of 3.96. Research limitations/implications The research is conducted in the Indian scenario which could be extended to global context by selecting the suitable barriers. Practical implications The results from the study can be used by the managers of organizations to enhance the possibility of Sus-EoL practices by incorporating suitable strategies which is the significant contribution of this study. Originality/value In the past, few authors discussed about the barriers of Sus-EoL practices; however, the analysis of complex interrelationship does not exist. Thus, the global and group interrelationship has been studied which is expected to pave way for future research in the direction of elimination of barriers and so on.
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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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.025 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".