A DEMATEL approach for evaluating barriers for sustainable end-of-life practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it