Prioritizing Key Factors in Refrigerant Substitution for GHG Emission Reduction: An Integrated DEMATEL-ISM-MICMAC Approach
Bibliographic record
Abstract
To implement the Kigali Amendment to the Montreal Protocol, the global academic community has intensified its research on environmentally friendly refrigerant substitutes. This effort aims to effectively reduce greenhouse gas emissions and facilitate the achievement of carbon neutrality goals. In this study, 14 key influencing factors were identified through the Delphi method, and the Decision-making Trial and Evaluation Laboratory (DEMATEL) approach was innovatively applied to systematically analyze the interrelationships among these factors. The results indicate that technological innovation related to refrigerant substitution ranks first with a centrality score of 5.429, confirming it as the core driving factor for refrigerant substitution. Subsequently, through the integration of Interpretive Structural Modeling (ISM) and Cross-impact Matrix Multiplication Applied to Classification (MICMAC), a hierarchical structure of influencing factors was further developed. This clarified high-driving factors such as government policies and life-cycle costs, as well as highly interrelated factors including climate conditions, greenhouse gas emissions, and performance coefficients. The key contribution of this paper is its success in overcoming the limitations of single-factor analysis by integrating multiple dimensions of influencing factors to construct a hierarchical classification. This innovative and systematic theoretical framework not only offers a scientific basis and decision-making support for refrigerant substitution but also possesses substantial theoretical value and practical guidance. Furthermore, it serves as an essential reference for advancing the development of low-carbon refrigeration technologies.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".