Exploring the enablers for building resilience in solar photovoltaic Energy supply chains
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
Abstract A solar photovoltaic energy supply chain (SPvESC) is a global network with several linkages, including mineral and metal mining, material processing, and module and panel manufacturing. Due to the wide range of uncertainties and the unfavorable environmental effects associated with current linear business models, this global network is vulnerable to disruptions. Strengthening the resilience of SPvESCs is crucial for addressing any disturbances. This requires identifying the key enablers of resilience in SPvESCs, an area that has been understudied in the existing literature. An enabler is an aspect that facilitates the achievement of a goal by another aspect. This research contributes to the existing literature by systematically investigating the enablers for SPvESCs to achieve resilience. Thus, the objective of this analysis is to identify enablers that have the potential to enhance the resilience of SPvESCs in Türkiye. This was done by applying the Nominal Group Technique (NGT) in conjunction with a review of the current literature. Neutrosophic (N)-DEMATEL was then utilized to determine the relationships between the identified enablers. Finally, the results were validated using N-DELPHI. The results revealed that sensing and seizing new business models, adaptability to changes in novel energy generation and information technologies, and business contingency plans for natural and man-made disasters were the most influential enablers. The findings provide implications for practitioners, policymakers, and researchers to help ensure improved resilience in SPvESCs.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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