Critical Success Factors for Building Resilience in Circular Supply Chains of Electric Vehicle Batteries: Evidence from an Emerging Country
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
Given the global expansion of electric vehicles (EVs), decision makers in developing and emerging countries must address important challenges across EV battery supply chains (EVBSCs) toward circularity. For example, batteries usually make up about 40% of an EVs’ value, and the race to achieve net zero emissions will further underscore the critical need for vital minerals and metals, such as lithium, cobalt, and graphite, necessary to make batteries. Stakeholders routinely question the resilience of circular (C) EVBSCs worldwide, from mining valuable materials to manufacturing the batteries necessary to support the widespread deployment of EVs. Identifying and investigating critical success factors (CSFs) of any system is a necessary step in achieving its targets. Little research, however, has been performed to investigate the CSFs for building resilience in EVBSCs, particularly those focused on building a circular supply chain. The goal of this research is, therefore, to systematically scrutinize the CSFs of resilient C-EVBSCs in Türkiye. To this end, a decision framework applying inter-valued neutrosophic ISM-MICMAC is proposed. Based on expert opinions, an application of the decision framework finds that effective government policies, directives, and incentives and well-established dynamic capabilities, are key driving CSFs to building resilience in a C-EVBSC.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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 it