A Global Assessment of Skills, Strategies, and Policy Frameworks for Sustainable Electric Vehicle Adoption in South Africa
Bibliographic record
Abstract
South Africa's automotive industry is a key pillar of its economy, contributing around 4.9% to gross domestic product and 27.6% to manufacturing output.However, the global shift from internal combustion engine vehicles to electric vehicles (EVs) presents both a challenge and an opportunity for the country considering the European Union proposed restriction on the importation of ICE vehicles, effective from year 2035, due to the urgent need to decarbonize the global economy in response to accelerating climate change and the need to preserve the ecosystem.Presently, EVs adoption in South Africa remains unexpectedly low, even as imported ones face a 7% higher tax than ICE vehicles, high upfront costs which make EVs unaffordable for most citizens, limited public fast-charging infrastructure and the country's vast geographical area which has led to range anxiety.This paper explores the current state of South Africa's EV landscape, identifies the key skills required for a just transition, and evaluates the strategic and policy frameworks necessary for developing a globally competitive EV industry.Drawing on recent studies, government documents, and automobile industry reports, the review offers analysis on how South Africa can build a resilient and inclusive EV ecosystem, as the country transits to electric mobility.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".