Predicting the emergence of the EV industry: A product space analysis across regions and firms
Bibliographic record
Abstract
The automotive industry is undergoing a profound transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends are increasingly blurring the boundaries between the automotive sector and other industries. The pace of adaptation to electrification varies considerably between regions and firms. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominated, competitiveness in electric vehicle (EV) production increasingly depends on expertise in electronics, batteries, and software. This study investigates whether and how firms’ ability to leverage cross-industry diversification contributes to their competitive advantage in this evolving landscape. We develop a country-level product space covering all industries, and an industry-specific product space covering over 900 automotive components. This allows us to identify clusters of parts which are exported together, revealing shared manufacturing capabilities. Closeness centrality in the country-level product space, rather than simple proximity, is a strong predictor of where new comparative advantages are likely to emerge. First, we examine this relationship across all industrial sectors to establish general patterns of path dependency, diversification and capability formation. Then, we focus specifically on the electric vehicle (EV) transition. It is argued that new strengths in vehicles and aluminium products in the EU will generate 5 and 4.6 times more EV-specific strengths, respectively, than other EV-relevant sectors over the next decade. In contrast, these sectors are expected to generate only 1.6 and 4.5 new strengths, respectively, in already diversified China. However, a different pattern emerges when these country-level results are compared to the firm-level product space. Countries such as South Korea, China, the US and Canada show the greatest potential for diversification into EV-related products. Established producers in the EU are likely to come under pressure. These findings suggest that the success of the automotive transformation will depend on the ability of regions to mobilize existing industrial capabilities, particularly in related sectors such as machinery and electronic equipment.
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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 | 0.000 |
| 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".