The impacts of financial support on technological innovation in fuel cell electric vehicles (FCEVs) from a national inter-comparative perspective
Bibliographic record
Abstract
To understand the role of financial support in fostering technological innovation in fuel cell electric vehicles (FCEVs), the impacts of policy/market-oriented financial support are empirically examined by using the Tobit model and panel threshold model, based on data from 58 listed enterprises in China, the United States, the United Kingdom, Germany, Japan , Canada, and Korea (UKGJCK). The obtained results show that: i) Both policy- and market-oriented financial support have an incentive effect on FCEVs' technological innovation; however, technological innovation in China's FCEVs depends on government subsidies and equity financing, whereas UKGJCK relies more on tax incentives and debt financing. ii) Government subsidy has a dual-threshold effect with the first and second threshold values of $39.82 million and $46.27 million, causing a nonlinear effect of debt financing and equity financing on technological innovation in China's FCEVs; while debt and equity financing have only one threshold. iii) On the other hand, most financial support instruments do not have nonlinear effects on FCEVs' technological innovation in UKGJCK, except for tax incentives. Increasing tax incentives for highly indebted FCEV enterprises would instead undermine their capacity for technological innovation. Thereby, the present study provides significant policy implications for tailoring financial support for FCEVs' technological innovation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".