Electric vehicles and sustainable development goals: A multi-level governance analysis
Bibliographic record
Abstract
Electric vehicle (EV) adoption is driven by government incentives and strategies aimed at achieving Sustainable Development Goals (SDGs). A coordinated, multi-level governance (MLG) approach is essential as fragmented efforts generate societal costs and undermine long-term sustainability commitments. This study analyses EV and EV supply equipment (EVSE) policies in Australia, Canada, Germany, the United Kingdom and the United States to determine how they contribute to the SDGs. Using MLG theory, it examines vertical and horizontal government integration for policy coherence for sustainable development (PCSD). A thematic analysis of 108 policies shows that most incentives support SDG 13 (climate action), SDG 3 (good health) and SDG 8 (economic growth). Policy discrepancies between national and local governments are observed for SDG 11 (sustainable cities). Governments integrate vertically through funding and horizontally through informal collaboration, increasingly engaging stakeholders in information sharing. The findings highlight the role of MLG in strengthening PCSD, as well as the contribution of transport electrification strategies to achieving the SDGs. The study provides insights for policymakers and academics and highlights the need for integrated policy design and implementation for sustainable transport.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".