ArticleScientific data2026
Chinese Electric Vehicle Policy Database: A Dataset of Policy Goals, Instruments, and Supply Chain Stages.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Chinese Electric Vehicle Policy Database: A Dataset of Policy Goals, Instruments, and Supply Chain Stages.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
China's electric vehicle sector has expanded rapidly, with policies playing a central role. Yet empirical resources that capture the comprehensive evolution of China's EV policies remain fragmented. We present a database of 7,300 policy documents issued by national, provincial, and municipal bodies from the early 2000s to 2023. Each record is annotated with issuing agency, level, date, policy goals, instrument type, targeted supply-chain stage, and policy scope (EV-specific or policies that indirectly shape the EV supply chain). The resource advances four fronts: (i) broad coverage including indirect measures; (ii) balanced central-local representation; (iii) theory-informed classifications of goals, instruments, and targets; and (iv) a whole-of-supply-chain perspective. Technical validation combines rule-based filters, model-assisted coding, and manual checks. We obtain rule-based accuracies of 100% for EV-specific identification and 94.1% for supply-chain labeling, alongside LLM-based accuracies of 96.5% for instrument classification and 82.9% for goal coding. This dataset enables analyses of policy design, sequencing, and diffusion across levels of government, providing an important foundation for comparative research on industrial policy and clean-technology transitions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.