Evidence map›Paper›PMID 42386786›Full record

ArticleScientific data2026

Chinese Electric Vehicle Policy Database: A Dataset of Policy Goals, Instruments, and Supply Chain Stages.

Peng Gao, Wangyang Hu, Yuhao Ba, Sebastian Sewerin

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Peng GaoLee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore.
Wangyang HuSchool of Engineering, Westlake University, Hangzhou, China. huwangyang@westlake.edu.cn.
Yuhao BaLee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore. yba@nus.edu.sg.
Sebastian SewerinInstitute of Political Science, Leiden University, Leiden, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID42386786
PMCPMC13687548

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.