Who decided the new energy vehicles policy in China? From the perspective of policy objects and policy makers
Bibliographic record
Abstract
The new energy automobile industry is a comprehensive system that contains Exploration and Manufacture, Consumption and Promotion, Infrastructure Construction and Supporting Industries, which coordinate and supplement with each other. Accordingly, from the perspective of policy object, NEVs policies since 1991 to 2022 could be divided into four fields in China. With policy bibliometric analysis and social network analysis, in each field of policies, its policy networks can be drawn, with statistic of policies released separately, in order to comprehensively analyse the features of NEVs policy making. It is found that: (1) The structure of policy system is balanced among four fields of NEVs policies in China, though with a bias towards Consumption & Promotion, Exploration & Manufacture. (2) Policy makers in all four fields of NEVs policies preferred slightly to formulate policies jointly, rather than acting alone. While policies made by sole actors are part of policy system. (3) GOOSC, MIIT and MOT, as sole actor, played more significant roles in industry-wide, supply-side and demand-side of NEVs industry respectively. (4) Policy networks of all four fields started with the "iron four" (MIIT, NDRC, MOF, MOST), ultimately forming two different ways of development, specialization and sociability. (5) In addition to the government departments, social organizations and enterprises also influenced the policy network, at the edge of network. This paper is of positive significance for understanding the current status and characteristics of policy making in different fields of the NEV industry, beneficial to distinguish potential effective ways to impact on NEVs policy system in China.
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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 | 0.000 | 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".