Text Analysis of Policy Coherence between the Central Government and the Provincial Governments in the New Energy Vehicle Charging Infrastructure
Bibliographic record
Abstract
In the context of China’s burgeoning new energy vehicle industry, the development of charging infrastructure plays a pivotal role. This study examines 648 new energy vehicle charging infrastructure policies enacted by central and provincial governments between 2012 and 2022, investigating the alignment between central and local policies in terms of thematic focus and policy synergies. Concerning policy themes, the coherence between central and local policies is evaluated through the analysis of high-frequency words and the construction of co-word networks. The analysis of policy coherence involves a two-dimensional framework that considers Policy tools and objectives, employing fuzzy mathematics to measure the degree of coherence. This research sheds light on the current status and challenges in the formulation of policies related to new energy vehicle charging infrastructure. Notable findings include the congruence of core principles between central and regional policies, albeit variations in the distribution of thematic content and high-frequency terms. Regarding policy coherence, supply-based policies in each region exhibit alignment with central policies, while disparities emerge in the coherence of environmental and demand-based tools. The central and eastern regions display strong coherence with central policy objectives, whereas the northeastern and western regions require improved alignment, particularly in aspects like technological quality, operational efficiency, and planning. The policy analysis underscores future pathways: provincial governments can take action through environment and demand-oriented policies, enhancing collaboration with the central government. Adapting policies to local nuances and flexibly aligning them with regional characteristics ensures the effective implementation of policies on the ground.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".