The Impact of Charging Infrastructure on the Promotion of New Energy Vehicles
Bibliographic record
Abstract
In the context of the current situation, with the rapid development and expansion of new energy vehicles, the accompanying charging equipment industry has also experienced significant growth. The construction and operation of charging infrastructure play a crucial role in the promotion and development of new energy vehicles. However, the charging facility industry also faces certain challenges. From a holistic perspective, the lagging development of the charging infrastructure industry hinders the growth of new energy vehicles. Simultaneously, it greatly influences the execution and development of China's long-term energy-saving and environmental protection strategies. Therefore, this paper first proposes the research questions to be explored. By analyzing the current status of the development of charging infrastructure for new energy vehicles and considering practical needs, scientific development trends and solutions are formulated. This aims to promote the healthy development of the industry and pave a new path for the growth of the charging infrastructure for new energy vehicles. Additionally, an analysis of the main factors influencing individual users' decisions to purchase or not to purchase new energy vehicles in China, along with policy recommendations aligned with the country's vigorous promotion of new energy vehicles, emphasizes the importance and urgency of charging infrastructure. The ultimate goal is to foster a healthier development of new energy vehicles in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".