Analysis of China’s Pure Electric Vehicle Sales Based on Spatial Econometric Models
Bibliographic record
Abstract
This paper empirically investigates the spatial dependence and serial correlation structures among different China’s brands of pure electric vehicle (EV) sales using spatial econometric models. Based on the newly proposed economic distance spatial weight matrix, the empirical results show that EV endurance mileage, power battery capacity, charging time, government subsidy, retail price, and each brand market share have important impacts on EV sales. The per capita disposable income of urban households, gasoline price, loan rate and the number of charging pile are statistically significant determinants of EV sales. In particular, the improvements of the number of charging pile and the rise of gasoline price can increase EV sales, while the rise of loan rate or tight monetary policy may increase the consumers’ cost of purchasing EVs and then decrease EV sales. Another interesting finding is that though the per capita disposable income of urban households increases the EV sales decreases. A plausible explanation would seem to be that the impact of the per capita disposable income of urban households on the EV sales is offset by the decline in government subsidies or the incomplete infrastructures such as the inconvenient of charging stations. Besides, the significantly positive spatial dependence and serial correlation exist among EV manufactures indicates that when developing EV sales strategies, EV manufacturers must consider not only the properties of the EVs they produce, but also the properties of similar types of EVs produced by other brands in the EV market.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".