Analysis of the Environmental and Socio-economic Benefits of Introducing Cleaner Vehicles in China: Policy Implications
Bibliographic record
Abstract
Along with its impressive economic growth China has experienced not only serious environmental pollution but also a very rapid increase in Green House Gas emissions and is now the largest emitter of CO2. Together with the power and steel sectors the transportation sector is the main contributor to CO2 emissions. In addition the transportation sector is also associated with air pollution and health damage. In order to address these challenges, at the COP15, the Chinese government set the target to decrease its CO2 emission per GDP by 40%-45% by 2020 compared with 2005 levels and increase non-fossil fuels rate at primary energy sector of 15%. The government has also put especial efforts to reduce air pollution through the Five Year Plans by introducing targets to reduce SO2, NOx, PM, among others. In order to determine the feasibility to reduce GHG emissions this research evaluates the potential of environmentally friendly motorized road vehicles (Hybrid Vehicle and Electric Vehicle). The research proposed 4 scenarios and designed social-economic model & environment model & automotive model based on Input-Out analysis. The results show that HV could be the most suitable option for promoting both GHG reduction and GDP increase with 1% GDP per GHG Emission (GpE) increase under 0.23 ton /Yuan carbon tax rate in China in the short term. The results of the study also show that these options should be followed by a transition to introduce EV in the long term.
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 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 | 0.000 |
| 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".