Study on the Beijing Transportation Energy Saving and Consumption Reduction under the Total Energy Consumption Restriction
Bibliographic record
Abstract
Total energy consumption and intensity in transportation industry are the critical factors to the success of future energy safety and greenhouse gas emission control.Total energy consumption in aviation transportation, pollution emission in road transportation, especially the exhaust gas emission from the private vehicles and road freight transport vehicles shall be the most crucial constraints for Beijing transportation industry to reach the energy saving and emission reduction target responsibility and obtain the air pollution governance effect in 13th Five Year Plan (2016-2020).Combining the 12th Five Year Plan period(2010-2015) analysis of transportation industry and study on energy demand prediction of private vehicles in Beijing, and after using the predict technique of a exponential growth model and the moving average method to revise the predicted result, the study predicts a quantitative range of energy consumption demand in Beijing transportation industry for the next five years, meanwhile suggested a three-level estimation of the total control of the energy consumption target in Beijing transportation industry, and proposed policy suggestions for energy saving and consumption reduction in Beijing transportation area. Keywords: total energy consumption, transportation, energy saving and consumption reduction Energy-Saving and Consumption Reduction are the Important Supports to Complete the National Energy Saving Target.In order to save energy and reduce consumption from the source, the Energy Development Strategy Action Plan (2014-2020) by National Energy Administration precisely proposed to strictly control the increase of the total energy consumption, strive to implement the energy efficiency improvement plan, and promote the energy consuming reform in urban and rural areas.National Development and Reform Commission (NDRC) also clarified that the inspection guide for
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".