Chinese Electric Power Development Coordination Analysis on Resource, Production and Consumption: A Provincial Case Study
Bibliographic record
Abstract
In line with the pressures of energy shortage and economic development, Chinese government has adopted a series of measures and policies to promote the exploitation and utilization efficiency of electric power. China is urgently reconsidering its electric power development level and coordinating between power supply and demand sides. Therefore, in this paper, Chinese industrial structure of electric power was constructed according to its production process from resource, production and consumption sides. With the constructed industrial structure, the influencing factors on each side were selected to build a measurable evaluation system. Thirty-one Chinese provinces were chosen to explore their development coordination level based on the projection pursuit model and coupling coordination model. By the projection pursuit model, improved projection directions and best projection vectors of each province were found to describe the development level of each side. The coupling coordination model was adopted to explore the provincial supply and demand relations between electric power industry side via the indexes of coupling degree, coordination degree and relative development degree. By using ArcGIS mapping analysis, the results show the changes in Chinese provincial coupling and coordination development levels from 2011 to 2014. Finally, using the evaluation results, optimal strategies were discussed for improving the coordination of Chinese electric power development from different aspects, such as technical support, project approval and supervision, and demand side management. The findings prove that projection pursuit model and coupling coordination model can evaluate the electric power development level and describe their dynamic changing coordination relations effectively.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".