Energy Efficiency Estimation Based on Bayesian Method and Industrial Economic Transition: Taking Shandong as an Example
Bibliographic record
Abstract
This paper studied the total factor energy efficiency of industrial sector’s in Shandong. First, theoretical models of stochastic frontier approach on energy efficiency were structured, and then the referred parameters were estimated by using panel data of thirty-seven industries in Shandong from 2006 to 2013 and Bayesian estimation method. Finally Tobit model was applied to empirically study the influencing factors on energy efficiency of industrial sector’s. The study indicates that: (1) The input of capital and energy is notably positively correlative to output, while the input of labor quantity is negatively correlative to output. This means labor redundancy exist in industrial sectors. (2) Chemical industry, machinery industry, equipment manufacturing industry and food processing industry which have high energy efficiency should be further developed, especially marine chemical industry and marine biological medicine should be focused on to realize traditional industry upgrading. (3) Enterprise scale, international trade, the level of foreign investment and technology progress are notably positively relative to energy efficiency, while the proportion of state-owned economy have negative impact on energy efficiency. Therefore, it is necessary for further improvement in industrial energy efficiency in Shandong to decrease the proportion of stated-owned, encourage private capital entrance, extend opening up, and speed up the technical innovation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".