Analysis of Energy Productivity and Determinant Factors: A Case Study of China’s Provinces
Bibliographic record
Abstract
The objective of this paper is to explore the structure of how energy productivity in China’s provinces is determined to draw useful energy policy implications for sustainable development. First, energy productivity is decomposed into two attributes; technology; and input factor which is necessary for economic activities such as labor and capital. The paper then estimates energy technology levels as an indicator across provinces in China through 2004 and 2007 using a growth accounting method. The estimation results show that disparity in energy technology level exists across the provinces even after controlling for differences in the contribution of input factor to energy productivity, implying the importance of technology for energy productivity enhancement. We then identify factors that affect the technology level using regression analysis. The regression results indicate that investment in the energy technology and the quality of human and man-made capitals determine the level of energy technology. Furthermore, we show resource abundance and industrial structures affect incentives to make investment, thereby leading to more efficient technology for energy use. Thus, appropriate energy policies including price setting and a better environment for investment is vital to achieve both economic development and energy conservation. The proposed energy productivity analysis system is also applicable to other countries and regions to draw useful implications for policy making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".