Study on the Evolvement of Technology Development and Energy Efficiency—A Case Study of the Past 30 Years of Development in Shanghai
Bibliographic record
Abstract
Previous research in regional energy efficiency by using macro statistical data has demonstrated that technology development could improve regional energy efficiency. Since the start of reform and opening up in 1978, China has mainly adopted energy import and foreign direct investment to promote economic growth. At the same time, the country has also increased the input of technology and R&D to prompt technological reformation and imported technology absorption. However, there is limited research on the relationship between technology development and energy efficiency. Using the grounded theory method, the authors of this paper study the relationship between technology input-output and energy utilization efficiency in Shanghai over the past 30 years. They conclude that although the tactics of technology import and foreign direct investment can improve energy efficiency in the initial stages of modern industrialization, they cannot improve it continuously. In the more advanced stages of modern industrialization, the improvement of energy efficiency relies not only on increased R&D investment but also on R&D investment structure optimization and independent technological 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".