Evaluation of Low-Carbon Scientific and Technological Innovation-Economy-Environment of High Energy-Consuming Industries
Bibliographic record
Abstract
The coordination of scientific and technological innovation with economy and environment is conducive to the sustainable development of high energy-consuming industries. Under the background of realizing the “carbon peak and neutrality” goal in China, this paper constructs the evaluation index system of scientific and technological innovation, economy, and environment of high energy-consuming industries. Based on the coupling coordination theory, this paper analyzes the coordinated development of scientific and technological innovation, economy, and environment of high energy-consuming industries from 2011 to 2019 and analyzes the factors restricting the coordinated development of the three systems. The results show that with the emphasis on scientific and technological innovation and ecological environment, the coordination degree of the complex system of scientific and technological innovation, economy, and environment of high energy-consuming industries is gradually increasing. R & D investment, the proportion of total industrial output value in GDP, and coal consumption per 10000 yuan of industrial output value are the main influencing factors of the coordination of the three systems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".