The Construction and Empirical Study on Evaluation Index System of International Low-Carbon Economy Development
Bibliographic record
Abstract
Global climate change has become one of the core issues of world governance. Many countries have put forward the goal of carbon neutrality one after another, leading to the intensification of international low-carbon economy competition. To assess the current low-carbon competitiveness among countries, this article constructs an evaluation index system of international low-carbon economy development, and obtains the scores and rankings of countries in energy, society, economy and environment, as well as overall. Taking 20 countries with the highest carbon emissions in the world in 2019 as samples, starting from the concept of low-carbon economy and five evaluation principles, this article selects 40 low-carbon evaluation indicators from five aspects, including economy, society, science and technology, environment, and energy structure. By using the principal component factor analysis method to calculate and test, the four factors, energy factor, society factor, economy factor, and environment factor, are finally extracted to construct the evaluation index system. Results show that South Korea, France, China, Canada, and Germany are among the world’s top five low-carbon economies. The overall competitiveness of China’s low-carbon economy is in a relatively favorable position (3rd overall), with the most outstanding performance in terms of economic strength (1st), but poor performance in terms of social development (9th) and environmental carrying capacity (9th), and the biggest disadvantage in terms of energy structure (13th).
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 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".