THE IMPACT OF DUBAI'S WORLD GREEN ECONOMY SUMMIT ON CHINA'S NEW THINKING OF GREEN ECONOMY
Bibliographic record
Abstract
As the global epidemic continues to have an impact on the world economy and public health, the issue of climate change is still the core threat facing the world. The "2020 Global Risk Report" issued by the World Economic Forum (WEF) pointed out that the five major risks facing the world in the next 10 years are all related to the environment. A study on this pointed out: If governments adopt greener economic recovery plans, the world can reduce the temperature rise by 0.3°C by the middle of this century. In other words, accelerating green economic growth after the epidemic and promoting green transformation in all aspects have become the top issues facing countries. Some of the economic recovery plans proposed by Western Europe, South Korea, Canada and other countries may have a positive effect on the environment. Economic stimulus plans such as China, the United States, Australia, Italy, and Japan will invest most of the funds in non-green areas. Among them, the US economic stimulus plan may the negative environmental impact is the greatest. The Dubai's World Green Economy Summit held this year undoubtedly produced a revolutionary change in thinking for the largest developing country like China.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".