Population Size,Economics Growth and Carbon Emission:Based on the Empirical Analysis and International Comparison
Bibliographic record
Abstract
According to the data of CO2 Emissions from International Energy Agency from 1971 to 2008,this paper discusses the differences of population size,economics growth and Carbon emissions among G8+5 countries in the world.The Results show that the Carbon emission per capital of China has exceeded the average level of the world.The Carbon emission per GDP of China keeps on reducing at present,but it still surpasses the average level of the world.Analyzing the rank coherent coefficient of Carbon emission per capital and Carbon emission per GDP,this paper indicates most of developed countries present declining tendency.In the developing countries,China and South Africa present declining tendency,at the same time,other developing countries presents rising tendency,such as Brazil India and Mexico.Analyzing the rank coherent coefficient of GDP per capital and Carbon emission per capital,this paper indicates most of developing countries present rising tendency of Carbon emission per capital.In the developed countries,America,France,German and UK present declining tendency obviously,at the same time,Canada does not present rising tendency obviously,Japan and Italy present rising tendency obviously.Finally,there are three types of model about population growth,economics growth and Carbon emission to be generalized of the most of developed and developing countries in the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".