Comparison of Greenhouse Gas Emissions Per Capita Per Year Among Countries Considering Methane Emissions
Bibliographic record
Abstract
The cumulative emissions of CO2 and CH4 had a great impact on the global climate, and the responsibility of countries around the world to achieve greenhouse gas (GHG) emission control goals should be based on the concept of fairness and sustainable development. In this paper, from the perspective of interpersonal equity, based on the annual GHG emissions per capita, using the CO2 and CH4 emissions data of 23 major countries from 1961 to 2017, the ratio for GHG emission per capita per year and the ratio for carbon dioxide emission per capita per year in various countries were calculated with 1961 and 1990 as the starting years, the countries were also sequenced and sorted to analyze the extent to which major countries occupy limited global emissions space at different time scales and GHG ranges. The results showed that the ratio of GHG emission per capita per year in developed countries such as the United States and Canada were far higher than the world average, China was significantly lower than the average, India was much lower than the average. In addition, lengthening the time scale and incorporating the methane emissions from the planting and breeding industry (agriculture activities) had a significant impact on the the ratio of GHGemission per capita and national classification. It can be more conducive to judge the world's average annual GHG emissions, reflect the global emission space occupied by each countries comprehensively and objectively, and scientifically support policymakers in formulating action plan for GHGemission reduction and control, which was of practical significance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.001 | 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".