A quantitative comparison and analysis on the assessment indicators of greenhouse gases emission
Bibliographic record
Abstract
Anthropogenic greenhouse gases (GHG) emission and related global warming issues have been the focus of international communities for some time. The international communities have reached a consensus to reduce anthropogenic GHG emissions and restrain global warming. The quantitative assessment of anthropogenic GHG emissions is the scientific basis to find out the status of global GHG emission, identify the commitments of each country, and arrange the international efforts of GHG emission reduction. Currently the main assessment indicators for GHG emission include national indicator, per capita indicator, per GDP indicator, and international trade indicator etc. The introduction to the above indicators is put forward and their merits and demerits are analyzed. Based on the GHG emission data from the World Resource Institute (WRI), the US Energy Information Administration (EIA), and the Carbon Dioxide Information Analysis Center (CDIAC), the results of each indictor are calculated for the world, for the eight G8 industrialized countries (USA, UK, Canada, Japan, Germany, France, Italy and Russia), and the five major developing countries including China, Brazil, India, South Africa and Mexico. The paper points out that all these indicators have some limitations. The Indicator of Industrialized Accumulative Emission per Capita (IAEC) is put forward as the equitable indicator to evaluate the industrialized historical accumulative emission per capita of every country. IAEC indicator can reflect the economic achievement of GHG emission enjoyed by the current generations in every country and their commitments. The analysis of IAEC indicates that the historical accumulative emission per capita in industrialized countries such as UK and USA were typically higher than those of the world average and the developing countries. Emission indicator per capita per GDP, consumptive emission indicator and survival emission indicator are also put forward and discussed in the paper.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".