ECOSYSTEM'S OCCUPATION OF DIFFERENT COUNTRIES VIEWING FROM ECOLOGICAL FOOTPRINT
Bibliographic record
Abstract
As a new method for quantitatively measuring natural resources use by human kind, ecological footprint can illustrate regional sustainable development through the analysis of energy and other resources consumption. Since the early 1990s when ecological footprint was first proposed by some Canadian eco-economists, it has been used in evaluating regional sustainability, calculating regional ecological capital and other fields. In this paper, after intruding the concept and principles, the authors used and analyzed the calculated results in some references, ecological footprints, bio-productive capacities and ecological surplus or deficit. The results show that developed countries or regions have more ecological footprint, occupy more ecosystems and have more ecological threats on other countries than developing countries. For example, the ecological appropriation is about per capita 10.9 hm~2 in USA which is the highest all over the world and the 2.4 times of the world average. The total footprint is 2901.7×10~4 hm~2 in USA which is also the highest in the world. Therefore, ecological footprint can reflect the consumption degree to natural resources. The more the ecological footprint is in some country or region, the more the natural resources used in the country or region and the more the potential influence of the country or region on others. The comparison between different countries or regions can illustrate the different contributions of different countries or regions to global change which has strongly been influenced by the consumption of natural resources and appropriation of ecological systems. Therefore, this method can be used in assessing the assignments of carbon emission and other related issues and resources and ecological aggression existed in the real world.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".