Assessment of Resource Sustainable Utilization in Southwest Mountainous Area Based on Ecological Footprint Model:the Case of Qiandongnan Miao & Dong Autonomous Prefecture
Bibliographic record
Abstract
The model of ecological footprint has been advanced by William Rees who was a eco-economist in Canada in 1990s.It is a way to quantitatively measure the sustainable situation in cities,regions,countries,and the world.This article uses the model of ecological footprint to calculate the ecological footprint and the ecological capacity of Qiandongnan Miao Dong Autonomous Prefecture from 1997 to 2006.The result shows that the ecological footprint per capita of Qiandongnan Miao Dong Autonomous Prefecture increased from 0.727 hm2 in 1997 to 1.2 hm2 in 2006,and the ecological capacity per capita of Qiandongnan Miao Dong Autonomous Prefecture decreased from 1.003 hm2 in 1997 to 0.879 hm2 in 2006.The ecological deficit per capita increased from-0.277 hm2 in 1997 to 0.321 hm2 in 2006.This means that social development of this area changs from sustainable development to unsustainable development.In order to analyze resource use efficiency of this area,this article also calculates the eco-footprint per 104 GDP of autonomous region from 1997 to 2006.The results showed that the autonomous region of eco-footprint per 104 GDP declind year-by-year, from 4.948 hm2 in 1997 to 3.212 hm2 in 2006.This indicates that Qiandongnan Miao Dong Autonomous Prefecture improves the efficiency of resource use quickly,and along with China's western development policy,the religion's economic growth mode develops step by step.Through analysis,this article final sums up the methods and measures of Qiandongnan Miao Dong Autonomous Prefecture to achieve sustainable use of resources from the five aspects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".