Research on Sustainable Development of Xi’an City Based on ecological Footprint Model
Bibliographic record
Abstract
This paper evaluates the sustainable development of Xi’an city with the ecological footprint method. Based on the ecological footprint method, it calculates the per capita ecological footprint of Xi’an city from the year 2000 to 2017. With the calculation result, the paper forecasts the per capita ecological carrying capacity and per capita ecological deficit, and draws the following conclusion: during the study period, ecological deficit occurs every year, and the ecological deficit in each year exceeds global average per capita ecological deficit. Besides, both ecological footprint and ecological deficit have a tendency to increase year by year, and the growth rates of both are higher than the growth rate of ecological carrying capacity. According to the calculation results of ecological footprint, ecological carrying capacity and ecological deficit of Xi’an city from the year 2000 to 2017, the GM (1, 1) model is used to predict the above three indicators. The predicted results show that the load of ecological environment in Xi’an region is over the ecological carrying capacity caused by the production and living activities of human beings. The resources and environmental system are under great pressure, and the regional development mode is in an unsustainable state. It is a necessity to reduce ecological footprint by improving industrial economic efficiency, cultivating the consumption habits of energy saving and environmental protection, promoting investment in environmental protection and stimulating technological progress, so as to promote the sustainable development of Xi’an city.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".