Developing model of Wuwei City under the control of water resources with the STIRPAT model
Bibliographic record
Abstract
With the rapid economic development and the increasing demand of water resources,the study on what kind of development pattern arid region of China should take to slow down the water consumption of economy development has become one hot spot of the current research.In this paper,using the IGT equation,the impact of the economic development on water consumption in Wuwei City,Gansu Province,China,from 1997 to 2010 was analyzed.Based on this,using the STIRPAT model,this study analyzed quantitatively the relationship between the total consumption of the water resources and population,affluence,the level of the urbanization and technological advances.Ridge regression results showed that 1% change of population,per capita GDP,urbanization level and technology advance would cause a corresponding occurrence of 0.790 1%,-(0.014 1+0.0018 ln A)%,-0.088 0% and 0.0312% change in the total consumption of the water resources.Based on the analysis of the above model,this paper took Wuwei City in Shiyang River Basin as an example and set 10 developing scenarios and analyzed the best model of reducing the total consumption of water resources.Results show that under the scenario of keeping the economy in high-speed growth,speeding the process of urbanization,making great progress in water-saving and high population control,it would be best for Wuwei to reduce the total consumption of water resources,and the total consumption of water resources in 2015,2020 would be 188 927.27 and 184 409.79 ten thousand cubic meters,respectively.Although the total consumption of water resources has been reduced,it still cannot effectively change the situation that the groundwater overexploited and the ecological security in Wuwei City cannot be effectively contained,Wuwei City's environmental load will remain in a state of high alert for a long time.In this paper,the STIRPAT model and ridge regression fitting were used to find out how to alleviate the pressure of the water resources in Wuwei City,data analysis is well-found,but it is lack of the practical guiding significance.Although it is an innovation point that the STIRPAT model was used to research the best approach for Wuwei to reduce the total consumption of water resources in this paper,there are still some shortages,such as all the factors(P,A,U,T)are how to influence the total water resources and each factor is how to influence each other,it needs to be further in-depth researched.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".