What are the main challenges facing the sustainable development of China’s Yangtze economic belt in the future? An integrated view
Bibliographic record
Abstract
Abstract Interactions among human and natural systems are fundamental to many issues facing today’s sustainable development. Yangtze Economic Belt (hereafter Belt), one of the most dynamic regions in China, is of no exception. The economic prosperity of the Belt, however, comes at the price of ecological and environmental degradation, which poses severe challenges to its sustainable development. This paper describes the application of the ANEMI_Yangtze system dynamics model, aiming at identifying the main challenges facing the Belt and the potential way out towards its sustainable development. Three scenarios are proposed to (i) explore the potential impacts of climate change; (ii) examine how changes in birth control policy affect population dynamics and the natural-environmental systems; and (iii) investigate how policies aimed at improving the eco-environment conditions affect the Belt. Results show that a moderate rise in temperature is beneficial to the Belt’s economy and energy-food-water systems, but further temperature rise is harmful. Population in the Belt peaks around 2030, 2080, and 2100 under one-child, two-child, and three-child policies, respectively. Suppose no major changes in economic, technological, and policy developments are introduced. In that case, the Belt may face a serious energy deficit ranging from 10 to 17 billion tce. A food self-sufficiency ratio will fall from around 0.7 to 0.39 by 2100 as the country’s birth control policy loosens. Water scarcity occurs if surface water is considered as the only supply and this situation becomes even more serious when water pollution effects are considered. However, water stress will be greatly allievated if groundwater and wastewater reuse are introduced. The policy of increasing nutrient removal efficiency can save million lives. Finally, our results also suggest that the recently introduced 10-year fishing ban policy can not prevent the Yangtze fish stock from depletion in the long run.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".