Impact of Natural Hazards on Agricultural Economy and Food Production in China: Based on a General Equilibrium Analysis
Bibliographic record
Abstract
Based on a standard general equilibrium model for China’s macro economy with multi-regional sectors, including water, croplands, agricultural labor and rural households, this study estimated the impact on the agricultural economy and food production from natural hazards in 2007 and considered two simulations: i) the drought-exempt case, which supposed that a drought did not occur; ii) the flood-exempt case, which supposed that a flood did not occur. The discussion focuses on the results obtained from the drought-exempt case, which was similar to but more significant than the flood-exempt case, because the drought in 2007 was the most widespread in recent years and was also more serious than the flood. In both cases, real GDP obtained insignificant positive effects contributed by the rise of agricultural output, but the effects on nominal GDP was negative. All agricultural productions increased their outputs and exports, especially for sorghum, oil seed and corn. Another finding was that more capital and less labor were related to most crop productions. All food productions also increased their outputs and exports, thus their energy inputs increased, especially for sugar, meats and vegetables. Households benefited from lower prices for all agricultural and food products from more domestic outputs and fewer imports. However, more food consumption and higher welfare occurred in urban households rather than in rural households. This was due to the declines in the returns of cropland and in the wages of agricultural labor. The worst rural households were located in Shandong, Henan, Hebei, Yunnan, Anhui, and Heilongjiang.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".