Factorial CGE‐Based Analysis for the Indirect Benefits of the Three Gorges Project
Bibliographic record
Abstract
Abstract Large‐scale hydraulic projects (LHPs) have become increasingly significant in river basin flood‐risk management. Although LHPs’ direct flood‐retention benefits have been clearly shown, there has been a dearth of a comprehensive examination of their indirect implications, which can be more long‐lasting and substantial in both environmental and economic terms. Thus, this study develops a factorial computable general equilibrium (CGE)‐based LHP‐effect analysis approach (FLEA) to quantify the indirect impacts of LHPs and simulate postflood recovery strategies under diverse scenarios. The FLEA integrates a factorial analysis with a dynamic CGE framework, including a flood module that connects hydraulic initiatives to the economy during floods. The FLEA is applied to the Three Gorges Project (TGP). The results demonstrate that ∼$57 billion in Gross Domestic Product (GDP) and ∼12.9 Mt CO 2eq reduction would be created annually through supply chains by the TGP. When floods strike, the TGP has the potential to save ∼$21 billion in GDP directly and reduce long‐term GDP losses by ∼50% throughout the reconstruction period. The TGP can have a considerable indirect impact on manufacturing. Furthermore, improved regulations and maintenance for the TGP may be desired for mitigating long‐term flood‐related losses, which is more crucial than aggressive postflood fiscal stimuli.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.005 | 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".