Development of a factorial hydroengineering equilibrium analysis model for analyzing direct and indirect socio-economic and environmental effects of large-scale hydropower projects
Bibliographic record
Abstract
The assessment of the socio-economic environment (SEE) impacts of large-scale hydropower projects (LHPs) are both controversial and challenging, particularly due to their multifunctional development, further complicating the evaluation process. Therefore, a comprehensive assessment of SEE impacts of LHPs is essential for the future sustainability and scalability of hydropower. In this study, a factorial hydroengineering general equilibrium analysis model (FGEA) is developed for investigating both direct and indirect SEE effects of LHPs, as well as the effects from a variety of water-related parameters and their interactions. The proposed FGEA was employed to analyze multidimensional SEE effects of the Xiluodu Hydropower Project (XLD) which is the 4th largest LHP in the world. Results indicate that in 2017, the operation of XLD generated 23.92 billion yuan in indirect GDP for YREB through the supply chain, approximately 1.32 times the direct GDP. Among the contributing factors, factor B (electricity generation) accounted for 76.43% of this growth, while factor E (LHP management cost) contributed 23.48%. However, this economic growth resulted in a total of 1.64 million tons (Mt) of CO 2 emissions, comprising 0.28 Mt of direct emissions and 1.36 Mt of indirect emissions through the supply chain. It is expected that the modeling results of FGEA will help support the formulation of desired management policies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".