Development of a factorial hydroengineering equilibrium analysis model for analyzing direct and indirect socio-economic and environmental effects of large-scale hydropower projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it