Unveiling long-term indirect socio-economic and environmental effects of large-scale hydropower project
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
Large hydropower projects (LHPs) can generate significant direct socio-economic and environmental (SEE) impacts, which may radiate and accumulate gradually through the supply/consumption chains over different development periods. Therefore, a dynamic hydroengineering equilibrium analysis (DHEA) model is developed in this study to comprehensively quantify the cumulative indirect SEE impacts of LHPs during their construction and long-term operation period. The proposed DHEA model will be applied initially to the Baihetan hydropower project (BHT), the second-largest LHP in the world, which recently commenced operation. The results indicate that the construction of BHT generates approximately 0.81 billion yuan in GDP annually for the YREB region through supply/consumption chains. Starting in 2023, the operation of BHT will have a long-term positive indirect impact on the YREB region, with significant cumulative effects over time. It is expected that by 2033, the cumulative contribution of BHT's construction and operation to the YREB's GDP will exceed the initial government investment in BHT (220 billion yuan). Additionally, during the operation periods, BHT will significantly reduce the YREB's energy input/consumption and trade/local embedded carbon emissions through supply/consumption chains. The developed DHEA approach is expected to highlight the multi-dimensional, multi-phase, and multi-sectoral indirect impacts of LHPs and contribute to evaluating the SEE effects of other LHPs worldwide.
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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 | 0.002 | 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 | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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