Unveiling long-term indirect socio-economic and environmental effects of large-scale hydropower project
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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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) | 0.000 | 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) | 0.003 | 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".