Navigating trade-offs between dams and river conservation
Bibliographic record
Abstract
Non-technical summary There has been a long history of conflicts, studies, and debate over how to both protect rivers and develop them sustainably. With a pause in new developments caused by the global pandemic, anticipated further implementation of the Paris Agreement and high-level global climate and biodiversity meetings in 2021, now is an opportune moment to consider the current trajectory of development and policy options for reconciling dams with freshwater system health. Technical summary We calculate potential loss of free-flowing rivers (FFRs) if proposed hydropower projects are built globally. Over 260,000 km of rivers, including Amazon, Congo, Irrawaddy, and Salween mainstem rivers, would lose free-flowing status if all dams were built. We propose a set of tested and proven solutions to navigate trade-offs associated with river conservation and dam development. These solution pathways are framed within the mitigation hierarchy and include (1) avoidance through either formal river protection or through exploration of alternative development options; (2) minimization of impacts through strategic or system-scale planning or re-regulation of downstream flows; (3) restoration of rivers through dam removal; and (4) mitigation of dam impacts through biodiversity offsets that include restoration and protection of FFRs. A series of examples illustrate how avoiding or reducing impacts on rivers is possible – particularly when implemented at a system scale – and can be achieved while maintaining or expanding benefits for climate resilience, water, food, and energy security. Social media summary Policy solutions and development pathways exist to navigate trade-offs to meet climate resilience, water, food, and energy security goals while safeguarding FFRs.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".