Quantifying land-energy interactions of hydropower in the Western United States
Bibliographic record
Abstract
Estimates for the land use of electricity generation technologies rely on limited information, challenging the representativeness of assumptions in energy systems analyses that use tools like life cycle assessment and grid optimization. This study used facility-level geospatial image analysis to quantify the land directly impacted for over 500 hydroelectric power plants in the U.S. Western Interconnection to create a database of the land requirements of hydropower projects, classifying power plants by mode of operation. The area of the plant, reservoir, and conveyance channels was delineated using aerial imagery to determine land-use efficiency (W/m 2 ) and land transformation (m 2 /MWh). Run-of-river facilities without reservoirs have higher land-use efficiencies than storage facilities with reservoirs, with a median operational land-use efficiency of 150 W/m 2 (range 1.3–13,000 W/m 2 ) compared to storage projects with a median of 1.4 W/m 2 (0.010 W/m 2 – 98 W/m 2 ). Run-of-river projects’ median annual land transformation is 0.78 m 2 /MWh (0.0090–88 m 2 /MWh), two orders of magnitude lower than the median for storage projects, 84 m 2 /MWh (1.2–11,000 m 2 /MWh). Reservoirs are documented for 45 % of the run-of-river plants, resulting in median annual land transformation of 15 m 2 /MWh (0.028–6700 m 2 /MWh), one fifth of the median for storage projects. Results confirm the importance of using land sparing opportunities for new projects, such as upgrading hydropower plants and converting non-power dams to reduce or eliminate additional land requirements. The resulting inventory has broad applicability in future research focused on life cycle assessment, energy transitions, power systems planning, and the evaluation of ecological impacts of hydropower.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".