The Water-Energy-Environment Nexus in the Great Lakes Region: The Case for Integrated Resource Planning
Bibliographic record
Abstract
Water is a critical element of electric power production in the U.S., particularly in the Great Lakes Basin region. Thermoelectric power generation accounts for the majority of all water withdrawals in the Basin, in large part due to the comparatively heavy concentrations of coal and nuclear power generation that utilize open-loop cooling. This paper explores how different energy generation portfolios could affect the water resources of the Great Lakes Basin. The suite of power generation scenarios analyzed reflects a range of potential outcomes resulting from the implementation of key national and regional energy and environmental policies for the electric power industry. These policies include U.S. EPA’s pending power plant cooling water intake standards, state renewable energy portfolio standards, possible climate change legislation, and the 2005 Great Lakes regional water resource agreement (Great Lakes and St. Lawrence River Basin Water Resources Compact of 2005; Public Law 110–342). Five scenarios were analyzed, resulting in different levels and intensities of total water use (withdrawal and consumption) in hydrologically-sensitive watersheds. These results confirm the close relationship between water and energy in the Great Lakes, and point to the need to take into account water resource impacts in designing future energy and environmental policies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".