Potential water‐related risks to the electric power industry associated with changing surface water conditions
Bibliographic record
Abstract
Abstract This study identifies and summarizes potential risks to operations, regulatory compliance, supply chains, and infrastructure of the electric power industry from changing surface water conditions resulting from global climate change. The results help inform companies/utilities seeking to incorporate climate change risk in their planning and decision‐making processes by ranking risk severity and likelihood of occurrence on both a regional basis and by risk receptor. The assessment includes identification of potential risks to: (1) thermal generating, (2) hydroelectric, (3) land‐based renewable generating, and (4) transmission and distribution assets. These risks may result from such projected changes as reduced water availability (e.g., for hydroelectric or once‐through cooling), increased water temperatures (e.g., decrease in cooling efficiency, inability to meet discharge permit conditions), increased flood severity (e.g., increased streambank erosion and/or damage to river‐adjacent infrastructure), and decreased water quality (e.g., from increased transport of sediment and dissolved solids). The potential risks identified from this qualitative risk‐assessment are documented in a graphical format depicting both severity and likelihood. This approach allows for comparison of risks across a portfolio and for future prioritization of adaptation strategies. A total of 32 risks were identified in the study, including nine risks to infrastructure, six risks to operations, four risks to supply chain, and 13 environmental/regulatory risks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".