A Model for Institutional Phosphorus Damage Costs: A Case Study at the University of Virginia
Bibliographic record
Abstract
Calculating environmental footprints for carbon, nitrogen, and phosphorus can help individuals, communities, and institutions monitor their environmental impacts and inform efforts to improve their sustainability efforts. In addition to tracking emissions, footprints can help account for economic “damage costs” associated with resource use. Though damage cost estimates have been developed for carbon and nitrogen, there is little precedent for damage costs relating to phosphorus. We demonstrate a method to estimate institutional phosphorus damage costs using a case study of the University of Virginia, a large public university in the United States. We estimate damage costs related to eutrophication, distinguishing between water quality impacts in freshwater and coastal systems as well as between diffuse agricultural and wastewater point sources. We found that activities at the university incur approximately $76 500 per year of downstream costs to society due to its phosphorus footprint ($2.16/kg of phosphorus released to the environment). Almost half (48%) of those damages are incurred in the Chesapeake Bay while 7% are incurred in the Gulf of Mexico. The remainder (44%) of costs are incurred in freshwater systems across both watersheds. Our findings are likely an underestimate of true societal impacts, as impacts such as loss of ecosystem services are difficult to value. However, we emphasize that this method is transferable and can be used by other institutions to calculate their own phosphorus damage costs, providing a more holistic accounting of downstream environmental impacts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".