The influence of water quality on the demand for residential development around Lake Erie
Bibliographic record
Abstract
In early 1970s, it was said that Lake Erie was dead.In 1950s and 1970s, the water of the Lake was pea-green colored due to excessive phosphorous from sewage and runoffs from farmlands and homeowners.There were many closed beaches and fish from the Lake was not edible.However, water quality has improved dramatically since the Clean Water Act of 1972.The pace of residential and commercial development around the shoreline of Lake Erie increased considerably following substantial improvements in the Lake's water quality and clarity.A double-edged sword exists since increases in water quality are followed by increases in residential and lake-related development, which in turn can degrade the lake and the amenities it provides.In fact, although the phosphorous level declined in 1980s, we are observing an increasing trend starting from 1990s and the trend continues until today.As for water clarity, although its level hit the peak in 1995, we observe the decreasing trend afterwards.In this study, we focus on the effects of water quality on housing values to evaluate water quality-housing value as the relationship on the one side of the double-edged sword.Both the first and the second stage of hedonic price analysis are conducted with identified housing submarkets by using Hierarchical Clustering with quantized similarity measures in the region including Erie, Lorain, Ottawa and Sandusky Counties located Since the Clean Water Act of 1972, point source pollution has been controlled well.Nearly 100 percent of all industrial plants use control measures to reduce their toxic discharge, and the number of sewage treatment plants has doubled.Water quality of Lake Erie has improved since then.In 1983, phosphorous level in the Lake met its standard.The pace of residential and commercial development around the shoreline of Lake Erie increased considerably following substantial improvements in the lake's water quality and clarity in the 1970the 's and 1980the 's.Between 1982the and 1997, the amount of urban land use in the eight Ohio counties bordering Lake Erie increased 24.4%, an increase of 112,500 acres (USDA, National Resources Inventory).A significant portion of this development appears tied to Lake Erie.For example, the amount of urban development in Ottawa County, a county that contains a number of lake amenities and recreational sites, increased 53% during this fifteen year time period.The improvement in water quality enhanced the urban development along lakeshore counties.On the other hand, the impacts of urbanization and development in coastal areas threaten the very resources that make these areas attractive as places to live.The cumulative effects of non-point source pollution on coastal waters and aquatic life is a critical and increasing concern both nationally and in the Lake Erie watershed.Like many streams and rivers in the U.S., sedimentation and hydro-modification are cited as primary non-point sources affecting Lake Erie tributaries (Ohio EPA, 1996).Of particular concern is urban storm water pollution, which is fast becoming the most serious type of water pollution affecting Ohio's streams and near shore areas.This
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".