Cost-Benefit Analysis of the Clean-Up of Hazardous Waste Sites
Bibliographic record
Abstract
Hazardous waste, defined as any material that poses a substantial threat to human health, can potentially contaminate all the environmental media: atmosphere, groundwater, surface waters and soil, and through these media can be harmful or even fatal for human health. The prolonged exposure to toxic pollutants such as benzene derivatives, dioxins and trichlorophenol has been associated with acute health effects such as narcosis, skin irritation, or respiratory diseases such as asthma and allergies. Hazardous waste exposure has also been associated with chronic health effects such as leukaemia, liver tumour, lymphomas and, in the case of methylene chloride, premature mortality. Since the case of Love Canal, New York State, in 1980 an increasing number of cases of hazardous waste mismanagement have been reported. Studies suggest that children are the most vulnerable victims of toxic pollutants. Exposure to compounds increases the likelihood of miscarriage and birth defects. In the Love Canal, for instance, birth defects were found to be twice as likely to occur among those living near the dump site (Goldman et al.1985). In Canada, a large study conducted by Goldenberg et al. (1999), suggested that individuals living close to landfill sites have an increased risk of liver, kidney, pancreas cancers and non-Hodgkin's lymphomas. Another study conducted by Pukkala (2001) in Finland found that the prevalence of asthma was significantly higher in individuals living near landfill sites. Lack of resources requires policy makers to prioritise competing alternatives. Despite the potential gains for both environmental and human health, it remains uncertain whether the benefits of interventions to clean-up hazardous sites would outweigh the costs. The analytical tool of cost-benefit analysis provides a powerful and transparent method to evaluate and select risk management strategies. Nevertheless, cost-benefit analysis has rarely been used to assess hazardous waste site cleanup interventions. There are several reasons for this: the effects of hazardous waste exposure are often ignored; there are difficulties indentifying the causal link between waste exposure and health effects; and estimating the value of the potential impacts resulting from cleanup interventions. Costs of cleanup interventions are also subject to great uncertainty because it is difficult to quantify them a priori, especially where more than one media has been affected by hazardous pollutants. The aim of this chapter is to provide an overview of the major steps necessary to conduct a cost-benefit analysis of cleanup interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".