Negotiation characteristics in brownfield redevelopment projects
Bibliographic record
Abstract
Brownfield areas are contaminated lands that lie unused and unproductive. Because of the serious economic, social, political, and environmental damages that brownfield problems can cause to society, governments are focusing their attentions on the redevelopment of these contaminated sites. However, the excess costs of reconstruction projects over their benefits often stall the initiation of projects. Moreover, brownfield projects involve several uncertainties that seriously contribute to the challenges of brownfield redevelopment, such as uncertainty about the extent of contamination and the uncertainty in cleanup costs. To overcome these challenges, negotiation among the involved parties (government, owner, purchaser, and their stakeholders) is one of the most efficient tactics to arrive at a mutually acceptable solution and, as such, saves an enormous amount of time, cost, and resources. This paper aims at discussing the negotiation process associated with remediation and redevelopment of brownfield projects. Timing, type of contaminate, extent of contaminate, zoning, offsite impact, and the number of players are some of the most important factors affecting the study of brownfield negotiation and their ultimate redevelopment. The needs and interests of the various parties involved in a brownfield negotiation process are discussed. Initial steps towards the development of a decision support system for resolving brownfield conflicts through negotiation are then highlighted.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".