The costs and risks of Brownfield redevelopment versus Greenfield development : a private sector perspective on the effectiveness of community incentive packages. A case study of Waterloo, Ontario
Bibliographic record
Abstract
Traditionally, there has been minimal interest on behalf of developers, land owners as well as private sector stakeholders to redevelop brownfields (De Sousa, 2000). The fears of real or perceived contamination have made the redevelopment project too expensive and risky to develop profitably. Limited government funding and assistance to the private sector for brownfield redevelopment further complicates brownfield redevelopment. This research investigated Ontario’s Community Improvement Plans with brownfield provisions and how they quantitatively aid investor returns. Hypothetical scenarios for a multifamily residential development were generated for both hypothetical brownfield and greenfield sites where brownfield incentives could be implemented. The pro forma analysis revealed that a full exemption from regional development charges (RDC) had the greatest effect on investor returns (NPV and IRR) followed by the joint TIEG offered in the City of Waterloo. Greenfield development is the most financially feasible option with no added costs or risks from contamination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".