Brownfields over the years: from definition to sustainable reuse
Bibliographic record
Abstract
Urban development is often confronted with a lack of available space. Brownfield sites offer great potential for sustainable urban planning because of their often-central location and the benefits associated with their redevelopment. Although the interest in brownfield regeneration has led to a lot of research on this topic, there is not yet a comprehensive review of brownfield research. In this review, we analyze the research on brownfields and provide a picture of the published case studies. We focus primarily on brownfields research in the United States, Europe, and People’s Republic of China. This exploratory research is based on an analysis of the published scientific literature available in the Web of Science database. Initially used in North America, the term “brownfield” quickly became popular in the rest of the world, particularly in Europe. However, with the exception of the United States, there is no specific legislation for these sites; their protection is often based indirectly on directives or laws related to soil pollution, biodiversity, or the environment. The perception of the potential use of brownfield sites varies considerably from one part of the world to another, and international collaborations among researchers from different parts of the world remain limited. Most of the described reuses of brownfield sites are forms of soft reuse (53%), and the type of reuse of a site depends more on the surrounding urbanization levels and the specific region than on the past use of a brownfield site. Despite the continued interest in the rehabilitation of these sites, especially due to the increasing demand for nature in the city following the COVID-19 crisis, many questions regarding the future of brownfield sites remain unanswered. The factors influencing their successful redevelopment are unclear, and further research is urgently needed to ensure a truly sustainable re-use of these sites.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".