From Brown to bright - the development of renewable energy on marginalized land
Bibliographic record
Abstract
Most post-industrialized countries are experiencing marked changes in the reutilization of land and the generation of electricity. On the one hand, the redevelopment of previously developed and potentially contaminated lands, so-called brownfields, has led to urban revitalization, rural wasteland recycling, as well as an increased protection of greenfields. On the other hand, the rapid growth of renewable energy installations has contributed to a more diverse, more distributed, and cleaner energy mix, albeit often on greenfield land. While brownfield redevelopment and ‘conventional’ green energy address land reuse and sustainable energy goals independently, brightfields could kill two birds with one stone. This nascent concept has thus far produced a scant amount of literature, regarding site typology, policy support and its barriers. This research addresses these significant gaps in the literature. The typology or former land use of existing brightfields is examined in an international context, finding that Canada has so far few ‘true’ brightfields, while the United States and Germany can boast hundreds of projects. The brightfields in the United States seem to have a ‘type, as the majority are located on landfills, while ex-military sites are the dominant former land use of so-called Konversionsflächen in Germany. The examination of technical, regulatory, financial and social barriers to the implementation of brightfields constitutes a large contribution to the literature. It provides a useful insight into the challenges to develop brightfields and shows that its barriers are not simply the sum of brownfields and renewable energy barriers. Lastly, this research finds that different types of brownfield owners may have different agendas and site selection priorities, which are not reflected in current site selection tools and a more context-dependent site identification tool is created using Analytical Hierarchy Process. This dissertation presents original research that contributes to the understanding of brightfields and its literature. It analyses brightfield typology and support in an international environment, their advantages and disadvantages, while also providing a practical tool for brownfield owners to identify and compare candidate sites. By doing so, this research provides a significant contribution to this emerging field of study.
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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 | 0.002 | 0.002 |
| 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 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".