A conceptual framework for ex ante valuation of ecosystem services of brownfield greening from a systematic perspective
Bibliographic record
Abstract
ABSTRACT Introduction: Although Brownfield greening (BG) can be a crucial solution to green space deficiency in dense urban areas, the potential benefits of different BG initiatives have rarely been quantitatively pre-evaluated. Here, the concept and main features of BG and its costs and benefits are firstly depicted. Next, a conceptual framework is presented which combines ecosystem service (ESS) valuation, economic cost-benefit analysis, and spatial pattern analysis. The framework is used to perform ex ante valuation of the ESS value of BG in Xuhui District, Shanghai. Outcomes: The scenario comparison results show that it can spatially reflect a closer-to-reality value of ESS at the urban scale instead of a theoretical value at the site scale. The ultimate values of ESS (After economic and landscape adjustments) in the composite scenario is more than 21% higher than those in the full-eco scenario, whereas the initial values of ESS (before the adjustments) in the former was 14% lower than those in the latter. These results suggest that a considerable part of brownfield greening projects need to be implemented in the more populated and economically vibrant areas instead of solely in the ecological construction zone, thereby correspondingly providing more cultural and regulation services and forming a better urban green space network. Conclusion: The framework provides a simple, science-based, and feasible tool for making BG decisions from a systematic perspective in dense urban areas. It can help meet the need for applying ESS knowledge to BG-based green space planning and policy making in the context of urban sustainable development.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".