Enhancing urban sustainability: An emergy-based framework to support green infrastructure planning
Bibliographic record
Abstract
Green infrastructure (GI) is a vital strategy for climate change adaptation and urban sustainability, yet integrating its multifunctionality remains significantly challenging. This study develops a sustainability-oriented optimization framework for GI systems, incorporating emergy analysis, multi-objective optimization, and regional climate models. It highlights a sound cross-domain assessment and addresses real-world challenges such as limited data availability and tight timelines. This framework is applied to two cases across different countries and reveals that GI project sustainability varies by site-specific factors and national contexts. Common findings underscore the high priority of green roofs, which, combined with rain gardens or sunken greens, are eco-friendly, cost-effective, and multifunctional. Permeable pavements, despite previous economic advantages, show lower sustainability. Sensitivity analysis reveals the importance of parameters related to green roofs and the emergy money ratio. This research provides robust decision support for stakeholders and advances GI planning, offering novel insights into sustainable urban practices under climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".