A conceptual analytical framework for green infrastructure (GI) towards resilience building in urban contexts: A Stakeholders' collaboration perspective
Bibliographic record
Abstract
The conceptualization of green infrastructure (GI) has evolved from a limited focus on individual green spaces to a more systemic approach that considers the interconnectedness of green spaces to offer a long-lasting, all-natural remedy for climate and urban challenges. This evolution has led to greater recognition of the importance of integrating GI into urban planning, significantly shaped by stakeholder participation . This paper presents a conceptual framework aimed at supporting GI planning and implementation, with a strong emphasis on stakeholders' collaboration. The framework is built upon the evolving understanding of GI, influenced by societal values, scientific advancements, collaborations, and policy frameworks. Through a systematic review of literature from 2013 to 2023, the paper examines the changing conceptions of GI, highlighting synergies and trade-offs in its application. Findings reveal that successful GI integration in urban planning requires a collaborative approach involving government, the private sector , and community groups. However, leading such collaboration effectively remains a challenge. The final conceptual framework presented in this paper outlines four stages of collaboration: the silo approach, multidisciplinary, interdisciplinary, and transdisciplinary models. By adopting a collaborative, evolving approach to GI implementation, urban areas can fully realize the potential benefits for both people and the environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".