Assessing the Validity of a Green Infrastructure Conceptual Framework for Urban Transport Planning: Insights for Building Resilient Cities
Bibliographic record
Abstract
Green Infrastructure (GI) has increasingly been recognised as a crucial strategy for enhancing urban resilience, particularly in urban transportation systems facing the challenges of climate change. Although several conceptual frameworks for GI planning have been proposed, empirical studies examining their application in urban transport planning contexts remain limited. This study aims to validate a recently developed GI conceptual framework by evaluating its applicability in urban transportation systems. A structured questionnaire was administered to 94 participants in Aotearoa New Zealand comprising urban planners, engineers, architects, policymakers, and academics involved in transportation and sustainability planning with special focus on GI. The framework was assessed across key dimensions including the perceived benefits of GI in transportation, stakeholder and collaborative practices barriers to implementation, and indicators of perceived effectiveness. The results confirm that the stakeholders’ perceptions of GI are significantly aligned with the dimensions of the conceptual framework, reinforcing its validity in assessing GI effectiveness. Key findings highlight a disconnect between stakeholders’ general familiarity with GI and their understanding of its multifunctional benefits beyond stormwater management. Also, the prevalence of multidisciplinary collaboration suggests that additional interdisciplinary and transdisciplinary approaches are required for more holistic GI planning. This study recommends that the conceptual framework be considered for city adaptation to GI integration, and to do so effectively, these knowledge and cooperation gaps must be addressed
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".