A Climate-Resilient Urban Green-blue Infrastructure Framework Model

Thesis Abstract

Urban centers increasingly rely on integrated green-blue infrastructure (GBI) to buffer climate risks, yet existing frameworks often treat green and blue components in silos, limiting holistic resilience outcomes. This study addresses the need for a climate-resilient GBI framework that synthesizes ecological, hydrological, and social dimensions into a decision-support model for urban planners and policymakers. The aim is to develop and validate a framework model that operationalizes climate-resilient GBI across urban scales, linking biophysical performance with governance and community engagement. Specific objectives are (1) to review conceptual foundations and theoretical perspectives on urban GBI, resilience, and ecosystem services; (2) to identify critical indicators and thresholds for climate-resilient performance in distinct urban typologies; (3) to construct a framework model integrating ecological functionality, hydrological dynamics, climate risk indicators, and governance mechanisms; (4) to test the model using empirical data from four representative metropolitan districts; and (5) to articulate practical guidance for policy and design implementation. The study adopts a mixed-methods, theory-driven design anchored in the Adaptive Urban Systems and Extended Ecosystem Services theories. It employs a sequential explanatory approach, with quantitative data informing subsequent qualitative interpretation. The population comprises urban neighborhoods within four metropolitan districts characterized by varying exposure to heat waves, flood risk, and water scarcity. A stratified random sample targets 400 household surveys, 60 stakeholder interviews, and 24 site assessments of GBI installations. Data collection instruments include structured questionnaires measuring perceived climate risk, ecosystem service valuation, and accessibility to green-blue features; semi-structured interview protocols with city planners, landscape architects, and community leaders; and field checklists for GBI condition, species diversity, water balance, and connectivity metrics. Instrument validity is established through expert panel validation and pilot testing (n=40) with reliability confirmed via Cronbach’s alpha (.82 for risk perception, .89 for governance clarity). Spatial data are compiled from high-resolution GIS layers (land-use, imperviousness, green canopy, proximity to waterways) and hydrological models to estimate runoff attenuation and peak discharge reduction potential. Analytical techniques include multivariate regression and structural equation modeling (SEM) to quantify relationships among ecological performance indicators, hydrological outcomes, climate exposure, and governance variables. The framework’s predictive capacity is further evaluated using scenario analysis with Monte Carlo simulations to assess resilience under projected climate trajectories (RCP 4.5 and RCP 8.5). Qualitative data from interviews and open-ended survey responses are analyzed through thematic analysis, triangulated with site assessment findings to refine the framework components and their interactions. A cross-case synthesis identifies common design principles and context-specific adaptations. The study integrates participatory mapping with deliberative stakeholder workshops to validate framework relevance and feasibility. Expected findings include (i) a validated set of composite indicators for climate-resilient GBI performance, (ii) empirical quantification of the co-benefits of GBI configurations in terms of heat mitigation, flood attenuation, and water security, (iii) evidence of governance and community engagement as critical mediators of GBI effectiveness, and (iv) a scalable framework model with modules for ecological design, hydrological performance, climate risk assessment, and governance mechanisms. The model will demonstrate that integrated blue infrastructure, when designed with ecologically informed species mixes, water-sensitive urban design, and inclusive governance, yields higher resilience scores under multiple climate scenarios. Contributions to knowledge include (a) a theory-informed, operational GBI framework that bridges ecological performance with social and governance dimensions, (b) methodological integration of SEM and scenario-based simulations for urban resilience assessments, and (c) actionable guidance for metropolitan planning that aligns engineering feasibility with community-specific climate adaptation needs. The study concludes that climate-resilient GBI requires co-creative planning processes, transparent governance structures, and performance-based design targets that are adaptable across urban contexts. Recommendations emphasize embedding the framework within city-level adaptation plans, adopting standardized reporting of resilience indicators, and fostering ongoing monitoring through community-based sensing networks and periodic stakeholder reviews.