Bridging Adaptation Theory and Measurement: A Multi-Scalar KPI Framework for Urban Resilience

Measuring progress in climate adaptation remains a critical challenge, particularly in the Global South, where ecological degradation, climate risks, and governance complexities are high. While existing adaptation metrics often focus either on governance processes or implemented actions, fewer approaches provide spatially explicit, outcome-oriented tools capable of informing targeted urban policies. This research proposes an integrated framework that translates biophysical indicators of urban nature into operational Key Performance Indicators (KPIs) for adaptation monitoring, evaluation, and learning (MEL), using a GIS-based spatial analysis. The study develops a structured indicator system encompassing ecological, environmental, and socio-governance dimensions of urban nature, grounded in urban resilience, ecosystem services, and socio-ecological systems literature. Indicators are prioritised through an interdisciplinary expert elicitation process, generating weighted KPIs that reflect their relative contribution to adaptation-relevant outcomes such as heat mitigation, flood regulation, ecological connectivity, and environmental quality. The framework further aligns the indicators with a MEL logic by distinguishing between process-oriented KPIs (e.g., governance mechanisms, land-use controls), output-oriented KPIs (e.g., green–blue infrastructure coverage), and outcome-oriented KPIs (e.g., reduced exposure to urban heat and flooding, improved ecological functioning). By integrating prioritised biophysical indicators, spatial analytics, and MEL-oriented KPIs, the proposed approach advances a practical and scalable method for adaptation measurement. It contributes toward more robust, transparent, and policy-relevant urban adaptation metrics, with applicability across diverse socio-ecological and institutional contexts.

Keywords: KPI framework, Biophysical indicators, Climate adaptation, Measurable KPIs, MEL, Urban resilience