Building a Scalable Climate Risk Framework from Template to Archetype

I chose to work with this client because I align strongly with its mission to make sustainability meaningful through high-quality, data-driven insights that enable action. I believe technology plays a critical role in strengthening sustainability by translating complex climate data into decision-relevant intelligence.

As I aspire to become a sustainability executive who integrates climate risk into corporate strategy, understanding how physical risk translates into asset-level economic impact is essential. Climate change increasingly manifests through extreme weather events that disrupt infrastructure, supply chains, and economic systems (IPCC, 2023). Translating these physical hazards into structured impact assessments is therefore central to resilient decision-making.

Our project focuses on strengthening the client’s climate scenario analysis by translating building-level physical hazard metrics into defensible, quantitative impacts that vary across asset archetypes such as offices, warehouses, and data centres.

We structured the engagement into three phases.

Phase 1: Build Hazard Template.

We are designing a structured hazard-to-impact template applicable across multiple hazard types. This involves defining hazard metrics, identifying evidence-based thresholds, establishing median loss assumptions, and standardizing impact outputs such as asset value degradation and service disruption. The goal is methodological discipline before applying the framework to a specific asset type.

Phase 2: Pilot Vulnerability and Exposure Profile.

After validating the template, we apply it end-to-end to one selected pilot archetype, currently an office asset. We identify key asset attributes, define failure modes, and build vulnerability functions that map hazard intensity to quantified impacts.

Phase 3: Scale to Additional Archetypes.

Finally, we convert insights from the pilot into a repeatable method and scale the framework to additional archetypes to ensure consistency and comparability.

This structured approach aligns with my long-term goal of designing scalable climate risk frameworks that move beyond compliance and inform strategic decision-making.

My research responsibilities include coastal flooding, extreme heat, wildfire, and extreme cold. We rely on historical loss databases and benchmarking resources to calibrate hazard-to-impact relationships.

During the first weeks, we are empirically testing the hazard template to identify inconsistencies across datasets and refine median loss assumptions. This iterative validation process ensures the framework is defensible before scaling.

One key challenge is balancing global generalization with asset specificity. A geographically agnostic template improves scalability, but overly broad assumptions may reduce asset-level relevance.

Another challenge is data variability. Hazard datasets use different metrics and thresholds, requiring careful justification when translating exposure into quantified economic impact. These trade-offs will be discussed with the client to ensure practical applicability.

My teammates bring diverse academic and professional backgrounds, strengthening our analytical discussions. Each member is responsible for specific hazards, and we maintain a shared appendix documenting assumptions and sources.

Open debate, structured task division, and working backward from weekly milestones have improved accountability and coherence in our modeling approach.

We engage with the client through bi-weekly calls and regular updates. Our initial meeting focused on aligning scope, timeline, and deliverables before advancing into deeper research. Clear communication at the outset helped prevent misalignment and establish realistic expectations.

I have learned the importance of:

• Establishing methodological clarity before scaling
• Testing assumptions early
• Maintaining structured milestone planning
• Listening carefully to teammates and clients
• Remaining flexible when data constraints arise

Climate risk modeling requires analytical rigor, collaboration, and disciplined execution.

Our next step is to finalize the pilot vulnerability profile before the midterm presentation. After validation, we will refine the repeatable framework and begin scaling to additional archetypes.