Limitations, Assumptions, and Data Transparency
At AlphaGeo, we aim to provide robust, transparent, and financially relevant insights into climate risk. While our methodology is grounded in the best available science, financial modeling, and engineering evidence, it is important for users to understand the inherent limitations, assumptions, and uncertainties in our approach. This ensures results are interpreted with appropriate context and caution.
1. Climate Data and Projections
Historical Data Gaps: Observational datasets vary in coverage and resolution across regions. In some areas, climate baselines are interpolated from limited data points.
Uncertainty in Projections: Our models use IPCC-endorsed scenarios (SSP245, SSP370, SSP585) to explore a range of plausible climate futures. These do not represent precise predictions but rather structured scenarios.
Downscaling Methods: We translate global or continental-scale climate model outputs into local impacts using statistical downscaling and proxy measures (e.g., degree days, damage ratios). These methods balance accuracy and practicality but inevitably smooth over local variability.
2. Model Scope and Use
Asset Classes: Our methodology covers a broad set of asset classes (residential, commercial, data centers, infrastructure). It does not account for unique bespoke designs or adaptive management strategies at individual sites.
Temporal Scope: Most projections are benchmarked against 2025–2050 horizons. Beyond this, uncertainty compounds rapidly, and results should be viewed as indicative rather than predictive.
Systemic Interactions: Our model assesses direct asset-level impacts. Wider economic feedback loops (e.g., supply chain disruptions, policy shifts, insurance retreat at systemic scale) are not explicitly modeled.
3. Methodological Assumptions
Two-Step Framework: Our results combine (1) location-based climate exposure metrics with (2) asset-specific sensitivity adjustments. This simplifies complexity but may underrepresent unique micro-level asset conditions.
Sensitivity Coefficients: Asset class vulnerabilities are represented by multipliers on a 1–5 scale. This approach ensures transparency and scalability but does not capture every engineering nuance.
Aggregation Choice: Metrics (e.g., Maintenance Cost Increase) are calculated as blended scores before sensitivity adjustment, rather than adjusting each underlying climate driver separately. This avoids false precision but may mask cases where opposing sensitivities exist.
4. Financial Modeling Assumptions
Insurance Premiums: Future premiums are projected using long-term mean damage ratios with a fixed average loss ratio (70%) to avoid short-term volatility. Real-world insurance markets may deviate significantly from this assumption.
Discount Rates: Climate discount rates are capped (e.g., 0.75% to 2.0% depending on time horizon) to reflect tail risks. These assumptions are consistent with current financial practice but may shift as markets evolve.
CapEx for Retrofits: Additional capital expenditures are estimated on an exponential scale (up to 10% of annual income per hazard). Actual retrofit costs may vary widely depending on design choices, regulatory changes, and supply chain dynamics.
Operational Impacts: Metrics such as downtime or workforce productivity loss are simplified as percentage changes in annual outputs. Real-world impacts may be non-linear and event-specific.
5. Uncertainty Beyond the Model
Extreme Events: While acute hazard metrics are included, unprecedented “black swan” events cannot be reliably quantified.
Policy & Regulation: Future changes in building codes, carbon pricing, or adaptation subsidies may materially alter cost structures.
Market Dynamics: Insurance, financing, and valuation markets are dynamic. Our assumptions represent today’s best estimates but may diverge from future market behavior.
Ethical and Transparency Commitment
AlphaGeo subscribes to the principle of “decision-useful transparency”: providing clear, interpretable, and bounded insights rather than overstated precision. We recommend using our analytics as one input among others—complemented by local expertise, engineering assessments, and market intelligence—for robust decision-making.
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