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Uncertainty Analysis

URBADAPT-HEAT addresses uncertainty through a combination of structural and parametric sensitivity analyses, targeting each major source of uncertainty in the modelling chain.

Implementation

Uncertainty quantification runs in NB09 (09_uncertainty_0126_improved_fast.ipynb), a thin wrapper around cityheat.nb09_improved_fast. Relative to the original March-2026 uncertainty workflow (cityheat.nb09_improved, kept untouched), the fast/improved variant adds:

  • spatially explicit daily raster modifiers for tree cooling and AC waste heat, instead of city-level scalars;
  • event-level warning-day logic for EWS (warnings evaluated day-by-day rather than aggregated);
  • EWS CBA uncertainty outputs alongside the impact-uncertainty outputs.

All results are written to outputs_variants/<variant>/<city>/tables/uncertainty_improved_fast/, leaving the original NB09 outputs unchanged.


Uncertainty dimensions

1. Impact function uncertainty

Source: the choice of age-specific heat-mortality dose-response family and its high-temperature extrapolation (see Impact Functions).

Sensitivity dimension Values examined
IF family Masselot (main, city+age-specific) vs. Burke polynomial / power-law (sensitivity)
High-temperature extrapolation Constant tail vs. log-linear tail (Masselot)
Anchor-point level scaling ±50% (Burke family)
Reference temperature T_ref Multiple lower threshold choices (Burke family)
Mortality displacement Range of short-term displacement fractions (gross → net conversion)

2. Climate uncertainty

Propagated through the low, central, and high warming-delta bands from the hazard module: - Low: P25 of GCM ensemble - Central: P50 of GCM ensemble - High: P75 of GCM ensemble

Each band generates a distinct set of annual T2M fields and corresponding mortality outcomes.

3. Parametric uncertainty

One-at-a-time sensitivity runs covering: - AC efficacy ranges (ε_a conservative / central / optimistic) - Vegetation cooling coefficients - Tree maturity timelines - EWS efficacy - Discount rate assumptions

4. EWS cost uncertainty

  • Choice of cost model (per-capita vs. per-day formulation)
  • Empirical range of EWS efficacy estimates from the literature
  • These can yield materially different BCRs for the same mortality reduction; both are reported as sensitivity bounds.

5. Vulnerability projection uncertainty

  • SSP scenario choice for long-run SVI evolution
  • Diverging social-inequality trajectories generate different distributional profiles of avoided deaths
  • Affects equity assessments and optimal targeting of spatially differentiated policies

6. Spatial uncertainty (vegetation and AC waste heat)

  • Vegetation cooling applied through monthly cell-level cooling maps scaled by policy intensity and maturity (preserves within-city spatial heterogeneity).
  • AC waste heat represented by a city-level intensity term spatially redistributed using the AC coverage pattern and added to each day's hazard field.

Global sensitivity analysis (PAWN)

A Monte Carlo sample is propagated through the full modelling chain and PAWN sensitivity indices are computed for key output variables:

Output variable Dominant sensitivity factors
Annual average impact (AAI, deaths/year) Impact function reference temperature (T_ref) — dominant; then baseline hazard construction mode, vulnerability persistence k, GVI projection scale
25-year EWS avoided deaths EWS efficacy, AC penetration, climate scenario
2050 SVI equity gap Spatial smoothing φ₂₀₅₀, thermal retrofit rate

AAI Monte Carlo summary (illustrative central case)

Statistic Value
Median 0.33 deaths/year
P5 0.01 deaths/year
P95 1.92 deaths/year

Reporting convention

  • All main results are reported for the central climate and demographic scenario.
  • Uncertainty bounds are reported as shaded bands or explicit low/central/high columns in result tables.
  • Sensitivity runs are summarised in standardised diagnostic tables exported alongside main results.
  • Output manifests record every configuration choice for full reproducibility.

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