## Abstract

This research extends prior Monte Carlo simulation work on global datacenter location optimization by introducing climate-dynamic TCO modeling and extreme weather impact quantification. Analyzing 10 strategic global markets across 100,000+ simulation iterations, we demonstrate that climate change amplifies existing location advantages—widening the TCO gap between Nordic and traditional hub locations from approximately 74% (static baseline) to over 80% under RCP 8.5 by 2050. We integrate IT hardware costs (4 tiers including GPU and ARM architectures) to show that hardware energy is the dominant channel through which climate change affects datacenter economics, with a 4.6x climate exposure ratio between Boden, Sweden and Atlanta, Georgia.

## Key Findings

1

### Climate Amplifies Location Inequality

The facility-only climate gap grows 4.9x from 10-year to 25-year horizons. Atlanta faces +$37.1M in climate premium vs +$16.6M for Boden over 25 years.

2

### Hardware Energy is the Real Story

For a 100MW AI facility (500 racks), hardware energy costs range from $203M (Boden) to $1,074M (Atlanta) under RCP 8.5—a 5.3x spread driven by PUE degradation.

3

### Nordic Resilience Confirmed

Boden, Sweden maintains cost leadership under all climate scenarios with only +$47M climate premium over 25 years, compared to +$179M at Atlanta.

4

### ARM Efficiency Offsets Climate Risk

ARM-based hardware (Apple silicon) reduces hardware energy by 85%, saving $131–491M per facility. Partially compensates for climate-vulnerable locations.

5

### Real Disaster Data Validates Risk

Georgia averages 149 FEMA disaster declarations/year vs Wyoming's 9—a 16x disparity. Survival model concordance: 0.94 using real data.

6

### BNN Validates Physics Model

Bayesian Neural Network uncertainty bands (2σ) encompass physics predictions across all locations, with 94–100% calibration coverage.

## Combined 25-Year TCO: Facility + AI Hardware

100MW reference facility, 500 Standard AI racks (H100-based), climate-adjusted

Location | Tier | RCP 2.6 | RCP 4.5 | RCP 8.5 | Climate Gap
--- | --- | --- | --- | --- | ---
Boden, Sweden | Nordic | $14,300M | $14,314M | $14,347M | +$47M
Iceland (Reykjanes) | Nordic | $14,336M | $14,355M | $14,388M | +$52M
Kristiansand, Norway | Nordic | $14,328M | $14,347M | $14,381M | +$53M
Evanston, Wyoming | US Secondary | $15,200M | $15,224M | $15,262M | +$61M
Sines, Portugal | Emerging | $14,831M | $14,873M | $14,935M | +$104M
Florence, SC | US Secondary | $15,487M | $15,534M | $15,602M | +$115M
Johor, Malaysia | Emerging | $16,037M | $16,105M | $16,206M | +$170M
Atlanta, Georgia | Traditional | $16,220M | $16,292M | $16,398M | +$179M

## Data Sources

### EIA API

US retail electricity prices and generation mix by state (2015–2025). 55 price records + 4.5MB generation data for 5 US locations.

### NOAA CDO API

Historical daily temperature, precipitation, and wind data. 46,044 records across 9 weather stations near datacenter locations.

### FEMA OpenData

69,769 disaster declarations (1953–2026). Georgia: 149/yr vs Wyoming: 9/yr—real extreme event frequency for insurance modeling.

### DCCore Paper

Monte Carlo simulation parameters for 10 global markets. Facility TCO, CapEx, PUE, power rates, and risk metrics from 100,000 iterations.

## Methodology

### Dynamic Monte Carlo

Physics-based shift functions replace static distributions with year-by-year climate-adjusted parameters. PUE degrades at 0.008–0.012 per °C with humidity compounding.

`PUE(t) = PUE_base + ΔT × β × (1 + H_f) × (1 + 0.05ΔT)`

### Survival Analysis

Weibull AFT model predicts time-to-outage from climate covariates and real FEMA disaster data. Concordance index: 0.94.

`log T = βTx + σε, ε ~ Gumbel`

### Insurance Regression

Quantile Gradient Boosted regression with nonlinear event scaling. Premium ~ event_ratio1.5. RMSE: $0.71M.

`σ_ins(t) = (E(t) / E_0)1.5`

### Bayesian Validation

MC Dropout BNN (12K params) independently validates physics shifts. 94–100% of true values within 2σ uncertainty bands.

`p(y|x) ≈ (1/N) ∑ f(x; θ_n)`

## Hardware Tier Analysis

Climate impact multiplied across IT equipment at 100MW scale

### Standard AI Rack

H100 GPU $3.5M/rack 100 kW 4yr refresh

Climate Δ (25yr): Boden +$99M vs Atlanta +$456M

### ARM Efficiency

Apple Silicon $800K/rack 15 kW 5yr refresh

85% energy reduction. Saves $131–491M per facility.

### Hybrid GPU+CPU

H100 PCIe + CPU $2.0M/rack 60 kW 4yr refresh

Balanced training/inference. Mid-range climate exposure.

### Traditional PC

Enterprise CPU $500K/rack 20 kW 5yr refresh

Lowest CapEx, limited AI throughput. Edge/inference only.

## Resources

### GitHub Repository

Full source code, pipelines, configs, and LaTeX paper. MIT License.

### Kaggle Dataset

6 executed notebooks, processed data, FEMA records, hardware TCO results.

### Paper (LaTeX)

Full manuscript with methodology, results tables, and bibliography.

### Notebooks

6 Jupyter notebooks covering EDA, dynamic TCO, survival analysis, BNN, and synthesis.