Climate Risk Assessment and Adaptation Planning

Module: Sustainable Tech | Difficulty: Premium

Expected Annual Damage

Vulnerability Index

Comparison

Python Implementation

import numpy as np
from scipy.stats import genextreme

class ClimateRiskAssessor:
    def extreme_value_analysis(self, data, periods):
        shape, loc, scale = genextreme.fit(data)
        return dict(zip(periods, genextreme.isf(1 / np.array(periods), shape, loc, scale)))

    def vulnerability_index(self, indicators, weights):
        norm = (indicators - indicators.min()) / (indicators.max() - indicators.min() + 1e-10)
        return np.sum(norm * weights)

    def adaptation_benefit_cost(self, cost, avoided_loss, years=30, r=0.03):
        npv = sum(avoided_loss[t] / (1 + r)**t for t in range(years))
        return npv / cost

Research Insight: ML models combining climate projections with socioeconomic data reduce adaptation risk by 40-60% per dollar.