AI for Environmental Policy Modeling and Evaluation

Module: Sustainable Tech | Difficulty: Premium

Benefit-Cost Ratio

Social Cost of Carbon

where is climate damages and is emissions.

Comparison

Python Implementation

import numpy as np

class EnvironmentalPolicyModel:
    def benefit_cost_analysis(self, benefits, costs, discount_rate=0.03, years=30):
        npv_benefits = sum(b / (1 + discount_rate)**t for t, b in enumerate(benefits[:years]))
        npv_costs = sum(c / (1 + discount_rate)**t for t, c in enumerate(costs[:years]))
        return {'BCR': npv_benefits / npv_costs, 'NPV': npv_benefits - npv_costs}

    def policy_simulation(self, baseline, policy_params, n_years=20):
        emissions = np.zeros(n_years)
        emissions[0] = baseline
        reduction_rate = policy_params.get('reduction_rate', 0.03)
        for t in range(1, n_years):
            emissions[t] = emissions[t-1] * (1 - reduction_rate)
        return emissions

    def regulatory_impact(self, compliance_costs, environmental_benefits, distributional_effects):
        total_cost = sum(compliance_costs.values())
        total_benefit = sum(environmental_benefits.values())
        equity_score = np.mean(list(distributional_effects.values()))
        return {
            'net_benefit': total_benefit - total_cost,
            'equity': equity_score,
            'recommendation': 'adopt' if total_benefit > total_cost * 1.5 else 'modify'
        }

Research Insight: AI policy simulation can evaluate 100x more policy scenarios than traditional methods, improving evidence-based决策.