CATASTROPHIC THRESHOLDS, BAYESIAN LEARNING AND THE ROBUSTNESS OF CLIMATE POLICY RECOMMENDATIONS

摘要: How does risk and uncertainty in climate thresholds impact optimal short-run mitigation? This paper contrasts the near-term mitigation consequences of using an expected value, stochastic programming (SP), control model to capture policy effects uncertain thresholds. The threshold outcomes increases damage. passive learning associated with SP creates extra incentive mitigate promptly reduce damage from remaining hazards. active a yet another do order postpone reaching harmful