气候情景模糊下的稳健贷款决策建模：基于NGFS短期情景的极小极大遗憾框架
Modelling Robust Lending Decisions under Climate Scenario Ambiguity: A Minimax-Regret Framework with NGFS Short-Term Scenarios
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中文总结 AI 辅助
本文提出一个基于NGFS情景的极小极大遗憾框架，利用公共数据评估气候情景模糊下的银行贷款决策，显著降低最大预期损失遗憾，为金融机构提供决策脆弱性分析工具。
中文摘要 AI 辅助
本文开发了一个公共数据框架，用于评估在合理气候情景意味着不同行业信贷结果但缺乏可靠情景概率时的增量银行贷款。校准结合了2025年共享国家信贷行业承诺、2026年1月美国行业杠杆率、来自Damodaran的利息覆盖率和股票波动率，以及NGFS CLIMACRED模型的官方2027年行业级违约概率调整。一年期Merton概率为16个非金融行业提供了独立的基准信贷风险代理，这些行业代表5.2808万亿美元的承诺。在两种主要决策评分下，将极小极大遗憾与对称情景加权和极大极小进行比较：预期损失和使用Damodaran合成利差的信贷补偿支付。DAPS将承诺加权的一年期违约概率从0.0592%提高到0.0897%。在12.5%的行业上限下，相对于对称情景加权，极小极大遗憾将最大预期损失遗憾降低了29.1%；在信贷补偿评分下，降幅为69.1%。对集中度限制、交叉权重、Merton输入和气候违约概率传输的敏感性分析表明，极小极大遗憾的价值取决于在接近约束性投资组合约束时情景特定行业排名的差异。其贡献是为金融机构和监管机构提供了一个可操作的决策脆弱性框架，而非关于已实现银行行为或普遍最优气候贷款的证据。
英文摘要
This paper develops a public-data framework for evaluating incremental bank lending when plausible climate scenarios imply different sector credit outcomes but defensible scenario probabilities are unavailable. The calibration combines 2025 Shared National Credit industry commitments, January 2026 U.S. industry leverage, interest coverage and equity volatility from Damodaran, and official 2027 sector-level probability-of-default adjustments from the NGFS CLIMACRED model. One-year Merton probabilities provide independent baseline credit-risk proxies for 16 non-financial sectors representing \$5.2808 trillion of commitments. Minimax regret is compared with symmetric scenario weighting and maximin under two principal decision scores: expected loss and a credit-compensation payoff using Damodaran synthetic spreads. DAPS raises the commitment-weighted one-year PD from 0.0592\% to 0.0897\%. At a 12.5\% sector cap, minimax regret reduces maximum expected-loss regret by 29.1\% relative to symmetric scenario weighting; under the credit-compensation score the reduction is 69.1\%. Sensitivity to concentration limits, crosswalk weights, Merton inputs and the climate-PD transport shows that the value of minimax regret is conditional on scenario-specific sector rankings differing near binding portfolio constraints. The contribution is an operational decision-fragility framework for financial institutions and supervisors, not evidence about realized bank behavior or universally optimal climate lending.
