Climate aware lending allocation under NGFS scenarios - A Monte Carlo approach

This paper examines climate-sensitive credit risk and lending allocation over a five-year horizon by mapping NGFS short-term transition and physical-risk scenarios into obligor default probabilities. NGFS shocks enter mean-reverting log-production dynamics, yielding analytical terminal default probabilities. Monte Carlo simulation with covariance-based PCA estimates joint losses, while constrained nonlinear optimization accounts for exposure-dependent default risk and compares alternative lending policies. Under a synthetic 1,000-obligor portfolio, mean five-year terminal default probabilities range from 13.88% (HWTP) to 13.95% (DAPS). Relative to conventional allocation, climate-aware lending reduces expected credit losses by 0.55--0.71% under HWTP, SWUC, and DiRe, with negligible improvement under DAPS. Tail risk is sensitive to dependence assumptions, and fixed-PD allocation generates markedly greater concentration. Results are conditional on NGFS scenario inputs and synthetic borrower calibration. Sectoral shocks cannot fully represent within-sector differences in emissions, geography, technology, or adaptation, motivating future institution-specific validation. The framework helps banks link NGFS scenarios to sector-level credit risk and portfolio loss outcomes. It can support ICAAP and climate stress testing by highlighting exposures most sensitive to climate risk, adding a forward-looking overlay to existing models. The study advances climate-aware credit allocation by linking NGFS sectoral projections to analytical obligor-level default probabilities and endogenous lending decisions. It provides a unified framework for examining how climate-related credit risk influences lending allocations, expected losses, and portfolio concentration, supported by quantitative policy comparisons and robustness analysis.