Hydrometeorological Disaster Insurance Modeling Based on Fractional Differential Equations for Climate Change Mitigation Within the Framework of SDG 13
Hanifah Al Affiani, Muhamad Deni Johansyah, Endang Rusyaman, Sukono, Nurfadhlina Binti Abdul Halim, Alim Jaizul Wahid, Moch Panji Agung Saputra, Astrid Sulistya Azahra, Aceng SambasRainfall-index-based disaster insurance is an efficient approach to mitigating hydrometeorological losses. However, conventional premium pricing models generally assume memoryless stochastic dynamics that do not fully capture the long-range dependence inherent in rainfall data. This study develops a hydrometeorological disaster insurance model within a fractional Black–Scholes framework to incorporate long-memory effects. The model is formulated using fractional differential equations and solved semi-analytically by integrating the Daftardar–Jafari Method (DJM) with the Kashuri–Fundo (KF) transform, yielding a closed-form solution expressed in terms of the Mittag–Leffler function. The proposed contract is structured as parametric rainfall insurance with a multi-layer payout mechanism based on percentiles corresponding to minor, moderate, and severe housing damage. The results show that variations in the fractional-order parameter significantly affect premium estimation. In particular, δ = 0.5 recovers the classical model and tends to generate higher premiums than the fractional model with δ = 0.23153, whereas the model with δ = 0.73153 yields lower premiums. These findings indicate that fractional-order parameterization can accommodate diverse risk characteristics and policyholders’ economic capacities, enabling more adaptive, risk-sensitive premium structures. In line with SDG 13 (Climate Action), the proposed framework offers a climate-responsive disaster-mitigation strategy through accessible, actuarially relevant insurance design. recovers the classical model and tends to generate higher premiums than the fractional model with , whereas the model with yields lower premiums. These findings indicate that fractional-order parameterization can accommodate diverse risk characteristics and policyholders’ economic capacities, enabling more adaptive, risk-sensitive premium structures. In line with SDG 13 (Climate Action), the proposed framework offers a climate-responsive disaster-mitigation strategy through accessible, actuarially relevant insurance design.