Document Type : English paper for special issue on "climate change & insurance industry"
Authors
1 Assistant professor, Management Department, Faculty of Administrative Sciences and Economics, Arak University, Arak, IRAN.
2 Assistant Professor, Department of Accounting, Payame Noor University, Tehran, Iran
Abstract
BACKGROUND AND OBJECTIVES: Climate change poses profound risks to emerging markets, where socioeconomic vulnerabilities and limited adaptive capacity amplify financial instability. The insurance industry, a cornerstone of financial resilience, faces increasing pressure to integrate climate-related risks into valuation frameworks. However, data scarcity, regional heterogeneity, and stakeholder ambiguity remain significant barriers. This study introduces a pioneering transdisciplinary framework to integrate climate change risks into insurance valuation models, focusing on stakeholder perceptions and adaptive strategies in Iran—an emerging market characterized by diverse climatic zones (arid, semi-arid, and Mediterranean), water scarcity, and a developing insurance sector. The framework synthesizes environmental economics, risk management, and accounting to quantify climate impacts for actuarial and financial practices. Three core hypotheses are tested:(H1) Localized climate risk models significantly enhance insurance valuation accuracy and premium pricing;(H2) Stakeholder perceptions significantly influence the adoption and effectiveness of climate-responsive insurance products;(H3) Green accounting practices significantly enhance insurers' financial resilience by improving risk management and stakeholder trust.
METHODS: The study employs a sequential explanatory mixed-methods design, combining quantitative and qualitative phases. A semi-quantitative questionnaire was administered to 310 stakeholders across three strategically selected regions: Tehran (urban-industrial), Khuzestan (agriculture-oil), and Sistan and Baluchestan (arid-vulnerable). The sample included insurance professionals (29.0%), policymakers (22.6%), sectoral stakeholders (35.5%), and academic/NGO experts (12.9%), selected via stratified purposive sampling (Cochran’s formula; 95% confidence, 5% margin of error; 80% response rate). The questionnaire captured climate risk awareness, perceived likelihood and severity (5- and 7-point Likert scales), adaptation preferences (ranking), and expectations for climate-responsive insurance products. Qualitative data were collected through 30 semi-structured interviews and three participatory workshops (15–20 participants each).
Advanced computational modeling included autoregressive neural networks (ANN) with Long Short-Term Memory (LSTM) units—trained on historical data (2000–2025; 70-20-10 split)—to forecast valuation outcomes under RCP4.5 and RCP8.5 scenarios. Fuzzy cognitive maps (FCM) were developed in workshops to model causal relationships among climate risks, adaptation strategies, financial impacts, and social factors. Multi-criteria decision analysis (MCDA) using the Analytic Hierarchy Process ranked adaptation strategies. Quantitative analyses comprised descriptive statistics, exploratory factor analysis (EFA; varimax rotation, KMO=0.82), confirmatory factor analysis (CFA), structural equation modeling (SEM; maximum likelihood), and hierarchical regression. Qualitative data were thematically analyzed using NVivo.
FINDINGS: Descriptive results showed high awareness of droughts (mean=4.35/5, 75.2% high) and floods (mean=4.12/5, 68.4% high), particularly in Sistan and Baluchestan (drought mean=4.65) and Tehran (flood mean=4.20). Perceived likelihood and impact were highest for droughts (likelihood mean=6.20/7, impact mean=6.45/7). Parametric insurance ranked as the most preferred adaptation strategy (mean rank=1.85/5, 64.5% top rank), followed by infrastructure upgrades (2.10) and green accounting (2.45). EFA extracted four factors explaining 72.6% of variance: Climate Risk Awareness (34.2%), Adaptation Strategy Preferences (24.1%), Financial Integration (14.3%), and Stakeholder Engagement (10.0%). CFA confirmed model fit (χ²=128.45, df=84, p=0.002, CFI=0.94, RMSEA=0.05, Cronbach’s α≥0.85).
SEM results supported all hypotheses: H1 (localized climate risk models → valuation accuracy; β=0.65, p<0.001), H2 (stakeholder perceptions → product adoption; β=0.60, p<0.001), and H3 (green accounting → financial resilience; β=0.58, p<0.001). An exploratory path (stakeholder engagement → product adoption; β=0.45, p<0.001) was also significant. Hierarchical regression showed incremental R² changes of 25% (localized models), 12% (stakeholder perceptions), and 8% (green accounting), with total R²=0.45 (F(3,306)=83.45, p<0.001). MCDA confirmed parametric insurance as the top priority (weight=0.45), excelling in feasibility and risk reduction. The hybrid ANN-FCM framework successfully captured non-linear interactions and stakeholder ambiguities, offering robust projections for premium pricing, reserve allocation, and loss ratios under climate scenarios.
CONCLUSION: This study presents a validated, transdisciplinary framework integrating localized climate risk models, stakeholder perceptions, and green accounting to enhance insurance valuation and financial resilience in emerging markets. Parametric insurance—particularly for droughts and floods—emerged as the most viable adaptation tool for data-scarce regions. Policy recommendations include: (1) developing and piloting parametric insurance products using satellite-derived triggers (e.g., soil moisture indices), with initial pilots in Khuzestan and Sistan and Baluchestan; (2) adopting green accounting frameworks aligned with TCFD, supported by actuary training and voluntary CII guidelines; (3) enhancing stakeholder engagement through participatory workshops and a national climate risk dashboard; and (4) fostering InsurTech collaboration for real-time data integration and machine learning-based pricing.
Limitations include reliance on simulated extensions due to the absence of disaggregated climate–insurance microdata (addressed via scenario-based stress testing), potential response bias (mitigated by triangulation), and regional focus. Future research should test the framework in other emerging markets (e.g., Sub-Saharan Africa), incorporate additional hazards (sea-level rise), explore blockchain for claims processing, and conduct longitudinal perception studies. Despite these constraints, high reliability (Cronbach’s α≥0.85) and robust model fit (CFI=0.93, RMSEA=0.05) underscore the framework’s validity. This study bridges critical gaps by combining advanced simulation modeling with stakeholder-driven insights, offering actionable strategies for insurers, regulators, and policymakers to navigate the fiscal tempest of climate change in vulnerable regions.
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