مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
Mohammad Reyhani
چکیده
BACKGROUND AND OBJECTIVES: The escalating frequency and severity of climate-induced hydro-meteorological hazards, particularly flooding, present unprecedented systemic challenges to the global insurance and reinsurance industries. Conventional actuarial methodologies, which rely on the fundamental assumption of stationarity, fail to capture the highly non-linear, dynamic, and spatially interconnected dependencies characteristic of contemporary flood risks. Consequently, these limitations lead to systemic premium mispricing and a widening insurance protection gap, particularly within developing ...
بیشتر
BACKGROUND AND OBJECTIVES: The escalating frequency and severity of climate-induced hydro-meteorological hazards, particularly flooding, present unprecedented systemic challenges to the global insurance and reinsurance industries. Conventional actuarial methodologies, which rely on the fundamental assumption of stationarity, fail to capture the highly non-linear, dynamic, and spatially interconnected dependencies characteristic of contemporary flood risks. Consequently, these limitations lead to systemic premium mispricing and a widening insurance protection gap, particularly within developing economies. To mitigate these challenges, the insurance sector has increasingly adopted deep learning (DL) paradigms, such as Convolutional Neural Networks (CNNs). Although these models offer superior predictive capacity, two critical limitations impede their operational deployment: first, their high data dependency requires extensive historical datasets that are unavailable in data-scarce markets, such as Iran, where historical loss records are typically fragmented, non-digitized, or brief; second, their black-box architecture violates stringent regulatory transparency mandates—such as those enforced by the Central Insurance of I.R. Iran (Insurance Supervisory and Regulatory Body - Bimeh Markazi)—which require fully explainable and auditable risk-premium calculations. This study addresses these dual limitations by developing and validating the Physics-Informed and Causally-Aware (PICA) framework, a novel conceptual and computational paradigm for flood-risk pricing. Specifically, the objectives are to: (1) formulate the PICA framework by synthesizing advanced artificial intelligence architectures, including Graph Neural Networks (GNNs) and Physics-Informed Neural Networks (PINNs); (2) mitigate data scarcity constraints by embedding fundamental hydrodynamic laws, specifically the shallow water equations, directly into the model loss function; (3) ensure regulatory compliance by integrating causal inference constraints that prioritize physical risk drivers over spurious correlations; and (4) empirically validate the performance of PICA against conventional benchmarks under simulated data-scarce conditions representative of the Iranian insurance market.
METHODS: To achieve these objectives, a multi-phase research design was executed. Phase 1 comprised a systematic literature review following the PRISMA 2020 guidelines across the Scopus, Web of Science, and IEEE Xplore databases (covering the period 2020–2025), synthesizing 52 peer-reviewed articles to identify research gaps in hybrid artificial intelligence (AI) modeling and guide the structural architecture of PICA. Phase 2 focused on the quantitative validation of the framework using a high-fidelity synthetic dataset. Due to the absence of consistent historical empirical records, a baseline ground-truth environment was generated utilizing the industry-standard HEC-RAS 2D hydrodynamic simulation engine. This hydrographic simulation modeled 50 distinct flood events over a heterogeneous 100 km² synthetic urban watershed containing 10,000 exposure assets, yielding high-resolution spatial data for inundation depth, flow velocity, and asset-level financial loss. The PICA framework was formulated as a hybrid GNN, wherein the watershed topology was modeled as a directed graph to capture flow routing and spatial dependencies between adjacent hydrological units. The model was trained utilizing a composite loss function that penalized empirical prediction error, violations of physical conservation laws (conservation of mass and momentum), and violations of specified causal structures. The proposed framework was benchmarked against two baseline models: a spatial 2D-CNN and a Random Forest (RF) regressor. All models were evaluated under two operational scenarios: a data-abundant scenario (utilizing 100% of the generated dataset) and a simulated data-scarce scenario (restricted to 20% of the dataset) to represent data constraints typical of the Iranian market. Predictive accuracy was measured using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and the statistical significance of predictive differences was assessed via the Diebold-Mariano (DM) test. Furthermore, SHapley Additive exPlanations (SHAP) analysis was employed to evaluate model explainability, while hyperparameter sensitivity analysis was conducted to confirm model stability.
FINDINGS: The systematic review in Phase 1 confirmed a critical gap in literature, indicating that no existing actuarial pricing framework integrates GNNs, PINNs, and causal machine learning (ML). Quantitative results in Phase 2 demonstrated that physics-informed regularization significantly enhances predictive accuracy. Under the data-abundant scenario, PICA (RMSE = 0.119) achieved a modest improvement over the 2D-CNN (RMSE = 0.137) and the RF regressor (RMSE = 0.141), indicating that purely data-driven DL models remain competitive when data are abundant. Conversely, in the simulated data-scarce scenario, the baseline models exhibited severe overfitting and performance degradation; the 2D-CNN error increased by over 250% (RMSE=0.482), and the RF error rose to 0.530. In contrast, PICA leveraged its physics-informed constraints to maintain generalization performance, yielding an RMSE of 0.165. This performance represents a 65.8% reduction in prediction error relative to the 2D-CNN under identical data-scarce conditions. The Diebold-Mariano test confirmed that this performance divergence was statistically significant (p < 0.001). Furthermore, SHAP attribution analysis revealed that PICA consistently prioritized physical risk drivers, such as maximum inundation depth, whereas the 2D-CNN relied heavily on spurious spatial coordinates , indicating that the baseline DL model captured statistical artifacts rather than underlying physical causal mechanisms. Finally, parameter sensitivity analysis demonstrated model stability across a broad spectrum of physical loss weights (0.1 ≤ ≤ 1), indicating that physics-informed regularization did not induce underfitting.
CONCLUSION: This research validates the PICA framework as a robust and mathematically sound solution for flood risk pricing in data-scarce insurance markets. The empirical findings demonstrate that embedding physical conservation laws within deep learning architectures serves as an effective mathematical regularizer, acting as a prior that compensates for missing historical loss data to ensure accurate model generalization. These findings have critical operational implications for the Iranian insurance sector. PICA provides Bimeh Markazi and commercial insurers with a viable pathway to circumvent the data limitations present in high-risk basins, such as the Karun and Karkheh river basins. The framework facilitates the rapid development of high-resolution, regulator-compliant premium pricing models without requiring decades of historical loss-data collection. Furthermore, the proposed architecture supports the design of parametric insurance products, which are triggered by physics-validated hazard metrics rather than traditional, manual loss adjustment processes. Future research should address the simulation-to-reality (sim-to-real) transfer gap by developing transfer learning strategies to fine-tune this physics-based framework on empirical claims data, thereby operationalizing the model for national-scale risk underwriting.
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
قیمتگذاری محصولات بیمهای
Fatemeh Atatalab؛ Mahboubeh Aalaei
چکیده
BACKGROUND AND OBJECTIVES: Catastrophic risk refers to the exposure of a large population or insurance portfolio to extreme, low-frequency yet high-severity events that can simultaneously generate substantial financial, human, and infrastructural losses. Such events exceed the volatility typically captured by conventional risk models and frequently challenge the solvency and underwriting capacity of insurance and reinsurance markets. Examples include large-scale earthquakes, floods, droughts, and acts of terrorism, all of which can severely disrupt national economies and necessitate external financial ...
بیشتر
BACKGROUND AND OBJECTIVES: Catastrophic risk refers to the exposure of a large population or insurance portfolio to extreme, low-frequency yet high-severity events that can simultaneously generate substantial financial, human, and infrastructural losses. Such events exceed the volatility typically captured by conventional risk models and frequently challenge the solvency and underwriting capacity of insurance and reinsurance markets. Examples include large-scale earthquakes, floods, droughts, and acts of terrorism, all of which can severely disrupt national economies and necessitate external financial assistance for recovery. In the context of Iran—recognized as one of the most disaster-prone countries globally, particularly in terms of seismic activity—the management of catastrophe risk constitutes not only an actuarial necessity but also a public policy priority. Over the past three decades, more than 80,000 fatalities have been attributed to earthquakes alone, underscoring the urgent need for robust catastrophe risk modeling and scientifically grounded reinsurance pricing mechanisms. Traditional actuarial models that rely on limited historical observations or standard frequency–severity distributions often fail to capture the heavy-tailed nature of catastrophic losses. This limitation motivates the adoption of advanced statistical and probabilistic approaches, such as Extreme Value Theory (EVT), which provide a more accurate characterization of rare but devastating events. The primary objective of this study is to develop and empirically validate a pricing framework for catastrophe (CAT) reinsurance based on a hybrid treaty structure that integrates Quota Share (QS) and Excess of Loss (XL) arrangements. This hybrid design enables flexible allocation of both frequency and severity risks across multiple loss layers, thereby enhancing the efficiency and stability of catastrophe coverage.
METHODS: To quantify the tail behavior of catastrophic losses, earthquake fatality data for Iran covering the period 1950–2023 are analyzed using the Peaks Over Threshold (POT) method within the EVT framework. The POT approach focuses on exceedances above a specified high threshold, allowing precise modeling of extreme observations while minimizing bias from moderate losses. The Generalized Pareto Distribution (GPD), the canonical distribution under the POT framework, is employed to model excess fatalities. Its parameters are estimated using Maximum Likelihood Estimation (MLE), ensuring statistical efficiency and consistency under large-sample conditions. Based on the fitted GPD, tail probabilities and expected losses beyond the threshold are incorporated into a reinsurance pricing model grounded in the standard deviation premium principle. By accounting for both the expected value and the dispersion of losses, this principle provides a theoretically sound basis for risk loading and premium determination. The hybrid QS–XL structure is formally specified to capture the interaction between the retention level (𝑑) and the quota share coefficient (𝛼), thereby establishing a unified framework for calculating the reinsurer’s expected indemnity and corresponding premium. In addition, sensitivity analyses are conducted across a range of retention levels and quota share ratios to evaluate the effects of contractual parameter variations on premium dynamics. The model is calibrated under realistic assumptions derived from Iranian earthquake data, reflecting both empirical severity patterns and practical treaty configurations commonly adopted by reinsurers operating in high-risk environments.
FINDINGS: The empirical results indicate a clear inverse relationship between the reinsurance premium and the retention level 𝑑. As the primary insurer retains a larger portion of the loss, the reinsured layer narrows, resulting in a lower reinsurance premium. Conversely, the premium exhibits a positive and approximately linear relationship with the QS coefficient (𝛼); as the reinsurer’s proportional participation increases, the total cost of coverage correspondingly rises. These findings confirm that catastrophe reinsurance pricing is highly sensitive to the structural parameters of the contract. For a fixed quota share proportion, higher retention levels substantially reduce premium costs. For a fixed retention level, increasing the reinsurer’s proportional participation raises premiums due to the higher expected indemnity. This evidence highlights the inherent trade-off between risk transfer and premium affordability—a central consideration for actuaries and policymakers involved in disaster risk financing. Moreover, the POT-based tail modeling framework captures extreme quantiles of the loss distribution, providing more robust and transparent estimates of potential catastrophic payouts. Compared with conventional approaches that may underestimate tail risk, the EVT-based methodology enhances actuarial fairness and strengthens resilience against large and unpredictable claims. Supported by rigorous tail risk quantification, the hybrid QS–XL model offers a balanced contractual mechanism suited to both primary insurers and reinsurers seeking efficient coverage in highly exposed environments.
CONCLUSION: This study demonstrates that integrating EVT with hybrid reinsurance structures provides a rigorous actuarial framework for pricing catastrophic risk. The proposed QS–XL model effectively combines proportional and non-proportional components, ensuring both analytical tractability and operational flexibility. By grounding premium determination in statistical inference and long-term empirical data, the framework contributes to the financial stability of insurers and reinsurers operating in disaster-exposed regions.
In the case of Iran, where earthquake-related losses pose systemic threats to economic resilience, adoption of this hybrid model may enhance capital adequacy, promote equitable pricing of catastrophe coverage, and support sustainable disaster risk management. Beyond its national application, the proposed framework offers a replicable methodological approach for other emerging markets confronting similar challenges in estimating and managing catastrophic risk. Overall, the integration of EVT-based tail estimation with hybrid treaty design narrows the gap between statistical rigor and practical actuarial implementation, thereby contributing to the development of more resilient and data-driven catastrophe insurance systems.
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
مدیریت ریسک در صنعت بیمه
Mohammadreza Farzaneh؛ Faezeh Banimostafa Arab
چکیده
BACKGROUND AND OBJECTIVES: Climate change increasingly manifests through systemic extreme weather events—such as heatwaves, floods, storms, and wildfires—that pose complex challenges to interconnected social, economic, and biophysical systems. While the insurance sector is highly exposed to these perils through property, infrastructure, and agricultural liabilities, it is simultaneously pressured by transition risks from emerging low-carbon development policies. Nonetheless, insurance mechanisms hold significant potential to enhance social resilience, mobilize adaptation finance, ...
بیشتر
BACKGROUND AND OBJECTIVES: Climate change increasingly manifests through systemic extreme weather events—such as heatwaves, floods, storms, and wildfires—that pose complex challenges to interconnected social, economic, and biophysical systems. While the insurance sector is highly exposed to these perils through property, infrastructure, and agricultural liabilities, it is simultaneously pressured by transition risks from emerging low-carbon development policies. Nonetheless, insurance mechanisms hold significant potential to enhance social resilience, mobilize adaptation finance, and incentivize risk-reducing behaviors. Existing literature, however, frequently treats climate change adaptation, mitigation, and disaster risk management (DRM) in isolation, rarely exploring their integration within comprehensive insurance and financing frameworks. To address this gap, this study conceptualizes insurance within a holistic climate governance structure based on Elinor Ostrom’s Social-Ecological System (SES) framework. By reframing insurance from a reactive, corrective mechanism to a proactive agent within multi-level governance, this study extends the applicability of the SES framework from conventional common-pool natural resources to the governance of climate finance, using global best practices and expert insights from the specific context of Iran.
METHODS: A three-level analytical approach was employed to investigate the integration of insurance into climate governance. First, Ostrom’s SES framework was adapted to construct a conceptual model consisting of a governance core and six interacting subsystems: economy-wide, public, finance, rural, urban, and social development. Within this model, insurance serves as a critical conduit in the finance subsystem, facilitating risk transfer, resource allocation, and adaptation, while capturing institutional linkages and feedback loops between climate impacts, regulatory frameworks, and financial decision-making. Second, an international comparative analysis was conducted using case studies and policy data from the Climate Change Laws of the World (CCLW) database—comprising 931 policies, 499 laws, and 1,520 targets in developed countries, and 1,321 policies, 429 laws, and 1,015 targets in developing countries. This analysis identified global trends in regulatory design, compulsory pooling, agricultural schemes, disclosure requirements, and parametric solutions. Third, empirical validation was gathered through a semi-structured survey of 181 experts. Qualitative data from open-ended responses were analyzed inductively using manual line-by-line coding and iterative thematic consensus, while quantitative subsystem rankings were analyzed using variance and bootstrap 95% BCa confidence intervals.
FINDINGS: The proposed SES framework positions insurance as a crucial systemic connector that operationalizes risk transfer and adaptation. International evidence demonstrates that insurance acts as an active policy instrument rather than a passive buffer: Norway’s Natural Damage Insurance Act mandates post-disaster risk pooling; the UK’s Flood Re program ensures flood insurance affordability; France and Spain integrate risk transfer with agricultural and forestry policies; and Turkey and Kyrgyzstan leverage insurance platforms to meet national resilience targets. In evaluating the transferability of these models to Iran, public-private pooling and parametric solutions emerged as viable pathways, though constrained by international sanctions, restricted reinsurance access, and data infrastructure deficits. While experts prioritized immediate interventions in urban and rural subsystems, the finance subsystem remained a critical long-term strategic anchor. Key implementation barriers included fragmented institutional coordination, deficient hazard and climate-risk modeling, and weak integration between environmental monitoring and insurance underwriting. Addressing these barriers requires expanding satellite-based parametric products for drought, enhancing spatial risk mapping, and improving access to innovative financing.
CONCLUSION: Under changing climate regimes, insurance serves as a vital strategic tool for integrating adaptation, mitigation, and DRM. By shifting the paradigm from reactive risk transfer to proactive, multi-sectoral coordination, the adapted SES framework addresses a critical gap in climate finance literature. Operationalizing this framework requires bridging risk-information deficits, developing localized and sector-specific products, and embedding insurance mechanisms within broader governance subsystems. Ultimately, implementing targeted, short-term regional interventions alongside long-term structural finance and insurance reforms will allow policymakers to align environmental objectives, financial stability, and social equity, fostering climate-resilient development pathways.
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
حاکمیت شرکتی در شرکتهای بیمهای
Keramatollah Heydari Rostami؛ Yassaman Khalili
چکیده
BACKGROUND AND OBJECTIVES: Climate change poses substantial risks to emerging markets, where socioeconomic vulnerabilities and limited adaptive capacity exacerbate financial instability. The insurance industry, a critical pillar of financial resilience, faces growing pressure to incorporate climate-related risks into valuation frameworks. However, persistent challenges—including data scarcity, regional heterogeneity, and stakeholder ambiguity—continue to hinder effective integration. This study proposes a novel transdisciplinary framework for incorporating climate change risks into ...
بیشتر
BACKGROUND AND OBJECTIVES: Climate change poses substantial risks to emerging markets, where socioeconomic vulnerabilities and limited adaptive capacity exacerbate financial instability. The insurance industry, a critical pillar of financial resilience, faces growing pressure to incorporate climate-related risks into valuation frameworks. However, persistent challenges—including data scarcity, regional heterogeneity, and stakeholder ambiguity—continue to hinder effective integration. This study proposes a novel transdisciplinary framework for incorporating climate change risks into insurance valuation models, with particular emphasis on stakeholder perceptions and adaptive strategies in Iran—an emerging market characterized by diverse climatic zones (arid, semi-arid, and Mediterranean), chronic water scarcity, and a developing insurance sector. The framework integrates insights from environmental economics, risk management, and accounting to operationalize climate impacts within actuarial and financial practice. Three core hypotheses are examined: (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; and (H3) green accounting practices significantly enhance insurers’ financial resilience by strengthening risk management and stakeholder trust.
METHODS: The study adopts a sequential explanatory mixed-methods design combining quantitative and qualitative approaches. 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 comprised insurance professionals (29.0%), policymakers (22.6%), sectoral stakeholders (35.5%), and academic or NGO experts (12.9%), selected through stratified purposive sampling (Cochran’s formula; 95% confidence level, 5% margin of error; 80% response rate). The instrument measured climate risk awareness, perceived likelihood and severity (5- and 7-point Likert scales), adaptation preferences (ranking), and expectations regarding climate-responsive insurance products. Qualitative data were collected through 30 semi-structured interviews and three participatory workshops involving 15–20 participants each. Advanced computational modeling incorporated autoregressive neural networks (ANN) with Long Short-Term Memory (LSTM) units, trained on historical data from 2000 to 2025 using a 70–20–10 data split, to forecast valuation outcomes under RCP4.5 and RCP8.5 climate scenarios. Fuzzy cognitive maps (FCM) were developed during the 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, was applied to rank adaptation strategies. Quantitative analyses included descriptive statistics, exploratory factor analysis (EFA; varimax rotation, KMO = 0.82), confirmatory factor analysis (CFA), structural equation modeling (SEM; maximum likelihood estimation), and hierarchical regression, while qualitative data were analyzed thematically using NVivo.
FINDINGS: Descriptive results indicated high levels of awareness regarding droughts (mean = 4.35/5, 75.2% reporting high awareness) and floods (mean = 4.12/5, 68.4% reporting high awareness), with particularly elevated drought awareness in Sistan and Baluchestan (mean = 4.65) and flood awareness in Tehran (mean = 4.20). Perceived likelihood and impact were highest for droughts (likelihood mean = 6.20/7; impact mean = 6.45/7). Parametric insurance emerged as the most preferred adaptation strategy (mean rank = 1.85/5, 64.5% ranking it first), followed by infrastructure upgrades (2.10) and green accounting (2.45). Exploratory factor analysis identified four factors explaining 72.6% of the total variance: climate risk awareness (34.2%), adaptation strategy preferences (24.1%), financial integration (14.3%), and stakeholder engagement (10.0%). Confirmatory factor analysis demonstrated satisfactory model fit (χ² = 128.45, df = 84, p = 0.002; CFI = 0.94; RMSEA = 0.05; Cronbach’s α ≥ 0.85). Structural equation modeling supported all three 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 additional exploratory path from stakeholder engagement to product adoption was also significant (β = 0.45, p < 0.001). Hierarchical regression analysis indicated incremental R² increases of 25% for localized climate risk models, 12% for stakeholder perceptions, and 8% for green accounting, yielding a total R² of 0.45 (F(3,306) = 83.45, p < 0.001). MCDA results confirmed parametric insurance as the highest-priority adaptation strategy (weight = 0.45), demonstrating strong performance in terms of feasibility and risk reduction. The hybrid ANN–FCM framework effectively captured nonlinear interactions and stakeholder uncertainty, producing robust projections for premium pricing, reserve allocation, and loss ratios under alternative climate scenarios.
CONCLUSION: This study develops and validates a transdisciplinary framework integrating localized climate risk modeling, stakeholder perceptions, and green accounting to strengthen insurance valuation and financial resilience in emerging markets. Parametric insurance—particularly for droughts and floods—emerges as the most viable adaptation mechanism in data-constrained contexts. Policy implications include the development and pilot implementation of parametric insurance products using satellite-derived triggers (e.g., soil moisture indices), initially in Khuzestan and Sistan and Baluchestan; the adoption of green accounting frameworks aligned with the recommendations of the Task Force on Climate-related Financial Disclosures (TCFD), supported by actuarial training and voluntary guidelines developed by the Central Insurance of Islamic Republic of Iran (CII – Insurance Supervisory and Regulatory Authority); enhanced stakeholder engagement through participatory workshops and the development of a national climate risk dashboard; and increased collaboration with InsurTech firms to enable 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 through scenario-based stress testing, as well as potential response bias mitigated through methodological triangulation and the study’s regional focus. Future research should test the framework in other emerging markets, such as Sub-Saharan Africa, incorporate additional hazards including sea-level rise, explore blockchain-enabled claims processing, and conduct longitudinal studies of stakeholder perceptions. Despite these limitations, high reliability (Cronbach’s α ≥ 0.85) and strong model fit (CFI = 0.93; RMSEA = 0.05) support the framework’s validity. By integrating advanced simulation modeling with stakeholder-driven insights, the study offers actionable guidance for insurers, regulators, and policymakers seeking to manage climate-related financial risk in vulnerable regions.
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
Mohammad Amin Natanzi؛ hassan jabari
چکیده
BACKGROUND AND OBJECTIVES: Drought is one of the most critical climate-related hazards affecting Iran, exerting substantial impacts on agricultural production, water resources, and socio-economic stability. The predominance of arid and semi-arid climatic regimes, coupled with high interannual precipitation variability and sustained warming trends, has intensified both the frequency and severity of drought events nationwide. Traditional drought assessments in Iran have predominantly relied on univariate drought indices and linear correlation measures, which often fail to capture the compound nature ...
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BACKGROUND AND OBJECTIVES: Drought is one of the most critical climate-related hazards affecting Iran, exerting substantial impacts on agricultural production, water resources, and socio-economic stability. The predominance of arid and semi-arid climatic regimes, coupled with high interannual precipitation variability and sustained warming trends, has intensified both the frequency and severity of drought events nationwide. Traditional drought assessments in Iran have predominantly relied on univariate drought indices and linear correlation measures, which often fail to capture the compound nature of drought processes driven by concurrent precipitation deficits and elevated atmospheric water demand. In recent decades, temperature-driven evapotranspiration has emerged as a key determinant of drought severity under climate change, exposing the limitations of precipitation-only metrics such as the Standardized Precipitation Index (SPI). In this context, the Standardized Precipitation Evapotranspiration Index (SPEI), which explicitly integrates potential evapotranspiration (PET), provides a more comprehensive characterization of drought dynamics. However, robust drought risk characterization requires not only evaluating individual indices but also quantifying their joint behavior and underlying dependence structure. Because interactions among climatic drivers can substantially amplify environmental impacts, recent advances in climate risk assessment emphasize modeling compound extremes—such as concurrent drought and heatwave events—via multivariate frameworks. Accordingly, this study provides a probabilistic, multivariate assessment of drought risk across the major climatic regions of Iran by jointly analyzing SPI and SPEI through copula theory. By modeling the joint dependence structure of these indices, this study captures nonlinear and asymmetric relationships, with particular focus on the tail dependence associated with extreme drought events. Beyond advancing methodological frameworks, this study demonstrates the utility of copula-based drought modeling for drought risk management, climate adaptation planning, and the conceptual formulation of index-based insurance schemes.
METHODS: This study utilized long-term meteorological observations spanning 1981–2020 from synoptic stations across Iran. Monthly precipitation and temperature records were employed to compute SPI and SPEI across multiple accumulation timescales, facilitating the evaluation of both short- and long-term drought conditions. The computation of SPEI integrated PET to account for the impact of rising temperatures on drought intensity. Marginal probability distributions for SPI and SPEI were fitted using the Maximum Likelihood Estimation (MLE). To model the dependence structure between the two indices, several copula families—namely Gaussian, Student- , Clayton, and Gumbel—were evaluated across diverse dependence behaviors, including symmetric dependence, tail dependence, and asymmetric lower- or upper-tail dependence. Optimal copula models were selected based on the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and goodness-of-fit (GoF) tests. Lower- and upper-tail dependence coefficients were quantified to evaluate the joint probability of concurrent extreme drought conditions. Furthermore, joint probabilities and bivariate return periods for severe and extreme drought events were derived from the fitted copula models across distinct climatic regions of Iran and subsequently validated against historical drought records. FINDINGS: The results revealed pronounced spatial heterogeneity in drought characteristics and dependence structures across Iran. Arid and semi-arid regions—particularly the Central Plateau and eastern Iran—exhibited strong lower-tail dependence between SPI and SPEI, indicating a high likelihood of concurrent extreme droughts driven by simultaneous precipitation deficits and elevated evaporative demand. In these regions, the Clayton copula consistently demonstrated the best fit, effectively capturing the asymmetric lower-tail dependence characteristic of severe drought events. Conversely, humid regions along the Caspian Sea coast exhibited weaker dependence structures, negligible tail dependence, and longer drought return periods. Transitional climatic zones were better characterized by the Student- copula, which captures symmetric tail dependence and intermediate drought dynamics. "These probabilistic findings are consistent with major documented drought episodes within and after the study period, particularly those occurring during the late 1990s and late 2010s. Earlier historical drought events, such as those reported during the early 1970s, are considered as contextual evidence from previous studies rather than direct validation cases for the proposed modeling framework."
CONCLUSION: These findings provide a probabilistic characterization of drought risk in Iran and establish a quantitative basis for analyzing compound drought behavior. Although this study does not formulate an actuarial pricing model, the findings provide actionable insights for drought risk management, climate adaptation strategies, and the conceptual design of index-based insurance frameworks. By integrating multivariate drought indices with copula-based dependence modeling, this study demonstrates the necessity of advancing beyond univariate and linear correlation-based paradigms in drought risk assessment. Furthermore, the findings underscore the escalating influence of temperature-driven evapotranspiration under climate change, confirming the critical utility of SPEI in warming environments. Overall, the proposed framework offers a robust, flexible, and policy-relevant tool for assessing drought risk across Iran, establishing a scientific foundation for strengthening climate resilience, enhancing agricultural risk management, and developing innovative risk-transfer mechanisms.
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
ارزیابی ریسک در رشته های بیمه
Mohammad Ariamanesh؛ Mansoureh Farzaneh
چکیده
BACKGROUND AND OBJECTIVES: Iran's water crisis poses a fundamental threat to national security, sustainable development, and social stability. The convergence of climate change, unsustainable consumption patterns, and institutional challenges has created an unprecedented water emergency, characterized by severe groundwater overdraft and the deterioration of traditional water-management systems. The present study addresses this crisis by developing a comprehensive insurance model tailored to Iran’s distinctive hydrological, agricultural, and industrial conditions. The study has three primary ...
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BACKGROUND AND OBJECTIVES: Iran's water crisis poses a fundamental threat to national security, sustainable development, and social stability. The convergence of climate change, unsustainable consumption patterns, and institutional challenges has created an unprecedented water emergency, characterized by severe groundwater overdraft and the deterioration of traditional water-management systems. The present study addresses this crisis by developing a comprehensive insurance model tailored to Iran’s distinctive hydrological, agricultural, and industrial conditions. The study has three primary objectives: first, to design innovative insurance mechanisms to protect and revitalize qanat systems as sustainable groundwater-management solutions by directly linking financial protection to maintenance activities; second, to develop incentive-based insurance products that promote efficient water use in industrial and household sectors through premium structures linked to verified conservation efforts; and third, to establish a robust framework for insuring critical water infrastructure, including treatment plants and dams, against climate-related damage and operational failures, thereby ensuring service continuity. The study focuses on adapting international best practices in water-related insurance to Iran's institutional and environmental context while addressing data scarcity, limited market capacity, and the need for effective public-private partnerships.
METHODS: This study employed an advanced mixed-methods approach that combined rigorous quantitative analysis with in-depth qualitative assessment through a carefully structured three-phase methodological framework. The first phase systematically reviewed water-related insurance schemes, ranging from flood insurance in Germany to index-based agricultural insurance in developing countries, including Iran, and analyzed their design features and impacts. The second phase conducted a detailed assessment of Iran’s water-risk landscape, incorporating climate-scenario modeling based on World Meteorological Organization (WMO, 2024) projections, infrastructure-vulnerability assessments of key water assets, and a comprehensive evaluation of the domestic insurance industry’s technical and financial capacity using data from the Central Insurance of Iran (2022). This phase was supplemented by case studies of critically endangered qanat systems. The third phase synthesized the findings to develop a validated roadmap for water-related insurance in Iran, informed by interviews with more than 20 stakeholders from the insurance, water, agricultural, and environmental sectors.
FINDINGS: The study identifies substantial market potential for water-related insurance in Iran. Based on historical data on water-related losses, estimates of the value of water infrastructure and agricultural assets at risk, and assessments of the premium-paying capacity of relevant stakeholders, water-related insurance could potentially account for 15-20% of Iran’s insurance market, compared with the current coverage rate of less than 2% for natural disasters. This quantitative assessment indicates substantial growth opportunities for insurers while addressing critical national needs. Moreover, insurance products can create strong economic incentives for water conservation when premium structures are directly linked to verified reductions in water use and investments in water-efficient technologies. International examples indicate that industries have reduced water use by 20-30% to obtain more favorable insurance terms, illustrating how appropriately designed premiums can align financial incentives with sustainable water management. However, significant implementation barriers remain, including data limitations that constrain accurate risk assessment, limited technical capacity for insurance risk modeling, and regulatory challenges. In particular, product-approval processes, the establishment of clear legal frameworks, and the provision of appropriate government support require further examination to ensure the feasibility of water-related insurance. In addition, cultural barriers to insurance adoption necessitate comprehensive awareness campaigns to promote stakeholder engagement and the uptake of these products.
CONCLUSION: Implementing a specialized water-insurance framework in Iran would represent a crucial strategic advancement toward water security and enhanced climate resilience. By providing financial protection for critical water infrastructure, creating economic incentives for sustainable water use through carefully designed premium structures, and supporting the preservation of traditional qanat systems through dedicated conservation insurance, the proposed model could significantly strengthen Iran’s adaptive capacity to address escalating water-scarcity challenges. The study demonstrates that successful implementation requires strong government support, strategic investment in data infrastructure, and effective public-private partnerships that leverage international expertise and reinsurance capacity. Given the current financial constraints of domestic insurers, international reinsurance arrangements and conditional government-backed funds are essential to ensure solvency, distribute risk, and maintain premium affordability. Specifically, the study recommends establishing a “Qanat Conservation Insurance Program,” with premium subsidies linked to maintenance activities; developing parametric agricultural-insurance pilots in high-risk regions; and creating a “Conditional Government Guarantee Fund” to support market development while ensuring compliance with water-conservation standards. The proposed model provides a practical and actionable pathway for integrating insurance mechanisms into Iran’s national water-management strategy. Its immediate applications in the agricultural, industrial, and municipal water sectors could contribute substantially to long-term water sustainability, economic stability, and climate resilience. Future implementation should prioritize pilot projects in severely water-stressed regions, coupled with continuous monitoring and evaluation to refine the approach and demonstrate tangible benefits for stakeholders across sectors
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
مدیریت ریسک در صنعت بیمه
shamsolah salami؛ Mohammad Ariamanesh
چکیده
BACKGROUND AND OBJECTIVES: The increasing frequency and intensity of earthquakes and floods in recent years have exposed fundamental weaknesses in conventional building insurance systems. With annual global economic losses exceeding $300 billion, the need to rethink insurance structures is urgent. Traditional public–private models provide only partial solutions, leaving challenges related to efficiency, transparency, and equity unresolved. This study addresses these shortcomings by developing an integrated framework that combines digital innovation and hybrid governance mechanisms specifically ...
بیشتر
BACKGROUND AND OBJECTIVES: The increasing frequency and intensity of earthquakes and floods in recent years have exposed fundamental weaknesses in conventional building insurance systems. With annual global economic losses exceeding $300 billion, the need to rethink insurance structures is urgent. Traditional public–private models provide only partial solutions, leaving challenges related to efficiency, transparency, and equity unresolved. This study addresses these shortcomings by developing an integrated framework that combines digital innovation and hybrid governance mechanisms specifically designed for earthquake- and flood-prone environments.
METHODS: This study used a mixed-methods design implemented in three interconnected phases. First, we conducted a detailed comparative review of insurance systems in ten countries, with a focus on governance structures, financial instruments, technological adoption, and patterns of private-sector involvement. The quantitative component employed fuzzy AHP and SmartPLS modeling, supported by insights from thirty international experts representing government, academia, and industry. To strengthen the empirical foundation, we also analyzed extensive data from Türkiye’s insurance sector (2015–2023), including more than 200,000 claims, which served as the basis for validating our simulation results.
FINDINGS: The proposed framework consists of four mutually reinforcing components. High-resolution radar satellite imagery and remote sensing technologies significantly accelerate damage assessment, reducing processing time by half and improving accuracy by 35%. Blockchain-enabled smart contracts enhance transparency in payment processes by 35% and substantially reduce fraud. A conditional government guarantee covering 30% of losses for buildings that meet seismic resilience standards creates strong incentives for risk-reduction investments. Finally, an integrated participatory monitoring platform enables real-time stakeholder engagement and continuous verification of building conditions. Simulation results indicate a 40–60% increase in private-sector participation, a 25% reduction in operational costs, and a 35% improvement in policyholder satisfaction.
CONCLUSION: The findings demonstrate that combining advanced digital technologies with innovative governance structures can fundamentally reshape building insurance systems in natural-disaster contexts. The framework offers practical policy tools ranging from conditional guarantees to intelligent monitoring platforms and automated payment mechanisms that can be adapted to the needs of developing economies. At the same time, the study underscores the importance of tailoring implementation strategies to the specific social, economic, and technological conditions of each country.
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
مدیریت ریسک در صنعت بیمه
Somaieh Babaei؛ Shamsolah Salami؛ Najmeh Molaei
چکیده
BACKGROUND AND OBJECTIVES: In recent decades, the global community has witnessed a marked increase in both the intensity and frequency of natural disasters, resulting in catastrophic human and economic losses. Iran, located in a region characterized by high seismic and climatic risk, faces unique challenges in disaster risk management. Conventional disaster management frameworks predominantly emphasize post-disaster recovery, often neglecting proactive risk-reduction strategies. Therefore, integrating modern financial and insurance mechanisms with advanced technological solutions is a strategic ...
بیشتر
BACKGROUND AND OBJECTIVES: In recent decades, the global community has witnessed a marked increase in both the intensity and frequency of natural disasters, resulting in catastrophic human and economic losses. Iran, located in a region characterized by high seismic and climatic risk, faces unique challenges in disaster risk management. Conventional disaster management frameworks predominantly emphasize post-disaster recovery, often neglecting proactive risk-reduction strategies. Therefore, integrating modern financial and insurance mechanisms with advanced technological solutions is a strategic necessity. This study aims to conduct a comprehensive gap analysis and prioritize disaster risk management strategies in Iran, with a particular focus on incorporating innovations in financial instruments, technology, and governance to address existing operational deficiencies. By evaluating the current infrastructure and operational effectiveness of the Natural Disaster Insurance Fund (NDIF), this research seeks to provide a roadmap for transforming Iran’s disaster management system from a reactive model into a resilience-oriented, proactive framework.
METHODS: This descriptive-analytical study employed both qualitative and quantitative approaches. The research framework was operationalized through a self-administered questionnaire comprising six dimensions and 38 initial indicators. The statistical population included 30 experts and specialists from the NDIF, the Climate Research Center, and the International Institute of Earthquake Engineering and Seismology. Participants were selected through purposive sampling on the basis of their expertise in insurance, disaster risk management, and modern financial instruments. In the first stage, field experts rigorously assessed the content validity of the questionnaire, resulting in refinement and reduction of the indicators to 35 final items. Internal consistency and reliability were confirmed using Cronbach’s alpha, yielding a total coefficient of 0.85. Data normality was examined using the Kolmogorov-Smirnov test, which indicated a non-normal distribution; consequently, the non-parametric binomial test was used to verify the research hypotheses and assess the suitability of the proposed dimensions. In the subsequent stage, the Full Consistency Method (FUCOM), a modern multi-criteria decision-making tool, was implemented in a Python programming environment to calculate weight values. FUCOM was selected for its superior ability to handle expert judgments with a lower deviation rate than traditional methods such as AHP.
FINDINGS: The binomial test confirmed that the technological, financial, and governance dimensions are essential for natural disaster risk management in Iran. The gap analysis revealed a dual reality: while conventional reactive approaches and structural/physical infrastructure have reached relative maturity, significant gaps persist in modern risk management domains. Critical deficiencies were identified in the integration of advanced technologies, including Early Warning Systems (EWS) and big data analytics, as well as in the financial sector’s use of contemporary risk-transfer instruments such as Catastrophe Bonds (Cat Bonds) and blockchain-based smart insurance contracts. FUCOM analysis provided precise prioritization of strategic phases, indicating that the “pre-occurrence” or “prevention and preparedness” phase holds absolute priority with a dominant weight of 0.648. This finding underscores the urgent need to transition from a reaction-oriented (ex post) system to a proactive, risk-oriented (ex ante) framework. Among the specific indicators, “Blue Resilience and Emergency Water Supply” (C1312) emerged as the highest-priority item (weight 0.096), followed by “Land Use Planning and Hazard Zoning” (C1311) with a weight of 0.083. The FUCOM model demonstrated exceptional mathematical precision, with a consistency rate (deviation factor) of 0.0018, reinforcing the reliability of these prioritizations for policymaking. Based on these results, a comprehensive conceptual model for natural disaster risk management was developed according to the priorities derived from FUCOM, highlighting the central role of technological, financial, and governance innovations.
CONCLUSION: This study concludes that the traditional passive role of insurance and financial institutions in Iran is no longer sufficient to cope with the growing risks posed by natural hazards. There is a strategic imperative to redefine the functional role of the NDIF and other stakeholders, transforming them from mere compensators of losses into proactive leaders in risk governance. Institutionalizing technological innovations, such as blockchain to ensure transparency in payments and big data for real-time risk assessment, is essential for this transition. Prioritizing the pre-disaster phase, as highlighted by the FUCOM analysis, will reduce future economic liabilities and significantly enhance national resilience and sustainable development. The findings provide a scientifically validated roadmap for policymakers, emphasizing that proactive, technology-driven, and financially innovative approaches are critical for effective disaster risk reduction in Iran, while also offering a scalable framework transferable to other developing economies facing similar climate risks.
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
مطالعات اجتماعی بیمه
Mohammad Amin Zandi؛ Mostafa Mansouri؛ Mohammad Ehsan Zandi
چکیده
BACKGROUND AND OBJECTIVES: Environmental crises, including rapid climate change, global warming, and resource degradation, pose major challenges to the global community. Rapid industrial growth, urbanization, and expanding consumerism have contributed to a sharp increase in greenhouse gas emissions, creating significant threats to biodiversity, human health, and ecosystem quality. In this context, promoting pro-environmental behavior has become a critical priority that extends beyond traditional environmental sectors and places increasing responsibility on financial and insurance institutions to ...
بیشتر
BACKGROUND AND OBJECTIVES: Environmental crises, including rapid climate change, global warming, and resource degradation, pose major challenges to the global community. Rapid industrial growth, urbanization, and expanding consumerism have contributed to a sharp increase in greenhouse gas emissions, creating significant threats to biodiversity, human health, and ecosystem quality. In this context, promoting pro-environmental behavior has become a critical priority that extends beyond traditional environmental sectors and places increasing responsibility on financial and insurance institutions to support sustainable development. As a cornerstone of the modern economy, the insurance industry is uniquely positioned to mitigate environmental risks through risk-transfer mechanisms and the development of green insurance products. This financial instrument incorporates sustainability considerations into product design, contractual arrangements, and investment policies, thereby discouraging environmentally harmful activities while promoting environmentally responsible projects. Despite its considerable potential to alleviate environmental pressures, the market penetration of green insurance remains limited in developing countries such as Iran, where environmental challenges—including water scarcity and severe air pollution—are particularly acute. Evidence suggests that although environmental awareness is increasing, it does not necessarily translate into purchasing behavior, indicating a persistent gap between knowledge and action. Accordingly, this study seeks to provide a comprehensive explanation of the psychological and structural mechanisms shaping individuals’ willingness to purchase green insurance in Iran. Specifically, it examines how environmental awareness, environmental concern, environmental attitude, and trust in insurers interact to influence consumer decision-making in a developing market context.
METHODS: To identify the determinants of green insurance adoption, this study employs a theoretical framework integrating the Theory of Planned Behavior (TPB), the Knowledge–Attitude–Behavior (KAB) model, and the Environmental Values framework. This integrated model enables the examination of both direct and indirect relationships between cognitive, affective, and attitudinal constructs and individuals’ behavioral intentions. Data were collected through a structured questionnaire administered to citizens residing in major Iranian metropolitan areas. The final sample comprised 397 valid responses, providing adequate statistical power for the analysis. A key methodological feature of the study is the operationalization of the principal constructs. Environmental concern was modeled as a second-order construct grounded in the Value–Belief–Norm theory and composed of three dimensions: egoistic concern, social-altruistic concern, and biospheric concern. Environmental attitude was measured to capture individuals’ worldviews regarding the relationship between humans and nature. Trust in insurance companies was assessed using a multidimensional scale evaluating perceptions of honesty, competence, and benevolence. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), which is particularly suitable for analyzing complex models with latent constructs and predicting behavioral intentions.
FINDINGS: The results of the structural equation modeling support the main hypotheses of the study. Environmental awareness functions as a cognitive driver and exerts a significant positive effect on individuals’ willingness to purchase green insurance. The findings indicate that awareness primarily operates as a foundational factor that reinforces other psychological determinants. Environmental concern—comprising egoistic, social-altruistic, and biospheric dimensions—also demonstrates a significant positive effect and acts as a powerful emotional driver. The greater the threats individuals perceive to themselves, society, or the biosphere, the stronger their willingness to purchase green insurance. Environmental attitude likewise exhibits a positive influence, indicating that a pro-environmental worldview is a meaningful predictor of green financial behavior. Importantly, trust in insurance companies emerges as a critical determinant with a significant positive effect on willingness to purchase intention. Given the intangible nature of insurance services and the novelty of green insurance products, trust serves as a key mechanism for reducing perceived risk and uncertainty. Analysis of the control variables reveals that income level, education, and prior insurance experience positively and significantly influence purchase intention. In contrast, demographic variables such as age and gender do not exhibit a statistically significant effect on the decision-making process.
CONCLUSION: The findings indicate that expanding the green insurance market in Iran requires a multifaceted strategic approach that extends beyond awareness campaigns alone. While increasing environmental knowledge remains essential, the results highlight the importance of fostering deeper emotional engagement and, most importantly, strengthening public trust in insurance institutions. To reduce consumer skepticism, insurance providers must demonstrate a genuine commitment to sustainability through transparent policies and practices. Furthermore, since higher levels of income and education are associated with stronger purchase intentions, early marketing strategies may benefit from targeting these socio-economic segments. Ultimately, developing a robust green insurance market requires more than raising environmental awareness; it demands a comprehensive strategy that combines educational initiatives, value-based engagement, and the strengthening of institutional trust. Such an approach is essential for bridging the gap between environmental concern and actual purchasing behavior.
مقاله انگلیسی برای ویژهنامه "تغییر اقلیم و صنعت بیمه"
مدیریت ریسک در صنعت بیمه
Mahmoud Asad Samani؛ Sahar Haghnazari
چکیده
BACKGROUND AND OBJECTIVES: Climate change, recognized as a systemic risk, has posed unprecedented challenges to the foundations of the insurance sector. As the global environment undergoes rapid transformations, the historical predictability of insurable risks is being fundamentally undermined. This study aims to examine the bidirectional impacts of climate change on the insurance sector, with a particular focus on physical and transition risks. The study further investigates how these risks are reflected in the financial statements and operational strategies of insurance firms. Globally, the insurance ...
بیشتر
BACKGROUND AND OBJECTIVES: Climate change, recognized as a systemic risk, has posed unprecedented challenges to the foundations of the insurance sector. As the global environment undergoes rapid transformations, the historical predictability of insurable risks is being fundamentally undermined. This study aims to examine the bidirectional impacts of climate change on the insurance sector, with a particular focus on physical and transition risks. The study further investigates how these risks are reflected in the financial statements and operational strategies of insurance firms. Globally, the insurance sector is undergoing a fundamental transformation in response to climate change, with insurers increasingly recognizing it as a material financial and strategic risk. This recognition is driving a shift toward more sophisticated climate risk disclosure and internal stress testing. However, responses remain uneven across jurisdictions, and practical adaptation is often limited to pricing and reinsurance adjustments rather than comprehensive long-term resilience planning. Many markets still struggle to integrate forward-looking climate projections into their core business models. Similar challenges persist in Iran, where regional climate volatility adds another layer of complexity to the domestic financial landscape. On the one hand, the country is highly exposed to a wide range of natural hazards, particularly earthquakes and floods, which frequently result in severe socioeconomic losses due to inadequate insurance coverage. On the other hand, the domestic insurance market continues to rely on traditional insurance products and pricing models without an effective, specialized climate risk management framework. This institutional inertia has widened the insurance protection gap and poses an increasing threat to national economic stability.
METHODS: Using a descriptive–analytical approach, this study examines the physical and transition risks associated with climate change and their implications for the insurance sector. The methodology is based on an in-depth review of the existing literature and industry reports to identify emerging patterns of risk and vulnerability. The analysis also draws on key international frameworks, including the Task Force on Climate-related Financial Disclosures (TCFD) and the Network for Greening the Financial System (NGFS), to evaluate how strategic adaptation and innovation can enhance the resilience and sustainability of the insurance sector. By assessing these global benchmarks, the study establishes a link between international best practices and the specific regulatory environment of Iran. It further employs comparative analysis to demonstrate how global adaptation trends can be contextualized within the Iranian insurance market to mitigate future financial shocks.
FINDINGS: Economic losses from natural catastrophes reached USD 318 billion in 2024, resulting in approximately 14,000 fatalities, while 57% of global losses remained uninsured. This highlights a critical vulnerability in the global financial safety net. These losses increased from USD 303 billion in 2023 and exceeded the 10-year average of USD 254 billion, confirming that climate-induced volatility is becoming the new normal. The findings indicate that the sharp rise in losses from natural disasters has rendered insurers’ traditional risk pricing and management models increasingly ineffective. Conventional actuarial methods, which rely heavily on historical loss frequency and severity, are no longer sufficient to capture the non-linear risk dynamics associated with a changing climate. At the same time, climate change presents opportunities for innovation by enabling insurers to expand their portfolios with products such as green insurance and parametric insurance. The findings further suggest that developing innovative solutions, including weather index insurance and renewable energy insurance, can enable insurers to access new markets while supporting environmental objectives. In addition, the analysis demonstrates that investment in digital transformation and catastrophe modeling is essential for maintaining solvency in an increasingly uncertain risk environment.
CONCLUSION: This study examines the physical and transition risks associated with climate change and their implications for insurance, proposing a framework that repositions insurers from passive claims payers to proactive leaders in climate risk management and the sustainable transition. This transformation requires a fundamental rethinking of the insurer–policyholder relationship. Insurance mechanisms should not only compensate losses but also encourage and facilitate preventive action. By incentivizing risk-reduction measures through premium discounts and specialized advisory services, insurers can reduce aggregate losses and strengthen resilience. The study concludes that strategic adaptation and sustained investment in innovation are not only essential for the survival of insurers but also critical to the long-term sustainability of Iran’s insurance sector. Without a proactive approach, the domestic market is likely to face severe capacity constraints and heightened capital volatility. Accordingly, the study proposes several strategic measures, including the establishment of a national climate risk database, the promotion of public–private partnerships for catastrophe insurance, and the adoption of Environmental, Social, and Governance (ESG) criteria in insurance investment portfolios. Implementing these measures can strengthen the sector’s long-term resilience while enhancing its contribution to national climate resilience.
