Countries in Emerging Asia are increasingly exposed to various types of disasters, and the frequency and severity of these events have grown in recent years. Disaster risk finance is a critical tool for mitigating the impacts of disasters. To enhance financial resilience to disaster impacts, a comprehensive framework will be needed. Finding new and innovative policy solutions to enhance disaster risk financing is important. These can take various forms, including risk pooling mechanisms, disaster insurance and catastrophe bonds. Several common challenges confronting the region are discussed in this chapter. In particular, strengthening regulatory frameworks and institutional capacity, facilitating and broadening policy options, enhancing disaster risk finance education, and strengthening regional co-operation are key to strengthen disaster risk finance.
Economic Outlook for Southeast Asia, China and India 2025
Abstract
Growth in countries in Emerging Asia – the eleven ASEAN countries and the People’s Republic of China (hereafter “China”) and India – is expected to remain resilient in the near-term, supported by strong domestic demand, and continuing to account for the largest part of global growth amid a changing global environment. Robust private consumption, and ongoing public investment, remain key growth drivers, along with supportive macroeconomic policies and easier financial conditions. Export performance was holding up well so far in the face of adverse external shocks (OECD, 2025[1]).
However, this growth outlook is still subject to risks. In particular, policy uncertainty related to US tariff rates and broader global trade tensions poses downside risks for the region. Further challenges include climate- and disaster-related shocks, which are especially relevant for many Emerging Asian economies.
Emerging Asian countries are increasingly exposed to various types of disasters,1 with the frequency and severity of such events in the region continuing to escalate in recent years (Figure 1.1). Countries in the region have faced approximately 100 disasters annually, affecting an average of 80 million people each year over the past decade (EM-DAT database). Floods are generally the biggest concern, although the Philippines and Viet Nam are more exposed to tropical storms, and China and Indonesia face a higher risk of earthquakes (Figure 1.2). Some of the region’s most notable disasters include the 2011 floods in Thailand, Typhoon Yolanda in the Philippines in 2013, the 2018 Kerala floods in India, the 2018 Sulawesi earthquake and tsunami in Indonesia, and Cyclone Amphan in India in 2020 (Table 1.1).
Emerging Asia remains particularly prone to catastrophes, although levels of risk vary considerably across the region. According to the WorldRiskIndex 2025, several Emerging Asian countries rank among the most vulnerable globally, including the Philippines (1st out of 193), India (2nd) and Indonesia (3rd). By contrast, Brunei Darussalam (169th) and Singapore (188th) face much lower risk levels.
Note: EM-DAT database does not record significant disaster events for Singapore and Brunei Darussalam.
Source: (EM-DAT, 2024[2]).
Note: EM-DAT database does not record significant disaster events for Singapore and Brunei Darussalam.
Source: (EM-DAT, 2024[2]).
Strengthening disaster risk finance (DRF) has grown increasingly important as both the damages and frequency of disasters rise2 (Figure 1.3). Effective financial preparedness is essential to ensure timely funding for relief, recovery and reconstruction. To enhance financial resilience to disaster impacts, a comprehensive framework will be needed (OECD, 2022[10]) (Box 1.1).
An OECD (2022[10]) report provides a framework to strengthen financial resilience to climate and disaster impacts. A comprehensive framework should involve implementing several interconnected steps. These include identifying, assessing and reporting climate- and disaster-related risks and their financial implications, mitigating financial losses from climate- and disaster-risks, as well as preparing integrated, multipronged government financial strategies.
Assessing how climate and disaster events may result in additional future costs for governments, identifying which sectors and communities are most exposed to these risks, and how damages affect the economy in general, are crucial as a foundation for designing effective responses. Identifying, assessing, and reporting these risks would allow governments to better anticipate how climate and disaster impacts can create future needs of public resources and enable risk-informed planning and decision-making processes.
Promoting and investing in risk prevention, reduction and adaptation can contribute to lowering the potential costs that governments must bear in response to disasters. Protecting households and businesses through insurance and access to credit, aligning incentives across all government levels and ensuring clear public financial assistance arrangements for households and businesses are also important.
Finally, financial resilience would also depend on a coherent mix of financing tools and integrated government financial strategies that can provide adequate and timely resources before and after disaster events. Various financing mechanisms each have different advantages and limitations and thus can complement one another within a co-ordinated multipronged government strategy.
A combination of the above measures and strategies can help prevent escalating and unpredictable public financing needs (OECD, 2022[10]). As a result, the risks of climate- and disaster-related financial losses and damages can be managed in a more effective and resilient manner. Beyond strengthening national financial strategies, promoting global climate and disaster financial resilience is also necessary, considering that climate change is a global phenomenon that requires collective global action.
Source: (OECD, 2022[10]), Building Financial Resilience to Climate Impacts: A Framework for Governments to Manage the Risks of Losses and Damages.
Note: EM-DAT database does not record significant disaster events for Singapore and Brunei Darussalam.
Source: EM-DAT (2025) database.
This chapter discusses recent developments and main challenges in disaster risk finance in Emerging Asia. The structure of the chapter is as follows. It begins with an overview of current policy and financing practices across the region, followed by a discussion of key challenges, including the need to strengthen regulatory frameworks and institutional capacity, expand and diversify policy instruments, enhance disaster risk finance education, and deepen regional co-operation. The chapter concludes with a summary of the main findings and policy implications.
Evolution of disaster risk finance schemes in Emerging Asia
The concept of disaster risk finance is becoming increasingly prominent in Emerging Asia as countries confront more frequent and devastating disasters. In 2016, for instance, the ASEAN Disaster Risk Financing and Insurance Initiative (DRFI) established a framework for regional co-operation that highlighted the need to integrate measures taken before (ex-ante) and after (ex-post) disasters to bolster resilience. Since then, several initiatives have signalled a shift away from reactive, fragmented financing towards comprehensive frameworks that support resilience, sustainable development, and rapid liquidity when disasters strike (ASEAN, 2025[11]). Several initiatives have also been advanced in the framework of APEC to improve disaster risk finance.3
Combining the use of ex-ante and ex-post financing tools is pivotal. Ex-ante instruments, such as insurance, are pre-arranged to provide immediate access to funds once a disaster strikes. Ex-post mechanisms, such as budget reallocation, are mobilised in the aftermath of an event to support recovery and reconstruction. In most countries across Emerging Asia and the OECD, ex-post mechanisms – particularly budget reallocations and in-year reallocations – are widely used. Many countries in Emerging Asia also rely on international assistance, highlighting their reliance on reactive measures to cover post-disaster costs. At the same time, Emerging Asian countries are gradually shifting towards more ex-ante and market-based instruments.4 Indonesia, for instance, has operationalised its national Pooling Fund for Disasters (PFB), institutionalising pre-arranged liquidity (World Bank, 2025[12]). The Philippines has advanced the use of Catastrophe Deferred Drawdown Options (Cat-DDOs) and pioneered parametric insurance as sovereign protection tools5 (World Bank, 2021[13]). Thailand has launched a national Insurance and Risk Finance Roadmap to diversify its financial protection instruments (UNDP, 2024[14]). Thailand and Malaysia have also begun exploring parametric and microinsurance products (Box 1.2). Viet Nam has piloted agricultural insurance schemes to extend financial protection to farmers (Pham and Dao, 2017[15]). However, tools such as catastrophe bonds remain at an early stage across the region (ASEAN, 2025[11]). Indeed, countries differ widely in how far they have developed DRF instruments – and DRF systems remain unevenly institutionalised (Table 1.2).
Parametric insurance – a central type of coverage in disaster risk finance – is triggered when an event meets or exceeds a pre-defined threshold, such as a specific water level, wind speed, precipitation amount or earthquake magnitude. When this threshold is met, payments of a fixed amount are dispersed regardless of the magnitude of losses incurred by the policyholder. Parametric insurance-based coverage can support affordability of coverage by reducing underwriting and loss adjustment costs.
Traditional indemnity insurance, which is based on the amount of assessed losses or damages incurred, differs from parametric insurance that provides coverage when a previously agreed set of conditions or thresholds is met. Although indemnity insurance still predominates in catastrophe bonds and insurance-linked securities (ILS), parametric triggers have become more prominent in the last decade (Figure 1.4).
Source: Artemis Catastrophe Bond and Insurance-linked Securities Deal Directory (database), www.artemis.bm/deal-directory/.
Parametric insurance can potentially offer faster post-event claims payouts, supporting a quicker and more efficient recovery and reducing the impact of natural hazard events. Microinsurance products often incorporate a parametric trigger, for example, for agricultural risks. Parametric triggers can also be utilised as a complement (or serve as an alternative) to traditional indemnity-based property insurance coverage.
Technologies such as drones, satellite imagery, and more advanced generations of weather stations have made it easier to analyse triggering events and disaster thresholds, sustaining momentum in the expansion of parametric insurance coverage. Combining these technologies with data provided by policyholders (e.g. precise locations of insured sites) has enabled more robust and responsive insurance models and risk assessments.
Parametric insurance is becoming increasingly popular in Emerging Asia. For example, in Indonesia, there has been an effort by insurers to develop parametric insurance products for agriculture producers that cover earthquakes and weather-related events. In these agricultural insurance schemes, farmers receive predetermined payouts when certain indicators cross predetermined thresholds. This parametric structure allows insurance programmes for agriculture producers to deliver more predictable and faster support. Some insurers in the Philippines have explored parametric products for natural hazard, though none have been broadly introduced.
Source: (OECD, 2025[16]), Protection Gaps in Insurance for Natural Hazards and Retirement Savings in Asia; (Generali GC&C, 2024[17]), Parametric insurance to build financial resilience; (Artemis, 2025[18]), Artemis website.
Note: At the regional level, SEADRIF provides disaster risk finance support that is available to member countries.
Country-specific developments of DRF
Against this backdrop, this subsection discusses key features of DRF practice and recent policy evolution for each Emerging Asian country (Table 1.3).
Brunei Darussalam benefits from a low-risk exposure, relying on fiscal reserves and stabilisation funds as its primary buffers. While disaster management is well institutionalised under the National Disaster Management Centre (NDMC), demand to develop various DRF instruments remain limited (Government of Brunei Darussalam, 2023[26]). Its policies emphasise the preservation of strong fiscal buffers and co‑operation at the ASEAN level (ASEAN, 2025[11]).
Cambodia’s post-disaster financing relies on contingency funds, budget reallocations, and donor grants, though progress is being made thanks to microinsurance pilots and financial preparedness activities (ADB, 2024[27]). Fiscal constraints and growing risk exposure lead to recurrent financing gaps, reinforcing the need for a national DRF strategy, stronger contingency budgeting and scaled insurance solutions for households and small businesses (World Bank and GFDRR, 2019[21]; ASEAN, 2025[11]).
China presents a different model altogether: it has a public finance response system and an insurance and reinsurance market, although household insurance penetration remains relatively low given the level of economic exposure (World Bank, 2020[28]; OECD, 2015[29]). Key DRF instruments include central contingency allocations, and a growing set of public asset insurance pilots (CBIRC, 2022[30]). Persistent gaps include fragmented DRF responsibilities.
India combines strong ex-post financing structures, a National Disaster Response Fund (NDRF) and State Disaster Response Funds (SDRF) and contingency budgets with large-scale sectoral insurance programmes such as PMFBY, a national crop insurance scheme (UNDP, 2024[31]; OECD, 2015[29]). Despite a relatively mature foundation, reliance on ex-post response, large protection gaps for households and small businesses, and co-ordination challenges among states remain significant. India nonetheless proceeds implement parametric insurance for floods and droughts, while the International Financial Services Centres Authority (IFSCA) is developing regulations to enable ILS and sovereign catastrophe bonds (IFSCA, 2025[32]).
Indonesia faces an average of around 2 900 disasters annually, with average annual losses of around USD 1.37 billion (UNDP, 2025[33]) underpinning a DRF system that blends ex-ante and ex-post mechanisms (United Nations, 2024[34]; UNDP, 2023[35]; BGS and BNPB, 2024[36]; OECD, 2015[29]). Ex-ante instruments like the State-Owned Asset Insurance and the Disaster Pooling Fund are complemented by ex-post instruments like budget reallocations and international aid (UNDP, 2023[35]). However, outdated regulations and legal frameworks (e.g. Law No. 24/2007; and Government Regulation No. 22/2008) and limited subnational participation and co‑ordination (UNDP, 2023[35]) constrain implementation.
Lao PDR faces several constraints, relying predominantly on contingency budgets, reserve funds, and ad -hoc reallocations. It differs, however, in its early adoption of sovereign parametric insurance through the Southeast Asia Disaster Risk Insurance Facility (SEADRIF) and its progress in developing anticipatory action protocols for drought – providing evidence of incremental movement towards pre-arranged financing despite budgetary constraints and weak co‑ordination systems (SEADRIF, 2023[24]; World Bank and GFDRR, 2019[21]; ASEAN, 2025[11]).
Malaysia’s primary hazard is flooding, which has triggered 85% of disasters since 2000 (World Bank and BNM, 2024[37]). While its average annual loss (AAL) is relatively low at around USD 225 million (0.05% of GDP), severe events such as the 2021‑2022 floods have caused losses exceeding USD 1.3 billion (Department of Statistics Malaysia, 2022[38]). Malaysia relies mainly on risk retention tools, federal and state contingency funds, emergency reallocations, and loan moratoria. Sovereign risk transfer tools remain underdeveloped (NADMA Malaysia, 2022[39]; ASEAN, 2025[11]). Institutional fragmentation and outdated directives (e.g. NSC Directive No. 20/2012) constrain co‑ordination. Legal reforms, digital data systems, and expansion of insurance access for small businesses and households are key to strengthening resilience (OECD, 2025[16]; OECD, 2015[40]).
Myanmar remains one of the most vulnerable countries in the region. It has confronted repeated shocks from cyclones, floods, and landslides, with average annual losses estimated at nearly 0.9% of GDP (GFDRR, 2012[41]). Its DRF system is still nascent and heavily dependent on external support, drawing on contingency budgets and donor-funded recovery programmes (ASEAN, 2025[11]). Weak fiscal buffers, the absence of a formal DRF strategy, and fragmented subnational co‑ordination continue to hinder financial preparedness. Key priorities include the development of a national financial protection strategy and improved rapid disbursement systems.
The Philippines has one of the region’s most advanced DRF systems, blending national contingency funds, CAT bonds, the Catastrophe Deferred Drawdown Option (Cat DDO), local risk pools (Philippine City Disaster Insurance Pool), and agricultural insurance, yet still faces fragmented fiscal structures and low insurance penetration (ASEAN, 2025[11]; ADB, 2024[42]). It employs a multi-layered DRF framework combining contingency funds (e.g. the National Disaster Risk Reduction and Management Fund (NDRRMF) and Quick Response Funds (QRFs), and the World Bank’s Cat-DDO facility. Notably, the 2019 sovereign CAT bond provided a USD 52.5 million payout following Typhoon Rai (Odette). Local risk pools and crop insurance programmes supplement these efforts, though low insurance penetration (3% of GDP) and fragmented fiscal classifications persist. Reforms should focus on integrating DRF into the national budgeting system, streamlining fund access, and leveraging green finance for resilience (ASEAN, 2025[11]; ADB, 2024[42]). These developments are consistent with findings from the OECD case study on the Philippines, which highlights the country’s relatively advanced DRFI framework, strong institutional arrangements, and ongoing efforts to scale parametric solutions and local-level financial preparedness (OECD, 2024[43]) (Box 1.3).
Singapore has limited exposure to natural hazards but maintains a highly advanced fiscal resilience framework that is underpinned by substantial reserves and forward-looking climate finance initiatives (Allen & Gledhill, 2025[44]). The country continues to innovate through green bond issuance, climate risk disclosure standards, and enhanced integration of climate risks into public finance (Government of Singapore, 2025[45]; ASEAN, 2025[11]).
Thailand’s exposure to recurrent floods and storms results in an average annual loss of USD 821 million (0.17% of GDP) (UNDP, 2023[46]). Fiscal stress following the 2011 floods prompted reforms, including crop insurance schemes and participation in SEADRIF (SEADRIF, 2024[47]). Yet, the insurance market remains underdeveloped, with less than 5% of at-risk households insured (UNDP, 2024[14]). Legal gaps in Thailand’s Public Finance Act and overlapping institutional mandates hamper sustained DRF integration (The Asia Foundation, 2024[48]). Strengthening DRF under the National Adaptation Plan and expanding coverage for drought-prone agriculture are key priorities (Green Climate Fund, 2025[49]).
Timor-Leste depends heavily on external assistance, transfers from the Petroleum Fund transfers, and contingency reserves to finance disaster response (World Bank, 2024[50]). Limited institutional capacity, and slow access to contingency funds hinder timely disaster response, though recent reforms have started to align contingency procedures and social protection systems with the Sendai Framework.
Viet Nam faces growing disaster losses, with an average annual loss of USD 2.7 billion (0.42% of GDP), primarily from floods and typhoons (UNDP, 2023[51]). Financing continues to rely heavily on contingency budgets and ad-hoc reallocations. Legal and institutional fragmentation under the Disaster Prevention and Control Law (2013) limits co-ordinated DRF implementation. Reforms should include a national DRF strategy, flexible budget transfer mechanisms and systematic expenditure reporting. Expanding agricultural and inclusive insurance via digital platforms and smart subsidies is essential for broadening coverage (UNDP, 2023[51]; World Bank, 2018[52]; ASEAN, 2025[11]).
The Philippines has developed multiple long-standing instruments that form the backbone of its disaster risk finance system, including the National and Local Disaster Risk Reduction and Management Funds (LDRRMF) and the People’s Survival Fund. Specifically, local governments units are explicitly required to allocate 5% of their budget for resilience through the Local Disaster Risk Reduction and Management Fund (OECD, 2024[43]) which ensures reliable financing. The People’s Survival Fund also provides long-term financing streams to finance climate change adaptation measures at the local level (OECD, 2024[43]). Recent reforms have accelerated its use, with utilisation rising from 32% to 89% after 2023 due to stronger inter-agency support (OECD, 2024[43]). These elements position the Philippines among the more institutionally developed DRF systems in the region, reflecting years of investment in planning, governance and adaptation finance.
In practice the use of the LDRRMF primarily incorporates ex-post responses and rehabilitation, with preventive and risk-reduction investments accounting for a smaller share of expenditures. It is highlighted that disaster funds at both national and local levels have “mostly been used to support emergency response, recovery and rehabilitation, at the detriment of risk prevention” (OECD, 2024[43]). Capacity constraints along with uneven technical expertise and competing local priorities can often hinder investment in ex-ante measures (OECD, 2024[43]). Recent reforms and instruments attempt to improve the effectiveness of local disaster financing. With improved guidance and technical assistance along with more inter-agency co-ordination there is improved utilisation of disaster funds, including at the local level. Strengthening local government units’ capacity and improving the monitoring of preventive spending, along with better connecting local financing with planning frameworks, is critical to moving from reactive to preventive disaster risk finance. These steps could further strengthen the LDRRMF as a cornerstone of local DRF and reinforce the role of subnational governments in preparing for rising disaster risks (OECD, 2024[43]).
Source: (OECD, 2024[43]), Adapting infrastructure to changing climatic conditions: The case of the Philippines.
Despite recent progress, countries in Emerging Asia still face several common challenges in advancing disaster risk finance. Key challenges include the need to strengthen regulatory frameworks and institutional capacity, broaden and diversify the range of available policy instruments, and enhance public and stakeholder understanding of DRF. Deeper regional co-operation is also needed to support knowledge sharing, promote market development, and build more coherent and resilient disaster risk financing systems across the region.
Strengthening regulatory frameworks and institutional capacity
Emerging Asia has made notable progress in institutionalising DRF, though the degree of policy integration remains uneven, as discussed before. Most countries, including Indonesia (Law No. 24/2007), the Philippines (Republic Act No. 10121/2010), and Viet Nam (Law on Natural Disaster Prevention and Control, 2013), have established disaster management laws that provide partial mandates for financial preparedness (GFDRR, 2021[53]; UNESCAP, 2022[54]). These disaster risk management laws are often introduced or significantly strengthened in the aftermath of major disasters. In Indonesia, for example, Law No. 24/2007, which serves as the country’s DRM legal framework was enacted following the 2004 Indian Ocean tsunami. However, explicit legal frameworks linking DRF to broader fiscal risk management remain limited, with existing legislation largely emphasising post-disaster response rather than ex-ante financial protection. Provisions for pre-arranged instruments such as insurance, catastrophe bonds, and contingent credit lines are still scarce (ADB, 2024[42]). Emerging reforms in Indonesia and the Philippines, supported by the ASEAN Disaster Risk Financing and Insurance (DRFI) Roadmap, seek to bridge these gaps by harmonising disaster laws with fiscal legislation and by integrating DRF within public financial management (PFM) systems (ASEAN, 2025[11]).
Legal and institutional fragmentation persists within Indonesia and the Philippines. In both countries, decentralised governance across various government agencies complicates the mobilisation of pre-arranged disaster financing (OECD, 2024[9]). In the Philippines, a multi-layered institutional framework spreads authority across different agencies, which can delay financing decisions and limit the predictability of funds for local-level recovery. Similar challenges have been observed in Thailand, where fragmented responsibilities and unclear divisions of authority between national and subnational agencies complicate the government co-ordination during flood events, affecting both the emergency response and fiscal decision-making (OECD, 2024[9]).
Institutional arrangements for DRF are typically dispersed across ministries of finance, disaster management agencies, line departments, and subnational governments, which often results in overlapping mandates and co‑ordination challenges. Only a few countries, notably Indonesia and the Philippines, have established dedicated DRF units within their Ministries of Finance to ensure better fiscal alignment and faster fund mobilisation (World Bank, 2021[55]). Nonetheless, fund disbursement delays and the absence of fiscal risk statements remain persistent weaknesses in many countries (UNDRR, 2023[56]). While most countries maintain contingency funds and emergency reserves, the assessment of disaster-related fiscal risks is rarely integrated with the rest of the fiscal risk assessment frameworks and the budget statements, with some exceptions (ADB, 2024[42]). Embedding DRF effectively would require several elements that underpin quality budget institutions (Box 1.4), including setting multi-annual expenditure baselines and binding ceilings, building reliable forecasting capacities, preparing comprehensive fiscal risk statements, and attaching them to budget submissions, as well as ensuring well-co-ordinated multi-year budget planning across government levels and agencies (OECD, 2025[57]; OECD/ADB, 2025[58]). DRF mechanisms in many Emerging Asian countries are not fully integrated within the national public financial management system. Addressing these challenges requires modernisation of legal and regulatory instruments, stronger co‑ordination mechanisms, more transparent monitoring systems and investments in digital infrastructure.
High-quality budget institutions provide the foundation for credible and risk-informed fiscal management. A central feature is the use of realistic baselines, which set out the forward cost of existing policies and ensure that new measures are assessed against a transparent and consistent starting point (OECD, 2025[59]). These baselines help identify fiscal pressures early and support alignment between annual budgets and medium-term plans.
Strong forecasting capacities are equally important. Reliable projections of revenues, expenditures and macroeconomic conditions reduce uncertainty and allow governments to anticipate risks and adjust policies in a timely manner. Complementing this, fiscal risk statements provide a structured approach to identifying, analysing and disclosing risks, from macroeconomic volatility to contingent liabilities, that may cause deviations from expected fiscal paths (OECD, 2025[59]).
To anchor these processes, binding ceilings on expenditure or other fiscal aggregates help maintain discipline, reinforce medium-term targets and create predictable space for responding to shocks. Together, these elements strengthen the coherence, transparency and resilience of the budget framework.
Beyond institutional rules, effective budgeting also depends on the quality of processes and capabilities that support implementation (OECD, 2025[59]). This includes transparent reporting, reliable data systems, clear accountability arrangements and performance-oriented budgeting practices that help ensure that fiscal objectives are met in practice. These features act as important complements to formal budget institutions, enabling governments to translate rules and frameworks into sustained improvements in fiscal management.
Source: (OECD, 2025[59]), Quality Budget Institutions: Developments in OECD Countries.
Source: Authors’ compilation based on (GFDRR, 2021[53]); (UNESCAP, 2022[54]); (ADB, 2022[60]); (ADB, 2023[61]); (UNDRR, 2023[62]) (SEADRIF, 2023[24]); (World Bank, 2020[22]) (World Bank, 2021[55]); (World Bank, 2023[63]); (Government of the Philippines, 2010[64]); (Government of Indonesia, 2007[65]); (Government of Viet Nam, 2013[66]).
Align disaster risk finance legislation with public financial management systems
Aligning disaster risk finance legislation with national public financial management systems is essential for effective institutionalisation of DRF instruments (OECD, 2015[40]; OECD, 2022[67]). Fragmented and outdated legal and regulatory frameworks constrain the use of modern DRF tools in Emerging Asia. Countries such as Indonesia and Thailand still rely on fiscal laws that do not explicitly accommodate parametric insurance, catastrophe bonds, or contingent credit lines, while mechanisms in the Philippines remain partly discretionary and donor-driven (ASEAN, 2025[11]). In countries with less DRF capacity (e.g. Cambodia, Lao PDR, and Myanmar), most initiatives remain external to domestic fiscal systems. Aligning and co-ordinating disaster and public financial management (PFM) laws would establish legal clarity for risk-layered instruments, streamline the release of funds, and ensure that DRF is embedded in national budget cycles. Updated fiscal legislation should also institutionalise the use of ex-ante financing mechanisms, allowing financial resources allocation before disasters occur. Moreover, this legislation should establish explicit mandates for DRF agencies to co-ordinate with treasury, budgeting, and audit authorities. Such reforms would enhance accountability, reduce bureaucratic delays and enable more agile and predictable disaster financing across the region.
OECD (2022[68]) emphasises that disaster risk financing is most effective when disaster-related fiscal risks are embedded in public financial management systems instead of being treated as ad-hoc emergency measures. Integrating DRF into public financial management frameworks supports more predictable financing and reduces reliance on post-disaster reallocations (OECD, 2022[68]). To support this, there is also a need to better measure and monitor disaster-related losses, as data is often missing at the national level. Moreover, aligning DRF strategies with core budget processes, such as budget preparation and execution, is critical to the improvement of fiscal resilience. Linking risk assessments with contingency planning and budget baselines, as well as the transparency of disaster-related fiscal risks remains essential. Without this alignment, disaster spending can be fragmented and poorly embedded into the budgeting process (OECD, 2022[68]). Clear institutional roles and co-ordination mechanisms are also essential to harmonise DRF with public financial management systems. OECD (2022[68]) underscores the need for well-defined responsibilities across treasury, budgeting and subnational authorities, as well as shared roles for triggering and executing DRF instruments. In addition, it is also important to establish clear and explicit cost-sharing mechanisms and financial arrangements for post-disaster assistance between national and subnational governments. Clear rules for financial assistance help national government assess fiscal risks and reduce implicit liabilities by encouraging subnational governments to manage the risks they assume. Strengthening these arrangements can improve accountability and accelerate fund disbursement all while enhancing the effectiveness of disaster-related spending, particularly in decentralised systems. Ensuring that different levels of government share clear procedures for triggering and executing DRF instruments would improve the timeliness and effectiveness of disaster-related spending across Emerging Asia.
Strengthen institutional co-ordination between national and local governments
Institutional fragmentation and co-ordination gaps are pervasive challenges. Institutional fragmentation is a major constraint to timely and effective disbursement of disaster risk finance. DRF responsibilities are dispersed among disaster management agencies, finance ministries, and line departments, often causing duplication, delayed disbursement, and inconsistent implementation (UNDRR, 2023[56]). Local governments, especially in Indonesia and Viet Nam, face lengthy approval processes for accessing national funds, and disaster spending is rarely itemised in local budgets (Table 1.4). Clarifying institutional roles, strengthening inter-agency co‑ordination and providing subnational capacity-building on DRF, procurement and risk-informed budgeting will be critical for improving efficiency and accountability. National governments play an important role in aligning DRF strategies. Predictable transfers, clear budget guidance and communication of national policies help subnational governments avoid unplanned adjustments (OECD, 2024[69]).
Among countries in the region, the Philippines provides the clearest example of a well-defined local government disaster contingency mechanisms (Table 1.4). Local governments in the Philippines maintain Local Disaster Risk Reduction and Management (LDRRM) Funds, with 5% of regular local government units' revenue earmarked for disaster risk reduction and emergencies. Of that sum, 30% is earmarked for the Quick Response Fund (QRF) – a rapid-release budget facility for line agencies and local governments to support immediate relief and recovery needs following disaster events. The remaining 70% is set to finance prevention, mitigation and preparedness measures. Most other countries in the region do not require a uniform statutory earmark at the local level. While many have central or provincial contingency budgets, local requirements vary widely. Indonesia and Viet Nam use a mix of general subnational budget allocations and small reserve funds but lack standardised or explicitly mandated local budgets for disaster expenditure.
% of annual local budget
Note: Viet Nam law requires all levels of government to maintain contingency reserves of 2-4%. Philippines allocates 5% of local government units regular revenues.
Source: (OECD, 2024[9]); (JICA, 2012[70]); (ADB, 2024[42]); (World Bank, 2020[28]); (AON, 2023[71]); (Swiss Re, 2024[72]); (GFDRR, 2017[73]); (Malaysian Re, 2021[74]); (Zurich Malaysia, 2023[75]); (World Bank and GFDRR, 2019[76]); (World Bank, 2021[13]); (Swiss Re, 2020[77]); (OIC, 2014[78]); (World Bank, 2018[52]).
Update monitoring, evaluation, and data systems
Weak monitoring, evaluation, and data governance systems impede transparent and risk-based management. Many countries lack standardised DRF risk exposure mapping, and real-time information systems (UNDRR, 2023[79]). Existing audits emphasise procedural compliance rather than performance or outcomes. Integrating risk analytics into budgetary frameworks and linking expenditure lines with quantified contingent liabilities and average annual losses, would enable proactive fiscal planning. The introduction of digital platforms and early-warning triggers combined with forecast-based financing (FBF) mechanism can further enhance timeliness, allowing funds to be released before disasters occur. Updating audit frameworks to assess efficiency and value for money, alongside improved digital infrastructure and risk modelling, would strengthen the fiscal discipline of DRF.
Updating monitoring, evaluation, and data systems is essential for improving transparency and risk-informed fiscal management (Box 1.5). Weak data governance and fragmented monitoring frameworks limit the ability of governments to assess DRF performance and allocate resources effectively. Governments should digitise expenditure tracking, and develop interoperable databases that consolidate information on hazards, exposure, and losses (OECD, 2015[29]).
Sound risk assessment begins with a reliable evidence base, built on systematic collection of data on hazards, exposures, vulnerabilities, and losses, which is a fundamental step in effective risk assessment for disaster insurance. Data availability can directly impact the market through various ways. For instance, if data on hazards and exposures is either unavailable or unreliable, insurers may either overestimate or underestimate risks, leading to products that are either too costly for consumers or unprofitable for insurers. With better data, insurers can accurately estimate the potential costs of a disaster, allowing them to set premiums that are both competitive and sufficient to cover losses. It also helps them to better manage their capital and reinsurance needs, thus making the market more resilient to large- scale disasters. The inherent uncertainty of disaster risk makes it hard to price risk transfer products or set appropriate terms – hence the recommendation to use alternative data sources (Zhao and Yu, 2020[80]). Satellite and drone imagery, for instance, offer real-time insights into risk exposure - but these technologies require sophisticated machine vision tools (Sheehan et al., 2023[81]).
ASEAN countries are taking steps to improve the availability and reliability of disaster data. For instance, in Indonesia the National Disaster Management Agency (BNPB) and the Central Statistical Agency (BPS) jointly developed Satu Data Bencana Indonesia (SDBI) – the official sectoral statistical platform – under UNESCAP’s Disaster Statistical Framework (DRFS). This initiative aligns with SDBI’s principles, ensuring consistent data standards and shared goals across various statistical and geospatial output. The initiative integrates various existing disaster data platforms like InaRISK, InaSAFE, DIBI and IRBI, enabling flexibility in the disaster database for analysis at different geographic scales using the same basic inputs (BNPB, n.d.[82]). There is still room for improvement, however, with weak co‑ordination, non-standardised methodologies, and poor communication among the biggest challenges (Marihot et al., 2023[83]).
The Viet Nam Disaster Management Authority (VNDMA) has established a disaster data management system that includes demographic information, evacuation and emergency response networks, as well as a historical archive of natural disasters since 1998, categorised by location, frequency and intensity (UNDP, 2023[51]).
Source: Zhao and Yu (2020[80]), Sheehan et al. (2023[81]), (BNPB, n.d.[82]), Marihot et al. (2023[83]), (UNDP, 2023[46]) (UNDP, 2023[51])).
Partnerships between scientific and financial institutions are vital for up-to-date monitoring, evaluation and data systems. The geohazard science partnership between Indonesia and the United Kingdom demonstrates how integrating geospatial data, risk modelling and capacity sharing can enhance fiscal risk analytics and inform anticipatory financing programmes (BGS and BNPB, 2024[36]). Integrating these approaches into treasury and disaster agencies, supported by ASEAN or ASEAN Disaster Risk Financing and Insurance (DRFI)-led regional data hubs, would allow real-time monitoring, early warnings, linked-fund releases, and transparent evaluation of outcomes, ensuring that public spending is aligned with quantified risks and measurable resilience gains. More recently, the effective use of artificial intelligence (AI) and Big Data is proving critical for disaster data management (Box 1.6).
As disasters become more frequent and severe, artificial intelligence (AI) and Big Data are posed to play important roles in enhancing disaster resilience. AI can be developed in numerous ways, such as contributing to early warning systems, as well as for forecasting and prediction. It can also be used for damage assessment, risk mapping, communication support and real-time monitoring and detection. By speeding risk predictions, AI can enhance readiness and facilitate more timely, efficient responses and recovery planning: integrating AI into disaster management and mitigation efforts is not just beneficial: it is essential.
Indeed, AI and Big Data have already been developed in disaster risk reduction. For instance, AI-based approaches have contributed to projections of rising sea levels (Bahari et al., 2023[84]), which is a pressing issue for many Southeast Asian countries. Various models based on machine learning offer short- and long-term projections. Haasnoot et al. (2021[85]) used Big Data to project how much sea levels would rise by 2100 and 2150, respectively, under scenarios (noting that needs differ by country). Planning for extreme heat events is another use case. AI-based modelling has proven to be more accurate than conventional techniques that rely on scarce observational data (Miloshevich et al., 2023[86]). Rare event algorithms, which are already used for studies in biology, chemistry, and physics can be designed to sample extreme heatwaves in models and reveal their characteristics (Ragone, Wouters and Bouchet, 2017[87]).
Additionally, AI can facilitate the estimation of disaster damage costs. Benefits include accelerated disaster response, lower human-resource requirements (particularly for specialists, such as electricians or various types of engineers), and less human error. Not only does this reduce costs for policymakers and insurers, but it can also improve the credibility of their assessments.
Big Data primarily refers to datasets that are too large or complex to be dealt with by traditional data processing application software. The number of available datasets is expanding rapidly. This increases the difficulty of data processing but also provides valuable sources for more granular analysis (Yu, Yang and Li, 2018[88]). Due to its effective application in other fields and previous trials in disaster management, the most recent literature on post-disaster cost assessment recommends the collection and analysis of diverse and extensive datasets, comprising pre-data information, spatial imagery, and mobile data (Jeggle and Boggero, 2018[89]; World Bank/Global Facility for Disaster Reduction and Recovery, 2023[90]).
AI systems are increasingly being used to analyse satellite imagery, sensor data and other sources in real time to anticipate natural disasters and trigger faster emergency response. Advanced applications are also emerging in law enforcement and disaster risk management, where AI supports early-warning systems, rapid damage assessments and improved co‑ordination during crisis events (OECD, 2025[91]).
It is important to note that appropriate tools and methods for data collection differ by assessment target. In this context, traditional analytical methods are not necessarily sufficient for timely and accurate analysis of Big Data. AI, with its exceptional data processing and analytical capabilities, is a powerful tool for post-disaster damage cost assessments that require extensive datasets (Sun, Bocchini and Davison, 2020[92]). The main categories of Big Data sources include social media, crowdsourcing, mobile GPS, geographic information systems (GIS), Internet of Things (IoT), satellite imagery, aerial imagery and videos from unmanned aerial vehicles (UAVs), as well as airborne and terrestrial Light Detection and Ranging (LiDAR). Some types have been incorporated into analysis due to advancements in data processing techniques (e.g. social media), while others gained traction thanks to better data collection technology (e.g. aerial imagery).
Source: (Bahari et al., 2023[84]), Predicting Sea Level Rise Using Artificial Intelligence: A Review; (Haasnoot et al., 2021[85]); Long-term sea-level rise necessitates a commitment to adaptation: A first order assessment; (Jeggle and Boggero, 2018[89]), Post-Disaster Needs Assessment (PDNA): Lessons from a Decade of Experience; (Miloshevich et al., 2023[86]), Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data; (Molnar-Tanaka and Shao, 2025[93]), Using AI to measure disaster damage costs: Methodology and the example of the 2018 Sulawesi earthquake; (Ragone, Wouters and Bouchet, 2017[87]), Computation of extreme heat waves in climate models using a large deviation algorithm; (Sun, Bocchini and Davison, 2020[92]), Applications of artificial intelligence for disaster management; (World Bank/Global Facility for Disaster Reduction and Recovery, 2023[90]), Global Rapid Post-Disaster Damage Estimation (GRADE) Report: February 6, 2023 Kahramanmaraş Earthquakes - Türkiye Report; (Yu, Yang and Li, 2018[88]), Big Data in Natural Disaster Management: A Review.
Facilitating and broadening policy options for disaster risk finance
Various disaster risk finance policy options and their characteristics
As discussed before, disaster risk finance includes a range of ex-ante and ex-post policy options. Figure 1.5 illustrates funding approaches to cover contingent liabilities from disasters (OECD, 2022[67). This framework recognises that no single financial instrument can provide adequate protection against all disaster risks. Instead, a mix of policies is encouraged and a holistic approach to DRF is essential to ensure that disaster preparedness is sustainable, inclusive and effective. Consequently, countries adopt a multifaceted strategy, that combines risk retention and risk transfer. Risk retention involves allocating domestic resources (e.g. contingency funds, reserves, or budget reallocations) to absorb disaster costs internally, while risk transfer shifts the financial burden through instruments such as indemnity insurance, event-based financial protection (OECD, 2022[67]).
In general, low-frequency but high-severity events – such as major earthquakes or cyclones - are optimally managed through such risk transfer tools as insurance (OECD, 2022[67]). Overall, risk transfer instruments provide liquidity quickly and reduce fiscal exposure. Conversely, high-frequency but low-severity shocks can be addressed through risk retention instruments – reserve, contingency funds, and budget allocations. Though the optimal mix will depend on country circumstances and the relative cost of insurance and borrowing, which is affected by interest rates and market cycles (OECD, 2022[67]).
Source: (OECD, 2022[10]).
Budgetary instruments, such as budget allocations and reallocations, are key elements of disaster risk finance. They can provide financing tools used for both ex-ante preparedness and ex-post responses. Ex-ante budget allocations can help secure funding for prevention, preparedness, response and other disaster risk reduction activities. These help governments minimise future losses and avoid sudden and unplanned financial disruptions. They ensure disaster risk finance through pre-allocated regular budget lines or contingency funds set aside for unexpected events (ASEAN, 2021[94]; AuctusESG, n.d.[95]). On the other hand, ex-post budget reallocations become critical once disasters happen. They allow governments to respond to disasters by quickly reprioritising and shifting funds from one budget category (usually less urgent expenditures) to another, or create supplementary budgets to finance relief, recovery and reconstruction efforts especially in the wake of major disasters (Cevik and Huang, 2018[96]). Together, these mechanisms should operate within a disaster risk-based budgeting framework, which highlights the importance of embedding disaster risk considerations throughout the government budget cycle and into the country’s fiscal frameworks in general (Skalon et al., 2024[97]; Akanbi, Kilembe and Park, 2025[98]), ensuring that fiscal and budget planning and post-disaster adjustments are aligned with assessed risks of disasters. This can help speed up emergency responses and action in the case of disasters. Evidence from international experience shows that well-designed budgetary instruments play a central role in strengthening fiscal resilience and ensuring timely post-disaster financing (OECD/The World Bank, 2019[99]). Moreover, they can also strengthen disaster risk resilience by national governance and planning, since an optimal DRF budgeting process will need to be supported by proper risk assessments (for risk-informed budget decision), and expenditure tracking and monitoring, as well as adequate institutional co‑ordination (Skalon et al., 2024[97]; UNDRR, 2023[79]; Choi, S. et al., 2023[100]; Cevik and Huang, 2018[96]; OECD, 2012[101]).
Governments can use taxation to fund recovery programmes and disaster risk management (Box 1.7). Government also often offers temporary relief from taxes and other obligations that would otherwise burden disaster affected households – either as a bespoke measure or through existing policy provisions (e.g. temporary tax-relief provisions). Chatterjee (2019[102]) notes that tax regimes have sometimes been modified to pay for disaster recovery, with some jurisdictions introducing new taxes to raise the necessary funds. These taxes are often national, making it possible to distribute the added burden across all regions. One example is the one-off national flood reconstruction income tax introduced in Australia following the 2011 Queensland floods (Australian Parliament, 2011[103]). Japan also introduced of a temporary 2.1% income tax increase for 25 years after the 2011 earthquake (NTA, 2024[104]).
In addition, international aid and donation from philanthropic organisations are also some of the most common DRF tools, particularly, often used to cope with disasters in less developed countries.
The different financial tools – budgetary, risk financing, and risk transfer – that may be employed to secure funding for disaster damages involve different advantages and limitations, particularly in terms of speed of access and cost (OECD, 2022[10]).
Ex-ante budgetary tools, such as dedicated reserve funds, generally involve low transactional costs relative to market-based instruments but potentially high opportunity costs, depending on potential alternative use of the funds as well as on the ability of governments to earn a return on any invested funds. Funding through reserve funds can generally be accessed quickly, depending on legislative requirements related to their use, although there may be political risks (such as appropriation) if funds are not accessed. Credit-based financing, including public debt finance, involves transaction costs, including issuance, underwriting and interest costs for debt securities and interest costs for commercial bank loans. Bond issuance and commercial bank loans may take longer to access, depending on a country’s existing relationships with investors and lenders. Official financing, while (usually) accessible only to developing countries, is generally provided at low or no-cost. Humanitarian assistance and concessional loans (arranged ex-post) can be volatile and uncertain and subject to political considerations in the donor country. Contingent credit arrangements provided by official lenders will provide the quickest and most secure access to funding and can mitigate some of the risks related to the volatility of official financing.
Risk transfer tools, including insurance and insurance-linked securities such as catastrophe bonds, involve transaction costs in the form of premium payments in the case of insurance and reinsurance (which incorporate commissions, insurer operating expenses and profits) and issuance, underwriting and interest costs in the case of catastrophe bonds. These costs can be significant depending on the level of diversification in the risks transferred, the expected frequency of payouts and the quality of risk information – and will ultimately depend on loss experience. Indemnity-based insurance would likely be more costly than event-based (parametric) coverage given higher underwriting and claims adjustments costs but would also provide coverage consistent with losses and damages incurred (event-based coverage can involve basis risk which could lead to uncovered losses or damages). Event-based coverage will payout much more quickly than indemnity insurance, including in the case of catastrophe bonds that apply event-based triggers (including modelled loss triggers). Risk transfer tools involve opportunity costs relative to alternative uses of the funds used to pay premiums or interest on catastrophe bonds and political risks if premiums are paid over many years without the occurrence of a covered event.
Figure 1.6 provides a simplified illustration of the relative speed of access, amounts available and (approximate) cost of different types of disaster risk finance instruments, though the relative cost, speed of access and available amounts could vary significantly for different countries (OECD, 2022[10]).
Note: This chart provides an illustration of relative (approximate) cost, amounts available (relative size of shape) and speed of access for different types of budgetary, risk financing and risk transfer tools (the different categories are linked by lines in the graph and shaded in different colours. The actual cost and speed will depend on specific country circumstances.
Source: (OECD, 2022[10]).
Developing adequate strategies for DRF is critical. This could include the establishment of reserve and contingency funds, budget reallocation procedures, debt management strategies and risk transfer arrangements. Financial management strategies should benefit from strong co‑ordination across the different crisis financing facilities available from development banks and other official donors and creditors to respond to identified funding needs. There may be opportunities for innovation in development partner contributions to climate-related financial instruments – some options for consideration include: support for the inclusion of hurricane (or more general) catastrophe clauses in debt issuances by climate-exposed developing countries or financial sector capital requirements; support for developing other forms of catastrophe protection for borrowing by climate-exposed developing countries (e.g. catastrophe wrappers that provide debt relief upon occurrence of a climate event); supporting further diversification of risk across regional risk pools and catastrophe risk insurance programmes in order to reduce reinsurance costs; and re-orienting ex ante premium subsidies for regional risk pool participation to ex post loss sharing, which could provide similar benefits in terms of reducing the cost of participation without subsidising the profits of (re)insurance companies. Development partners could potentially transfer some of their own exposure to loss sharing to reinsurance and retrocession markets (OECD, 2022[10]).
Furthermore, the choice and combination of various policy options for disaster risk finance will depend not only on various economic conditions such as fiscal space, capital market development, and access to debt market, but also the types and magnitudes of disruptions of the disasters. These various factors will need to be considered to determine best policy mix of DRF instruments (Box 1.8).
At the aggregate level, tax revenues in ASEAN countries and China rose from 13.8% in 2010 to 15.0% in 2023, showing slow but steady fiscal capacity expansion. All economies in Emerging Asia reported levels below the OECD average (OECD, 2025[105]) ratios ranged from 11.0% in Lao PDR to 20.4% in China. This narrow fiscal room means that large, unexpected costs from disasters could place significant pressure on budgets. Trends over time also vary markedly: between 2010 and 2023, some economies, such as Cambodia, achieved substantial increases in their tax-to-GDP ratios (up by 7.1 percentage points), while others have remained broadly stable or recorded declines, including China and Viet Nam, where ratios fell by more than 3 percentage points over the period (OECD, 2025).
Beyond the overall level of revenue raised, the composition of taxation in Emerging Asia differs from OECD economies. ASEAN countries and China rely more heavily on consumption taxes – particularly value-added taxes and other taxes on goods and services – while collecting a relatively smaller share from personal income taxes and a larger share from corporate income taxes (OECD, 2025[105]). These patterns reflect the prevailing tax structures in the region and shape overall revenue mobilisation trends.
Source: (OECD, 2025[105]), Revenue Statistics in Asia and the Pacific 2025: Personal Income Taxation in Asia and the Pacific.
The need for comprehensive and effective disaster risk finance frameworks is attracting attention as countries confront more frequent and severe disasters. Bolstering resilience to cope with rising costs and worsening disaster impacts requires expanding DRF options and combining tools effectively.
A theoretical study using a macroeconomic equilibrium model examined two disaster risk financing measures – the issuance of sovereign (government-issued) catastrophe bonds and taxation – and compared their impact on economic welfare and growth (Molnar-Tanaka and Sakamoto (2025[106]). Given the strengths and weaknesses of both catastrophe bonds and taxation, using mix of these tool is key for effective disaster risk management. Figure 1.8 Panel A shows that the sovereign catastrophe bond option has an advantage from an economic welfare point of view. At the same time, tax financing leads to higher long run economic growth (Panel B).
Note: Welfare and growth effects are derived from a macroeconomic equilibrium model comparing sovereign catastrophe bonds and tax-based financing for disaster risk management. Results illustrate model-based calibrations.
Source: Molnar-Tanaka and Sakamoto (2025[106]).
Based on the theoretical model, they highlighted the importance of having the right balance between those two financing tools by considering the benefits of these two instruments for effective disaster risk finance.
Source: Molnar-Tanaka and Sakamoto (2025[106]), Financing the costs of disasters: Catastrophe bonds or taxation?, OECD Development Centre Working Papers No. 354.
The expanding use of disaster risk insurance
Among the diverse ex-ante DRF tools, insurance – including parametric insurance – offers useful solutions. Insurance mechanisms can ensure predictable and rapid payouts after disasters, enabling countries to fund recovery efforts. Indeed, the use of insurance schemes to cover disaster risks has expanded significantly in Emerging Asia in recent years. From 2004 to 2018, the penetration of non-life insurance in the ASEAN region grew from 1.1% to 1.7% of GDP, and shifting the financial burden in the event of disasters (Ikeda, Palakhamarn and Anbumozhi, 2024[107]). Yet, insurance penetration in ASEAN remains relatively low compared to other regions (Table 1.5), indicating significant potential for expansion in disaster insurance markets.
Protection gaps remain a major constraint in the effectiveness of DRF. The disparity between total economic losses and those covered by insurance is immense, with 92% of losses uninsured over the past 20 years (Swiss Re, 2021[108]). In 2023, this "protection gap" for the Asia Pacific region stood at 91%, with only 9% of losses – roughly USD 6 billion – covered by insurance (AON, 2024[109]). That is well below the average annual insured loss of about USD 15 billion, since the start of the century – highlighting the urgent to expand insurance protection in the region. Additionally, this gap is even more pronounced in medium- and low-income ASEAN countries, which leaves governments and individuals to bear the financial burden.
Several supply and demand factors affect protection gaps in insurance (OECD, 2025[16]). Low Insurance coverage across Asia is driven by a limited understanding of its necessity and advantages – as well as a lack of trust in insurance. Confusion about what is covered – and whether additional coverage is needed – often reflects insurers’ approaches to natural hazard coverage. Moreover, insurance coverage may simply be unaffordable for lower-income segments of the population and those located in high-risk areas. Insurers may also face constraints to their willingness or capacity to provide natural hazard coverage. Limited access to quality data and risk assessment tools like catastrophe models can make it difficult for insurers to accurately assess the potential frequency and severity of losses, and to accurately price premiums based on risk. The same dynamic affects the reinsurance market.
Note: Estimates reflect publicly available indicators of insurance penetration. Flood-specific household insurance data remain limited across Emerging Asia and are presented qualitatively.
Source: Authors’ compilation based on (OECD, 2024[9]); (JICA, 2012[70]); (ADB, 2024[42]); (World Bank, 2020[22]); (World Bank, 2021[23]); (AON, 2023[71]); (Swiss Re, 2020[77]); (Swiss Re, 2024[72]); (GFDRR, 2017[73]); (Malaysian Re, 2021[74]); (Zurich Malaysia, 2023[75]); (World Bank and GFDRR, 2019[21]) (OIC, 2025[110]).
National insurance or reinsurance schemes have been used in OECD countries as well as Emerging Asian countries. Examples include essentially public insurance carriers such as the National Flood Insurance Program (NFIP) in the United States and the Turkish Catastrophe Insurance Pool (TCIP), as well as the French Caisse Centrale de Réassurance (CCR), which acts as a public-sector reinsurer (OECD, 2024[111]). In parallel, governments should foster private insurance markets, so that private insurance companies could cover the main part of disaster losses suffered by households and firms (OECD, 2021[112]) (Box 1.9).
The (OECD, 2021[112]) discusses several important points to develop disaster insurance programme. While establishing a catastrophe risk insurance programme to broaden insurance coverage, governments need to carefully consider the potential trade-offs inherent in different approaches to programme design, including:
Approaches designed to ensure coverage availability do not always result in broad coverage as policyholders may underestimate the risk of losses or have an expectation of government financial support should a large catastrophe occur and therefore not acquire the available insurance coverage.
Efforts to support affordability through cross-subsidisation between policyholders can blunt incentives for risk reduction and can raise issues of fairness if cross-subsidies benefit wealthier policyholders that could afford to pay higher premiums, although some mutualisation may be necessary for some risks to become insurable.
Subsidisation of the aggregate cost of programme coverage can put taxpayers at risk and might also raise competition concerns if the coverage provided by catastrophe risk insurance programmes competes directly with coverage provided by private (re)insurers.
Limiting the scope or amount of coverage provided by a catastrophe risk insurance programme to specific perils or policyholders can reduce public sector exposure although may lead to gaps in coverage and can also reduce the ability of the programme to benefit from diversification.
Catastrophe risk insurance programmes can play an important role in developing modelling and risk analytics tools – particularly for perils that have not traditionally created significant exposure for private (re)insurers – although limiting private sector involvement in the assumption of risk could hamper the development of private sector models and analytics.
Catastrophe risk insurance programmes can provide a source of expertise and funding to support risk reduction although their capacity to contribute will depend on the scope of the coverage that they provide (and the amount of premiums that they collect).
Careful consideration should also be given to the differences in the characteristics of the underinsured peril. By nature, some perils are more challenging to quantify or lead to high levels of correlation in losses:
Quantifying the financial consequences of infectious disease outbreaks, for example, involves uncertainties related to not only the frequency and severity of outbreaks, but also to the response of public authorities and individuals as well as the capacity of public health systems to manage the health impacts.
A number of perils (e.g. cyber risk, infectious disease outbreaks) can materialise as both low- and high-severity events with not all occurrences of the peril leading to catastrophic losses.
Perils also differ in terms of the level of correlation across countries and the diversification benefits that can be achieved in a global portfolio. Cyber risks and pandemics, for example, cannot necessarily be diversified by assuming risk in different countries.
Source: (OECD, 2021[112]), Enhancing Financial Protection Against Catastrophe Risks: The Role of Catastrophe Risk Insurance Programmes.
Expanding types of disaster insurance schemes
Several types of insurance schemes are used to manage disaster risks in Asia. A study by Surminski, Panda and Lambert (2019[113]) found that the majority of schemes (71%) provide microinsurance mainly because they are small and are also easily linked to existing microfinance schemes in developing Asia. Microinsurance schemes across Asia typically operate at the local or state level or apply to a small subgroup. Meanwhile, 14% of existing programmes are larger sovereign risk schemes. These schemes range from single-country schemes to regional ones (e.g. the Pacific Catastrophe Risk Assessment and Financing Initiative, which pools together sovereign disaster risks across 15 Pacific nations). A small minority (5% of schemes) provide coverage to private property held by small and medium-sized enterprises, while a further 10% insure institutions at an intermediary (meso) level (e.g. Vision Fund, which insures microfinance institutes across Cambodia and Myanmar). The schemes listed lean towards multi-risk contracts, with about 60% offering bundled coverage and offering single-peril insurance contract. Recent assessments show microinsurance is expanding steadily, with 330 million people covered across 36 countries, but still reaching only 11.5% of the potential market (Micro Insurance Newtwork, 2024[114]). Despite this growth, global utilisation remains below its estimated market potential of 15% (Glemarec, 2025[115]).
The Philippines, for instance, has developed a key microinsurance mechanism for extending market-based protection to low-income households. The Center for Agriculture and Rural Development Mutually Reinforcing Institution’s (CARD MRI) Mutual Benefit Association now covers 7.5 million members, while Card MRI Insurance Agency (CaMIA), disbursed EUR 1.8 million to 58 000 disaster-affected claimants in 2023 (Allendorf, 2025[116]). These examples illustrate how microinsurance can scale effectively when supported by clear regulation and strong community-based delivery models.
Crop insurance in Emerging Asia plays a critical role in protecting farmers. For example, in vulnerable areas like the Surakarta region in Indonesia, a study found that roughly 128 000 hectares of farmland, were vulnerable to disasters, of which about 42 000 hectares prone to drought (Suryanto, 2020[117]). The estimated economic loss due to climate-induced crop failure in these regions was estimated at more than IDR 207 billion (around USD 12.4 million) per harvest season derived from the vulnerable agricultural land area to flooding or drought and then multiplied by the potential loss of production (Suryanto, 2020[117]). Indonesia’s Rice Crop Insurance (AUTP) scheme has effectively shielded farmers from substantial disaster losses, though the compensation is not necessarily sufficient. A separate study of Thailand underscores the value of area-yield crop insurance, backed by AI machine learning models, and stress the important role that government can play in supporting and sustaining agricultural insurance programmes. In particular, the study investigates the suitability of area-yield crop insurance for rice farmers of Jasmine-105 rice. Area-yield insurance differs from traditional named-peril cover by linking compensation to the average yield of a defined area rather than the loss of an individual farm. This lowers administrative costs and mitigates adverse selection and moral hazard, making it a more efficient insurance model for smallholder farmer (Chaiyawat et al., 2023[118]). India offers one of the most prominent examples of large-scale crop insurance implementation: with 25 million farmers insured, the National Agricultural Insurance Scheme (NAIS) in India is the largest crop insurance scheme in the world, and its transition to the modified NAIS introduced a market-based crop insurance programme with actuarially sound premium rates and early weather-index payments (World Bank, 2012[119]). China has also developed agricultural insurance systems, based on a government-guided, market-based model with substantial premium subsidies. Since the introduction of pilot programmes in the late 2000s, agricultural insurance coverage has expanded, playing an increasingly important role in protecting farmers against losses from droughts, floods and other climate-related hazards (World Bank, 2020[120]).
Parametric insurance is also gaining importance across Emerging Asia as a tool for useful disaster risk finance. Unlike indemnity insurance, parametric insurance releases funds automatically once a predetermined and measurable trigger, such as wind speed or rainfall deficit, is reached, allowing faster relief and lower administrative costs. The global market is expanding with projections reaching USD 34.4 billion by 2033 (World Economic Forum, 2025[121]). An example of parametric insurance development comes from Indonesia, where a drought-index product for Central Java has been designed using the Standardized Precipitation Index (SPI) to help protect farmers and provincial authorities from the impacts of dry spells. This is intended to provide quick liquidity during prolonged drought periods when production losses place pressure on households and local governments (World Bank, 2018[122]). In the Philippines, Benni (2023[123]) proposes a region-based parametric insurance scheme in BARMM for rice and corn producers, using PAGASA rainfall thresholds to trigger payouts. With strong collaboration between public and private sectors and targeted investment, the scheme could create a more competitive insurance landscape and attract long-term interest from both private local development partners and insurers. This highlights how parametric mechanisms can complement traditional insurance by providing rapid liquidity following climate shocks.
Challenges in setting disaster insurance premiums
Insurance involves the payment of premiums by many individuals, with premium rates tied to their respective levels of risk. The degree of risk is determined by the probability of the loss event occurring and the likely magnitude of the loss should it happen (OECD, 2021[112]). Insurance premiums are, therefore, established based on the anticipated losses over a specified timeframe (Box 1.10). However, some disaster insurance programmes do not fully reflect the diverse risk levels among policyholders, either because implementing a fully risk-based pricing model is challenging or because they prefer a simpler pricing structure (OECD, 2021[112]). Establishing differentiated pricing based on risk levels can serve as an incentive for both policyholders and insurers to invest in risk reduction measures. In some cases, premium rates are primarily established based on broad hazard zones or types of construction. For example, in the Philippines, the rates for insurance coverage against typhoons are determined according to the location of the insured asset within designated zones. These rates remain the same, regardless of the building’s characteristics.
Fixed premium approach is widely used in ASEAN countries, compared with risk-based premium approach. In general, fixed premium approach consists of several types, including the fixed premium, fixed sum insured, and flat rate premium.
Fixed premium rates remains constant throughout the policy period, regardless of changing risk factors. This approach offers predictability for policyholders as they know exactly how much they will pay for the life of policy. One example of a disaster insurance programme that use fixed premium is Flood Re in the United Kingdom. Its premiums are fixed and based on the property’s council tax band, reflecting the household’s financial capacity (National Flood Forum, 2023[124]).
Fixed sum insured scheme provides a predetermined payout in the event of loss, regardless of the one actual damage or changing circumstances. Premiums often tied to the fixed insurance sum. In Romania, for example, a special law mandates coverage for earthquakes, river floods and subsidence for residential properties using a combination of fixed premium and a fixed sum insured (Radu, 2022[125]). Depending on the type of construction, there are two main options: a fixed EUR 20 000 sum insured for a EUR 20 premium, or a EUR 10 000 sum insured for a EUR 10 premium (Kiohos and Paspati, 2021[126]).
A flat rate premium is applied uniformly across a defined risk group, often as a percentage of the property value. In this case, all policyholders sharing the same level of risk would have identical premium rates, regardless of individual risk levels. Kousky (2019[127]) highlights that a flat rate premium approach views disasters as exogenous events that impact society as a whole, with the cost being shared collectively, commonly known as a solidarity approach. This model is used in several Asia Pacific countries, including Indonesia and New Zealand (JICA, 2021[128]).
Source: (National Flood Forum, 2023[124]), Flood Re Explained; (Radu, 2022[125]), Disaster-Risk Financing: Limiting Fiscal Costs; (Kousky, 2019[127]), The Role of Natural Disaster Insurance in Recovery and Risk Reduction and (JICA, 2021[128]), Data Collection Survey to Improve the Public Insurance System.
Catastrophe bonds as a potentially useful tool
CAT bonds are a potentially useful tool for transforming sovereign risk, but they require specialised expertise (Box 1.11). Indeed, CAT bonds remain relatively new to ASEAN region. Several points need to be addressed for a successful development of CAT bond markets (OECD, 2024[111]). First, it is crucial to formulate a grand design for disaster risk financing, while recognising the importance of an integrated approach to disaster risk management and the contribution of risk assessment, risk awareness and risk prevention to the disaster risks financial management. Developing tailor-made catastrophe risk models is important, and so are creating meteorological, hydrological and seismological services and investing in measurement infrastructure. Moreover, accurate and timely data are critical for effective disaster risk transfer. Data providers must be trustworthy, independent and have reliable processes plus trained personnel, and they need to fulfil high standards of data security. In addition, it is important to broaden investor bases and capacity building needs to be strengthened further. Building up know-how should involve establishing expertise and experience regarding CAT bonds through training sessions and cross hirings and partnering with private firms and business schools. Minimising basis risk could be done by establishing a risk pool with insurance portfolio to enable indemnity triggers, while maximising the correlation to losses if using a parametric trigger. Preparing distribution schemes is also needed. To ensure a rapid and targeted distribution of the proceeds from sovereign CAT bonds, contingency plans must be put in place ex ante (OECD, 2024[111]).
In addition, developing robust market for CAT bonds will require specific legal and structural frameworks to facilitate secure risk transfers and protect investor rights, which would build market confidence and drive demand. It will also need a standardised regulatory framework for the insurance and financial markets that will facilitate issuance and ease the entry of catastrophe bonds into region’s financial markets. Many ASEAN member states have yet to develop their legal and regulatory frameworks to match the sophisticated tools. This includes enabling the use of various financial instruments such as derivative contracts and parametric insurance schemes, which are essential for implementing comprehensive fiscal risk management (OECD, 2024[58]).
Catastrophe bonds are financial instruments that utilise a process called securitisation to wrap disaster risk into a tradable format. This process is depicted in Figure 1.9. A typical transaction requires the sponsor or cedent (the entity that would like to lay off the risk) to set up a special-purpose vehicle (SPV), which acts as a facilitator to transfer the catastrophe risk from the sponsor to the investors. The SPV (also called a special-purpose entity or single-purpose company) is a firm with the solitary goal of enabling the transaction. The SPV grants reinsurance coverage or catastrophe swap protection to the sponsor and collects the required risk capital by issuing the CAT bond to investors. During the term of the reinsurance contract between the sponsor and the SPV, the investor’s capital is held in the form of highly liquid and low-risk collateral in a trust account.
Source: OECD (2024[111]).
CAT bonds offer a coupon stream consisting of the floating interest rate (term premium) from the collateral securities and a fixed spread (risk premium) that is determined at issuance. The fixed spread represents the Rate on Line (ROL) paid under the reinsurance contract or catastrophe swap. CAT bonds carry minimal interest rate and credit risk due to their floating rate and the high quality of the collateral. Yet investors may lose their principal, because it is paid out to the sponsor if a predefined trigger event occurs during the term of the bond. The payout function can be binary or proportional to an underlying trigger metric. To determine whether a payout is due under the embedded reinsurance contract or catastrophe swap, CAT bonds use different trigger mechanisms. CAT bond trigger mechanisms vary and provide investors with various levels of transparency. Mechanisms can be broadly classified into indemnity and non-indemnity triggers. Non-indemnity triggers can be further divided into parametric (index) triggers, industry-loss triggers, modelled-loss triggers and hybrid triggers. While the most common CAT bonds are those that feature indemnity triggers, index-triggered CAT bonds, including parametric, industry-loss and modelled loss, have some advantages in terms of their simplicity and higher transparency, hence CAT bond can reduce potential for moral hazard issue. While these types of CAT bonds may pose greater basis risk for sponsors, the payout can be disbursed faster than with indemnity-triggered CAT bonds.
Source: (OECD, 2024[111]), Fostering Catastrophe Bond Markets in Asia and the Pacific.
Active use of monetary and macroeconomic policy in disaster risk finance to manage disaster risk
Meanwhile, monetary and macroeconomic policy can play a bigger role in disaster response and need to be utilised further. Examples in Asia include the government responses to flooding in Thailand in 2011 and in India in 2014. The central bank of Thailand cut policy rate by 50 basis points to facilitate recovery from the flooding in December 2011 (Bank of Thailand, 2012[129]). The Reserve Bank of India, rescheduled loan repayments and placed a temporary moratorium on interest rates, following the Jammu and Kashmir flooding in September 2014 to ease liquidity conditions for the sectors of economy that were affected by the disaster (Reserve Bank of India, 2017[130]).
Monetary and financial policies can support disaster-affected regions and countries by providing low-interest rate loans and ensuring sufficient credit flows to facilitate reconstruction and recovery (Mittnik, Semmler and Haider, 2020[131]). This approach aims to prevent hysteresis effects – long term damage to productive capacity – so that economies can recover more swiftly and efficiently after a disaster.
Monetary authorities and central banks could also ease credit after disasters to help alleviate bottlenecks in the supply of goods and services, in infrastructure, transport, and in other private and public sectors of economy. Getting monetary policy is also vital for funding for disaster recovery, especially in the immediate aftermath. Cantelmo et al (2022[132]) caution, however, that “while monetary policy is not a substitute for structural and financial climate adaptation policies, welfare losses from ill-devised monetary policy rules may compound with those deriving from the devastating impacts of disasters.”
Conventional wisdom holds that a looser monetary policy – through affordable credit as well as loan concessions and restructuring – may be needed to ease the flow of credit into the affected sectors. However, in instances of price spikes, and volatile exchange rates, a tighter monetary policy stance can be the more appropriate course of action (Cantelmo et al., 2022[132]).
Foreign exchange reserves can also be a useful tool for cushioning shocks against disaster, but the discussion on how best to use them remain limited. Effective foreign reserve policy helps mitigate the consequences of disasters and can help stabilise financial markets. Molnar-Tanaka, Dutu and Ibrahim (2025[133]) examined foreign reserves policy responses to large external shocks in Indonesia, Malaysia and Thailand. They used a DSGE (dynamic stochastic general equilibrium) model to analyse foreign reserve policies against three types of large external shock – trade, interest rates and productivity shocks – and concluded that foreign reserve policies should be tailored to the type of shock. For instance, in the case of a negative export shock or falling interest rates, the optimal policy would be to reduce foreign exchange reserves. By contrast, a negative TFP (Total factor productivity) shock calls for increasing them.
Countries in the region have already used foreign exchange reserve and other monetary policies in response to major disasters, though detailed public information on these interventions is rather limited. In the aftermath of typhoon Haiyan, for instance, the central bank of the Philippines (BSP) conducted foreign exchange operations, contributing to a 4.4% drop in gross international reserves by end of 2014 compared to end-2013 levels (Bangko Sentral ng Pilipinas, 2014[134]). Similarly, Bank Indonesia intervened in the foreign exchange market, leading to a decline in the country’s foreign exchange reserves following the Indian Ocean (Aceh) earthquake and tsunami in 2004 (Bank Indonesia, 2005[135]). There are also several examples of foreign exchange reserve interventions in the region during the COVID-19 pandemic to cope with the exchange rate pressures and volatility (IMF, 2020[136]).
Enhancing disaster risk finance education
Disaster risk finance education is a key enabler of robust DRF. Without it, even well-designed DRF frameworks remain underutilised (OECD, 2022[67]). This section outlines challenges and policy directions for improving disaster risk finance education.
Integrating disaster risk finance literacy programmes into the education system
Government can increase understanding of disaster risk finance through the formal education system as well as targeted financial literacy programmes. This helps to ensure that, from an early stage, individuals develop a strong understanding of the concept of disaster risk finance, including the importance of risk management, protecting assets from disasters, how insurance works and why it is necessary. This foundational knowledge equips young people with the tools to make informed decisions as they grow older, fostering a culture of preparedness and responsible financial planning.
Many countries in the region incorporate financial education into their school systems to varying degrees, often integrating it into the national curriculum through a cross-disciplinary approach (Messy, 2016[137]). For instance, the 2021-25 National Strategy on Indonesian Financial Literacy (SNLKI) focuses on building and reinforcing strategic alliances to implement financial literacy and education programmes. As part of this initiative, a series of financial literacy books has been developed for Early Childhood Education, as well as for students at the elementary, junior high and senior high school levels, as well as university (OJK, 2021[138]). Additionally, a public university in Indonesia has introduced a community service initiative called “Insurance Goes to School,” aimed at helping high school students understand and use financial products and services, make good financial plans, and be responsible for their financial decisions (University of Indonesia, 2023[139]). In the Philippines, the Department of Education has issued a Financial Education Policy designed to enhance the integration of financial education across the K-to-12 Basic Education Curriculum in various subjects and grade levels (Philippenes Department of Education, 2021[140]). Meanwhile in Thailand, the Office of Insurance Commission has set a goal of promoting and facilitating the inclusion of insurance courses across all levels of education (UNDP, 2023[46]). Several other initiatives on disaster risk finance exist across the region (Table 1.6).
Note: Examples reflect publicly available descriptions of national financial-literacy education initiatives.
Expanding disaster insurance literacy: Engaging public officials, practitioners and the public
Disaster risk finance literacy should not only be limited to schools – it should also be provided to a broader audience, including public officials, insurance practitioners and the general public. For instance, public officials often play key roles in making and implementing policy. A strong understanding of disaster insurance helps them to better assess the financial market’s capabilities and needs, and to advocate for effective policies within the broader disaster management framework. Among financial market practitioners and the public, disaster insurance literacy is especially important to increase insurance penetration, especially among communities in high-risk areas, low-income households, small businesses, and those who are not familiar with the benefits of disaster insurance. An empirical study conducted in China concluded that improving households’ understanding of earthquake disaster insurance can significantly influence their decision to purchase coverage, highlighting the crucial role that literacy campaigns can play (Sun and Yuan, 2024[144]).
A comprehensive approach to disaster risk finance education ensures that all stakeholders are well-informed and capable of making sound decisions. The ASEAN Insurance Education Committee (AIEC) established in 2003 is an example of a regional initiative aimed at addressing the critical skill shortage within the insurance industry (AIEC, 2024[145]). Such initiative equips professionals with the technical expertise and leadership skills necessary to meet customers’ long-term financing and disaster risk mitigation needs.
In Thailand, building a risk management culture among individuals and businesses is one of the key strategies of country’s Insurance Development Plan Vol. 4 (2021-25). Additionally, boosting insurance literacy in Thailand could build on the government’s efforts to improve financial literacy through its national savings agenda, which has made notable progress. The National Savings Fund (NSF) grew from 400 000 subscribers in 2016 to 2.45 million subscribers in 2021, representing roughly 3.7% of the population (UNDP, 2023[46]).
In Viet Nam, initiatives include the development of both insurance manuals for trainers and teaching materials that can be easily understood by the target audience (e.g. farmers). While the first provides guidance to trainers, enabling them to support farmers in making informed decisions on joining an insurance scheme, the latter provides essential information to farmers on the basics of agriculture and livestock insurance, such as how the risk transfer instrument works, the scope of coverage, the costs of the insurance, its potential benefits and limitations, how to enrol in the schemes, and accessing premium subsidies, as well as how to make a claim (IFAD, 2025[146]). The programme also empowers farmers to adopt sound farming practices and risk management practices, which can make them eligible for reduced premiums.
In Indonesia, an executive education programme on disaster risk finance has been developed for senior officials across key ministries and agencies, focusing on Indonesia’s DRFI strategy and real‑world financing scenarios. By strengthening public officials’ technical understanding of disaster risk financing tools, the programme supports more informed policy decisions and more coherent implementation of DRF across government. The curriculum typically combines lectures on fiscal risk assessment and instrument design with group exercises where officials simulate disaster‑response funding decisions under different hazard and budget conditions, helping them practise how to sequence grants, contingency funds and risk‑transfer instruments (World Bank, 2025[147]).
Adapting disaster insurance literacy efforts across Emerging Asia
Given the region’s geographical and cultural diversity, disaster insurance literacy efforts need to be balanced, locally relevant, and regularly updated (OECD, 2024[9]). It is also important to address the unique economic and social structures within each Emerging Asian country. Governments should invest in the continuous development of localised educational programmes that are both culturally sensitive and appropriate for the region.
In Viet Nam, for example, the target market for disaster insurance often consists of large, family-owned farms. Yet, most of the country’s agricultural production is small-scale and fragmented, and smallholder farmers often do not understand how agricultural insurance works or appropriate its importance (ASEAN, 2021[148]).This highlights the need for developing literacy programmes that not only inform but also address the specific challenges that low-income communities face. The Vietnamese government works with development partners to increase access to agricultural insurance and boost farmer demand through education and awareness campaigns. Efforts taken include a hybrid capacity-building programme with online and in-person training for trainers across several provinces, including professionals, extension workers, and local co-ordinators working with farmer co-operatives (IFAD, 2025[146]). Collaborating with local leaders and leveraging modern technology would enable tailoring of disaster insurance literacy programmes to region-specific needs, ensuring that campaigns are both effective and relevant to the populations they serve.
The Philippines also offers useful examples of targeted education initiatives that have strengthened the resilience of vulnerable groups. The Department of Trade and Industry’s MSME Development Plan (2017‑22) stresses the importance of promoting microinsurance at the “barangay” (community) level, noting that MSMEs are particularly vulnerable to the devastating effects of disasters (Wiedmaier-Pfister, 2010[149]). To address this vulnerability, the government, in collaboration with the Insurance Commission, has adopted key strategies – including improving insurance literacy among low-income households, the informal sector and MSMEs – to support the microinsurance market. By targeting these groups, the government aims to tailor disaster insurance education to the needs of local communities. However, the government has mainly acted as a catalyser, delegating most of these activities to private sector (UNESCAP, 2015[150]). While this approach encourages private sector participation in promoting insurance literacy, it may also point to a need for a more balanced approach that involves the government, private sector and local communities and other stakeholders. As in the Philippines, Indonesia’s economic landscape is dominated by micro, small, and medium enterprises (MSMEs). Small business owners are particularly vulnerable to disasters due to low levels of both financial and disaster literacy (Setiawan et al., 2020[151]). Tailored educational programmes are therefore critical in Indonesia.
In India, microinsurance‑based disaster risk reduction programmes developed by the All India Disaster Mitigation Institute provide training to low‑income households and small businesses on how microinsurance works, how to enroll, and how to file claims after disasters. By coupling disaster preparedness education with hands‑on guidance on microinsurance, these initiatives improve insurance literacy among vulnerable groups that often lack formal financial and disaster knowledge. Training sessions are usually delivered through local workshops using pictorial materials and case studies of past disasters, and participants are guided through simplified explanations of policy terms and documentation requirements so they can understand premiums, exclusions, and payout rules even if they have limited formal education (UNDP, 2015[152]).
Using digital platform to bolster disaster risk finance literacy
Disaster insurance literacy and digitalisation should go hand in hand, since digital skills are essential for developing many of today’s disaster insurance tools. The insurance sector is under increasing pressure to adopt digital technologies in various aspects of its operations, including underwriting, claims management, marketing and sales. Disaster insurance literacy programmes must therefore incorporate digital elements and teach participants the skills needed to use them effectively.
Governments can also leverage digital platforms and social media to expand the reach of disaster insurance literacy initiatives and make them accessible to a broader audience. In the Philippines, for example, insurance companies actively promote microinsurance on Facebook and their corporate websites (UNESCAP, 2015[150]). The Financial Services Authority (OJK) in Indonesia has also been proactive in leveraging digital media, using mini-sites and social platforms to complement traditional, in-person financial education. The OJK’s has launched an Instagram Live series called “Sikapiuangmu”, where influencers and experts discuss financial topics, ranging from financial planning to products and services awareness in a format that is engaging and easy to understand. As part of its National Strategy for Financial Literacy 2019-23, Malaysia has developed a simulation-based game called “Mind Your Ringgit”, which immerses players in scenarios that mimic real-life financial solutions, helping them to understand the consequences of their choices and educating them on key financial themes such as digital payments, insurance, investments, loans, savings, and financial scams (AFI, 2021[153]).
While these initiatives demonstrate the progress being made in financial and digital literacy, many still lack a focused emphasis on disaster insurance. Therefore, existing digital literacy programmes should explicitly include disaster insurance literacy as a core component. This can be achieved by adding dedicated modules within broader financial literacy platforms or games that focus on the unique aspects of disaster insurance.
Strengthening regional co-operation in disaster risk finance
Regional efforts are expanding within ASEAN and ASEAN+3 frameworks
As disaster risks grow, strengthening regional co-operation to support national and regional preparedness and response remain essential. Regional efforts are anchored in the ASEAN Agreement on Disaster Management and Emergency Response (AADMER), a legally binding accord that has been in effect in December 2009. The agreement spells out national commitments on disaster risk reduction, emergency response and recovery. It encourages ASEAN Member States to take concrete steps towards building a safer and more resilient community through various DRR and climate adaptation initiatives. AADMER remains the core regional policy framework for reducing disaster losses and furthering collective emergency response efforts.
AADMER outlined four strategic components to its first five-year Work Programme 2010-2015: risk assessment, early warning and monitoring; prevention and mitigation; preparedness and response; and recovery. Six foundational “building blocks” were identified as essential for implementation: institutionalisation of AADMER; partnership strategies; resource mobilisation, outreach and mainstreaming; training and knowledge management; and information management and communication technology (ASEAN, 2013[154]). Although the initial work programme made considerable progress, gaps remained. The subsequent Work Programme 2016‑2020 expanded the priority programmes to include: risk assessment; disaster prevention and mitigation; preparedness and emergency response; and recovery and innovation (ASEAN, 2016[155]). The current Work Programme 2021‑2025 aims to further strengthen ASEAN’s capabilities in disaster risk reduction and disaster management through inter-sectoral collaboration, scalable innovation, resource mobilisation, expanded partnerships, and enhanced co‑ordination among Member States (ASEAN, 2020[156]). Implementation is organised around five pillars covering risk assessment and monitoring, prevention and mitigation, preparedness and response, resilient recovery, and global leadership.
In 2011, ASEAN also adopted the Disaster Risk Financing and Insurance (DRFI) roadmap to guide efforts to improve financial resilience to disasters and to support the development of effective DRF and insurance mechanisms across the region. The first phase of the roadmap launched in 2013 with the establishment of a Cross-Sectoral Committee in 2013. Due to its inter-sectoral nature, the programme requires extensive co-ordination and relies heavily on technical assistance from partners (ASEAN, 2025[11]). To further better financial resilience for climate-related impacts, ASEAN+3 (ASEAN plus China, Japan and Korea) set up the Southeast Asia Disaster Risk Insurance Facility (SEADRIF) in 2018. This initiative provides member countries with access to individualised disaster risk finance solutions and provide them with administrative and technical support. SEADRIF’s first product was a three-year flood insurance policy designed at the request of Lao PDR.
The ASEAN+3 recently launched a 2023-2025 action plan aimed at improving regional co‑operation on DRF, by strengthening joint financial solutions (including existing mechanisms like SEADRIF) and developing additional joint insurance products, and introducing new risk transfer instruments (ASEAN+3, 2023[157]). The initiative also emphasises that new financial structures combining risk finance and preparedness (e.g. risk reduction investments, preparedness planning, mitigation and adaptation) should be considered to help strengthen overall resilience. The plan aims to introduce at least one innovative product that combines risk finance with resilience building, depending on members’ demand (ASEAN+3, 2023[157]).
The SEADRIF initiative has focused primarily on addressing flood risks. Over the past three years, it has developed a system to assess flood risk levels and monitor ongoing flood events in two countries: Lao PDR and Myanmar. Since joining the initiative in 2021, Lao PDR has gained access to improved regional risk assessment systems and taken a more structured approach to managing flood-related financial losses. The partnership has already provided substantial support, including a USD 1.5 million payout in 2023 and a total of USD 3 million in response to the severe floods that followed Typhoon Yagi in 2024. The renewal of Lao PDR’s policy in 2024 further strengthened its protection, expanding the parametric trigger from two to four levels and lowering thresholds to better reflect the country’s risk profile. These developments highlight SEADRIF’s growing role in bolstering Lao PDR’s disaster preparedness, financial resilience and ability to respond quickly and effectively to major flood events. In Myanmar, SEADRIF’s support has centred on improving flood risk monitoring and regional access to technical systems. Although Myanmar has not created a sovereign risk finance product like Lao PDR, it benefits from the shared flood risk assessment platform and the early-warning systems that have been developed under the initiative. These tools enhance Myanmar’s ability to evaluate risk and allows for more systematic preparedness planning. It also lays the groundwork for new financial protection instruments in the years to come (SEADRIF, 2024[47]).
Strengthening multi-country risk-sharing pools across Emerging Asia
Risk-sharing pools offer an important opportunity to improve regional DRF co-operation. Many countries are highly exposed to disasters yet have limited capacity to absorb the associated costs, underscoring the need for more collaborative approaches. Regional risk-sharing pools, where conditions allow, are one potential way to strengthen regional initiatives. A risk-sharing pool is a collective mechanism in which participants combine resources to share the financial burden of disasters, reducing costs for individual members and improving access to coverage. Further development and adoption of these mechanisms would greatly enhance disaster risk mitigation (OECD, 2022[67]).
Examples of multi-country risk-sharing pools already exist, including the Caribbean Catastrophe Risk Insurance Facility (CCRIF), the Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI), and the Southeast Asia Disaster Risk Insurance Facility (SEADRIF) (Table 1.7).
CAT bonds could also be issued within a multi-country framework (OECD, 2024[111]). This approach offers several advantages compared to single-country CAT bonds, including allowing participating countries to benefit from economies of scale which reduces issuance costs. Since the expenses for structuring, marketing, and legal services for the bond issuance are shared among several countries, each would bear a lower cost. Furthermore, it can also attract a larger range of investors looking for diversification of their portfolios. Early examples of regional co-ordination include the Pacific Alliance CAT bonds, issued simultaneously by Chile, Colombia, Mexico and Peru in 2018, although these were not a single jointly issued instrument.
Note: PCRIC evolved from the pilot programme developed under the Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI), an initiative launched in 2007. Company establishment date of CCRIF refers to the date of restructuring of the facility into a segregated portfolio company (SPC). Initiative inception date of SEADRIF refers to the signing date of the Memorandum of Understanding (MoU) for the establishment of SEADRIF by Cambodia, Indonesia, Japan, Lao PDR, Myanmar and Singapore.
Source: Molnar-Tanaka and Wu, (2025[158]).
Strengthening regional risk-sharing arrangements could offer significant benefits for ASEAN countries. A recent theoretical study by using a CAT bond pricing model examines the potential of regional disaster risk-sharing pools on multi-country CAT bonds, based on data on floods and storms in ASEAN from 1980‑2021 (Molnar-Tanaka and Wu (2025[158]). The annual damage correlations among countries indicate where diversification benefits may arise. Low or negative correlation coefficients between two countries in Table 1.8 suggest strong potential for mutual risk sharing. Several country pairs seem well-positioned to co-operate in strengthening their protection against extreme disaster events. The study further explores this discussion by theoretical model and suggests that certain groupings among Southeast Asia – specifically Malaysia, Thailand and Viet Nam (MTV) and Philippines, Thailand and Viet Nam (PTV) – could potentially be suitable for a disaster risk sharing pool as examples.
As noted above, the collaborative nature of regional pools allows participating countries to benefit from economies of scale. In this context, several implementation challenges arise, including setting premiums for risk-sharing pools, developing appropriate risk assessment models, and improving data collection and interoperability (UNESCAP, 2018[19]). Determining the scope of coverage and the appropriate premium is a key consideration for countries to join a multi-country risk pool, since they may face varying types and levels of disaster risks and have differing capacities and willingness to pay (Box 1.12). Developing risk assessment models suited to a particular disaster risk pool is crucial, given the unpredictable nature of disasters and the resulting difficulty of setting appropriate premiums. The models would also need to be updated regularly. And finally, smooth data sharing would allow for harmonised understanding of the risks they face and thus enable them to assess the bond’s parameters more accurately. Overall, strengthening regional pool would require participating countries to have a comprehensive discussion regarding the shared risks and willingness of each country to collaborate on harmonising disaster management-related frameworks and enhancing data sharing.
Establishing the scope of coverage and the appropriate premium to be paid are major considerations for a country wishing to join a multi-country risk pool. Countries within the pool may face distinct types and degrees of risk, which are ideally reflected in the premiums they pay. At the same time, countries may differ in their willingness or ability to pay such premiums, and this should be taken into account to encourage participation. This has indeed been a major barrier to establishing catastrophe risk pools. Participation in regional catastrophe risk pools can be closely related to how premiums are set (Molnar-Tanaka and Wu, 2025[158]).
Premiums are determined by several factors as outlined in a report by the World Bank (2012[8]). The first is the average annual loss (AAL), referring to the average annual amount that the pool would be expected to lose over an extended period. The expense load, charged by the insurer to cover the pool's administrative costs of establishing the risk pool, is another important component and may include expenses for setting up the pool, building a database, developing or purchasing a catastrophe risk model, and operational expenses such as underwriting, adjustment, and marketing and delivery costs. The frictional costs of transferring risk with CAT bonds – such as time and efforts required to create or adjust frameworks – also add to the cost from regulations or taxes (World Bank, 2012[159]). There is also contingency load, which refers to the amount an insurer charges as compensation for bearing the risk. Within the contingency load is the cost of equity capital, which can be defined as the opportunity cost to the insurer for tying up capital in a particular risk pool, Lastly there is the uncertainty load, which compensates the insurer for the possibility that actual losses may be higher (World Bank, 2012[8]).
At the same time, lowering the cost of capital delivers the biggest premium reductions. Bollmann and Wang (2019[160]) examine the benefits of catastrophe risk pools discussed by Ciullo et al. (2023[161]) in greater detail. According to them, risk pooling reduces the cost of capital by improving the ability of pool members to retain the first aggregate loss. Moreover, catastrophe pools have the potential to accumulate surplus (equity) capital over time, reducing reliance on global reinsurance and risk transfer through capital markets. Using data form the Florida Hurricane Catastrophe Fund (FHCF), Watson Jr. et al. (2012[162]) find that significant economic diversification benefits can be achieved for events with return periods of at least 25 years (i.e. events that are rare but severe). They find that a minimum geographic distance between exposure points is required to reap these benefits. Indeed, Watson Jr. et al. (2012[162]) find that a multi-state insurance portfolio requires only 50% of the reserves that a comparable single-state portfolio does (Bollmann and Wang, 2019[160]; Watson, Johnson and Dumm, 2012[162]). The limitations faced by geographically smaller states in spreading risk domestically makes them strong candidates for risk pooling. Insuring multiple uncorrelated risks in a pool also reduces the cost of capital. Bollman and Wang (2019[160]) illustrate their point by noting that an earthquake in one region of the world is unrelated to a tropical cyclone elsewhere. More concretely, they find that the combined modelled loss for earthquakes, typhoons, and crop failures – each with a 100-year return period – is far lower than the sum of the losses modelled for each event independently. Combining uncorrelated perils in a catastrophe risk pool can lower the risk capital requirements for the risk-bearing entity.
Source: (Bollmann and Wang, 2019[160]), International Catastrophe Pooling for Extreme Weather: An Integrated Actuarial, Economic and Underwriting Perspective; (Ciullo et al., 2023[161]), Increasing countries’ financial resilience through global catastrophe risk pooling; (Molnar-Tanaka and Wu, 2025[158]), Disaster risk-sharing pools and multi-country catastrophe bonds in Southeast Asia; (Watson, Johnson and Dumm, 2012[162]), The Impact of Geographic Diversity on the Viability of Hurricane Catastrophe Insurance; and (World Bank, 2012[8]), Advancing Disaster-Risk Financing and Insurance in ASEAN Countries.
Countries in Emerging Asia are increasingly exposed to various types of disasters, with both their frequency and severity rising in recent years. Disaster risk finance (DRF) is critical for mitigating the impacts of these events by ensuring timely support for relief, recovery and reconstruction. To enhance financial resilience to disaster impacts, a comprehensive framework will be needed. Broadening financing options through various innovative policy solutions can also strengthen disaster resilience. While ex-post mechanisms, particularly budget reallocations remain dominant in Emerging Asia, countries are gradually making a shift towards ex-ante and market-based instruments.
Yet, despite these recent developments, countries in the region face several common challenges, including strengthening regulatory frameworks and building institutional capacity; facilitating and broadening policy options; enhancing disaster risk finance education and strengthening regional co-operation.
Harmonising DRF legislation with national public financial management systems is crucial, as fragmented and outdated legal frameworks constrain the institutionalisation of DRF instruments. A lack of harmonised legislation leads to inconsistent budget execution, unclear accountability, and underutilisation of ex-ante instruments. Modernising disaster and public finance laws, setting clear legal mandates for risk-layered tools, and embedding DRF frameworks into national fiscal policy are urgent priorities. Persistent institutional fragmentation and co-ordination gaps make co-ordination between national and local governments essential to operationalise DRF at all levels. Updating systems for monitoring, evaluation, and data management is also needed to boost transparency and support risk-informed fiscal management.
Facilitating and broadening DRF policy options are also important. The optimal policy mix will depend on economic conditions, capital market development, and the types and magnitude of disruptions of the disasters. Policymakers in the region should be encouraged to take a holistic approach to DRF to identify the most effective response. For instance, insurance mechanisms can ensure predictable and rapid payouts after disasters, enabling countries to fund recovery efforts. Indeed, the use of insurance schemes to cover disaster risks has expanded significantly in Emerging Asia in recent years. Crop insurance plays a critical role in protecting farmers. Catastrophe bonds can be useful for transferring sovereign risk, but face challenges to implement, including measurement infrastructure, addressing basis risk, creating a strong investor base and improving data quality. Meanwhile, monetary and macroeconomic policies, including foreign reserve policy, can serve as useful tools in responding to disasters.
Improving disaster risk finance education is equally important, since even the most well-designed DRF mechanisms cannot achieve their objectives if they are poorly understood or under-utilised. Integrating DRF literacy into national education systems, expanding disaster insurance literacy beyond schools, tailoring disaster insurance literacy to diverse Emerging Asian contexts, and leveraging digital platforms for DRF literacy, are critical.
Finally, deeper regional co-operation offers significant potential as many disasters – especially major ones – cut across borders and demand co-ordinated responses, shared knowledge, and collective financial strategies. While several ASEAN and ASEAN+3 initiatives are underway, more must be done to strengthen multi-country risk-sharing pools for effective disaster risk finance.
References
[42] ADB (2024), Asian Development Outlook 2024: Financing Resilience in Asia and the Pacific, Asian Development Bank, https://www.adb.org/publications/asian-development-outlook-2024.
[27] ADB (2024), The Enabling Environment for Disaster-risk Financing in Cambodia: Diagnostic Assessment, Asian Development Bank, https://www.adb.org/sites/default/files/publication/1024246/enabling-environment-cambodia.pdf.
[61] ADB (2023), Jumpstarting a Microfinance Revolution in Indonesia to Boost Women’s Financial Inclusion, Asian Development Bank, https://development.asia/case-study/jumpstarting-microfinance-revolution-indonesia-boost-womens-financial-inclusion.
[60] ADB (2022), ADB Annual Report 2022: Building Resilience in Challenging Times, Asian Development Bank, Manila, https://reliefweb.int/report/world/adb-annual-report-2022-building-resilience-challenging-times.
[20] ADB (2021), Asian Development Outlook 2021: Financing a Green and Inclusive Recovery, Asian Development Bank, https://www.adb.org/publications/asian-development-outlook-2021.
[153] AFI (2021), POLICY AND REGULATORY REFORMS IN THE AFINETWORK 2021, https://www.afi-global.org/wp-content/uploads/2024/10/2021_Policy-Change-report.pdf.
[145] AIEC (2024), ASEAN Insurance Education Committee (AIEC), https://aseaninsurancecouncil.org/project/aiec/.
[98] Akanbi, O., J. Kilembe and D. Park (2025), “Strengthening Fiscal Frameworks in the Presence of Rising Risk of Natural Disasters”, IMF Working Papers, No. WP/25/38, https://doi.org/10.5089/9798400298851.001.
[44] Allen & Gledhill (2025), MAS Sustainability Report, https://www.allenandgledhill.com/sg/publication/articles/30977/mas-sustainability-report-20242025-sets-out-strategy-on-climate-resilience-and-environmental-sustainability.
[116] Allendorf, H. (2025), CLIMATE AND DISASTER RISK FINANCE: A MOSAIC OF INSTRUMENTS, https://www.adaptationcommunity.net/wp-content/uploads/2025/04/Disaster-Risk-Finance-Toolkit.pdf.
[109] AON (2024), “Natural Catastrophes Caused USD 65 Billion Economic Loss in Asia Pacific in 2023, Aon reports”, https://www.aon.com/apac/in-the-press/asia-newsroom/2024/natural-catastrophes-caused-usd-65-billion-economic-loss-in-asia-pacific-in-2023.
[71] AON (2023), Weather, Climate and Catastrophe Insight: 2023 Annual Report, Aon plc, https://www.aon.com/weather-climate-catastrophe.
[164] APEC (2015), “2015 APEC Finance Ministerial Meeting, Cebu, The Philippines, 11 September 2015”, https://www.apec.org/meeting-papers/sectoral-ministerial-meetings/finance/2015_finance.
[18] Artemis (2025), Artemis website, https://www.artemis.bm/deal-directory/.
[11] ASEAN (2025), Disaster-Risk Financing and Insurance in Southeast Asia: Trends, Challenges, and Strategic Approaches, ASEAN Secretariat, https://asean.org/wp-content/uploads/2025/04/20240401_Trend-Report_DM-12-2025.pdf.
[94] ASEAN (2021), ASEAN Disaster Resilience Outlook: Preparing for a future beyond 2025, ASEAN Secretariat, https://asean.org/book/asean-disaster-resilience-outlook-preparing-for-a-future-beyond-2025/.
[148] ASEAN (2021), Engaging on the ground: Empowering Farmers through Insurance Literacy, https://www.asean-agrifood.org/wp-content/uploads/2021/03/Topics-Engaging-on-the-ground-.pdf.
[156] ASEAN (2020), AADMER Work Programme 2021-2025, https://asean.org/wp-content/uploads/2021/08/AADMER-Work-Programme-2021-2025.pdf.
[155] ASEAN (2016), AADMER work programme, https://asean.org/wp-content/uploads/2021/01/AADMER-Work-Programme-2016-2020-v1.6.pdf.
[154] ASEAN (2013), ASEAN Declaration on Enhancing Cooperation, https://asean.org/wp-content/uploads/images/pdf/Final_Draft_ASEAN_Declaration_on_Disaster_Management_-_23rd_ASEAN_Summit.pdf.
[4] ASEAN/UN/World Bank (2008), Post-Nargis Joint Assessment (PONJA), ASEAN Secretariat, United Nations and World Bank, https://www.gfdrr.org/sites/default/files/GFDRR_Myanmar_Post-Nargis_Joint_Assessment_2008_EN.pdf.
[157] ASEAN+3 (2023), 2023–2025 Action Plan of the ASEAN+3 Disaster-Risk Financing Initiative, Ministry of Finance, Japan, https://www.mof.go.jp/policy/international_policy/convention/asean_plus_3/20230502_4.pdf.
[95] AuctusESG (n.d.), Disaster triggered financial instruments for vulnerable communities, https://auctusesg.com/disaster-triggered-financial-instruments-for-vulnerable-communities/.
[103] Australian Parliament (2011), Income Tax Rates Amendment (Temporary Flood Reconstruction Levy) Bill 2011, Australian Parliament, Canberra, https://www.aph.gov.au/Parliamentary_Business/Bills_Legislation/Bills_Search_Results/Result?bId=r4507.
[84] Bahari, N. et al. (2023), “Predicting Sea Level Rise Using Artificial Intelligence: A Review”, Archives of Computational Methods in Engineering, Vol. 30, https://doi.org/10.1007/s11831-023-09934-9.
[134] Bangko Sentral ng Pilipinas (2014), BSP Annual Report 2014, https://www.bsp.gov.ph/Media_And_Research/Annual%20Report/annrep2014.pdf.
[135] Bank Indonesia (2005), Bank Indonesia Annual Report 2005, https://www.bankmandiri.co.id/documents/38265486/38265687/2005_Q2+Summary+Financials.pdf/52f2c1eb-455a-c17c-7829-8a79b4d0642c.
[129] Bank of Thailand (2012), Thailand: 2012 Article IV Consultation, International Monetary Fund, https://www.elibrary.imf.org/view/journals/002/2012/124/article-A001-en.xml.
[123] Benni, N. (2023), The potential for agricultural insurance in the Philippines, https://openknowledge.fao.org/server/api/core/bitstreams/9af1a23f-8275-4cd6-b23a-d91a1310a6bd/content.
[36] BGS and BNPB (2024), UK–Indonesia Partnerships for Advancing Geohazard Science for Disaster Risk Assessment in Indonesia, British Geological Survey, https://www.bgs.ac.uk/news/new-report-sets-pathway-to-reduce-the-impacts-of-geohazards-in-one-of-the-worlds-most-hazard-prone-nations/.
[82] BNPB (n.d.), Strengthening National Disaster Databases in Asia, https://bnpb.go.id/storage/app/media/uploads/migration/pubs/450.pdf.
[160] Bollmann, A. and S. Wang (2019), International Catastrophe Pooling for Extreme Weather: An Integrated Actuarial, Economic and Underwriting Perspective, Society of Actuaries, https://www.soa.org/4a863f/globalassets/assets/files/resources/rese.
[132] Cantelmo, A. et al. (2022), “Monetary Policy in Disaster-Prone Developing Countries”, IMF Working Paper, No. 22/067, International Monetary Fund, https://doi.org/10.5089/9798400204371.001 (accessed on 25 November 2025).
[30] CBIRC (2022), Guiding Opinions on Accelerating the Development of Catastrophe Insurance, China Banking and Insurance Regulatory Commission.
[96] Cevik, S. and G. Huang (2018), “How to Manage the Fiscal Costs of Natural Disasters”, IMF How To Notes, No. 2018 (003), A001, https://www.elibrary.imf.org/view/journals/061/2018/003/article-A001-en.xml.
[118] Chaiyawat et al. (2023), Enhancing the Effectiveness of Insurance Risk Management in Thailand, AgBioForum, https://agbioforum.org/menuscript/index.php/agb/article/view/220/168.
[102] Chatterjee, A. (2019), Financing Disaster Relief Through Taxation, https://blogs.adb.org/hi/blog/financing-disaster-relief-through-taxation.
[100] Choi, S. et al. (2023), Tracking the money for climate adaptation and disaster-risk reduction: trends and experiences, https://www.greenfinanceplatform.org/sites/default/files/downloads/resource/Tracking%20the%20Money%20for%20Climate%20Adaptation%20and%20Disaster%20Risk%20Reduction_IIED_0.pdf.
[161] Ciullo, A. et al. (2023), “Increasing countries’ financial resilience through global catastrophe risk pooling”, Nature Communications, Vol. 14/1, https://doi.org/10.1038/s41467-023-36539-4.
[38] Department of Statistics Malaysia (2022), SPECIAL REPORT ON IMPACT OF FLOODS IN MALAYSIA 2021, https://www.dosm.gov.my/site/downloadrelease?id=special-report-on-impact-of-floods-in-malaysia-2021&lang=English&admin_view=.
[2] EM-DAT (2024), Emergency Events Database, Centre for Research on the Epidemiology of Disasters (CRED), Brussels, https://www.emdat.be.
[17] Generali GC&C (2024), Parametric insurance to build financial resilience.
[53] GFDRR (2021), Annual Report 2021: Bringing Resilience to Scale, Global Facility for Disaster Reduction and Recovery, Washington, DC, https://www.gfdrr.org/sites/default/files/publication/GFDRR_AR_FY21_Feb_21_2022.pdf.
[73] GFDRR (2017), Lao PDR Country Disaster-Risk Profile, Global Facility for Disaster Reduction and Recovery, Global Facility for Disaster Reduction and Recovery, https://www.gfdrr.org/en/lao-pdr.
[41] GFDRR, W. (2012), Disaster Risk Finance Country, https://www.financialprotectionforum.org/sites/default/files/DRFI_Myanmar_0.pdf.
[115] Glemarec, Y. (2025), Closing the Climate and Disaster Insurance Protection Gap, https://sdgs.un.org/sites/default/files/2025-07/Thematic%20Report%20on%20Finance.pdf.
[26] Government of Brunei Darussalam (2023), Voluntary National Report for the Midterm Review of the Sendai Framework 2015–2030.
[25] Government of India (2024), National Disaster Response Fund (NDRF) and State Disaster Response Fund (SDRF), Disaster Management Division, Ministry of Home Affairs, Government of India, New Delhi, https://ndmindia.mha.gov.in/ndmi/response-fund.
[65] Government of Indonesia (2007), Law No. 24/2007 on Disaster Management, Government of Indonesia, Jakarta, https://faolex.fao.org/docs/pdf/ins114997.pdf.
[45] Government of Singapore (2025), Sustainable Finance, https://www.mas.gov.sg/development/sustainable-finance.
[64] Government of the Philippines (2010), Republic Act No. 10121: Philippine Disaster-Risk Reduction and Management Act of 2010, Government of the Philippines, Manila, https://downloads.caraga.dilg.gov.ph/Disaster%20Preparedness/DRRM%20Act%20Primer.pdf.
[66] Government of Viet Nam (2013), Law on Natural Disaster Prevention and Control, Government of Viet Nam, Hanoi, https://faolex.fao.org/docs/pdf/vie164788E.pdf.
[49] Green Climate Fund (2025), , https://www.greenclimate.fund/countries/thailand.
[85] Haasnoot, M. et al. (2021), “Long-term sea-level rise necessitates a commitment to adaptation: A first order assessment”, Climate Risk Management, Vol. 34, p. 100355, https://doi.org/10.1016/j.crm.2021.100355.
[146] IFAD (2025), , https://www.ifad.org/en/w/countries/viet-nam.
[32] IFSCA (2025), Insurance Linked Securities (ILS), https://ifsca.gov.in/CommonDirect/ViewFile?id=21626bde60601ef44a0ed0220160758e&fileName=2025_07_31_IFSCA___ILS_WG_Report___To_Publish_20250731_1220.pdf.
[107] Ikeda, M., T. Palakhamarn and V. Anbumozhi (2024), Study on the Importance of Investment in Disaster-Risk Reduction, ERIA, https://www.eria.org/uploads/Study-on-the-Importance-of-Investment-in-DRR-and-Advanced-DRR-Technology.pdf.
[136] IMF (2020), Regional Economic Outlook: Asia and Pacific – Navigating the Pandemic, https://www.imf.org/en/publications/reo/issues/2022/06/06/regional-economic-outlook-october-2020-asia-and-pacific-navigating-the-pandemic-a-multispeed-49794.
[143] Insurance Commission of the Philippines and GIZ (2011), Roadmap to Financial Literacy on Microinsurance, Insurance Commission of the Philippines and Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Manila, https://dynawealth.com.ph/resources/A-Roadmap-to-Financial-Literacy-on-Microinsurance.pdf.
[141] IRDAI (2016), Pradhan Mantri Fasal Bima Yojana: Operational Guidelines, Insurance Regulatory and Development Authority of India, New Delhi.
[89] Jeggle, T. and M. Boggero (2018), Post-Disaster Needs Assessment (PDNA): Lessons from a Decade of Experience, World Bank, https://doi.org/10.1596/30945.
[128] JICA (2021), Data Collection Survey to Improve the Public Insurance System, Japan International Cooperation Agency, https://openjicareport.jica.go.jp/pdf/12367157.pdf.
[70] JICA (2012), Natural Disaster Risks in Brunei Darussalam, Japan International Cooperation Agency, https://www.jica.go.jp/english/our_work/thematic_issues/disaster/c8h0vm0000b2hm1n-att/BRUNEI.pdf.
[126] Kiohos, A. and M. Paspati (2021), “Alternative to Insurance Risk Transfer: Creating a catastrophe bond for Romanian earthquakes”, Bulletin of Applied Economics, Vol. 8/1, pp. 1-17, https://doi.org/10.47260/bae/811.
[127] Kousky, C. (2019), “The Role of Natural Disaster Insurance in Recovery and Risk Reduction”, Annual Review of Resource Economics, Vol. 11, pp. 399-418, https://doi.org/10.1146/annurev-resource-100518-094028.
[74] Malaysian Re (2021), Flood Risk in Malaysia: Industry Assessment Report, Malaysian Reinsurance Berhad, Malaysian Reinsurance Berhad, https://www.malaysianre.com.my/research.
[83] Marihot, D. et al. (2023), “The Implementation Of Satu Data Bencana Indonesia (SDBI) For Sustainable Development”, International Journal Of Humanities Education And Social Sciences (IJHESS), Vol. 2/6, pp. 1955-1962, https://ijhess.com/index.php/ijhess/article/download/517/427.
[137] Messy, F. (2016), Financial Education Policies in Asia and the Pacific, https://www.oecd.org/en/publications/financial-education-policies-in-asia-and-the-pacific_5jm5b32v5vvc-en.html.
[114] Micro Insurance Newtwork (2024), Landscape of Microinsurance, https://microinsurancenetwork.org/resources/the-landscape-of-microinsurance-2024.
[86] Miloshevich, G. et al. (2023), Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data, https://arxiv.org/pdf/2208.00971.pdf.
[131] Mittnik, S., W. Semmler and A. Haider (2020), “Climate Disaster Risks-Empirics and a Multi-Phase Dynamic Model”, Econometrics, Vol. 8/3, p. 33, https://doi.org/10.3390/econometrics8030033.
[133] Molnar-Tanaka, K., R. Dutu and P. Ibrahim (2025), “Optimal Foreign Reserve Policy Amid Large External Shocks in Indonesia, Malaysia, and Thailand: Implications for Policy Response to Disasters”, ADBI Working Paper, No. 1493, Asian Development Bank Institute, Tokyo.
[106] Molnar-Tanaka, K. and H. Sakamoto (2025), “Financing the costs of disasters: Catastrophe bonds or taxation?”, OECD Development Centre Working Papers, No. 354, OECD Publishing, Paris, https://doi.org/10.1787/f652ad80-en.
[93] Molnar-Tanaka, K. and K. Shao (2025), “Using AI to measure disaster damage costs: Methodology and the example of the 2018 Sulawesi earthquake”, OECD Development Centre Working Papers, No. 355, OECD Publishing, Paris, https://doi.org/10.1787/b1fe3967-en.
[158] Molnar-Tanaka, K. and Y. Wu (2025), “Disaster risk-sharing pools and multi-country catastrophe bonds in Southeast Asia”, OECD Development Centre Working Papers, No. 356, OECD Publishing, Paris, https://doi.org/10.1787/5add24ef-en.
[142] Munich Re Foundation (2014), 9th International Microinsurance Conference 2013: Indonesia Country Report, Munich Re Foundation and Microinsurance Network, Munich.
[39] NADMA Malaysia (2022), National Security Council Directive No. 20, NADMA, http://1997_policy_and_mechanism_of_national_disaster_management_and_relief_national_security_council_directive.pdf.
[124] National Flood Forum (2023), Flood Re Explained, National Flood Forum, United Kingdom, https://nationalfloodforum.org.uk/wp-content/uploads/2023/03/Flood-Re-Explained.pdf.
[104] NTA (2024), Outline of the Special Reconstruction Income Tax, National Tax Agency, Government of Japan, Tokyo, https://www.nta.go.jp/english/taxes/individual/pdf/incometax_2024/01.pdf.
[163] OECD (2025), “Governing for the green transition”, OECD Net Zero+ Policy Papers, No. 13, OECD Publishing, Paris, https://doi.org/10.1787/5b0aa7d0-en.
[91] OECD (2025), Governing with Artificial Intelligence, https://www.oecd.org/en/publications/2025/06/governing-with-artificial-intelligence_398fa287.html.
[1] OECD (2025), OECD Economic Outlook, Volume 2025 Issue 2: Resilient Growth but with Increasing Fragilities, OECD Publishing, Paris, https://doi.org/10.1787/9f653ca1-en.
[16] OECD (2025), Protection Gaps in Insurance for Natural Hazards and Retirement Savings in Asia, OECD Publishing, Paris, https://doi.org/10.1787/294f044e-en.
[59] OECD (2025), Quality Budget Institutions: Developments in OECD Countries, OECD Publishing, Paris, https://doi.org/10.1787/8e811202-en.
[57] OECD (2025), Quality Budget Institutions: Developments in OECD countries, OECD Publishing, Paris, https://doi.org/10.1787/8e811202-en.
[105] OECD (2025), Revenue Statistics in Asia and the Pacific 2025: Personal Income Taxation in Asia and the Pacific, OECD Publishing, Paris, https://doi.org/10.1787/6c04402f-en.
[43] OECD (2024), “Adapting infrastructure to changing climatic conditions: The case of the Philippines”, OECD Environment Policy Paper, No. 46, https://doi.org/10.1787/a4c3f35a-en.
[9] OECD (2024), Economic Outlook for Southeast Asia, China and India 2024: Developing amid Disaster Risks, OECD Publishing, https://doi.org/10.1787/3bbe7dfe-en.
[111] OECD (2024), Fostering Catastrophe Bond Markets in Asia and the Pacific, The Development Dimension, OECD Publishing, Paris, https://doi.org/10.1787/ab1e49ef-en.
[69] OECD (2024), Managing rising subnational fiscal risks, https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/06/managing-rising-subnational-fiscal-risks_80fa76f4/58437ac8-en.pdf.
[10] OECD (2022), Building Financial Resilience to Climate Impacts, OECD Publishing, Paris, https://doi.org/10.1787/9e2e1412-en.
[68] OECD (2022), Building Financial Resilience to Climate Impacts: A framework to manage the risks of losses, OECD Publishing, Paris, https://doi.org/10.1787/9789264271442-en.
[67] OECD (2022), Strengthening Disaster-Risk Finance in ASEAN Countries, OECD Publishing, Paris, https://doi.org/10.1787/9789264271442-en.
[112] OECD (2021), Enhancing Financial Protection Against Catastrophe Risks: The Role of Catastrophe Risk Insurance Programmes, OECD Publishing, Paris, https://doi.org/10.1787/338ba23d-en.
[29] OECD (2015), Disaster Risk Financing A Global Survey of Practices and Challenges, https://www.oecd.org/content/dam/oecd/en/publications/reports/2015/11/disaster-risk-financing_g1g543e4/9789264234246-en.pdf.
[40] OECD (2015), Disaster-Risk Finance: A Global Survey, OECD Publishing, Paris, https://doi.org/10.1787/9789264234246-en.
[101] OECD (2012), Disaster-risk assessment and risk financing: A G20/OECD methodological framework, OECD Publishing, Paris, https://www.oecd.org/content/dam/oecd/en/publications/reports/2012/11/disaster-risk-assessment-and-risk-financing_eaeea9fd/8f48d476-en.pdf.
[58] OECD/ADB (2025), Government at a Glance: Southeast Asia 2025, OECD Publishing, Paris, https://doi.org/10.1787/bc89cb32-en.
[99] OECD/The World Bank (2019), Fiscal Resilience to Natural Disasters Lessons from Country Experiences, https://doi.org/10.1787/27a4198a-en.
[78] OIC (2014), Insurance Development after the 2011 Floods, Office of Insurance Commission, Thailand, https://www.oic.or.th/en.
[110] OIC, T. (2025), Office of the Official Information Commission, https://www.oic.go.th/web2017/en/main.html.
[138] OJK (2021), National Strategy on Indonesian Financial Literacy, https://ojk.go.id/en/berita-dan-kegiatan/publikasi/Pages/National-Strategy-on-Indonesian-Financial-Literacy-(SNLKI)-2021---2025.aspx.
[15] Pham, T. and T. Dao (2017), “Agricultural insurance pilot in Viet Nam: Protecting farmers from climate risks”, Journal of Economics and Development, Vol. 19/3, pp. 5-22, https://doi.org/10.33301/2017.19.03.01.
[140] Philippenes Department of Education (2021), DepEd expands financial education in K to 12 to improve literacy of Filipinos, https://www.deped.gov.ph/2021/07/08/deped-expands-financial-education-in-k-to-12-to-improve-literacy-of-filipinos/.
[125] Radu, D. (2022), Disaster-Risk Financing: Limiting Fiscal Costs, European Commission, https://doi.org/10.2765/270746.
[87] Ragone, F., J. Wouters and F. Bouchet (2017), “Computation of extreme heat waves in climate models using a large deviation algorithm”, Proceedings of the National Academy of Sciences, Vol. 115/1, pp. 24-29, https://doi.org/10.1073/pnas.1712645115.
[130] Reserve Bank of India (2017), Relief Measures by Banks in Areas Affected by Natural Calamities, https://www.rbi.org.in/commonman/English/scripts/notification.aspx?id=2326.
[47] SEADRIF (2024), SEADRIF Annual Report 2024, Southeast Asia Disaster-Risk Insurance Facility, Singapore, https://seadrif.org/annual-report/.
[24] SEADRIF (2023), Lao PDR–SEADRIF insurance pool: Policy overview and payout summary, Southeast Asia Disaster-Risk Insurance Facility.
[151] Setiawan et al. (2020), “Digital financial literacy, current behavior of saving and spending and its future foresight”, https://www.researchgate.net/publication/343556498_Digital_financial_literacy_current_behavior_of_saving_and_spending_and_its_future_foresight.
[81] Sheehan, B. et al. (2023), “On the benefits of insurance and disaster risk management integration for improved climate-related natural catastrophe resilience”, Environment Systems and Decisions, Vol. 43, pp. 639–648, https://doi.org/10.1007/s10669-023-09929-8.
[97] Skalon, T. et al. (2024), Conceptualizing Disaster Risk–Based Budgeting and Exploring Practical Applications, Prosperity Insight Series, World Bank, https://doi.org/10.1596/42519.
[144] Sun and Yuan (2024), “Earthquake disaster insurance literacy and earthquake insurance purchasing behavior: based on the mediating effect of risk preference”, Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, https://ideas.repec.org/a/spr/nathaz/v120y2024i12d10.1007_s11069-024-06647-5.html.
[92] Sun, W., P. Bocchini and B. Davison (2020), “Applications of artificial intelligence for disaster management”, Natural Hazards, Vol. 103/3, pp. 2631-2689, https://doi.org/10.1007/s11069-020-04124-3.
[113] Surminski, S., A. Panda and P. Lambert (2019), “Disaster Insurance in Developing Asia: An Analysis of Market-Based Schemes Asian Development Bank Economics”, ADB Economics Working Paper Series, No. 590, Asian Development Bank, https://www.adb.org/sites/default/files/publication/528026/ewp-590-disaster-insurance-developing-asia.pdf.
[117] Suryanto, E. (2020), “Crop insurance as farmers adaptation for climate change risk on agriculture in Surakarta residency-Indonesia”, International Journal of Trade and Global Markets, https://www.inderscienceonline.com/doi/abs/10.1504/IJTGM.2020.106771.
[72] Swiss Re (2024), Sigma 4/2024: Natural Catastrophes and Inflation Dynamics, Swiss Re Institute, https://www.swissre.com/institute/research/sigma-research/sigma-2024-04.html.
[108] Swiss Re (2021), Nat Cat Protection Gap in Asia, https://corporatesolutions.swissre.com/en/japan/insights/knowledge/resilience/nat-cat-protection-gap-in-asia.html.
[77] Swiss Re (2020), Sigma 2/2020: World Insurance in 2019, Swiss Re Institute, https://www.swissre.com/institute/research/sigma-research/sigma-2020-02.html.
[48] The Asia Foundation (2024), Thai Public Views on International Issues, https://asiafoundation.org/wp-content/uploads/2024/06/Thailand_Thai-Public-Views-on-International-Issues.pdf.
[5] UN OCHA (2014), Philippines: Typhoon Haiyan (Yolanda) – Situation Reports and Consolidated Damage Assessments, United Nations Office for the Coordination of Humanitarian Affairs, https://www.iom.int/sites/g/files/tmzbdl486/files/migrated_files/Country/docs/OCHAPhilippinesTyphoonHaiyanSitrepNo.33.20Jan2014.pdf.
[33] UNDP (2025), Climate Changte and Human Security in Indonesia, https://www.undp.org/sites/g/files/zskgke326/files/2025-10/climate_security_report_ntb_ntt_2024-16_2025.pdf.
[31] UNDP (2024), Inclusive insurance and risk financing in India Snapshot and way forward 2024, UNDP, https://irff.undp.org/sites/default/files/2024/Nov/india3.pdf.
[14] UNDP (2024), Insurance and Risk Finance for Thailand, UNDP Insurance and Risk Finance Facility, https://www.undp.org/sites/g/files/zskgke326/files/2025-01/irff-roadmap_en_22122024.pdf.
[35] UNDP (2023), Inclusive insurance and risk financing in Indonesia. Snapshot and way forward 2023, UNDP Insurance and Risk Finance Facility, https://irff.undp.org/diagnostic-reports/indonesia.
[46] UNDP (2023), Inclusive insurance and risk financing in Thailand: Snapshot and way forward 2023, UNDP Insurance and Risk Finance Facility, https://www.undp.org/sites/g/files/zskgke326/files/2023-03/Thailand%20insurance%20diagnostics%20summary.pdf.
[51] UNDP (2023), Inclusive insurance and risk financing in Viet Nam. Snapshot and way forward 2023, UNDP Insurance and Risk Finance Facility, https://irff.undp.org/diagnostic-reports/vietnam.
[152] UNDP (2015), Microinsurance for Disaster Risk Reduction, https://www.undp.org/asia-pacific/publications/microinsurance-disaster-risk-reduction.
[3] UNDP (2006), Tsunami Recovery: Lessons Learned, United Nations Development Programme.
[56] UNDRR (2023), DRR Financing in Asia and the Pacific, https://www.undrr.org/publication/disaster-risk-reduction-financing-asia-and-pacific-scoping-study-midterm-review-sendai.
[62] UNDRR (2023), Global Assessment Report on Disaster-Risk Reduction 2023, https://www.undrr.org/gar.
[79] UNDRR (2023), Strengthening disaster and climate change finance: Budget tagging for disaster-risk reduction and climate change adaptation, Presentation for training workshop by UNDRR Global Education and Training Institute, https://www.undrr.org/media/91959/download?startDownload=20251126.
[54] UNESCAP (2022), Asia-Pacific Disaster Report 2022, https://www.unescap.org/kp/2022/asia-pacific-disaster-report-2022.
[19] UNESCAP (2018), Disaster-risk financing: opportunities for regional cooperation in Asia and the Pacific, https://hdl.handle.net/20.500.12870/543.
[150] UNESCAP (2015), Financing the Social Sector: Regional Challenges and Opportunities, https://www.unescap.org/sites/default/files/9-ESCAP_FfD_Social%20Sector%20Financing_24April2015.pdf.
[34] United Nations (2024), UNdata Country Profile: Indonesia, https://data.un.org/en/iso/id.html (accessed on 25 November 2025).
[139] University of Indonesia (2023), FIA UI and IOM 2023 Collaborate to Host Event on Financial Literacy and Career Tips for Students, https://fia.ui.ac.id/en/kolaborasi-fia-ui-dan-iom-2023-selenggarakan-acara-bahas-literasi-keuangan-dan-kiat-karir-bagi-mahasiswa/.
[162] Watson, J., M. Johnson and R. Dumm (2012), The Impact of Geographic Diversity on the Viability of Hurricane Catastrophe Insurance, Florida Catastrophic Storm Risk Management Center.
[149] Wiedmaier-Pfister (2010), “Microinsurance innovations in rural finance”, https://www.researchgate.net/publication/46442157_Microinsurance_innovations_in_rural_finance.
[147] World Bank (2025), An Executive Education Program on Disaster Risk Finance for Indonesia, https://www.financialprotectionforum.org/executive-education-program-disaster-risk-finance-indonesia.
[12] World Bank (2025), Indonesia Disaster-Risk Finance and Insurance Project Appraisal Document, Indonesia – Disaster-Risk Finance and Insurance Project, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/316601611543685552/indonesia-disaster-risk-finance-and-insurance-project.
[50] World Bank (2024), Timor-Leste Economic Report, https://documents1.worldbank.org/curated/en/099022224203533573/pdf/P500776116deb704f18e3215df68ee950f8.pdf.
[63] World Bank (2023), Philippines Country Climate and Development Report, https://openknowledge.worldbank.org/bitstreams/50e38e9f-eb8e-4ccf-94a0-d1d23e9f56bd/download.
[55] World Bank (2021), How Indonesia Strengthened Its Disaster Response, World Bank, https://www.worldbank.org/en/news/feature/2021/11/17/how-indonesia-strengthened-its-disaster-response-with-risk-finance-and-insurance.
[13] World Bank (2021), Philippines Cat-DDO and Parametric Insurance Progress, https://documents.worldbank.org/en/publication/documents-report/documentdetail/789821607382813101.
[23] World Bank (2021), Strengthening Financial Protection Against Disasters in the Philippines, World Bank Group, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/278261619134502523.
[28] World Bank (2020), China Flood Risk Governance and Insurance Development Study, World Bank Group, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/608791586669255989.
[120] World Bank (2020), Fostering Rural Resilience: A Closer Look at China’s Agricultural Insurance Pilot, https://documents1.worldbank.org/curated/en/148591594886678703/pdf/Fostering-Rural-Resilience-A-Closer-Look-at-China-s-Agricultural-Insurance-Pilot-Learning-from-Experience-Insights-from-China-s-Progress-in-Disaster-Risk-Management.pdf.
[22] World Bank (2020), Indonesia Disaster-Risk Finance and Insurance: Project Appraisal Document (P173249), World Bank Group, Washington, DC.
[52] World Bank (2018), Funds for Natural Disaster Prevention and Control in Vietnam, World Bank, https://www.eda.admin.ch/dam/countries/countries-content/vietnam/en/sdc-publications/wb-disaster_prevention-fund-review_EN.pdf.
[122] World Bank (2018), Indonesia - Developing parametric insurance for weather related risks for Indonesia, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/704171524632990898.
[8] World Bank (2012), Advancing Disaster-Risk Financing and Insurance in ASEAN Countries, https://openknowledge.worldbank.org/handle/10986/17006.
[159] World Bank (2012), Catastrophe Risk Insurance Pricing, DRFIP, No. 97457, https://documents1.worldbank.org/curated/en/173381468189234926/pdf/97457-BRI-Box391476B-PUBLIC-Pricing-CatRiskPricing-Concept-Final.pdf.
[119] World Bank (2012), National agricultural insurance scheme in India : making insurance markets work for farmers, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/471931468179672837.
[7] World Bank (2011), China – Wenchuan Earthquake Recovery Project: IEG Review, World Bank Group, Washington, DC, http://documents.worldbank.org/curated/en/748431494856876983.
[37] World Bank and BNM (2024), Managing Flood Risks: Leveraging Finance for Business Resilience in Malaysia, https://www.bnm.gov.my/documents/20124/3770663/wb-bnm-flood-risks24.pdf.
[21] World Bank and GFDRR (2019), Financial Protection Against Disasters: Operational Framework, https://openknowledge.worldbank.org/handle/10986/31219.
[76] World Bank and GFDRR (2019), Myanmar Disaster-Risk Financing and Insurance Diagnostic, World Bank Group, https://www.gfdrr.org/en/publication/myanmar-disaster-risk-financing-diagnostic.
[6] World Bank and Government of Kerala (2018), Kerala Post-Disaster Needs Assessment (PDNA), World Bank, Washington, DC, https://www.undp.org/publications/post-disaster-needs-assessment-kerala.
[90] World Bank/Global Facility for Disaster Reduction and Recovery (2023), Global Rapid Post-Disaster Damage Estimation (GRADE) Report: February 6, 2023 Kahramanmaraş Earthquakes - Türkiye Report, World Bank, https://doi.org/10.1596/39468.
[121] World Economic Forum (2025), What is parametric insurance and how is it building climate resilience?, https://www.weforum.org/stories/2025/01/what-is-parametric-insurance-and-how-is-it-building-climate-resilience/.
[88] Yu, M., C. Yang and Y. Li (2018), “Big Data in Natural Disaster Management: A Review”, Geosciences, Vol. 8/5, p. 165, https://doi.org/10.3390/geosciences8050165.
[80] Zhao, Y. and M. Yu (2020), “Predicting Catastrophe Risk: Evidence from Cat Bonds”, Journal of Banking and Finance, Vol. 121, p. 105982, https://doi.org/10.1016/j.jbankfin.2020.105982.
[75] Zurich Malaysia (2023), Understanding Flood Protection Gaps in Malaysia, Zurich Insurance Malaysia, https://www.zurich.com.my/en/about-us/news.
← 1. The term “disaster” can be used broadly to refer to a wide range of events, including conflicts, terrorism, and other man-mad shocks. In this chapter, however, the term refers specifically to events induced by environmental or natural phenomena such as earthquakes, floods, typhoons and other climate and weather-related hazards.
← 2. The cost of disasters and its impact on public budgets is likely to be compounded further by the economic impacts of climate change and the fiscal implications of decarbonisation. Tools like OECD-EDISON tool can help countries in calculating a comprehensive estimate of the fiscal impacts of environmental challenges and decarbonisation over the long-term (OECD, 2025[163]).
← 3. Under APEC, disaster risk financing initiatives include work under the Cebu Action Plan to enhance financial resilience, commitments to develop disaster risk financing and insurance mechanisms and to explore regional risk-pooling arrangements, as well as analytical work commissioned to support the management of disaster-related contingent liabilities (OECD, 2015[40]; APEC, 2015[164]).
← 4. Market-based instruments are especially important for countries that are highly vulnerable to disasters but constrained by limited fiscal capacity yet sufficiently developed to have the institutional and market readiness to engage with capital markets.
← 5. Moreover, the Philippines’ broader climate related DRF framework combines budgetary contingency measures, risk transfer tools, and other reforms to identify the state’s contingent liabilities from disasters (OECD, 2022[68]).