Editing IPCC:AR6/WGIII/Chapter-15 (section)

=== 15.6.4 Climate Risk Pooling and Insurance Approaches ===

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Since 2000, the world has been experiencing significant increase in economic losses and damages from natural disasters and weather perils such as tropical cyclones, earthquakes, flooding and drought. Total global estimate of damage is about USD4210 billion, 2000–2018 ( [[#Aon%20Benfield%20UCL%20Hazard%20Research%20Centre--2019|Aon Benfield UCL Hazard Research Centre 2019]] ). The largest portion of this is attributed to tropical cyclones (USD1253 billion), followed by flooding (USD914 billion), earthquakes (USD757 billion) and drought (approximately USD372 billion, or about USD20 billion yr –1 losses) ( [[#Aon%20Benfield%20UCL%20Hazard%20Research%20Centre--2019|Aon Benfield UCL Hazard Research Centre 2019]] ). In the period 2017–2018, natural catastrophe losses totalled approximately USD219 billion ( [[#Bevere--2019|Bevere 2019]] ). According to the National Oceanic and Atmospheric Administration, 14 weather and climate disasters cost USD91 billion in 2018 ( [[#NOAA%20NCEI--2019|NOAA NCEI 2019]] ). The European Environment Agency reports that ‘disasters caused by weather and climate-related extremes accounted for some 83% of the monetary losses over the period 1980–2017’ for EU Member States (EU-28) and that ‘weather and climate-related losses amounted to EUR426 billion (at 2017 values)’. For the EEA member countries (EEA-33), the ‘total reported economic losses caused by weather and climate-related extremes’ over the same period amounted to approximately EUR453 billion ( [[#EEA--2019|EEA 2019]] ). Asia Pacific and Oceania has been particularly impacted by typhoon and flooding (China, India, the Philippines) resulting in economic losses of USD58 billion, 2000–2017, and a combination of flooding, typhoon and drought totalling USD89 billion in 2018 (inclusive of loss by private insurers and government sponsored programmes ( [[#Aon%20Benfield%20UCL%20Hazard%20Research%20Centre--2019|Aon Benfield UCL Hazard Research Centre 2019]] ). Based on past historical analysis, a region such as the Caribbean, which has experienced climate-related losses equal to 1% of GDP each year since 1960, is expected to have significant increases in such losses in the future leading to possibly upwards of 8% of projected GDP in 2080 ( [[#Commonwealth%20Secretariat--2016|Commonwealth Secretariat 2016]] ). Similarly, Latin American countries, such as Argentina, El Salvador and Guatemala, experienced severe losses in agriculture totalling about USD6 billion due to drought in 2018 ( [[#Aon%20Benfield%20UCL%20Hazard%20Research%20Centre--2019|Aon Benfield UCL Hazard Research Centre 2019]] ). In the African region, where climate is projected to get significantly warmer, continuing severe drought in parts of East Africa, Tropical Cyclone Idai, had devastating economic impacts for Mozambique, Zimbabwe and Malawi ( [[#WMO--2019|WMO 2019]] ). According to Munich Re, loss from about 100 significant events in 2018 for Africa are estimated at USD1.4 billion (Munich Re 2019).

'''While there are questions about the sufficiency of insurance products to address the losses and damages of climate-related disasters, insurance can help to cover immediate needs directly, provide rapid response and transfer financial risk in times of extreme crisis (''' high confidence ''')''' '''( [[#GIZ--2015|GIZ 2015]] ; [[#Lucas--2015|Lucas 2015]] ; [[#Schoenmaker--2015|Schoenmaker and Zachmann 2015]] ; [[#Hermann--2016|Hermann et al. 2016]] ; [[#Wolfrom--2016|Wolfrom and Yokoi-Arai 2016]] ; [[#Kreft--2017|Kreft and Schäfer 2017]] ; [[#UNESCAP--2017|UNESCAP 2017]] ; [[#Matias--2018|Matias et al. 2018]] ; [[#UNECA--2018|UNECA 2018]] ; [[#Broberg--2019|Broberg and Hovani-Bue 2019]] ; [[#EEA--2019|EEA 2019]] ; [[#Martinez-Diaz--2019|Martinez-Diaz et al. 2019]] ). Commercial insurability is heavily driven by the predictability of losses and the resulting ability to calculate insurance premium levels properly. Climate change has become a major factor of increasing uncertainty. The previously strong reliance on historic data in calculation of premium levels may be but a starting point given the likely need for upward adjustment due to climate change and potential consequential economic damage. Different risk perceptions between policyholders and insurers will create contrary assessments on premium levels and consequently underinsurance. [[#McKinsey--2020b|McKinsey (2020b)]] also stresses the systemic effect of climate change on insurers’ business models and resulting availability of appropriate insu''' '''rance products.'''

The conventional approach to such protective or hedging position has been indemnity and other classical insurance micro-, meso- and macro-level schemes ( [[#Hermann--2016|Hermann et al. 2016]] ). These include micro insurance schemes such as index insurance and weather derivative approaches that cover individuals’ specific needs such as coverage for farm crops. Meso-level insurance schemes, which primarily benefit intermediary institutions, such as NGOs, credit unions, financial institutions and farmer credit entities, seek to reduce losses caused by credit default thereby ‘enhancing investment potential’, whereas macro-level insurance schemes ‘allow both insured and uninsured individuals to be compensated for damages caused by extreme weather events’ ( [[#Hermann--2016|Hermann et al. 2016]] ). These macro-level insurance schemes include catastrophe bonds and weather derivatives and so on, that transfer risk to capital markets ( [[#Hermann--2016|Hermann et al. 2016]] ). Over the last decades, there has been a trend towards weather-index insurance and other parametric insurance products based on predefined pay-out risk pooling instruments. It has gained favour with governments in developing regions such as Africa, the Caribbean and the Pacific because it provides certainty and predictability about funding – financial preparedness – for emergency actions and initial reconstruction and reduces moral hazard. This ‘financial resilience’ is also increasingly appealing to the business sector, particularly micro, small and medium enterprises (MSMEs), in developing countries ( [[#MEFIN%20Network%20and%20GI%20RFPI%20Asia--2016|MEFIN Network and GI RFPI Asia 2016]] ; [[#Woods--2016|Woods 2016]] ; [[#Schaer--2018|Schaer and Kuruppu 2018]] ).

To date, sovereign parametric climate risk pooling as a way of managing climate risk does not seem to have much traction in developed countries and does not appear to be attractive to actors in the G20 countries. No G20 members are yet party to any climate risk pooling initiative ( [[#Kreft--2017|Kreft and Schäfer 2017]] ). However, international bilateral donors such as the USAID and the UK Foreign, Commonwealth and Development Office (FCDO, formerly DFID), and the multilateral development banks are all, to different extent, supporters of the various climate risk pooling initiatives now operational in developing countries.

As noted also in IPCC AR5, risk sharing and risk transfer strategies provide ‘pre-disaster financing arrangements that shift economic risk from one party to another’ ( [[#IPCC--2012|IPCC 2012]] ). Risk pooling among countries and regions is relatively advantageous when compared to conventional insurance because of the effective subsidising of ‘affected regions’ using revenues from unaffected regions which involve pooling among a large subset of countries ( ''high confidence'' ) ( [[#Lucas--2015|Lucas 2015]] ). In general, the premiums are less costly than what an individual country or entity can achieve and disbursement is rapid and there are also fewer transaction costs ( [[#Lucas--2015|Lucas 2015]] ; [[#World%20Bank--2015|World Bank 2015]] ). The World Bank argues that the experience with the Pacific Catastrophe Risk Insurance Pilot (PCRIP) and Africa Risk Capacity risk pooling (ARC) show savings of 50% in obtaining insurance cover for pooled risk compared with purchasing comparable coverage individually ( [[#Lucas--2015|Lucas 2015]] ; [[#World%20Bank--2015|World Bank 2015]] ; [[#ARC--2016|ARC 2016]] ). However, it requires, as noted by UNESCAP, ‘extensive coordination across participating countries, and entities’ ( [[#Lucas--2015|Lucas 2015]] ).

At the same time, this approach has substantial basis risk (actual losses do not equal financial compensation) ( ''high confidence'' ) ( [[#Hermann--2016|Hermann et al. 2016]] ). With parametric insurance, pay-outs are pre-defined and based on risk modelling rather than on-the-ground damage assessment so may be less than, equal to, or greater than the actual damage. It does not cover actual losses and damage and therefore, may be insufficient to meet the cost of rehabilitation and reconstruction. It may also be ‘non-viable’ or damaging to livelihoods in the long run ( [[#UNFCCC--2008|UNFCCC 2008]] ; [[#Hellmuth--2009|Hellmuth et al. 2009]] ; [[#Hermann--2016|Hermann et al. 2016]] ). Additionally, if the required threshold is not met, there may be no pay-out, though a country may have experienced substantial damages from a climatic event. This occurred for the Solomon Islands in 2014 which discontinued its insurance with the Pacific Catastrophe Risk Insurance Pilot when neither its Santa Cruz earthquake nor the 2014 flash floods were eligible to receive a pay-out under the terms of the insurance ( [[#Lucas--2015|Lucas 2015]] ).

Increasingly, climate risk insurance schemes are being blended into disaster risk management as part of a comprehensive risk management approach ( ''high confidence'' ). The best-known example is the Caribbean Catastrophe Risk Insurance Facility ( [[#CCRIF%20SPC--2018|CCRIF SPC 2018]] ), which involves cooperation among Caribbean states, Japan, Canada, UK and France and international organisations such as the World Bank ( [[#UNESCAP--2017|UNESCAP 2017]] ). But there are growing platforms of such an approach mainly under the umbrella of the G7’s InsuResilience Initative ( [[#Deutsche%20Klimafinanzierung--2020|Deutsche Klimafinanzierung 2020]] ), including, the Pacific Catastrophe Risk Assessment and Financing Initiative for the Pacific Islands (PCRAFI), the African Risk Capacity (ARC Agency and its financial affiliate), and the African Risk Capacity Limited (ARC Ltd/ the ARC Group) ( [[#ARC--2016|ARC 2016]] ) and in the Asian region, the South East Asian Disaster Risk Insurance Facility (SEADRIF) and the ASEAN Disaster Risk Financing and Insurance Program (ADRFI), ( [[#SEADRIF--2018|SEADRIF 2018]] ; GIZ and World Bank 2019; [[#Martinez-Diaz--2019|Martinez-Diaz et al. 2019]] ; [[#Vyas--2019|Vyas et al. 2019]] ; [[#World%20Bank--2019a|World Bank 2019a]] ). The group of 20 vulnerable countries (V20) has also developed a Sustainable Insurance Facility (SIF), billed as a technical assistance facility for climate-smart [[#footnote-003|14]] insurance for MSMEs in 48 developing countries aswell as potentially to de-risk renewable energy in these countries and regions ( [[#ACT%20Alliance--2020|ACT Alliance 2020]] ; [[#V20--2020|V20 2020]] ; [[#V20--2021|V20 2021]] ).

However, as noted above, climate risk pooling is not a panacea. There are very obvious and significant challenges. According to [[#Kreft--2017|Kreft and Schäfer (2017)]] , limitations of insurance schemes include coordination challenges, limited scope, destabilisation due to exit of one or more members as premiums rise and inadequate attention to permanence ( [[#Schaeffer--2014|Schaeffer et al. 2014]] ). There are also challenges with risk diversification, replication, and scalability ( ''high confidence'' ). For example, CCRIF is extending both its membership and diversifying its geographic dimensions into Central America in seeking to lower covariate risk (similar shocks among cohorts such as droughts or floods). Under the SPC portfolio, CCRIF is able to segregate risk across the regions. Risk insurance does not obviate from the need to engage in capacity building to scale-up as well as having process for addressing systemic risk. Currently, risk pools have limited sectoral reach and may cover agriculture but not other important sectors such as fisheries and public utilities. Only recently (July 2019) has CCRIF initiated coverage of fisheries with the development of its Caribbean Oceans and Aquaculture Sustainability Facility (COAST) instrument ( [[#CCRIF%20SPC--2019|CCRIF SPC 2019]] ; [[#ACT%20Alliance--2020|ACT Alliance 2020]] ). Historically, risk pool mechanisms, like CCRIF and ARC, only cover a small subset of perils, such as tropical cyclones, earthquakes and excess rainfall but do not include other perils such as drought. Since 2016, ARC has increased its scope to cover drought and in 2019 launched ARC Replica, which not only covers drought but offers premiums and coverage to NGOs and the World Food Programme through the START Network and a pastoral drought product for protecting small farmers and ensuring food security. In some regions and countries, there may also be limited access to reinsurance ( [[#Schaeffer--2014|Schaeffer et al. 2014]] ; [[#Lucas--2015|Lucas 2015]] ). An important down-side of climate risk pooling is that it does not cover the actual cost of damage and losses. Though on the positive side, pay-out may exceed costs, but it may also be less than costs. Hence, the parametric approach is not a panacea and does not preclude having recourse to conventional indemnity insurance, which will cover full damage costs after a climate change event as it involves full on-the-ground assessment of factors such as the necessity and costs of repair versus, say, replacement value of damaged infrastructure. This may be important for governmental and publicly provided services such as schools, hospitals, roads, airports, communications equipment and water supply facilities. Given the growing popularity of parametric insurance and climate risk pooling, there are very ambitious attempts to expand this approach on several fronts ( [[#Scherer--2017|Scherer 2017]] ). [[#Schoenmaker--2015|Schoenmaker and Zachmann (2015)]] have proposed a global climate risk pool to help the most vulnerable countries. The pathway to this includes capacity building in underdeveloped financing sectors of developing countries. They argue that as climate extremes become more normalised, they will wipe out significant parts of the infrastructure and productive capacity of developing countries. This will have knock-on impact on fiscal capacity due to lowered tax revenue and high rebuilding costs. ‘Developing countries’, [[#Schoenmaker--2015|Schoenmaker and Zachmann (2015)]] argue, ‘cannot insure against such events on a market basis, nor would it be sensible to divert scarce fiscal resources away from infrastructure investment into accumulating a financial buffer for such situations’. In that context, [[#Schoenmaker--2015|Schoenmaker and Zachmann (2015)]] call for international risk pooling as ‘the only sensible strategy’, especially if it addresses the major gaps in climate risk insurance for poor and vulnerable communities by enhancing demand through ‘smart support instrument’ for premium support such as full or partial premium subsidies and investment in providing risk reduction ( [[#Schäfer--2016|Schäfer et al. 2016]] ; [[#Le%20Quesne--2017|Le Quesne et al. 2017]] ; [[#MCII--2018|MCII 2018]] ; [[#Vyas--2019|Vyas et al. 2019]] ). This, it is argued, may help to smoothen out the limited uptake of regional institutions such as ARC and CCRIF SPC, which are only in three regions of the world (with missing mechanism in South America) ( [[#Kreft--2017|Kreft and Schäfer 2017]] ). Existing regional mechanisms, while they may perform very well, only cover a portion of climatic hazards and tend to have limited subscribers. For example, across the key four sovereign risk pools (ARC, CRIFSPC, PCRAFI and SEADRIF), though there are 68 countries only one-third or 32% have purchased coverage in 2019 and 46% ‘did not deploy disaster risk financing instruments’ ( [[#ACT%20Alliance--2020|ACT Alliance 2020]] ).

Other gaps and challenges flagged by [[#Kreft--2017|Kreft and Schäfer (2017)]] include limited coverage of the full spectrum of contingency risks experienced by countries, inadequate role of risk management as a standard for all regional pools, though there are some emerging best practices in terms of data provision on weather-related risks, and incentivisation of risk reduction ( ''high confidence'' ). Here, they recognise the work of Africa Risk Capacity for not only providing the infrastructure to trigger disbursement but for also promoting national risk analysis. Another important gap in the landscape of climate risk pooling is lack of attention to financial institutions’ lending portfolios that are vulnerable to weather shocks. In this regard subsidies as part of innovative financing schemes facilitated by the donor community can encourage the uptake of meso-level climate risk insurance solutions ( [[#Kreft--2017|Kreft and Schäfer 2017]] ).

In the literature, there are two attempts at systematic evaluation or comprehensive assessment of regional climate risk pools: a comprehensive study by [[#Scherer--2017|Scherer (2017)]] and FCDO’s ten-year evaluation (2015–2024). Overall, neither of these studies draw adverse conclusions about regional climate risk pooling initiatives/mechanisms. According to Scherer, ‘it appears that insurances work in principle and there is certainly success’ and ‘initial experiences demonstrate regional climate risk insurances works’. The author cited the 28 pay-outs to 16 countries of USD106 millionarguing that it provides cash-starved countries with much needed cash ( [[#Scherer--2017|Scherer 2017]] , p. 4). The FCDO study ( [[#Scott--2017|Scott 2017]] ) examines the uptake of ARC and its impact on reducing vulnerability to disasters. It notes that there is scarce literature on disaster risk insurance mechanisms in terms of impacts. In its current sample of 20 countries as of November 2017, four are projected to experience food security crisis (IPC Level 3) but are not signatories to the ARC, which may signal that ARC is not attractive to all food insecure countries and that there is no overwhelming appetite for ARC among poorer countries. Additionally, [[#Panda--2020|Panda and Surminski (2020)]] research the importance of indicators and frameworks for monitoring the performance and impact of Coalition for Disaster Resilient Infrastructure (CDRI) but make no final assessment of any of the regional climate risk pool. However, they propose mechanisms to improve the transparency and accountability of the system. [[#Scherer--2017|Scherer (2017)]] , [[#Forest--2018|Forest (2018)]] and [[#Panda--2020|Panda and Surminski (2020)]] seem to indicate that there is ‘enthusiasm to support and scale-up regional climate risk insurance’ ( [[#Scherer--2017|Scherer 2017]] , p. 4) Examples of this support include: the Germany Ministry for Economic Cooperation and Development (BMZ) has provided USD5.9 million for the World Food Programme (WFP) to protect 1.2 million vulnerable African farmers with climate risk insurance, through ARC Replica, and the G7 InsuResilience Vision 2025, which has committed to ensuring 400–500 million poor persons are covered against disaster shock by pre-arranged finance and insurance mechanism by 2025; some of this will be through ARC ( [[#WFP--2020|WFP 2020]] ). Of course, this does not mean that risk pools are without challenges or are not failing on specific sets of metrics. [[#Forest--2018|Forest (2018)]] flags three failing areas: policy holder and hazard coverage, the cost of premium and risk transfer parameters, and the use of pay-out, which in most cases are up to the government. Here, ARC is flagged among the three regional risk pools, as the only one with contingency plan requirements that can support effective use of pay-outs. Other research exploring climate risk pooling and its impacts flag lack of transparency around pay-out, premium or risk transfer parameters. Ultimately, climate risk pools are not full insurance; they offer only limited coverage. Entities such as the U4 Anti-Corruption Help Desk are exploring how to mitigate potential corruption with regard to climate risk insurance.

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