IDRC Davos 2016 CONFERENCE AGENDA

The programme includes the IDRC Davos 2016 agenda of sessions, plenary sessions, special panels and workshops. Click on the session title for more details.

Please send minor changes and corrections (in affiliations, presentation order, or spelling) to idrc@grforum.org

 

IDRC Davos 2016 CONFERENCE AGENDA


Session
Session 28: Risk Transfer Mechanisms in DRR
Time:
Wednesday, 31/Aug/2016:
2:00pm - 3:30pm

Session Chair: Chloe DEMROVSKY, Disaster Recovery Institute International
Session Chair: Christian H. BARTHELT, Munich Re Foundation
Room: Seehorn

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Presentations

Resilient Cities, SMEs, Communities and Infrastructure: Four Pioneering Projects of the UN’s Principles for Sustainable Insurance Initiative

Butch BACANI

United Nations Environment Programme, Switzerland

This presentation will cover key findings of four pioneering projects—spanning cities, SMEs, communities and infrastructure—of the UNEP FI Principles for Sustainable Insurance (PSI). With nearly 100 members worldwide, including insurers representing more than 20% of world premium and USD 14 trillion in assets under management, the PSI is largest collaborative initiative between the United Nations and the insurance industry.

The first project is the PSI Global Resilience Project, a multi-year project led by IAG to increase awareness of climate and natural disaster risk; to understand behavioural, ecosystem and structural approaches to reduce disaster risk; and to drive greater investment in disaster risk reduction by governments, NGOs, businesses and communities. The project’s final report, “Collaborating for resilience: Partnerships that build disaster-resilient communities and economies”, was launched on Resilience Day of COP21. The report focuses on multi-stakeholder partnerships that have worked to address disaster risk reduction and climate change adaptation in order to build resilience at different levels of society—from districts and municipalities, to cities and countries. To complement the disaster risk reduction focus, the report features innovative climate and disaster risk transfer/insurance solutions from around the world, from micro to macro levels.

The second project is the AXA-PSI global study of how cities and SMEs are building climate resilience, based on a landmark survey of more than 40 city leaders and 1,100 SMEs from around the world.

The third project is the project of Santam, the PSI, ICLEI, ClimateWise and partners to create “City Innovation Platforms” for African infrastructure risk and resilience, with Dar es Salaam in Tanzania as the pilot city.

The fourth project is the PSI-World Bank Group global project, led by Munich Re and the International Finance Corporation, to develop guiding sustainability principles for underwriting surety bonds and infrastructure projects.


Integrated Finanical Risk Transfer Mechanisms for Urban Resilience

Matthias RANGE, Sandra SCHUSTER

Deutsche Gesellschaft für Internationale Zusammenarbeit, Germany

Extreme weather events are likely to become more frequent and intense with global climate change and cities and communities begin to encounter hazards that they have rarely, if ever experienced before. Insurance plays a significant role in spreading risk across communities and all sectors of the economy, over large geographical areas and time. The sharing and transfer of risk can assist the recovery to these natural catastrophes and therefore enhance the resilience of society to disaster.

Governments who are challenged by a timely disaster response and financial constraints can be assisted by a more integrated approach of risk analysis, risk prevention, preparedness and also transfer solutions. By integrating risk transfer solutions, such as insurance, into disaster risk management and climate adaptation efforts, governments and individuals are able to (1) soften the financial impact and access timely finance after a disaster impact, (2) increase the effectiveness of the implementation of contingency plans, and (3) plan preventive measures that mitigate disaster-induced poverty traps and long-term development setbacks. The insurance industry is uniquely placed to play a strong role in providing risk management and enhancing the resilience of communities. Internationally, policy makers are increasingly recognising this approach as highlighted by the G7 and private sector “InsuResilience” initiative and reference to risk transfer and insurance as being part of a comprehensive climate risk management approach in the Paris Agreement (COP21).

Integrated Climate Risk Management (ICRM) concepts are currently being developed for four countries by GIZ across urban, agriculture, energy and sustainable tourism sectors. This project is funded through the International Climate Initiative (IKI) of the German Ministry of Environment (BMUB). This presentation will outline integrated approaches for climate risk management and transfer and will highlight GIZ’s urban resilience work in China where an ICRM concept is being developed.


Use of Catastrophe Modelling Data to Help Earthquake Risk Assessment for Developers and Policy Makers

Alexandros GEORGIADIS1, Chris EWING1, Stuart FRASER2

1Impact Forecasting, United Kingdom; 2World Bank, GFDRR Innovation Lab

Catastrophe models are routinely used in the insurance market to analyse the financial implications of catastrophic events such as hurricanes, earthquakes and floods. In a nutshell, catastrophe models are tools that allow insurers and reinsurers to quantify the frequency (likelihood) and intensity of potential losses and help them to develop effective reinsurance programs. In addition, they are useful tools for risk appetite, portfolio and capital requirements management.

Beyond their traditional use in (re)insurance, catastrophe models and related model data (e.g. hazard and exposure maps) have been proven to be versatile tools, useful for a diverse number of risk related topics such as disaster risk reduction in developing countries and providing guidance for policy makers, think tanks, government agencies or NGOs. An example of such a use of catastrophe modelling data is the development of ThinkHazard!, a new tool focusing on providing hazard information for preparedness rather than post-event reaction. Impact Forecasting, Aon Benfield's catastrophe model development centre, teamed up with World Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR), to incorporate modelled earthquake data (e.g. hazard maps) into their online tool.

The EQ hazard data show the probability of earthquakes for a number of countries including Bosnia-Herzegovina, Kenya, Morocco, Serbia, Turkey and Uganda. The countries are labelled with high, medium or low rankings and are linked directly to recommendations on how to reduce risk. Overall, development professionals and policy makers can use the tool to determine the potential likelihood of earthquakes and provide guidance on actions to increase resilience against disaster, including advice surrounding enhancing evacuation plans and improving building codes.


Creating New Hybrid Products for Adapting the Insurance Mechanism to Drive Resilience

Robert MUIR-WOOD

Risk Management Solutions., United Kingdom

The IPCC 2012 definition of resilience focuses on the three elements of Anticipate, Absorb and Respond/Recover. Key to both anticipating and responding is to drive a reduction in the level of risk. While insurance can assist in absorbing the impact of an event, by helping to pay for some or all of the cost of the loss and damage, the insurance mechanism on its own does not reduce risk, nor ‘build back better’ after a disaster. However by combining a long term insurance contract with a bond that invests in risk reduction, it becomes possible to create a mechanism that both refunds the damage and reduces the risk. The talk will give several examples of how such hybrid resilience products can be structured, from the scale of a single homeowner all the way up to a whole city. In a simple example for a bond to build a flood wall sea defense, the premium paid to the investor in the bond is initially to compensate for the high level of flood risk, but once the flood wall is built the premium continues at the same level for several years so as to pay off the loan made to build the defense, while the insurance component to cover the residual flood risk continues at a low level. At the end of the contract the city has improved its resilience for the long term.


Where and What Kind of Weather Insurance Indexes Could be Potentially Used for Main Crops in China on the County-level?

Jing ZHANG1,2,3, Zhao ZHANG1,2,3

1State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University; 2Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University; 3Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education

With the background of climate change and urbanization of China, it’s a big challenge to increase the rural income and decrease the gap of urban-rural. Fortunately, agricultural insurance and reinsurance have been reported as a powerful tool to transfer risk of natural disasters and reduce loss for farmers. Additionally, agricultural weather index insurance (WII) is gradually taking the place of traditional insurance in more countries, without moral risk and adverse selection. But most research focus on the specific weather index or in a narrow area for one single crop. To our knowledge, there is still no comprehensive WII research for three crops in major crop planting areas in China. In the study, we examined where and what kind of WIIs are suitable for agricultural insurance, and further summarized the spatial characteristics of these indexes.

Total 727, 1026, 1526 counties were selected for maize, wheat, and rice separately. Daily meteorological variables, phenology data and county-level yield records were collected from 1980 to 2008. After calculating WIIs including chilling injury, heat stress and drought index for individual crop and county, the Pearson correlation coefficient between index and yield loss is used to judge the application of WII with a confidence level of 90%.

It’s obvious that not all counties are insurable. For maize, chilling injury and heat stress index in the north of China are more sensitive to the yield loss, while drought index in the south. Moreover, heat stress index has a good correlation with yield loss across the whole China, with the others having the best performance in the Sichuan Basin. For rice, the national common index is drought index, with cold injury and heat stress index are the regional optimum for northeast China and Sichuan Basin, respectively.


Study on Probability Distribution of Disaster Losses, Demographics and Social Security: A Case Study of Western China

Hong MI1,2, Xinhao LIN1, Guolong WANG1,2

1School of Public Affairs, Zhejiang University, People's Republic of China; 2Institute for Population and Development Studies, Zhejiang University, People's Republic of China

China is a vast but also a natural disaster-prone country since ancient times. The modern production mode and lifestyle is even increasing the occurrence of disasters. China has been suffering a huge economic loss every year. However, there is very little research about probability of disaster losses at a micro level.

The data used in this research is from a 5 year-retrospective questionnaire survey of 792 middle-aged and elderly residents, took place in Baoji City, Shaanxi province of western China in the year of 2013. Baoji is a city of heavy industry and moderately developing economy in China, located right between Xi’an and Wenchuan, which is an appropriate representation of average China. The 2008 Wenchuan big earthquake, whose magnitude was 8.0 and caused 87150 deaths and missing, was a miserable memory for all Chinese people. After this, issue about catastrophe exception clause in commercial insurance has been heated discussed. This research is mean to explore the influencing factors on property losses and provide useful suggestions for establishing Chinese state catastrophe social insurance and the modification of the social supporting system.

Based on the cross analysis and logistic regression, the paper explored the influencing factors on property losses in disasters. The main conclusions are below:

1. Increasing age, education level and annual income all lead to increasing probability of property losses.

2. Participation in pension insurance increases the probability of property losses significantly and dramatically.

3. People living at home show a significant lower probability of property losses than those living in a nursing house.

4. There is a strong correlation between property losses and self-expectation of future life.

*The project is supported by the Major Program of the National Natural Science Foundation of China (Grant No. 71490732) and the Key grant Project of Chinese Ministry of Education (Grant No. 12JZD035).


Study on the Impact of Economic Growth on Meteorological Disaster Losses in China

Zhuorong YING1,2,3, Peijun SHI1,2,3

1State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University; 2Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University; 3Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing Normal University

Natural disasters, especially meteorological disasters, have caused considerable fatalities and economic losses in China. According to statistics of the China Meteorological Administration, losses due to meteorological disasters account for about 70% or more.

In this paper, the spatial distribution and temporal trends of the losses are analyzed. Then the effect of meteorological disasters on macro-economic growth is explored, using social economic data and meteorological disaster data of 31 provinces in China from 2004 to 2013. The results show that in China, the meteorological disaster losses have significant positive effect on economic growth rate at the province scale. This positive effect is affected by some social and economic factors, including urbanization rate, government size, regional openness, education, transportation and medical level.



 
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