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Research & Engagement

CIRCAD is structured to provide a compelling value proposition that appeals to a broad spectrum of Members engaged in climate risk management. By offering networking events, collaborative workshops, access to tailored and cutting-edge research, and a pool of well-matched future talent, we aim to position CIRCAD as a hub for innovation, collaboration, and workforce development.

In the current Planning Phase, we are working with prospective CIRCAD members to understand their unique needs, challenges, and aspirations. This will allow us to organize our subsequent plans and proposals to address the concerns and opportunities important to them. In September, 2014, we will host a two-day Planning Workshop for prospective members, external collaborators, and site researchers. The goals of the Planning Workshop are to establish CIRCAD’s focus, value propositions, and potential research projects, with the output being a research agenda and a specific portfolio of proposed projects that appeal to our prospective members. These will provide the intellectual foundation for our full proposal, due in mid December, 2014.

While the specific research to be conducted by CIRCAD will be determined by a member advisory board, following is a representative set of prospective projects that we anticipate aligning industry’s research needs concerning climate risk management.

Establishing metrics and thresholds for parametric extreme heat insurance

Many rural residents work in occupational settings, such as agricultural or manufacturing environments, that can expose them to high heat levels, escalating their risk of heat-related health issues. An innovative approach to addressing heat vulnerability involves parametric insurance, a policy that pays out a predetermined amount based on the occurrence of a specific, predefined event, rather than on the actual losses incurred. Parametric insurance can be designed to offset loss of wages or increased electricity costs due to periods of extreme heat, reducing the economic burden of exposure and allowing insurers to pay claims rapidly and proactively. However, the thresholds being established for extreme heat exposure are not currently based on health outcomes, but rather on the probability of the event itself. For example, thresholds that trigger heat warnings often occur at the 98th percentile of all heat events, yet health outcomes are often experienced at temperatures that are in the 90th percentile. Identifying the appropriate health-outcome based metrics would allow insurers to properly calibrate policy pricing while reducing harm for policyholders.

Objectives: The purpose of this project is to assess which temperature metrics best predict health outcomes and establish statistically-based thresholds at which health outcomes occur to inform outcome-based triggers for parametric insurance. While many studies rely on measures such as maximum temperature to assess health impacts, this study will employ more reliable heat stress metrics, such as wet bulb globe temperature (WBGT). WBGT is a robust indicator of hot conditions since it accounts for the effects of air temperature, humidity, wind speed, and solar radiation. Using such a comprehensive index provides a more thorough understanding of the relationship between heat exposure and health outcomes. Along with a comparison against the standard metrics of maximum temperature and heat index, we will also examine the relationship between persistently high overnight temperatures and health outcomes.

Impact: When paired with a subsequent project to simulate the future occurrence of the established triggers, the results of this research could be leveraged to design cost-effective new parametric insurance products to mitigate the impact of extreme heat.

Aligning firm- and system-level incentives in managing climate-related financial risk

The US insurance exemplifies the magnitude of climate-related financial risk facing global finance markets and the challenges with aligning regulatory and market responses to these risks at the firm- and system-level. Recent insolvencies of property and casualty insurers driven by increasingly frequent and severe natural disasters demonstrate the potential financial consequences of inadequate firm-level management of climate-related financial risks. Yet, more proactive insurer climate risk management strategies—such as reducing coverage, increasing premiums, and exiting high-risk markets—may undermine the ability of the system as a whole to effectively and equitably manage climate risks. This disconnect between optimal risk management at the firm- and system-level has implications for economic and environmental resilience, including (i) impeding financial instability, as protection gaps amplify climate-related systemic financial risk; (ii) exacerbating racial and economic inequality, as low-income communities and communities of color are disproportionally vulnerable to both direct climate harms and financial exclusion; and (iii) inhibiting efficient public and private investment in climate mitigation and adaptation, as inaccurate price signals distort incentives for risk reduction.

Project Objectives: This project will focus on the analytical and governance barriers to aligning firm- and system-level incentives in climate-related financial risk management. Specifically, it will (i) map the policy, regulatory, and market dynamics shaping incentives for insurance sector climate risk integration; (ii) analyze complex interactions within and across insurers’ climate risk management strategies and the implications for resilience; (iii) develop a multi-objective decision framework that integrates these systemwide feedbacks; and (iv) identify approaches to institutionalize the framework in industry and regulatory practice.

Impact: This project will inform strategies to more effectively and equitably manage climate-related financial risk and surface opportunities to leverage insurance sector analytical capacity and investment capital to catalyze greater economic and environmental resilience. 

Enabling large-scale probabilistic modeling for multi-hazard risk characterization

Many reinsurers operate underwriting and investment portfolios across multiple hazards, sectors, and geographies, subjecting them to uncertain financial risks resulting from complex, climate-related interactions. Modeling such risks at the national or global levels requires computational models that can represent rich predictive covariance structures across space and time and accommodate large sets of diverse data. As insurance applications can often be naturally grouped according to factors such as geography, business sectors, and hazard types, hierarchical model structures are a natural choice. However the computational burden of inference for large data (n≈107 observations) and high-dimensional parameter sets (d>106 parameters) has made most existing statistical modeling frameworks and approaches intractable due to the O(d2) practical complexity of most Markov chain Monte Carlo (MCMC) algorithms. 

Project Objectives: We aim to jointly model occurrence and severity of multiple hazards, making use of likelihood functions appropriate for count-valued data, positive loss values, and extreme values, in a computationally-tractable manner. We also want to exploit the latest advances in remote sensing and machine learning to extract features from satellite imagery and other sources to improve predictive performance.  While individual elements of this work, such as multi-hazard modeling, GPU-based MCMC, and hierarchical insurance modeling have been explored separately, we will combine all of these into a single framework compatible with the latest advances in GPU-based probabilistic modeling.

Impact: This research will enhance risk characterization by allowing a probabilistic model to both share information across geographies and hazards and be parameterized at very fine scales (i.e., at city or district rather than a state or national-level). This will improve rate-setting and loss forecasting by using more information to enhance point estimates as well as better characterizing spatiotemporal and cross-hazard correlations. Unlike most machine learning methods, our proposed workflow will also allow for identification of unusually high- or low-risk data groupings.

Piloting community-based insurance frameworks in underserved and overburdened communities

More communities across the country are facing elevated climate risk, both intense and severe acute disasters and slow-moving chronic stressors. Not only are underserved and overburdened communities most likely to be impacted by climate change, they also have the least individual and institutional (local jurisdictions or Tribal governments) capacity to evaluate and manage climate risks, including via insurance products. For example, low income households and renters often cannot afford costly property insurance and may not meet the National Flood Insurance Policy floodplain management requirements to be covered by federal backstop insurance. These communities need additional information about their growing risks and how to manage them, as well as support in developing required plans, getting community buy-in, accessing and stacking funding, and building necessary partnerships.

Community-based insurance frameworks and technical assistance can support underserved and overburdened communities in building greater climate resilience. For example, community-based catastrophe insurance (CBCI)—disaster insurance arranged by a local government or quasi-governmental body (e.g., special purpose district) to cover properties within the community—may be a model to help those not being supported in the current system. These community-based insurance frameworks can enhance financial resilience, provide affordable and reliable disaster insurance, and create incentive for community-level risk reduction. Moreover, technical assistance—leveraging the insurance sector’s analytical and capacity building resources—can enable underserved communities to build resilience and reduce risks, which can keep insurance a more viable risk management tool in the long run. There is a need to convene and coordinate local jurisdictions, community members, state resilience offices, regulators, insurance providers, and experts to identify an effective path forward for addressing risk in these communities.

Project Objectives: This project aims to identify how the insurance sector can support climate resilience and risk reduction in underserved and overburdened communities in ways that keep current models of property insurance affordable, viable, and useful for risk management.

Impact: Community pilot projects will reveal: 1) technical assistance needs and approaches that work for communities, 2) the potential role of nature-based solutions for community risk reduction and 3) the value of CBCI for managing climate risks in underserved communities. 

Developing performance metrics for climate resilience planning

A fundamental challenge to building a climate-resilient society is achieving consensus on what this would entail and how we would measure its attainment. What should the goals of adaptation investment be? While for a single asset, the nature of the hazard or stressor and the relevant timeframe may be relatively straightforward (though not always), for society as a whole the picture is far more complicated. Which hazards and stresses should be addressed? Resilience for whom or what and over what area and timeframe? What systems, organizations, institutions, and stakeholders need to be involved? How do we balance adaptation to multiple hazards and avoid maladaptation? How can performance be measured? The lack of an widely-accepted planning framework with key performance indicators (KPI) hampers the ability of governments and institutions to establish climate-resilient goals, appropriately allocate adaptation investments, and track and communicate performance. 

For the insurance sector, establishing a common approach to adaptation planning and key performance indicators is vital to their investment strategies, including in municipal bonds. Very few municipalities have rigorously developed a plan and ability to track the resilience added by the public service or infrastructure that they are seeking to fund. The dominant role played by the insurance sector positions them well to influence how local municipalities plan for, invest in, and track climate resilience and adaptation. By providing and supporting municipalities with clear planning and KPI development guidelines, the insurance industry will be better able to allocate capital in relation to achievement of climate resilience. 

Project Objectives: We seek to develop a common approach to establishing key performance indicators (KPIs) for resilience planning and adaptation investment tailored for the insurance sector.

Impact: Our approach has already been adopted by the White House Council on Environmental Quality (CEQ) and has been incorporated into federal agencies’ Guidance Document for developing Climate Adaptation Plans. We will create a similar document tailored to the insurance sector that offers stepwise guidance for KPI development and climate resilience and adaptation planning. As with the process used for federal agencies, we will establish a working group with members from insurance companies, industry associations, climate resilience policy think tanks, and academia. Through a series of workshops, participants will determine specific needs for the insurance sector and then develop and evaluate a guidance document under a range of scenarios. 

Evaluating the impact of flood risk rating on property values, insurance uptake, and hazard mitigation

Evaluating the impact of flood risk rating on property values, insurance uptake, and hazard mitigation
The US Federal government spends billions of dollars each year on disaster relief and recovery through loans, assistance, and insurance payments, yet insurance is the only guaranteed source of post-disaster relief. Nonetheless, market penetration for flood insurance remains low, with many homes in high-risk flood zones foregoing coverage despite mandatory purchase provisions. In addition, FEMA incentivizes local flood hazard mitigation projects through the Community Ratings System (CRS) of the National Flood Insurance Program (NFIP). CRS awards points for hazard mitigation and information projects; the resulting CRS classification level results in lowering flood insurance costs for policy holders in the community. Existing research in this domain indicates that insurance demand is not very sensitive to price (given restrictions on demand) but is responsive to past flood damage and household perceptions of flood risk. Community flood risk mitigation efforts are influenced by resources (municipal capacity, tax revenue), competing priorities (crime and education), and “windows of opportunity” (circumstances that permit mitigation investments after a significant disaster experience). 

The past several years have witnessed significant changes in flood risk ratings in both the public and private sectors. In the public sector, FEMA has initiated Risk Rating 2.0, which is an attempt to make NFIP risk premiums better accord with actual flood risk. The CRS program has been through numerous revisions that change the provisions of points awarded for mitigation efforts. In the private sector, First Street Foundation has launched an ambitious effort to map parcel-level flood risk in the US, using state-of-the-art methods in flood modeling. Each of these programs have been rolled-out in the space of 12 to 36 months, making it difficult to assess the various influences of this “sea change” in flood risk management. 

Project Objectives: Recent research in coastal housing markets finds evidence of underpricing of flood risk, maladaptive tendencies in risk management and recovery, and variance in capitalization of climate risks according to market participant beliefs about climate change. We seek to elucidate the effects of information on property market transactions and individual and community risk management decisions, while exploring the potential for expanded investment in nature-based solutions (and how these investments might influence CRS scores).

Impact: This project will provide insights into how flood insurance premiums, external risk ratings, and incentives for flood mitigation influence housing price in high flood risk areas, insurance purchase decisions, and flood mitigation efforts.

Coupled evaluation of nature-based solutions and community-based catastrophe insurance

U.S. homeowners in high-risk areas currently face an insurance availability crisis, as private insurers exit states with high windstorm or wildfire risk. Many of these homeowners must resort to state-run risk pools to obtain insurance, which often are more expensive or provide less coverage than private market insurance. One potential option is community-based catastrophe insurance (CBCI), in which local community leaders arrange catastrophe insurance that can be purchased by members of the community. CBCI could help homeowners in high-risk areas obtain affordable coverage for the most significant risks they face, and can be structured in a way that transfers risk to private insurance markets (e.g., via reinsurance or parametric insurance). The concept of CBCI is a relatively recent development; no communities have yet implemented a CBCI program. 

In addition, there is growing recognition of the necessity and appeal of nature-based solutions (NbS) for short and long-term climate adaptation and risk mitigation. However, innovations in financing NbS projects (e.g., public-private partnerships) lag those for more conventional risk mitigation measures (e.g., publicly financed levees or floodwalls). One reason for this lag is the limited availability of relevant data on the returns to NbS investment. The multi-purpose/hazard potential of NbS, while advantageous in terms of a general business case, can pose challenges with respect to the stovepiped programs of government agencies and market segregation across the private sector. In the context of CBCI, information on the risk-reduction benefits of NbS could substantiate a reduction in insurance premiums over time. Evaluation of equity in the exploration, testing, and scaling of NbS is another missing link in financing and deployment of NbS. Research is needed to determine optimal ways to maximize uptake and prioritize NbS in marginalized communities in most need of infrastructure. 

Overall, the key challenge in both CBCI and NbS is the limited understanding of coupled systemic risks that may affect communities’ abilities to function during and after a disaster (whether natural or man-made).  This includes the need for modeling scale-free nature-based infrastructure along with scaled infrastructure, such as transportation, power, and water. A deep and truly effective resilience model with added value for policymaking and business continuity that enables scenario analyses focused on coupled systemic risks to communities is only achieved by accounting for the human, social, behavioral, and larger societal factors affecting physical and technological assets. In summary, in order to implement effective CBCI and evaluate the costs and benefits of NbS, there is a need for much better understanding of the inherent interdependencies that affect the ability of communities to operate both during and after disasters.

Project Objectives: The overall objective of the project is to develop and demonstrate the information and tools for establishing the business case for innovative insurance and infrastructure programs through coupling improved modeling and quantification of community resilience with scenarios focused on CBCI and natural hazard risk reduction.  Jointly with other projects in CIRCAD, the CBCI research team will also evaluate the feasibility of NbS to reduce the severity of disasters in target communities. Effective NbS will reduce the overall cost of CBCI and generate direct returns on investment to the community.

Impact: This project will serve as a case study for innovative solutions in the CBCI and NbS space. Results will provide a foundation for assessing policy challenges and alternatives with respect to the innovations. Outputs will include publicly available white papers intended for insurance industry experts and community leaders to collaborate on developing their own CBCI programs. Ideally, this project establishes a menu of feasible CBCI options for nearly “off the shelf” use in communities across the U.S., with special attention to CBCI in marginalized communities and integration of NbS in CBCI programs more generally. 

Evaluating the risk reduction benefits of nature-based solutions

Hazard modeling is advancing to support projections of both short and long-term risks under climate change.  There is a growing need to consider, evaluate, and quantify the risk-reduction value of Nature-based Solutions (e.g., coastal islands, inland wetlands and floodplains) at the scale of individual projects and in the form of networks of NbS over large spatial areas at the system-scale.  Incorporating the capability to fully evaluate NbS within modeling systems will support evaluations of the risk-reduction value of NbS for a range of hazards (e.g., coastal flooding, inland flooding, drought, heat, wildfire) across landscapes.

Project Objectives: One of the several co-benefits of NbS is flood hazard reduction. However, a major shortcoming is identifying NbS project types at spatial scales necessary for measurable flood hazard (and risk) reduction that warrants their implementation. To resolve this shortcoming, NbS must be included in flood hazard models (e.g., ADCIRC for coastal systems and ADH for inland, riverine systems) to evaluate with and without project conditions - for individual and systems-level projects. The USACE has been developing the Engineering With Nature (EWN) toolkit to assist with the modeling and quantitative evaluation of various NbS solutions to compare alternative combinations of measures, interventions, and infrastructure plans. There is a growing need to develop a quantitative evaluation framework to support NbS project design, to evaluate trade-offs in various potential projects, and support the analysis of systems-level risk reduction and resilience.
Alongside the other projects of CIRCAD, the project team will develop a series of case study applications in coastal and river systems where NbS will be evaluated at a range of scales to assess risk reduction value.  Various NbS types across different spatial scales will be evaluated using coastal and river hazard models to identify local and non-local / regional influences and the conditions under which hazards are reduced or increased.  The case studies will be documented to illustrate expanded modeling capability, needs for additional capability, and principles and factors to guide the use of NbS to address flood risks in combination with conventional engineering (e.g., levees). Experience gained in NbS modeling for flooding applications would provide a basis for follow-on projects for other hazards, (e.g., drought, wildfire). 

Impact: This project will develop case studies of best practice for hazard modeling applications for NbS and opportunities for future model development. The expanded and demonstrated modeling capability would provide a platform for evaluating policy alternatives regarding NbS, land use planning, and infrastructure financing. 

Improving assessment of subjective risk perceptions to understand risk management choices

Individual subjective perceptions of the likelihood and consequences of adverse events play a central role in conceptual and mathematical models of decision making under risk and uncertainty (e.g., expected utility, prospect theory, regret aversion, disappointment aversion). Yet, empirical measurement of subjective risk perceptions is difficult to achieve, particularly for low likelihood risks, like natural hazards.  Mental models of risk perception find significant heterogeneity in how individuals conceive, classify, and prioritize risks, and these constructs presumably influence their decisions, behavior, and opinions. Recent research explores ways to empirically measure latent risk perceptions, finding significant variability in how subjects respond to likelihood and consequence queries. 

Approach: We will design and implement a series of both laboratory and field experiments that explore fundamentals of risk conceptualization, context, and measurement. Lab experiments offer advantages in terms of control and replicability, permitting researchers to induce risk levels and control context and information flow. In the domains of insurance and mitigation, risky prospects can be described in terms of monetary outcomes (using real cash incentives), permitting subjects to assess and choose among prospects. Field experiments, on the other hand, present designed trials or naturally occurring quasi-experiments in native environments and typically engage with subjects that more closely align with the population of interest. The experiments will initiate with a very basic setup, then vary key parameters to assess what aspects of risk evaluation, beliefs, and information lead to variation in risk perception and which instruments are effective in measuring risk perceptions for which classes of subjects. 

Impact: Improved understanding of evaluation of risk, formation of beliefs about risky outcomes, updating of beliefs, and assessment of subjective perceptions of risk would benefit risk communication, decision science, and design and marketing of risk management products.

Mapping future climate vulnerability by connecting trends in extreme weather and socio-economics 

Climate change is driving a new generation of extreme events that amplifies impacts, threatens physical and societal infrastructure, and disproportionately harms marginalized segments of our society. The University of Georgia is well-positioned to develop better understanding of future spatio-temporal trends in severe weather (e.g., tornado, severe winds, hail), extreme rainfall, and hurricane risk. Investigators at UGA already have experience with applying downscaled climate modeling, observational frameworks, and risk-vulnerability assessments to such problems. Additionally, UGA-based research efforts have led to publication of the first county-level climate vulnerability index for the state of Georgia. That work was also extended to the entire nation with projections out to 2040. Outcomes from that research have yielded an understanding of the most extreme weather-climate vulnerable counties in the U.S.

Project Objectives: The objectives of this project are to refine our analyses based on the needs of insurance stakeholders and to include assessment of environmental and socio-economic factors that affect risk as well as identifying the disparities in current risk and potential trends in disparities over time. This project will use an existing ensemble of downscaled climate model projections to examine a range of scenarios of future severe weather environments. While climate models are unable to project individual storms that produce tornadoes, hail or severe thunderstorm winds, this project will use proxies of environmental factors that are known to produce severe and hazardous weather. We will explore ways of integrating downscaled projections of severe storm environments into the frameworks to characterize which. counties exhibit the greatest risk to emerging severe weather environments. Aforementioned studies of risk or vulnerability consist of social demographics, economic well-being, and infrastructure but have been anchored to extreme heat, flooding, and drought. A potentially novel aspect of this project is initial assessment of future severe weather environments and vulnerable communities as well as the cumulative risk of meteorological and socioeconomic factors. 

Impact: This project will advance climate vulnerability research by establishing a pathway to connect projections of severe weather to risk vulnerability (e.g., social, infrastructure, etc.). The use of an ensemble of climate model projections will provide a probabilistic assessment of the change in magnitude and seasonality of severe and hazardous weather environments.