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Please find below the judging results for your proposal.

Finalist Evaluation

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We would like to thank you for addressing the questions.It still seems that there would not be much impact in developing world. Targeting insurance companies in rich world. Relatively good presentation, but don't see proponents addressing social and ecological barriers.

Very important to bring private sector into adaptation. We need to make them better equipped. Mitigation may take more time. We need to have science-based, robust data which will help us to make better decisions. IUCN, we and others have taken this information to influence and inform policy-makers. Insurance companies an integral part. If we can address relevance questions, this would be a good tool to help the private sector engage.

Semi-Finalist Evaluation

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Congratulations! Your proposal, Management via Measurement: Translating Climate Projections into Actionable Data, in the Adaptation contest, has been selected to advance as a Semi-Finalist. Thank you for your work on this very important issue. We're proud of your proposal, and we hope that you are too. Again, congratulations!

Please see below the following comments to enhance your proposal:


This is a very well-written proposal that addresses the need for the translation of climate projections into data formats useful for insurance risk models and other decision-making processes. Given that I do not have specific modelling expertise I find it challenging to assess the quality of the statistical process itself, but I see that this tool will fill an important gap. I see a gap in the development of the data inputs and the actual uptake of the data by insurance companies and others - as we have seen in the past, provision of data is not sufficient to trigger behaviour change. While interesting, I don't think that this proposal is exceptionally novel or ground-breaking, so I don't think it should go on to the next round.

This proposal could possibly be interesting to some insurance companies with a goal of setting premiums properly for high value infrastructure in the developed world. If it indeed is, I'm sire those insurance companies can and will pay for this. On the ground in the developing world, where most of the adaptation challenges are, this approach is not likely to add much value. Most of the developing world (indeed much of the developed to) are already highly vulnerable to existing extreme events, and almost nothing is insured. Their priority is developing resilience to these extreme events, not gaining slightly nuanced (I know modelers may not like this turn of phrase for their work) improvements on forecasts. I find this proposal a bit too ivory tower to work in the real world, except possibly for relatively sophisticated users such as the insurance companies the authors mention.

There was a discussion about this proposal. In sum, while the scientific merit is established, the usefulness to regions outside the US/Europe is unclear. If that part is strengthened and better explained, this could be a strong proposal.

All the best,
The 2016 Climate CoLab Judges

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Colin Sullivan

Jun 15, 2016
09:13

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Dear judges,

Thank you for your feedback, many of the issues you've mentioned are things we've come across as we've talked with our partners in the insurance and other industries. Please see our responses and we'd love to get more feedback.

 

What is the difference between what risQ is doing and current methodology? Is there a practical difference?

  • Currently there are many climate models each projecting a different outcome. For decision makers, this is a serious problem: which model’s forecast should be used? Or would an average be better?  And even more pressing, many decision makers (particularly in the insurance industry) require probabilistic data. If they select a model (or average several) what are the chances of the model being wrong and how wrong can it be? Without this data it’s impossible to incorporate climate change predictions into the tools of many industries.


  • In the image above different model projections are represented in red and the observed data is in black. risQ's model is able to take these different projections and combine them into a probability band (represented in blue). While risQ's forecasts more accurate, more important is the fact that we can generate probability data at all. 

  • risQ’s methodology is the only one that can take global climate models -- all of which project different things about the future - and consolidate those different projections into a concise and accurate probability distribution. These distributions are incredibly important for decision making. We have received letters of support from AIG, Munich Re, AIR Worldwide, the City of Boston and other groups who recognize the importance and utility of the data risQ is providing. risQ is working closely with these groups to develop data products that WILL be able to cause behavioral change.

Why target the insurance industry?

  • risQ is ambitious. We want to enact systemic changes that will address climate change issues. By providing the insurance companies the knowledge and tools to understand the costs of climate change, we believe that the full financial and political power of the industry will be brought to bear on solving climate issues. Insurance companies are already taking actions toward climate change. In 2011, companies invested an estimated $23B into climate mitigation (projects such as clean energy).

How would this impact the developing world?

  • We agree that this data would not be directly usable by the developing world. However as previously mentioned, by pushing the insurance industry to directly address climate change we believe there will be cascading impacts across the global.

    One clear example is microinsurance. Microinsurance is currently provided to ~135 million people in the developing world, an estimated 5% of the total coverable. Companies like Partner Re, Swiss Re and Munich Re are becoming more and more active in the market (Munich Re is one of the insurance companies that has come out in support of risQ’s data). Furthermore in an insight published by Lloyd’s, many companies are also moving to “consider the related issues of adaptation, recognising that the effects of climate change related catastrophes may be better addressed through adaptation.”

  • https://www.lloyds.com/~/media/lloyds/reports/360/360%20other/insuranceindevelopingcountries.pdf

What else can be done with this data?

  • Directly selling this data is only a small portion of risQ’s goal of combating climate change. We hope to develop a suite of products using our methodology. One that is planned for the next 2-5 years is using the probabilistic data to generate flood projections that take into account climate change.