 I'm excited to be here today and really looking forward to seeing the feedback during the discussion section, so I want to thank wider for the opportunity. I should mention here that this project was really developed under Dr. Paul Shinowski, who is the lead on the project. He's not at this conference, but works with us at the Institute of Climate and Civil Systems in Boulder. I'll be talking about strengthening research capacity. We have developed a model that looks at infrastructure planning and climate change in developing countries. So we focus on understanding, evaluate, and analyzing the impacts of climate change on infrastructure. We focus especially on roads and look at the applications from both a technical standpoint and policymaking and budgeting constraints. Obviously, as we have talked about in this conference, there is a growing recognition of the need to take climate change research and turn it into policy and practical application for countries, especially in the developing world. So just a quick background to let you guys know why we have decided to focus on roads for this model specifically. I know that the MDG, the Millennium Development Goals, have their have their flaws, but seven of the eight goals actually mention metrics that are specific to transportation and road infrastructure. I think this really does highlight the breadth of impact that roads have on development and development priorities, ranging from social to environmental and economic. It's particularly relevant to the policy discussion because road infrastructure is coordinated, budgeted, and managed at the national policy level and therefore really requires an informed discussion when you're talking about things as diverse as climate change. So today the model that I will be talking about specifically is the IPSS model, Infrastructure Planning and Support System. It uses stressor response variables to help understand the potential impacts that climate change will have on infrastructure in the coming years. It was developed originally with wider for the Mozambique Ministry of Transport. They were really looking for a way to understand climate change in a way that could fit into their annual and medium term budgeting. We have expanded the model to work in Vietnam, South Korea, China, and Japan, and we are currently working on capacity and training building efforts in both Ghana and South Africa. And here at the bottom you can see a reference to one of our papers that looks at the climate change impact on developing and developed countries. Looking to answer the question, do developing countries bear a disproportionate burden of the climate change impacts? And I'll discuss a little bit of those results later, but overwhelmingly, yes, they do. So why IPSS? We really developed this work out of the 2009 study in the World Bank on the economics of adaptation to climate change. This was really the first study that looked at climate change from a quantitative perspective. So going beyond qualitative assessment that, yes, climate change is a concern, it will affect things. How do we take that into quantitative measures that matter and that can be useful at a policy level? This also, you know, we began to look at the questions of what impacts does road infrastructure have on development. So when, especially developing countries, you're discussing other policy implications, other investment opportunities. Where do roads fit into that and how does infrastructure fit into that bigger picture? You can see that with from the maps on the right here, that they're from the initial studies that we've done, that there is a big difference in the results and the cost of climate change from an adapt and a no adapt perspective. And this really informed how we built the system. So here's a little bit, a quick overview of what I mean when I say adapt in the no adapt policy scenarios. For the no adapt, this approach is really a business as usual. We assume that no adaptation options are put in place. So current design structure and maintenance schemes are used for future. And so costs are incurred as maintenance increase. We did this as a preference for design life, if infrastructure is designed to be built for 20 years. Even if there's increased climate stressors, you're going to increase your maintenance cost to retain that design life. When we are looking at the adapt, this approach assumes that there is a forward looking approach to climate change. So looking ahead and saying the infrastructure design life is 20 years. What is projected to happen in those 20 years from climate change? And then adapting the design at the construction. This obviously incurs upfront costs, but reduces both maintenance costs throughout the life span of the infrastructure. And it also has external benefits when you look at things like connectivity and transportation costs. Another thing that we have built into this system is the range of GCMs. So we've talked a little bit about the variation that's inherent when you're running 50 climate models. And so we do a quartile looking at the mean, the most extreme climate scenarios, and then provide some ways of interpreting this data in the output. Just an example here of some of the costs that are incurred from the different types of road infrastructure when you're looking at the adapt and the no adapt. For example, gravel roads. If you're going to adapt your roads, you'll be increasing your costs through an increased sub base drainage capacity and grading. Whereas if you're not adapting your roads, the costs will be required from increased annual maintenance and also loss of potential economic activity through washout or loss of use. So obviously the adapt and the no adapt can be looked at from a purely technical standpoint. And we have built the system pretty robustly on engineering design standards. But we also recognize that this fails to incorporate the broader development perspective. Here are six areas that we have built into IPSS. The climate and the flooding are really the technical standpoint. And then we have expanded that to look at the environmental, the transportation, the financial and some of the social concerns. Sorry. So this actually, we're looking at, for example, its social considerations. Some of the metrics that we use include the poverty rate, population density, labor intensive construction, road density. Some of these metrics, if you are adapting your roads, can be very positive. Increasing your paved road percentage, increasing access to education and healthcare, lowering transportation costs when you're looking at the time and cost of getting goods to market and also market access and knowledge. So what are the data sources we use? Obviously if you're going to be looking at climate change, you need to have climate models and projections. We don't generate these ourselves. We work very closely with the MIT Joint Program to get GCM models and HFDs. The model that we have built, IPSS, can run any number of models and this will be displayed in the RISL output. And obviously this is really where the first inherent uncertainty comes into climate change. You can see up here is just two models and they differ greatly in precipitation projections for 2100. So this is really just the first step to understanding that while the model is built on sound engineering, the climate change variation doesn't need to be acknowledged. So here's some of our data methodology, just kind of a high level overview. As I mentioned, we do use engineering design standard tests looking at how do stressors affect degradation. So how does flooding, precipitation, temperature, traffic, what does this do to roads? How does it degrade and how do we measure that? We take those material studies and put the climate change on top of it and then say, given certain climate futures, how will this affect the current road infrastructure? You can see on the right over there, just one example of our graph for the flooding impact that increased flooding and frequency and severity can have on roads. So now that that's kind of the quick background on what is IPSS, I'm going to show you guys a little bit about what the user experience is like and also some of the applications that we have done with this model. So this is our input screen. All of the data for the engineering, all the data for the climate is already in the model. And so from a user perspective, what we need is we need the road stock inventory data. This is for primary, secondary, tertiary roads, paved gravel and unpaved and the administrative regions. We also require population data, poverty rates, traffic projections, and building inventory. You can see that we upload all of these files through Excel, so they're actually stored in separate workbooks. We can help to create those and use local knowledge and local resources to generate this. We have these nine road classifications and we realize that, well, ideally, all roads could be climate-proofed. That is not always a reality for the use of funding. So we can run a partial analysis adapting only certain roads that may be of key importance in the country. And then we've incorporated visual things as well. So here's an example of Ghana, the administrative regions, and the current road network overlay. So when we're looking at the output, there are four metrics that you will see our results displayed in, the total cost, the opportunity cost, the maintenance savings, and the adaptive advantage. Each of these four metrics combine to guide the decision-making process. They each tell a different story about the impact of climate change and particularly important is the second one, the opportunity cost. This is really designed to help the developing countries in particular understand what is the real cost of climate change and how does that fit into their current development picture. And this is really designed to help prioritize investments. So the metric is the percentage of new roads that could be built relative to the existing infrastructure if climate change didn't happen and money didn't have to be diverted. Just a quick overview of what our output looks like. We have a series of workbooks. We do kind of a user-friendly less technical output. So at a glance, you can see what the mean GCM projects the cost to be, a slightly more detailed looking at the core tiles for the 2030, 2050, and 2090 decades. This is particularly important because obviously the inherent uncertainty in climate change, you see a big increase towards the end of the century. So it's from a policy perspective, are these climate change impacts happening in 2030, or are they happening in 2090? Because those are big differences in how you're going to allocate funding and planning and looking ahead. There's also more detailed results, so especially for technical road planners working within the Ministry of Transport or engineering firms or more technical areas, we can produce the results by GCM by year, by road type through 2100. So one of the ways that we've just looked at how do we understand the uncertainty of GCMs? So here's a histogram that looks at the adapt and the no-adapt results from one example. This is particularly important when from the climate science perspective, there isn't one right GCM. They're equally likely, I'm not the expert on that, but equally likely. So from a policy perspective, are you looking to climate-proofy roads? Are you looking to have a no regret, so minimum investment to mitigate impacts? So where does this fall? You can see from the no-adapt here on the left in the red that while the most extreme GCM projects very high cost, the vast majority, the trend of the GCMs is much less expensive. So that would be a consideration to look into as a policymaker and looking to allocate money. So just our first study, one of our first applications where I use this system was looking at a 10-country study. We wanted to answer the question, does climate change impact developing countries disproportionately? We chose 10 countries, five developing and five developed geographically about the same size, obviously very different amounts of infrastructure. And the results show that yes, developing countries bear a very large burden. You can see here, Ethiopian Cameroon could both nearly double their road infrastructure just by dealing with the cost of climate change, whereas Italy, while it sees a very high total cost in climate change, sees a very low opportunity cost, really showing that even if climate change happens at an extreme rate for Italy, the burden that it will affect on design and construction is very low. We also looked at the ADAPT and the NO ADAPT policy perspectives. For all 10 countries, by 2050 through the end of the century, there was a significant advantage to proactive adaptation and the costs that were incurred were much lower than if no adaptation was taken. We decided to take this actually to a continent level and say, okay, given Africa, what does this look like on a bigger scale? And the impacts were actually surprising. We found that for the 42 countries that we analyzed, in general, the costs were very high, but they varied significantly between countries. The total cost and the opportunity cost did not always line up. You can see South Africa here faces a very high total cost, whereas the opportunity cost is relatively low compared to many of the other countries. Across Central Africa, you see a pretty median total cost, but a very high opportunity cost with the ability to essentially improve the road infrastructure by over a thousand percent. So taking this to a regional level, in all the regions, there was an adaptive advantage of several billion dollars over the time period that we looked at, which was 2020 to 2100. But taking this down to a country level, this is not always the case. Malawi in the green does have an advantage if they adapt. Mozambique, for this GCM, actually shouldn't adapt. It can be far more costly, whereas Zambia sees about negligible either way, pretty even. But this is where we really see the other analysis elements I talked about, the environment, the social, the transportation constraints, because you're looking at, okay, if we're going to spend money on climate change anyway, should it be just to repair damages and repair unpaved roads, or should we adapt and upgrade our roads to paved roads? Do we see benefits in lower transportation costs, transportation times, improve social metrics? So that's where we see this system really coming from kind of the interdisciplinary perspective based on engineering and climate science, but useful at a higher policy level and incorporate in those broader elements of development. One of our recent projects was working in conjunction with wider and a larger study looking at Vietnam. We looked at road infrastructure, but it was the first time we'd included sea level rise. The costs here, you can see the mean GCM opportunity cost was 92%, and for many of the southern coastal regions, using the sea level rise projection that we used, there was almost total inundation. So definitely something to be concerned about, and definitely be considered in policy as Vietnam moves forward. So those are some of the finished applications we've done. I talked about road infrastructure because it's our most complete model, but we've also worked to expand this to building infrastructure. We did a preliminary study with the Asian Development Bank, and these are some of the results from South Korea. You can see that from the map, there's definitely kind of a swath through the middle of the country that's going to be much greater affected, and urban housing is of particular concern. This is something where we are really looking to refine the methodology and understand the difference between public and private. So in summary, the climate change obviously presents a significant and disproportionately costly impact to developing countries, and adaptation can significantly reduce these impacts, but understanding when and how and where these technical applications and budgeting should be considered is definitely important. So we have designed IPSS with that in mind and hope to see it used both technically and at a policy level.