 All right, welcome, thanks for joining us again here today. I'm Lawrence Buja, Director of RAL's Climate Science and Applications Program. If you're in the audience today, then you're probably already familiar with Patty Romero-Lengkau in her Cutting Edge Urban Futures Program. But for those of you who are new to Patty, well, today you're in for a real treat. Patty joined NCAR in 2006, and since then she's been a leading voice in expanding the scope of NCAR's interdisciplinary research areas, collaborating with both NCAR and external scientists on high quality integrated research at the intersection of urbanization and environmental risk. Patty tends to work at a very high level and has exercised strong scientific leadership on for many significant local, national, and international efforts. These include such global initiatives as Future Earth, UJEC, which is WCRP's Urbanization and Global Environmental Change Project, and the UN Habitat Program. Patty was also a co-leading author for Working Group Two, the Nobel Prize-winning IPCC Fourth Assessment Report, and was a convening lead author for the North American chapter of the more recent IPCC AR5. And yet she still takes the time to meet with and carefully explain the impacts of urbanization and climate change to the first graders in the elementary schools. So whether it's participating in international negotiations, like her presentation at the recent COP 21 in Paris, or working out one-on-one with residents in Islam in Mumbai, India, Patty brings a keen scientific curiosity and a deep theoretical background combined with an amazing level of passion and energy to her cutting-edge urban research. This keeps NCAR at the scientific forefront of this area. So at this point, I'm gonna turn it over to Patty for her presentation. There'll be a question-and-answer period at the end, so I'll ask you to hold your questions to that time. Thank you. I'm glad that you already saved me some minutes of my talk. Well, it is known for many of us that cities are crucibles of innovative experiments and interventions seeking to reduce the impacts of floods and heatwaves and to increase the capacities of populations to mitigate and to respond to environmental change. What is missing is our interdisciplinary efforts that help us understand how effective, how sustainable, how resilient those interventions are. In that context, the mission of urban futures is to explore key intersections between urbanization and environmental change in order to inform action and world views across scales in ways that foster sustainable and livable cities. And livable is very important, believe me. I am from a non-solivable city. We have focused our research in three research, or three research foci. The first tries to address how urban development impacts the environment. The second tries to understand what factors determine vulnerabilities and resilience across and within cities. And last, but not least, we also want to understand what factors, what limits, what barriers are out there to enhance decision makers and actors capacities to respond to these challenges. I won't be able to talk about all these three themes. I will focus on the second one. Let me insist that something I have engaged in since I moved to NCAR are research efforts that go from the global to the country to the city to the neighborhood and household level and backwards. And I will tell you why. It's very important for social scientists to understand how the history of a city and its institutions, which I will define later on, define current patterns of vulnerability and risk. But when I came to NCAR, I realized that we were faced with a challenge similar to the one doctors face. A doctor needs to be able to say whether someone is healthy or not. We social scientists need to also be able to understand what factors make populations and cities vulnerable, sustainable, or resilient. So our challenge is an upscaling challenge, which is similar but different to the downscaling challenge. Many modelers here at NCAR are faced with. And I really have learned to respect what they do, by the way. I will focus my presentation on five themes. First, I will share with you the rationale, the motivation for this work. And our conceptual framing of risk. Then I will use three examples of our research to highlight how this framework has been tested. The first focuses on the factors, the determinants of urban populations, vulnerability across and within cities. The second focuses on unpacking urbanization, because we all talk about urbanization, but sometimes we don't measure it and we just say blah, blah, blah. So what are the links between urbanization and risk? And this is an effort that we did focusing on global and the country scale. And then I will go into a case study, one of the most recent we have been working with with Joshua Sperling on a key dimension of urban development inequality as it relates with vulnerability of households. I will use these three examples to lay out my ideas, my suggestions for future research directions. Let me start with the first, with the motivation. Cities already face risk from climate relevant hazards. This is a map I did with Alex DeCervini for the UN Habitat Report on Cities and Climate Change. And what we measure here was the hazard risk of each city that represents a cumulative index based on the risk of impacts from exposure to four hazards, cyclones or hurricanes, flooding, landslides and droughts. We know from our work with Cynthia Rosenzweig that and also from my work in the IPCC that human activities are expected to change the Earth's climate in ways that can increase risks to cities. I know also that it is very difficult to attribute many of the dynamics of hazards to climate change. And therefore, and because I have learned that by working within the IPCC and collaborating with my colleagues here at NCAR, I am convinced that an integrated risk approach will be key to understanding and managing sustainability challenges in a changing climate. Equally important will be to understand the development context in which actors and decision makers make decisions. If we don't do, we will make many mistakes. How do we define risks? There are two big approaches to urban risk. For the first, top down, risk is the probability of a hazard occurrence multiplied by its consequences. This is the most used here in NCAR. Modellers use scaled down models to estimate future hazards such as floods, heat waves. They explore adaptation options under different climate and socioeconomic scenarios. There is also what I would call the bottom-up approach to risk. This is more of a social science approach for which risk is the potential for uncertain outcomes where something of human value, livelihoods, life's property is at stake. We need to keep that in mind because sometimes we think, oh, let's move that population from there. Well, that's not so easy. It's quite complicated. This approach uses vulnerability frameworks that are similar to the models that we use in the top down approach. It combines quantitative and qualitative methods and data, but it's very important. It helps us understand the culture, the history, the institutions of a city. And it combines that with quantitative methods. And I will refer to examples of how this is done. The framework we suggest integrates both approaches. And let me just ask you to remember that the top down approach tends to focus on the dynamics of environmental change as they affect hazard exposure. Why the second approach tends to focus on the societal factors, governance, one of those that explain differences in access to assets and options to respond to hazards. In this approach, which builds on the IPCC reports, the one on risks and the number five, a hazards are stresses such as flats and social unrest because people do not only deal with the environment, people, economic, social, and infrastructural assets are exposed to. The final impacts of exposure to this depends on societal factors and ecological factors. And those are defined with the concept of vulnerability, which is the propensity to be adversely affected. The contrary of it is resilience, the capacity to perceive risk and effectively adapt. I want also to insist that the exposed units can also be sensitive. And that we define as a degree by which they can be negatively or positively affected. The capacity of sociological systems and of populations, which I will focus on, is a pool of assets such as education, information, and social networks. Actors can use to manage risk while attending their development needs. Actors, who are those actors? Not Angelina Jolie, but actors are the government, the private sector, NGOs, scientists, and the media. These assets and options are unequally distributed and therefore we use the concept of social inequality defined as a condition that answers when assets are distributed unevenly. These actors do not work and operate in a vacuum. They operate in what we call governance and we define as a set of formal and informal. I want to insist informal rules because I will come back to that. Rulemaking systems such as laws and regulations, actor networks at all levels to steer cities towards or away from sustainability. With this framing, let me now show you how we have been testing it and what our findings are. And let me start by the first example, urban populations vulnerability. That's something I did with Katie Dickinson who is there and who is watching. We are aware that context matters, but we have hypothesized in this project that was awarded by NSF that it is possible to identify patterns of populations vulnerability across urban centers and what is equally important research approaches. We develop a meta-analysis and meta-knowledge or sociology of science, I'm a sociologist, so that's why I did it, right? Approach and we focus on temperature-related hazards such as heat waves. Why did we focus on those? Because they are associated to large impacts because they are clearly tied to climate change. We were able to assess a large number of studies and to cover 224 cities. So, okay, what is urban populations vulnerability? Also, everyone defines vulnerability the way I already share with you. Not everyone measures vulnerability equally. We identify three approaches to vulnerability. For the top-down applied by 80%, 88% of the papers and which is mostly used by epidemiologists, climate modelers and some natural hazards communities. Vulnerability results from exposure to a hazard and they measure it by exploring quantifying the temperature, health outcome and also quantifying confounding factors such as age and such as education. Let me drink some water. That's what these things do to you, huh? Okay, for the second approach, also remember I am mapping my framework and you are seeing how I'm going from concepts to methods to data to results and backwards. So, for the second approach applied by 11% of the papers and which is mostly a social science approach. Vulnerability is defined by differences in capacities that are driven by what we call structural factors such as inequality, governance and urbanization. While the top-down approach focuses on the individual without considering the social environment, the social science approach focuses on social dynamics and sometimes forgets about the environmental conditions. Therefore many scholars, particularly some of us at CISA have developed integrated approaches that integrate both approaches to risk and that focus on both the social and ecological determinants and also the underlying drivers of vulnerability. Sadly, this approach was only applied by 11% of the papers we analyzed. We also applied the IPCC approach to assessing uncertainty and what we did was to quantify the evidence, meaning the number of papers, identifying a factor as a determinant of population vulnerability and the agreement among scholars. And how did we measure this? It was hard, right Cathy? It was not an easy process, but we did it. And what we did was to identify those hazard indicators such as the timing of a heat wave, the levels of temperature and what is very important, the thresholds which we call temperature magnitude. We also use indicators of exposure such as population density, total population, vegetation and indicators of capacity such as access to education, social networks. And we also use a symbology to define whether the indicator is positively related to vulnerability, meaning increases it or negatively, meaning decreases it or there is no agreement such as the relationship is a no relationship. We found that 13 factors which are mostly located in these quadrants account for 60% of the tallies and we also found that only two determinants, two of the many determinants that play a role in defining vulnerability are analyzed by studies, H and temperature magnitude. These findings result from the dominance of the top-down approach. I mean, we need it, but it is not enough. That is our point. We also found and we mapped the cities covered in the studies and we found no surprise that most of the studies focus on the US and Europe. What we have found in our IPCC reports that most of the vulnerability tends to be located here. In summary, we found that it is possible to identify patterns of vulnerability across cities and research approach and also to make the city, the approaches, the research approaches comparable which is a huge challenge for scientific progress to happen. We also found that knowledge has examined only certain aspects and that scale has been key in defining also why some aspects are neglected. And this was the first time I was confronted with uncertainty linear. I couldn't believe it. Uncertainty given by the approach used that focuses on some things, forgets others, the methods and data and the scale of analysis. We were able also to justify why an integrated approach is needed. Let me now move to the second example. The urbanization dynamics shaping risk. Our hypothesis states that a focus on only the urbanization exposure interactions is not enough to understand urban risk. First of all, we need to really unpack urbanization. Second, we also need to include indicators of sensitivity and capacity. What we did, and I did this with a colleague, a very young and promising scholar from Germany, Garchagin, what we did was to use two indicators of urban levels or urbanization levels and economic growth and two indicators of the rate of urbanization and economic growth. We applied a hierarchical clustering analysis to group countries in 10 groups. And we correlated these numbers, right? These indicators with indicators of exposure, such as populations in presence and in contact with affected by storms, floods, sea level rise and droughts, sensitivity, such as population undernourished, dependency ratios and poverty indicators, and indicators of lack of adaptive capacity, such as the corruption perception index, which in our country is very important. But also in the US, it's very important. Access to medical services, gender equity, access to education and quality of ecosystems, we also use indicators around all these. And you will see that these indicators appear over and over in our analysis. What is what we found? Well, let me just give you some highlights. For sure, we were able to create the OECD group, which registers high levels of urbanization, low levels of urban growth, high levels of GDP per capita or GNI per capita, low levels of economic growth. We also have a typical case represented by Southeast Asia with high levels of urbanization. I mean, I think that China and India are on steroids. There is no other way I can describe it, for good and for not so good life. So again, high levels of urban growth, in this case, there are high levels of urbanization but low levels of economic growth. Which really, I mean, these are indicators that we get at one of the two of the key elements of urbanization. So we also found that rather than exposure, sensitivity and capacity indicators contribute to overall risk. And let me just show you how is it that we could test, prove this. These are plots where we have here on the Y axis, the index values and on the X, the country groups. And we found that in terms of exposure, there is not so much difference between country groups. That's not the case once we include indicators of sensitivity and lack of adaptive capacity. Once we include those, which are indicators of development or lack of. Once we include those, then this group, this group and this group are particularly vulnerable. We also found that rather than urbanization levels, it is a race of urbanization that influence risk. Of course, and this is what I summarize here, right? Again, rate of urbanization is more important. It's a key driver of risk. Sensitivity and capacity indicators contribute more to overall risk. But let me tell you, this is just the beginning of the conversation. Why? Because urban development is more than economics and more than demographics. It's also built environment characteristics. It's also governance and it's also equity. So there is a lot of stuff to do. And I will give an example of how we address the links between social inequality and vulnerability in the city of Mumbai. This is part of an NSF Pire Award I engaged with. And I'm really happy I did with George Sperling. I really learned a lot from you and by being with you, George. And I hope we stay together. So our hypothesis is similar to the prior one. Under current conditions, current climate conditions, poverty and capacity contribute largely to overall vulnerability. Why Mumbai, India? Well, I like large cities, I guess. No, not only that. I mean, Mumbai is one of the 10 largest cities depending on the boundary we use to define the city. It has between 18 and 22 million people. It has high levels of risks of impacts from sea level rise, storm surges and flooding. And that's why we did our field work during the monsoon. And these risk results not only from hydro meteorological conditions that are changing with climate change. It also results from human action. A key element here is a huge reclamation of large areas of what used to be seven islands of land just above sea level with many of these areas below the high, high level. And just now we are mapping this, this. But I cannot talk about that just now. So besides that, the city is faced with high levels of inequality. I am from Mexico City. I'm used to deal with poverty. I grew up in a poor neighborhood and I used to engage with how to address poverty. But once I moved, I started to work in Mumbai. I said, okay guys, we need to address this issue. The levels and qualities of poverty in Mumbai are special. Some data, some indicators, 70% of the population engages in the informal economy. Defined by the government as non-conforming with the regulations. One out of four children is stunted. And informality, remember the formal and informal rules. Informality is a key institutional component defining social inequality. In many ways. Informality is a state of regulatory flux. And I know it from being with my mom when she was selling on the streets. I mean, where the legal and the illegal are up for negotiation, contestation, and corruption. And this informality has implications for vulnerability as well. Because by not having access to secure land tenure, many of these populations are criminalized. And do not have access to many sources of capacity such as infrastructures and services. A key other finding, and this is for Caspar who is not here, Caspar Aman. And I will share that, we will see whether we can do something together. Although Caspar is already finding that heat waves will be a key hazard facing cities such as Mumbai, the levels of penetration of air conditioning are very low. Of between 8% and 12%. Equally important is the fact that the study, the analysis of the influence of wealth and vulnerability indicators offers a lot of methodological challenges. Not only because these are multi-dimensional concepts. I have been talking a lot about vulnerability but let me tell you poverty, which is one expression of social inequality, can be measured by income indicators, expenditure indicators, and assets indicators. And each approach offers very different results. And I also found that, again for you, it's a present to you, Linda, that uncertainties are key challenge when dealing with this. Why, to what is this challenge related? To indicator selection, to index construction, and to an accurate understanding of the influence of indicators on the outcome we want to explore. And I want to give you here an example. And I will use an image. To study vulnerability, we usually use the same ingredients, more or less the same ingredients, which are our indicators. But the characteristics of the indicators vary across context or with context. In Latin American cities such as Mexico, access to improve toilet facilities is of the essence. And it is mostly related to connection to the sewage system. In India, Indian cities such as Mumbai, besides that, a key challenge is given by the fact that between 40 and 80 households share these toilets. That's, I mean, they used to tell me, but did you like talking about climate change? Guess what? This is our challenge. How do we deal with that? Okay, how do we address that? Well, we develop, again, our indicators, and I already have talked about indicators of exposure, sensitivity, and capacity. I just want to say that we capture some indicators of hazard impact, such as the number of hazards people experience, households experience, and whether they suffer a health impact from exposure to those. I just want to insist that we added these indicators, which are access to material possessions and access to physical assets, which in all urban centers is key, right? And what we did was to combine qualitative methods, meaning knowledge from local experts with quantitative methods, which allow us to embrace the subjectivity involved in waiting indicators, and to build, and we combine that with the fuzzy logic to build four household classes. The low, moderate, high, and very high vulnerability class. The first finding is not surprising. 55% of our households are highly vulnerable, and 28% are very highly vulnerable. The two groups make two thirds of the population. That's not surprising, but what is surprising is that with the exception of the very high vulnerability class, which is faced with very high levels of exposure, the other groups, the other household classes are faced with similar levels of exposure, and what defines vulnerability are differences in poverty, which increase, yeah, when we go from the low to the very high vulnerability class, and with capacity indicators, which decrease when we go from the low vulnerability to the very high vulnerability class. Let me just really tell you a little bit more about the capacity indicators, which really define not only access to assets, but also agency, which for us social scientists is so key, because people do not only receive hazards and say, oh, let me go and be killed. No, people are active, and they really are very creative. Okay, so we found that in terms of awareness and priority given to mitigation, risk mitigation and adaptation policies, our household classes are more or less similar. But we also found that, and this is a finding that is similar to what we found in the other for Latin American cities I have studied, while the very high vulnerability class is very likely to rely in family support and able to respond to emergencies, the other classes are very likely to use social networks, such as NGOs and religious groups. Well, the education assets are really related to class, the socioeconomic status, not surprise, but interesting, and this is important for us at NCAR, with the exception of our very high vulnerability class, the other classes are very likely to use warning sources, such as TV and radio to inform themselves about risks. This is a very challenging table, and I'll see whether you like it this time. So, there's so much conversation about thresholds or points after which heat waves move from some conditions to the others, right? What we did with this figure, which is only one example of three we are working with, what this figure shows are threshold points for capacity and exposure. As we move from high to low capacity, a point or some points are reached in which levels of vulnerability start to ramp up. We move from one regime to another regime. Likewise, when we move from lower exposure to high exposure levels, in each case, a level is reached. At which further increases do result in higher vulnerability regimes. But what is interesting, and this is really bringing me back to one hypothesis I love so much, which is the idea that also the reach will be at risk. And why is that important? Because I have seen that historically, only when the wealthy are affected, they take care of things. So, there is also a point at which, and this is our next regime, there is also a point at which increases in the exposure effect populations equally across capacity levels. To summarize, under current climate conditions, poverty and capacity indicators contribute largely to households overall vulnerability. Another important conclusion is that these methods which are really a combination of qualitative and quantitative methods are key and complement what we do here at NCAR in that they capture the multidimensionality and uncertainty in vulnerability and race analysis, help us evaluate the contribution of wealth, sensitivity, and capacity indicators. And what is very important, they support policy interventions that target attributes under different combinations across household classes. Let me close with some conclusions and some considerations of what could be done with this area of research. I'm convinced that both approaches are needed. Both approaches to race are needed. One shed slides on some aspects, but it's not enough. Either one, even if it is social science or physical sciences that are coming together to address this issue. We need to combine qualitative and quantitative methods. Only by being and doing free work and interviewing people and listening to them and going to meetings and going to meetings and going to meetings, we get the trust we need to really get to understand the role of power, the role of governance, and how people really feel whether their interests are taken care of. I mean I have some quotes of how people from low income areas in Buenos Aires, Mexico City, Santiago, and Bogotá are aware of the fact that without governance, without governmental support, they won't be able to make it. Equally important is the fact that, and this is something I will include in another paper addressing some other data about Mumbai, the economics of this are key. And why are they key? Because you need also an economic threshold in terms of levels of income, city authorities need to invest in things. And when you have high levels of informality, you don't get those sources of income. And that you only understand if you go to the places and listen to people and you combine all these with quantitative methods. And also you learn to be humble about things, right? So I hope I was able to show how multiple scales bring shared light on different components of vulnerability and resilience, and also how some patterns cut across scales, right? That's interesting. I was like, wow, I cannot believe it. Wealth and capacity indicators contribute at least in Mumbai, I think also in other cities, contribute more to overall risk and vulnerability than exposure indicators. It's true that that might change and it's already starting to change under that changing climate. Before I go to the future directions, I want to insist that I only present our work on this theme. It is by working on the three themes that I have been able to find other connections and I will refer to those. What can I say about future directions? First of all, it's really important to unpack the key dimensions of urban development. We already have some work dealing with demographics and economics. We need to also deal with governance indicators with built environment indicators, such as infrastructure, the quality of housing. For instance, in Indian cities, having a separate cooking space is key. That's not the case in Latin American cities. We already have that. So we also need social indicators such as inequality indicators and I think that in this context, where everyone around the world is starting to say enough inequality has increased, we need to also address issues of inequality. Another important point is the issue of thresholds and tipping points and let me tell you, a threshold is defined as a point at which one relatively stable socio-ecological and governance regime gives way to another. What is interesting is that while socio-ecological thresholds are contingent upon the interaction among physical, hydro-ecological and social processes, governance thresholds are integral to the way societies work, with limits contingent upon ethics, meaning values, politics, knowledge and culture and politics meaning power. So, I want to move forward and I'm working on that with colleagues from the Future Earth Orban platform, UJEC, which is transitioning to Future Earth. And what we want to do, I know it's crazy, but what we want to do is to connect the problem space with the solution space and what I mean by that is that we social scientists, physical scientists, engineers, tend to always focus on, okay, how urbanization comes together with hazards and institutions to drive risk and resilience. That's nice, but once we come to decision makers, they say, well, what do I do with all this information? Well, we also need to understand how people make decisions. And I see ourselves inserting our groups in this solution space, but what we need to do is to understand whether there is a fit or lack of fit. I think there is a lack of fit between the two domains. And we are studying that in two projects. One is called Unicorn, urbanization, water management and flood risk across mountain regions. I'm working with Andy Monaghan on that and we are waiting to see whether the Belmont Forum will award our proposal. And with Unmask, a proposal on urbanization, food, energy, water systems, and extreme hazard risks that we will submit. So besides working on this, well, I'm working with Olja, which helped me and we try to understand uncertainty in urban vulnerability and risk. Linda Mearns is our mentor in this. So I mean, these are, again, just some examples of future directions. I wouldn't like to finish without saying that my ideas wouldn't be possible without the beautiful projects and collaborations and partners I'm engaged with at NCAR. They are with RED in the US and globally. Without them, I wouldn't be able to be challenged, particularly if engineers and physical scientists challenged me and I want to kill them. But next day I say, I need to see how to do this. They are right and let me get it, okay? So I also want to really thank my postdocs. I mean, I called them mine, but they are not mine. Because they also have been a source of inspiration for me. They always have good ideas. They come with interesting stuff and I also want to thank them for being part of this. I also want to insist that many components of what I do are the result of awards, but others are the result of just sitting together after meetings and saying, hey, I listened to what you say. Why don't we do this? For instance, the survey we conducted in Mumbai was a result of a collaboration with Indian partners who were conducting an atmospheric field campaign. They had 90 students. They said, if you train my students, we conduct the surveys. I said, voila, I'll do it. So there is a combination of many things involved here. And again, I want to thank them and thank you for taking the time to be here with me. So I'll let you choose who you want to answer the questions from. So we'll open it up to questions from the audience. Hi, I'm Daniel from Amsir Chemistry. Very inspiring talk. I really like the slide about the moving the solution space into the problem space. There are more connections. Do you have any examples of what that would look like specifically or any situations where you've seen a shift happen? Right. In my work on governance, I learned that there are always misfits in terms of the spatial and temporal scales at which vulnerability and risk operate and the temporal and spatial scales at which decision makers operate. I give you an example. For water management, you use the basin. I mean, as a researcher, right? Well, decision makers have made, I mean, there are some exceptions to that. But usually, they have many jurisdictions coming together to deal with that basin. And they overlap, they are fragmented, et cetera. That's a problem of fit. Another problem of fit is given by the fact that usually decision makers work at a temporal scale that goes from, I mean, if you are lucky, 10 years are the maximum. Well, many processes we are dealing with here are 50 years processes. For instance, just now, even if we were able to mitigate, to introduce mitigation policies and move us to a trajectory, it's a 4.5 trajectory. Whereby we could reduce, bring our temperature to 1.5 to 2. Even if we were able to do that, the effects of what we will do will be manifest in 50 years. Many decision makers tell me, Patty, I'll be here for three years. What are you talking about? So those are two examples. But now under a changing climate, there are two other issues of a fit that I want to work with. One is thresholds, meaning are we really identifying thresholds or not? And cascading effects. And what happened with the floods in 2013 here in Boulder is an example of that. I mean, the impacts of the floods were not only the result of the huge amount of precipitation we receive. But those were also the result of some ecosystem areas not working as multiple use areas, such as the walking and biking spaces we have in Boulder, that during floods can also help you mitigate the impact of floods. That together with how people act and operate creates a series of effects that we call cascading effects that perhaps we are not ready to deal with. So what I consider is that we need to add those two to our efforts to support decision makers and to engage with them and learn from them. I'm sure they will talk about more, but I really think we need to bring those spaces together to really connect the dots. So there's a lot of unauthorized construction so these cities, how do you get data and things like that? For what, again? Unauthorized constructions. Well, there are some methods. One is participatory GIS approach whereby you go with people. I mean, before the GIS, we did it by walking. It's in general a problem. So I mean, you can use participatory GIS now, but when I used to do it, we just walked with people and we really swept around places. And they started to say, look, this area is not acknowledged, but we have so many houses here, et cetera. So there are many approaches. And now again, the GIS is really making us do things. But what is important is you might have all this information if decision makers don't want to listen to you. Believe me, they won't listen to you. So the challenge is how you frame issues in such a way that you can engage with them. And that's not a challenge of us telling, you know, decision makers, you are not getting it. Because they will say, you know what, go away. I cannot, you know, you really need to. For instance, with George, we are just now analyzing our interviews in Mumbai. We were looking for climate change policies. And we found development policies that are driving risk. So. We go back to time for the. Vivek, from UIL. Next. Yeah, here, before that, yeah. OK, in the fourth one, you say, well, then capacity indicator contributes to more than exposure indicators. Could you differentiate between those two or the exposure indicators you meant? What connected somehow? Right, I will. Let me give you examples here. Capacity indicators include such factors such as education. We have found that people who are educated are more able to look for sources of information to respond to challenges and hazards. Also access to governmental support in general and during emergencies, and access to family members, to community grassroots, to church organizations, which are elements of social institutional capacity. And last, but equally important, are also indicators of information. How people perceive risks and whether they use the TV, the internet, or not to respond to hazards, to respond to early warning systems, are key elements defining vulnerability. And then in terms of exposure and sensitivity, people wouldn't be vulnerable if they were not exposed to something. You need to be exposed to a heat wave, to a flood, to be vulnerable. So we measure that by asking people to which heat waves, air pollution, and other hazards they have been exposed and whether they can identify impact levels and health outcomes from that exposure. And we have found in the literature and in our prior work that the elderly, for instance, are more sensitive to heat waves and that women are more sensitive to floods in some areas, particularly because they cannot go out. I mean, there are cultural issues that constrain them. And pre-existing medical conditions, if you already have a heart condition, it will be very hard for you to deal with a heat wave. So those are the exposure capacity and wealth indicators we use. So what we found, again, is that in terms of exposure, which we have here, these are our index values, average index values, and we have levels of uncertainty, by the way. In terms of that, many of the households we interview are more or less equally exposed, with the exception of the very highly vulnerable household. That's not the case when you include indicators of capacity. The more vulnerable you are, the less capacity you have. Remember those that we use. And then the poorer you are, the more vulnerable you are. So that's why we concluded that. Thank you. Marcus Mench, have you thought of taking and adding into your kind of key research issues the question of surprise? Because I think in terms of thresholds and both the cascades, where the relationship between wealth capacity might come apart is when you have things that are outside. And the boulder floods might be an example of that on a small scale. Right. Yes. And that's why, as you can see here, independently of your capacity levels, once you reach the 0.8 level of exposure, then you are at risk. So this is something that I already have been talking about when dealing with exposure to air pollution. And particularly atmospheric modelers really pay a lot of attention to this. And in our analysis of thresholds and cascading effects, we are including the issue of surprise. But again, this is a project we are to start. So yeah, we are including that. But that's a very important comment. So following on, does that go into the exposure index then, the surprise? Yes, it is here. And it was interesting in three cities of Latin America, Santiago, Bogota, and Mexico. We ran some Poisson regressions to examine the links between exposure to air pollution and health outcomes. We used mortality indicators, which are extreme, by the way. That's a caveat with that. And then we correlate that with spatial data, vulnerability or vulnerability indicators. And we found that impacts from exposure to air pollution in these cities cut across socioeconomic status. Only air pollution. And I'm only talking about extreme impacts. We are also aware of the fact that these wealthier households have a lot of assets to respond. But still, I mean, I think that we need to start pointing to the fact that wealthier populations are not spared. All right. Thank you for joining. Again, thank you. Linda, sorry. You blended. I blended in, as usual. Some Linda Mearns from NCAR. It's probably a tricky question to answer this late in the time. But I wonder if you could succinctly. I think the audience would probably be rather interested in some of your activities at COP 21, in terms of the side event of cities, if there's a few pearls that you could throw before the audience. Yes. I worked together with Cynthia Rosenzweig. And we put together a synthesis book, a report on cities and climate change. And what we did was to engage with stakeholders and to really also make them part of all these synthesis efforts where we did not only explore what will happen with climate hazards, but also how authorities are responding to these problems in the water, health, build environment, I mean, in many sectors. But what was interesting was that we really have been engaged with the Covenant of Mayors and with the ICLEI and with key authorities that are really addressing these issues at the global level. But it's also nice is that I am involved with the city of Boulder. I'm going to their meetings, learning a lot from them. We are having a very nice dialogue about the COP. So there are many things that are going on. And I'm very excited about them and what we could do here at NCAR to inform those decisions. All right. With that, I think we'll close today again. Thank you, Patty. Thank you for coming.