 Hello, everyone, and welcome to the Circular Metabolism Podcast. I'm your host, Aristeed, from Metabolism of Cities. And in this podcast, we interview thinkers, researchers, policymakers, and practitioners to better understand the metabolism of our cities and how to reduce their environmental impact in a socially just and context-specific way. On today's episode, I wanted to focus on a piece of our cities, which I've always found I have to admit a bit boring. It's a topic that it's dominated by engineers. I thought there was nothing new since the invention of sliced bread. And I then read more about it. And it seems that, wait, hang on, there is something more about it, especially if we want to reduce the environmental impacts of existing and new cities. Then this is a key element. And if you haven't figured it out already, I'm talking about infrastructures. Indeed, infrastructures and urban infrastructures are and will dictate the way our flows are circulating within our cities and how to reduce them. And I think they also have a hidden political and urbanistic implication. To talk about infrastructures, we have with us today Sibyl Derbal, who has written and edited two books and written countless articles on the topic. He is an associate professor of urban engineering in the Department of Civil Materials and Environmental Engineering at the University of Illinois of Chicago and the director of the complex and sustainable urban networks lab. What I'm reading now from his bio, his research interests include the planning, design, and modeling of urban infrastructures. More particularly, he looks both at the supply and demand for infrastructure. And he looks at the nexus of building water, electricity, gas, telecom, transportation, and even solid waste infrastructures as they are ubiquitous elements. That's a difficult word to pronounce in our cities. And so his main goal is to rethink infrastructure planning and design practices and inform new policies to help design smart, sustainable, and resilient cities. So that is a perfect fit to introduce us to the topic of urban infrastructures. So thanks again, Sibyl, for being on the podcast and please let us know who you are and tell us a bit more about your work. Yeah, sure. So there's a lot of big words there. So I'll try to make it a little bit simpler. So yeah, so I'm Sibyl. I'm originally French, but I lived in, I live in the US. I've been here for the past nine years. I've really enjoyed being in Chicago. I love Chicago, but originally, so I'm French, but I'm not from mainland France. I'm from one of those overseas territory that France has in France. Not many people know that, but France has a lot of islands everywhere in the world. Some really nice one, tropical islands and the Caribbean and the Indian Ocean and the Pacific Ocean. And it's got a tiny, tiny, tiny island in the fridge cold of the Atlantic Ocean really close to Canada called Sankir and Miclon. It's 6,000 people. It's off the coast of Canada and the US, but it's French-owned. So that means I'm a French citizen. We have the Euro there. I went for the French president. I went through the French school system. And the fact that I'm from an island, I think makes a lot of sense for the fact that I'm looking at infrastructure and all of them is because it was just around me. And I know how the island have to operate. So when you're on the island, like everywhere else, it comes from electricity, it comes from water. Your wastewater has to go somewhere. There's a transport system. And I actually do have access to all the places. I have access to the power plant, to the water treatment plant. And so that really got me to think of all those infrastructure systems. Again, not looking individually at one of them, which is typically the way that we do in engineering, typically the way that we educate engineers is by asking them to specialize into one infrastructure systems over others. So that's really it. So I'm just, from that island, I'm very curious. I did not like infrastructure in the first place. I really didn't like it because it was, yeah, really boring, like you're saying. Really, really boring. I got into engineering because I was fairly good in high school. And when you're fairly good in high school, especially in math, you know, it's kind of common to go to engineering. And I thought after engineering, I would go into business. And I went to my engineering school. I did that in the UK, actually. So from France, I went to the UK. Very French way, actually, when you have good grades in math, what you're gonna do is engineering. That's always the... It's always. So you do one of those schools and then you do engineering and then you do whatever else. So I went to France and I went to the UK. When I was there, I got to do a bit of research. And I really, really enjoyed it. Like I said, I'm just a very curious person. I just like to learn. And then I just, yeah, just really, really enjoyed it. I really liked transportation in particular because I thought transportation has a way to go into engineering, into politics, into economics, into social, urban planning. So that was really my foot into the world of civil engineering where my undergrad was in another kind of engineering. And then I got very lucky and I did my PhD with Chris Kennedy in Toronto. And Chris Kennedy is someone who looks at all those infrastructure systems. And so it was perfect for me. So I was mainly focusing on transportation when I was with them, but I was really exposed to buildings and water and wastewater electricity, natural gas, et cetera, et cetera. And that's really what I'm doing now. And it's funny because, and then, when you look around, if you're in a city anywhere, you just look around, it's full of infrastructure and what from being boring to being fascinating. And now wherever I go in the world, I just look around, I study infrastructure and I see everything that's common between cities and everything that's very different. So you go from boring to fascinating very quickly. Yeah, but it is funny because this is the importance of looking at the things that do not seem important in the first place. And once you get it, then you see it everywhere. It's like the new words that you once heard for the first time and then you hear it all of the time. It's more or less the same with infrastructures. Yeah. Well, and even with my students, right? Like I asked them when I see them, I say, when you open your tap, do you have water that comes out? It's like, yes, where does the water come from? And they don't really know. Or sometimes it's the way it comes from the lake. It's all right, when you have, when you do trash, when you don't want your trash, where do you put it? One in the garbage. Where does it go after? No people in the garbage truck, they pick it up. Where does it go after? They have no idea. And just asking those questions just really makes you curious about the place where you live. Yeah, yeah, I love this kind of urban investigation more or less that it lets you know what your city is. It gives you like a small magnifying glass and you look at things. One of the things I really enjoy is when I go to cities, I take pictures of the sewage plates or the electricity grids and all that. They have small planks and very frequently it is made by the city themselves, right? It's written the name of the city and then you have like, I don't know, energy system or gas supply or something like that and it provides like, what is the pressure of the pipes or something like that. And it's very nice because it tells you also some, you know, some chronological elements of your city. It tells you how did your city evolve, developed? It tells you like, okay, electricity arrived at that moment in that city. Or in Paris where you spent a bit of your sabbatical you had this very little famous plate that is gas ozettage, like you had natural gas in the above stories. And well, it tells you a lot about how a city evolved and you don't see it, but I mean, even behind you we see so much of it and we don't even think about it. Yeah, no, absolutely. And it's funny that you're looking at those plates, right? Most people don't really care. Most people don't even question it. You always think it's the sewers but it's not necessarily the sewers. And when you start reading about it you actually learn about the city itself. And by the way, I love your term urban investigation. I think I'll bore it. I think it's phenomenal. It's great, thanks. Well, you can send your students do some urban investigations. I did some workshops with kids back in the day and we were connecting these plates of water and electricity and we had like some small stencils with graffiti, how do you call it? Bombs or spray cans, yeah. And we're connecting the dots and so they was kind of following the thread of where did it came from? And then they saw a transformer and they said, oh, okay, that's the second node. And then another node and all of that. And well, thankfully we had called the police before doing that. So I don't recommend to do it just on your own but it really builds a mindset I think to people. Once you see it then it's baked in your brain and you continue watching that. I'll do that activity. It sounds really amazing. Thanks, thanks. How big was Saint-Pierre and Michelin? So in terms of population, it's 6,000, it's very small in terms of surface area. The whole thing is 250 square kilometers. Most people live in Saint-Pierre which is 40 square kilometers. So we imagine six kilometers by eight. So very, very small. Yeah, and isolated. So you know everything as you said, you walked everything around. So you yourself know the infrastructures. I mean, you could probably name them or draw them or something like that. Oh, yeah. Well, no, yeah, absolutely. And even if you don't know, you feel comfortable just calling the person, right? So I go back every two years or so now and so I try to always do something. So one year I visited the power plant, one year I visited the water treatment plant, one year I visited the new solar waste management facility that we have. And I just ask people, do you know who takes care of it? Yeah, yeah, here's the number of the person. Call them up, you know, and I just go for it. And they're normally very happy to talk about it. That's also something else that I recommend to everyone is usually the people who operate the infrastructure are passionate about them. So if you just call them up or try to go in and meet with them and talk with them, they'll be super happy to tell you about what they're doing. So it's, oh no, urban investigation is worth it all the way. Yeah. So you mentioned that you did your PhD with Chris Kennedy who is like one of the persons that raised urban metabolism out of the ashes because it existed back in the 60s and 70s but kind of died off. And then Chris and Sabin Barl in another, a small group of researchers kind of in the end of 1990s, early 2000s, let's say, they kind of picked it up and now it's like a solid investigation field or it has many sub-disciplines and all of that. So I'm wondering how was it to do your PhD with him because it was at that moment that UM urban metabolism had a renaissance moment. And I can imagine that you looking at the infrastructures in a systemic way perhaps has also an inspiration from urban metabolism. I mean, you're always very close to urban metabolism, right? So absolutely. So I'll tell you Chris. So it's funny, it's really like a family. When you grew up in a family, there are a lot of things that your parents do that they don't tell you to do, but you just see them doing and you kind of do the same thing. So Chris has always had his students who were doing kind of their stuff and he kept doing his research on the side. So he was teaching or the full thing he said, no, it's really important for him to keep doing research. And so I wasn't necessarily doing urban metabolism but he was doing all of these things and you just learn from that. So he never told me that it was a good thing to do. I just looked at him. I didn't mind things. So I focused mostly on public transportation networks. To networks, network science, looking at networks, the structure of the networks was very, very big in the late. So 2000s up to 2010, 2012. And now it's less big, but it was very, very big. So I studied public transmission networks from a networks perspective, not necessarily urban metabolism, but my colleagues were doing something related to other infrastructure systems. They were doing their own things. Sometimes really do urban metabolism, sometimes not. And then Chris was doing his own thing as well. And so you just learn from all of that. And he definitely infected me with that desire to look at urban infrastructure systems though completely. One of the reasons why he was, I think one of the first ones is because there was very, very little data. So urban metabolism means multi-infrastructure. Multi-infrastructure means that you need to get data from a lot of places. We've always had a lot of data when it comes to transportation at least how it's from the 1950s or 60s. But for the others, it was much more complicated. And so it was really an investigation job for him to go and to try to get data from all those places. And it was really, yeah, I mean, it took a long time. So I think that's why he was one of the early people to revitalize urban metabolism and to do well. And yeah, and he really instilled in me that the desire to look at everything. So again, so I did transportation. Then I went from my postdoc and then when I took my job, I really wanted to look at all those infrastructure systems. Plus, we know that the way CDs operate right now is not sustainable. We know that we consume too much energy. We consume too many resources. Something has to change. Some people think it's going to be all about technology. So using having the same systems, but using technology to change everything. I'm pretty much sure that people have completely debunked that now. So we know it's going to be, it's going to require a lot of social change. So we'll have to work. A lot of people, cultural change, social change, we'll have to change the way we live. But I also think that the way that infrastructure itself is set and especially the interconnections into relationships between infrastructure also has a big part to play in it. And that's really what I'm focusing on. So yeah, so Chris infected me with a desire, I think, to look at everything. Well, at least he showed me that it was possible to look at everything. And that's what I've been doing for the past, I don't know, I mean, yeah, at least 10, 15 years. And it's been great. Yeah, it seems that there is no end. Of course, you can never have an answer at the end and say, okay, I got it now. I can stop research, you know? I mean, there's always this other layer or, you know, another time in space or another time in time or a point in time or something like that. You never finish that, right? Oh, absolutely. And you know, it's really ironic when you think of it because infrastructure by definition is built by humans. So it was built by humans over decades or even centuries. And now we don't even know how they work because it was piecemeal, bit by bit, you know, in system by system, we don't know how the whole thing works together, right? I mean, it's really quite incredible. And so I'm really trying to do that investigation piece, I guess, of saying, all right. So we've done this thing, how does it actually work? And it's interesting. And it's not supposed to be that complex. I mean, I'm not a biologist. I mean, the human body is tremendously complicated, you know, or living organisms. Infrastructure is something that we built. So it's not supposed to be that complicated, but it actually quite is. So yeah, it's fun. It's a bit of a Frankenstein baby where with, you know, limbs that you attach to it every time and it's like a never ending weird organism. Yeah, exactly. Exactly, yeah. So you mentioned networks on your PhD that you studied the transportation systems as networks. What were the insights when we studied them as networks? Yeah, yeah. So here's what's happening. So a network is a system, right? You have a system, normally we have nodes and we have links. For the longest time, especially in transportation, we were really looking at the flows. So the flows on the link, so there's a lot of traffic on one road versus another. What can we do to alleviate traffic? So the flow was the whole thing. In the late 1990s and early 2000, there's that science of network science that came about. And the main thing about that science is that the structure of the network itself. So not the flows, but the structure of the network itself, the number of lengths, the number of nodes, how they're connected. That really has huge implications on what the network can and cannot do. And so again, just the structure and not the flows. And so I started to look at the structure of public transportation systems. I started to look at networks and I started to measure some network properties and number of links, number of nodes, number of links over number of nodes, number of connections. This is really a bunch of things that you can calculate, which are the most central nodes, so the most central stations, what's the most central station, the second most, third most, and then you can calculate metrics and then you can compare them with subway systems over there, public transportation. I mainly looked at metro over the world and you see that there are many common features, which was really odd because all those systems evolved in their own city by their own planners for different reasons, based on completely different social dynamics, but they all shared some properties. And so just really measuring, witnessing, identifying those properties was very, very interesting. And I remember at the beginning when I was trying to publish my work, the transportation people really don't understand, they say, no, no, I mean, that's just a network. I mean, that's not important at all, but the flows, I mean, that's what matters. They say, no, no, no. Look, the network property really has a big impact and it really, yeah, so it took time for them, but then once they saw it, a lot of people started to use network science in transportation. And you, so again, really purely the structure. And you mentioned properties, what are these common properties? Yeah, so, I mean, there's quite a few. One of them is, there's a very simple one, which is how many nodes, how many stations have a lot of transfer stations or a lot of transfers to other lines, how many nodes don't really have any transfers or maybe just one other line. And when just you think about it, think about a city, think about a, ideally a city with a metro system, how many big stations do you have, how many smaller stations do you have, and just count them. And if you count them, you put them on the graph, you're gonna see that it's gonna follow certain curve. And that curve usually really, you have a lot of stations with very, very few transfers and very few stations with a lot of transfers and follows kind of that like this. And this here actually has some implications and depending on whether it goes down very fast or not as fast, I always give the example of Tokyo because Tokyo is one system that has some very, very large stations, but it also has quite a few stations with just a few transfers, maybe two transfers or three transfers. So when you're in Tokyo, it's very easy if a station is not operating, is not working. It's very easy to find an alternative path to go somewhere. And that contrasts with Chicago where I am, for example, because in Chicago, almost everything goes to the loop to the downtown. And so if you wanna go from, I don't know, from west to south, you have to go downtown first, you can't bypass it. And so that has implications in terms of resilience. So you talked a little bit about resilience before, but it has really strong implications. And these I don't think are things that we initially thought about before we actually measured those properties. Yeah, and it's nice always to put some figures and some intuitions, you know what I mean? When you see, I guess, the Metro plan of Chicago, you can see that it does not work or you can experience it does not work. But adding numbers to it, it kind of adds a robustness to why or what are some features that we should plan for future infrastructures, I guess. Exactly, yeah. Oh yeah, absolutely, absolutely. That intuition, you need to prove that it's actually there and we did, that was nice. Yeah, that's interesting. I mean, I got a bit familiar with these and all of the allometric stuff from Betancourt and all of these. Actually, he was in Chicago. Is he still in Chicago? I don't remember. Yeah, yeah, he is. He moved here, I think, yeah, two years ago, he is. Yeah, that was a whole new topic of urban metabolism, which I'm still digesting. I don't know how comfortable I am with treating cities as static entities and all of that. But I think it's a very interesting way to look at cities. Regardless of what scale, are you gonna look at them? Are you gonna look at them at the building level, at the city level and compare them? Or what is the right scale for analysis? And I think when you work with networks, that's the same thing, right? Yeah, so the right scale and the right, so the whole science by those people is called urban science or the science of cities. It's Ben Court, it's Mike Batty. There's quite a few people. And there, the goal is really try to measure if there are common properties among cities. So, and that's all part of something called complexity science or complexity theory. And complexity theory is where you have a lot of things happening. You have a system with tons of agents. Everything is happening. There's no order necessarily. People do whatever they want. There's no central command that's directing some, for some things to happen. And yet when you look at the whole thing together, you measure some emerging order. There's order that's just there that emerges. The best analogy that I have for that is the invisible hand by Adam Smith in the economy. So there are market forces that are there and it's putting some order when there's no central command. And so that was also happening with the development of cities. And we can actually measure some of those. Although the math gets tricky sometimes, but that's what Betting Court did for a long time and Batty as well. All that was very, very hot, I think, until maybe 2012, 13, maybe 14. But then came machine learning and machine learning data science, artificial intelligence, really overtook everything since then. So right now, I think for the past five, six years, it's all been about natural language processing and deep learning and all these things. And I do a lot of them, you know, and it's fun. But I don't know what's gonna be next. I don't know if urban science is gonna come back or not. We'll see about that. Yeah, so I guess you have, how you call it? You are quite advanced in the analytical techniques that you use, right? I mean, I can imagine that through this network and now you mentioned machine learning. You also mentioned artificial intelligence and all of that. So I guess we can go in the future by we can start understanding cities in more in-depth ways, in more predictive ways, I guess, start modeling stuff as well because so far we're a bit looking at the past and try to make a correlation of what happens if we have sufficient points in time or sufficient units, let's say neighborhoods, and we have a sample big enough so that we can make any correlation already modeling. But I can imagine this will be extremely useful for forecasting, for modeling, for testing scenarios such as, okay, what happens if we have this infrastructure or the other? So I can imagine that these advanced techniques are gonna be really helpful, but can you let us know how will that evolve or what are there about? So yeah, no modeling. So I actually never really did any modeling. I'm not a big model. I've never considered myself a big modeler. For my PhD, I did no modeling, no coding whatsoever. And then I saw that it was, not that modeling was a thing. I saw that more coding, more Python was a thing. So I started to learn Python. And I was surprised by how easy it was. It was way easier than I expected it to be. And so I learned bit by bit and the more you learn, the more you know about it and then you become the modeler. But I don't have the modeling fiber inside of me. It's just something that I do because it was fun. Again, I'm just a curious person. It was fun, intellectually no fun at the beginning. So you use modeling for different purposes. We usually think only about the forecasting purposes, the testing policies purposes. That's the second one. The first purpose for you doing it usually is for something called inference. And inference means you're trying to learn about your system. So we see that things are happening. We have some intuitions that we discussed before, but then we develop a model and you see that, oh, one variable over another is important. For example, oh, income. Oh, if you have a higher income, you're more likely to drive. If you have a lower income, you might be more likely to use another mode, for example. Or, oh, if you live closer to the city, you're more likely to use transit or if you live in the suburbs or what, you're more likely to drive. So all of that is inference. And there are things that we know are gonna be there and it's kind of obvious. But others that are not necessarily obvious and in particular, the ranking of which variable is more important over others. So that's where for me was really, really fun. And so for the longest time, I focused purely on the infrastructure side, like the supply side of infrastructure. And more and more I go to the demand of the infrastructure services. So I try to model water consumption. And so when I try to model water consumption, it's not tried to forecast how much water we're gonna use. It's to understand how people consume water. And one of them in particular is the impact, for example, of the number of people in the household. If you have two people, do they consume twice as much as if you have one person? And if not, is their relationship. And that's where something like those more novel techniques, and that's where we're gonna go into machine learning and things like that are really good just because fundamentally they work differently from what we used before. And they don't have a lot of the assumptions that the other techniques that we used before had. The biggest one is linear. So before we would assume that if you have two people in their household, they consume twice as much as one person. If you have three people, the consumer, three times as much as one person. Now with machine learning, you don't assume that anymore. So, and the way machine learning works is you have some kind of a fairly complicated, I'm gonna say model, but you have something and the machine just learned by itself and just picks up by itself some of the behaviors. And so what we do after is we look at what did the model learn and what can we learn from that? And that's where it's becoming a lot of fun. So for me, it's really all about inference. Inference of how people consume water, inference about travel behavior. So transportation is a field that was far advanced, more advanced than others for the longest time because they have those big transportation model. More and more other systems are catching up. But how do people actually choose their travel mode? Now what I'm doing increasingly is trying to look at those interrelationships between infrastructure systems. So we'll have water, electricity, gas. Normally if you're in a house, for example, there are more people who are gonna consume more water, more electricity, more gas if you have gas. But these two are interrelationships. Sometimes you consume more, sometimes you don't consume as much. So just learning about all that is really what I'm trying to do right now. Eventually I'm not going to forecasting, but again, I don't have that modeling fiber inside of me. I have that curious fiber inside of me that wants me to understand how the world works, but not that modeling fiber we'll see. But then you were talking about all those models and eventually where we say we want to go, I don't know that's really where we want to go, but where we say we want to go is to something called a digital twin of the city. And the digital twin is when you have, the city is happening, we have the replica of the city as a model and we're trying to model people, behaving and consuming electricity and water and also even how the infrastructure responds. So one of the big ones is whenever it rains a lot. So flooding is a problem for many that most cities in the world, extreme flooding and extreme droughts, some cities, you know, almost at the same time, they see like Phoenix, for example, as extreme drought and then it really torrential floods. So that water goes to the sewer system, you know, where does it go? Is the sewer system going to be overwhelmed or not? All of that we can model. And what we do now is we have a wastewater model that's sitting here, transport model that's sitting here, et cetera, et cetera. So digital twin is supposed to combine everything together. So that's the theory. I think that's what we say where we want to go. Really right now we're using that goal as an excuse to try to learn about, you know, the different systems together to try to look at, even thinking in a completely different way, how we can model these things, just because we have this huge monolithic model that looks at individual systems. I don't know if we can really have them talk to one another, but we might think about the future of modeling. And so it's just a good discussion to be in. And being on both sides, so being on the side away from modeling for the longest time. I remember being very, I think the word is intimidated. Intimidated. And yeah, by the fact that, oh, they know all those models, they know all that math, it's really crazy. It's like, wow, I can't do that. And when that happens very quickly, I think you become a little bit defensive. So you just look at the wrong things about them as models. Oh, but they assume that that's crap, that doesn't work. As opposed to saying, well, these models have something to say. Part of it is real, part of it is not real. It's not about saying the model's getting at a result. That's the truth. But it's about saying, oh, that actually helps me to make a decision. Again, when I was doing my PhD, one of the faculty members there was Eric Miller. Eric Miller is a big person in transportation. And it was saying, for him the model is just one voice around the table. So it's not the voice that you're gonna listen to, but if you have different experts and you have the model to one of the voices that you're gonna listen to, to make some decisions. Yeah, what is, I don't know who said that, but it goes like, all models are wrong, but some are useful or something like that, or some are helpful. Yeah, exactly. Yeah, that box, I forget his first name, his last name is Box, Lucky Box. And I think it was in the 1970s. And he said that for something completely different, but transport people have really taken that to heart. How models are wrong, but some are useful. Yeah, there's a lot of things to unpack there, I think. So for the longest of time when I did my PhD, I was looking at the drivers of urban metabolic flows. So what does drive water consumption, what does drive energy consumption, all of that. And so I tried to go to different, or to look for data in different cities. And over there, the only way for me to do a good comparison between socioeconomic and territorial organization characteristics of a city and metabolic flows, you would need to have spatial data, right? You would need to have a sample big enough to be able to do such relationships. And so I started with Brussels because that's what I had in hand. They had 19 municipalities. And so there was public data about how much water, natural gas, electricity was consumed in these municipalities. And then what was the unemployment rate? What was the income levels? What is the density, the number of buildings, the square meters of buildings, et cetera, et cetera, et cetera. And so I tried to make relationships and it got blurry, of course. There was like big, let's say insights, but nothing actionable, let's say. However, what I saw and was fascinating is that the way you ask the question of what is the main driver of that and the metric that you use, then you're gonna get different answers. So if you measure, let's say consumption by consumption per capita, or if you measure it absolute, or if you measure it per square meter or per anything else, and then you do the same relationship, then you're gonna find a different order of these municipalities or other variables are more significant. And so I find the way that we ask questions as important as the answer. And very frequently we just, you know, muddle through all of this and we say, okay, this is the driver of energy. But yeah, but what energy and how do you account for energy and is it the right way to account for it? And yeah, and then of course, you stubble upon the limits of data availability. Like there are not a lot of cities that have accurate data at a small level. So Chicago is one of the best cities because they have like, I think at a zip code level, they have electricity or was it energy user data, which is like fantastic because you have, I don't know, 3,000 zip codes or something like that. So that's a fantastic case study, but how do we then, you know, people from data poor countries or data poor cities will say, well, this is nice and all, but how do we do it? Or how do we advance in this field? Yeah, no, no, yeah. And then otherwise you say, all right. So we have a lot of energy data. Let's try to compare energy to transportation and then we don't have transportation data at this level. Exactly. So we get stuck very quickly. You know, I like what you're saying a lot because I think fundamentally the brain, the way our brain works is very, very limited. When we get overwhelmed with information, we don't really know what to do. So we simplify everything, right? We just abstract a lot of complicated stuff into very simple rules that we can act on. And so for us as researchers, we wanna try not to do that. We wanna try to have a more accurate picture, but we still get overwhelmed, right? With all of that information. And it is very difficult. And that's also why I think I like, so I do work with some researchers, especially in those silos. And when I show them and then tell them about my work, they tell me, well, but that's their own kind of data. You can't do that because there's no relationship necessarily between this and this. It's like, well, there's a story there to tell. We can learn something. Let's work on that. And they're so used to being super rigorous into the finest thing of their field that they're very uncomfortable dealing with that much uncertainty. And I surf on that uncertainty. I love that. So I'm fine with it. Yeah, I completely feel the same way is that I'm not an energy specialist. I'm not an infrastructure specialist. I'm not a sociologist. So I'm probably wrong from every point of view, but there is no single person in the middle that has it right as well. So there is this middle piece where everybody can still be not wrong, which is I'm fine with it. So someone told me at some point, it's like, well, okay, so you have to be careful not to become a jack of all trades master of none. That's a very famous English expression. Jack of all trades master of none means a little bit of everything, but not an expert into anything. And I thought about that. And after a while, I thought, no, we need some people like this. We need some people with those interest in everything to kind of glue everything together. So it doesn't mean that we're more right than the others, but I think there's a need in the world, at least in the world that we're living for people like this. And so like you, I mean that position, I'm very comfortable with it and that's it. And even if it's not what they recommend them in, we feel comfortable, so yeah, that's how it is. Then I also enjoyed, so you mentioned the digital twins. I think now people are also trying to go from, building information models to city information models and probably they're more or less the same thing, but what I also enjoy in this, I still haven't figured it out at all. So I see this a bit like, as you say, like a 3D maquette of a city or a 3D model of a city. I haven't yet seen the use of it or the application of it. I'm still a bit waiting for it to happen because I see, okay, we have 3D buildings and we might even have the infrastructures and it's real-time monitoring for some stuff, not for others. That's fine, but I haven't yet seen how do we, group perhaps, one field of urban metabolism, there's material stock modeling. So we could say in the city information model, okay, I have that much still in my city and then you can take an action on that. And I think this modularity knowing that this piece belongs to that piece, belongs to that piece. And there's certain, no, you cannot just sell a window. You need to sell, I don't know, the frame with it or something like that. You know, some conditions that then tell us, well, this is why you can recycle that much or economically that's the amount of money you will save by planning infrastructure in the same way. So I think there are some decision-making things. So it's exactly, I think what I told you before, which is we say that the goal is to develop those big models. I don't think that's really what the goal is. I think we're just trying to learn bit by bit. So you have the people who just wanna make a model, an accurate model no matter what, and they completely forget the big picture of why do we wanna use that model for. So I think that's where we are. So we're still trying to figure out, you know, we're going in that direction, still trying to figure out how we're gonna use it, what we're gonna do with it. And I think it's fine. I think it's fine. I think part of it is, cultural change is needed. So cultural change within academia to know why we're doing something and we're just learning bit by bit as we go through it. You know, and talking about metabolism or the metabolism, I know one of the biggest complaints about urban metabolism is that it's a diagnostic tool, right, so we use it, we know how things are, we don't know what the right solution is. And I think it's kind of, you know, it's similar as well. You know, we're just gonna try to, you know, bit by bit, we advance, we go forward, we see how things are, and we just adapt. Now, if we stay here at the beginning, say, oh, once we have that big model, there's nothing we can do with it. So we should not do it. I don't think it's the right thing to do. No, no, yeah. Saying, you know, we should put all our, you know, investment funding into this because those big models are gonna solve all the problems. I don't think it's the right thing to do, but a little bit, right, a little bit by bit, it's gonna make a big difference. And it's incredible also how much I see change. And what I've mentioned before, it is, you know, social, cultural change, the way that we think. So for me, you know, I see those technical things to be there bit by bit, and hopefully when they're ready, when they're mature, we have all that cultural change that has happened, and then we can leverage these things to do something different. And that's especially important, I think, in infrastructure because the way we're gonna consume infrastructure, or the way we're gonna use infrastructure in the future is not the same, they're gonna use it now. And it's incredible how people are trapped into thinking that something that's here now will be like that forever. And I have my students, I tell them, it's like, you know, the Department of Transportation did not exist before the 1960s. There's a lot of things that are there now. We just assume that that's it. People want that. They've always wanted that. They're always gonna want that. And no one's gonna change, right? It's absolutely going to change. So I think it's multiple things that you kind of bring forward together and you hope that at some point there's a synergy and they're gonna come together. Knowing that we're never gonna solve all the problems of the world, there's always gonna be new problems. But if we couldn't do it together, right? Together. Yeah, no, of course, it's a daunting task, but I still see the progress every year and we're now in places where we couldn't have imagined, I think, when I started research. So I think we're getting there. You know, you said it right as well. There are some fancy words, such as smart CDs and stuff like that, which sometimes are a bit cringy that, yeah, I mean, technology and sensors and all of that is fantastic. I mean, I love when there is accurate data and real-time data. I don't say that, but what do you do with them then? What is the answer that you want to, no, what is sorry, the question that you want to answer and can, will it change as well? Because what I've, I think the one thing I learned from studying urban metabolism for now almost 10 years is that you have the theory, you can propose theoretical answers or proposals or actions, but they never work, right? I mean, they don't get adopted. And this is because of political will. This is because of, et cetera, et cetera. So, you know, great. You're gonna have a lot of data, but nobody's gonna use it or they're gonna use it against what you want. So, you know, data is nice, but at the end of the day, there is a lot of, I think negotiations, a lot of thinking how you're gonna achieve your goal. And I think this is especially, I mean, you mentioned it, you're now working into the nexus of infrastructures, right? So I think that's a layer even more complex of before. So I can imagine that there is a optimization or perhaps optimization is a big word, but at least you can, you know, plan infrastructures as a system instead of individual ones. And therefore you can probably reduce requirements of energy, requirements of materials, requirements of, you know, emissions of CO2 and all of that. Have you started a bit? So, that's a phenomenal question. I really don't like the word optimization. I don't do any optimization. I don't do any oppressive research. And that's, you know, partly coming from resilience when you optimize something, you're just really gonna make it vulnerable to something because it's optimized for a certain thing. The way I think about it right now is not so much to optimize all the systems together. It's to identify, so I always say, infrastructure are interdependent, they're interrelated. Right now those interdependencies are limiting factors to limit what we can do. They just happen like this. We have a water pump, it requires electricity. So we depend on the water, on the electricity, on the electric utility. And that's a limiting factor. Now if we identify those, maybe we can transform them from being limiting factors to enabling factors. What does that actually mean? I'm not too sure. And I don't think we wanna have, you know, was one entity planning for everything, but at least we know they're there and they're enabling factors. My favorite quote for that is from an artist called Frank Stiller. So Frank Stiller is an American artist who was a minimalist in 1960s, 70s, and then he completely changed and had those canvases and he really emerged from the canvas. So if you look at his art, it's pretty, it's grand, it's really nice, it's pretty big. And I remember visiting, well, I'm going to see one of these exhibitions and it was a quote, and it was a long quote, but at the end, you know, it really says basically that boundaries, the boundaries, and that was in the context of the canvas, boundaries are defining but not limiting. And I love that boundaries are defining but not limiting. It really spoke to me, you know, it really resonated with me. No, transportation is transportation. We can define what transportation is. Transportation is not electricity, it's not water, but it's not limiting. And right now what we're doing, by the way, we've been saying that for the past 10, 15 years, as long as we've been around, we've been saying that we work inside of those and we do change things. We still haven't done that. Hopefully bit by bit we will. But I like to say that boundaries are defining but they're not limiting. So if you work for a department transport, if you work for electric utility, if you work for water utility, yeah, yeah, you have your system, it's there, there are boundaries. Don't limit yourself. Just look at how, you know, what it depends on. You don't need to be an expert in electricity but just don't limit yourself. I think once we get more and more in that direction, it's gonna work better. And for that we do need to have different departments, institutions, companies working together. A good example that I have here in Chicago is that we do have a lot of storm water. So it rains a lot, the water has to go somewhere. Chicago grew, it's very, very, very large. So now whenever it rains a little bit, all that water goes to the sewers, the sewers are overwhelmed. And because they're overwhelmed, you have a few things, one of them is disposal of raw wastewater in surface water bodies. So, you know, really all that, the water from houses, the like the sewers from houses, all the storm water going directly in the river or in the lake. So that's happening, that's nasty. And you also have something called basement backups or sewer backups. That's when people had their basements and in the basement, you really had the sewer coming back up and flooding someone's basement, which is absolutely horrible. So the problem for that is because you have all that water that's going in the system, the system is very small. And so it's just, you know, it's overflowing. So in the 1970s, the solution that was found for that was a typical engineering solution, which is, well, it's too small because what we need is to make this conduit bigger. If we need to make it bigger than the flow, you know, we're gonna be able to accommodate it. And actually what we're gonna do is let's build those massive underground tunnels and we're gonna place them on, no, bigger than subway networks, bigger than metro networks. And like in tens of kilometers of them, we're gonna dump all the water there. And then when it's gonna stop raining, we're gonna pump it back up to the wastewater determine plan and we're gonna treat it. And of course it's not working because Chicago grew and it's not working. It's billions and billions and billions of dollars and it's not working. And my favorite part is, do you know where those things are, where those underground tunnels are? They're below the rivers. They're below the rivers. And why are they below the rivers? Because the rivers are owned by the Water Management Department. I said, why don't you put them below a highway? A highway, you just have cars. I mean, there's not much load. The highway belongs to the Department of Transportation. I don't wanna work with them, right? So I know, I just wanna work with myself. So I control the rivers, I'm gonna put them below the rivers. And so there's really people that work together and that was again, typical 1970s problem. We would never do that now. We're now, we're completely changing the way that we handle stormwater. And all of that is happening bit by bit. It's just as human beings, you want things to happen very, very quickly. You wanna change to happen now, but things take time. It really takes time. So that's where, as someone working in the field, it's very, very easy to become negative very quickly. And I do have a lot of colleagues and I know a lot of people are very negative, no, very toxic and depressed, et cetera, et cetera. And I think you just have to remember that things take time. So work for the better. And with progress, it's gonna go in the right direction. We just have to keep at it and do it with a lot of optimism and while being positive, and it will happen. Not as fast as we'd like, but it will happen. So I think this is segue to a difficult to articulate question. So let me try put some pieces together. So you synthesize this caricature of infrastructure which is let's make it bigger. It's gonna work, right? Let's add another lane. It's gonna solve traffic. Let's make the water collection thing a bit bigger so everybody can open the tap at full blast at the same time throughout the city. And so it can work the same with the electric grids, et cetera, et cetera. But we're seeing the limits of that, right? Or we're seeing, we're also seeing on the flip side how this affects our consumption, right? I mean, if the pipes were smaller then we couldn't consume as much water. It's trivial, right? And so in that sense flow, while infrastructure sizes dictates the amount of flows that are consumed more or less or at least very intricately related. Then we have decisions about new infrastructures. So let's take all of the urbanization that is happening in Africa and Asia and all of that, right? Where they're in a dire need of infrastructures, water infrastructures, transportation, health infrastructures and all of that. And so the way that we're gonna construct them now are gonna dictate how they're gonna consume for 40, 50 years old, 40, 50 years in the future. And so we're in this weird scenario where we have to go, go, go, but at the same time, if we miss this, then we're gonna explode in terms of CO2 emissions in terms of the 1.5 degree, we can forget about it and all of that. This is like a hugely daunting task. I don't know, what can we learn from the mature cities where they have a lot of infrastructure from the past for all of the new cities? Yeah, that's a great question. I think there's two parts to this question that I could mention. The first one is, so one of the things I learned recently from the telecom infrastructure is that for the longest time, water, electricity, transport, it was all about quality of service. What we want is to give people what they want right now and as much as they want. So we're just gonna do it bigger and bigger and bigger. We don't want you to change anything. We wanna give you what you want, so quality of service when they have 100% satisfaction. The telecom industry, so the internet is not owned by anyone. The internet is that sort of agreement between a lot of people and companies that we're gonna have cables and we're gonna have servers and some people own cables, some people own servers, some people, but we have different sets of cables. No one really owns it. And so it's a lot more vulnerable than other infrastructure systems. And so what they did is instead of focusing on the quality of the service, they focused the quality of the experience. And so your internet connection might go up or down. Even, I had a connection problem right before, but usually your experience is good because they wanna make sure that your experience is good. With the traditional infrastructure systems, I think that's kind of where we should go or that's kind of where we're going now. My best example for that is the electric utility. So I have my own electric utility. When you have a power outage, you're really not happy about that. So you call them up, things are not well. So what did it is they invested a lot into making sure that they had a communication system between them and their customer. And then this way whenever you have an outage where you're told you have an outage, we're working on it, we think it's gonna be restored in two hours or in three hours and four hours, but at least, you know, before you have the power outage, you don't know when it might come back in 30 minutes, might come back in eight hours, you don't know. All they want is quality of service, make sure there's no power outages. Now they say, we will have power outages, we have trees, there's a lot of wind, some lines are gonna go down. So let's focus on that communication. And so it's quality of the experience versus quality of the service, which is really one of the things I think that's big. And I think we're gonna go there with everything. So yeah, so that's probably something that's gonna happen in all those very rapidly changing cities. The second one is even changing the way we do things. So during my sabbatical, I did spend a few months in Paris, but also spent quite a few months in Vietnam. When I was in Vietnam, I was really impressed by, it's really changing rapidly. I spent most of my time in Hanoi. I was really impressed with the infrastructure there. Some of it was not that great, some of it was really, really amazing. And I got really surprised by their water system, water distribution system. Because I've been really annoyed with water distribution for quite a few years now. Think about it, what we're doing now is we have a surface water body. So let's say it's a lake, let's say it's a river, whatever it is, we collect the water in a big facility. We're gonna treat it to make sure it's portable. And then we're gonna propel it in thousands of kilometers of pipes that have leaks that can break anytime. And to make sure that it doesn't get too bad, we're just gonna keep the pressure there all the time. All the time, it's really under a lot of pressure. So usually around, so then we don't have to talk about units, but usually it's about 25 meters of pressure all the time, all the time, all the time. And that really requires a lot of energy. And I always think that, I would say that the water distribution systems are really ticking time bumps because as the pipes are getting older, they're gonna be more breaks. And so what you do is as a utility company, you just have to change the water pipes whenever you see that there's a problem, you just change them changing and you keep changing them. I was like, and that doesn't make sense to me. They're always, always, always under pressure. And when I was in Hanoi, I learned how to do things there. And I was really amazed. So there just for the longest time, they could not treat the water fast enough. They didn't have the facility there to provide for every neighborhoods at the same time. And so what they would do is to service water to some neighborhoods and then others and then others. And then they would just stop. And so people just to make sure they'd have enough water, they would build a water tank in their basement. So it's in the basement so that this way the water pipe comes because the water pipe is more elevated than the tank in the basement water comes in. So it doesn't need to be in the cylinder or a lot of pressure. And then if they stop distributing water for a few days, they have that basement tank and they're good with that. Now the basement tank is good but you still need the water pump to pump to your house. And sometimes you have an electrical power outage that, you know, you don't want that. So that the big basement tank in the basement, then they have a smaller tank and the roof. So first they pump water in the tank and the roof, it's a smaller one. And then they just use this gravity to go throughout the house. And I was really amazed with that because now you can have a water shortage for several days. They're fine. You can even have a power outage. You're fine. You can have a, you know, some kind of a fire in the house and no power, nothing. And at least you have some water and you can put it out. And so I was really, really impressed by that. And so because they have that, the pressure in Hanoi is a lot lower. And so they don't have to go to the 25 meters. They usually are around 10 meters and 10 meters is just for fire safety. It's just a completely different way, I think, to distribute water. And I thought it was phenomenal. Now people are gonna say, yeah, but you need to have those tanks and it's more, it's more money. And then, you know, people have to pay for it and what if people can't afford it? I said, wait, you know, let's, you know, this is one possible alternative. This is there. And I, and trust me, people I don't know, they're not all rich. And even though they're not all rich, you know, it is a middle income country. They can still, you know, work with it. And then I told that story. I haven't had a little paper about that. And then people told me in India, it's like that as well. You know, a lot of places in India. And I don't think everyone is in India is super rich. So anyway, so that thinking of just doing things differently and that thinking that what we do in mature cities is not necessarily the golden standard that we have, is not necessarily what we want to reproduce everywhere. I think is the way to go as well. So learning bit by bit. And hopefully not just saying, this is what we do. This is what we're good. That's good. And we're just going to produce that everywhere in the world. So if we do bit by bit learning, little by little, I think it's going to make a big difference. But I'm optimistic. And the reason why I'm optimistic is that we've been doing the same thing for so long that there's got to be better ways, right? And we're creative people. I'm sure we're going to find solutions. And so the 21st century infrastructure is what is decentralized, modular, adaptive. Or is it gonna, because you said it's not going to look like what we know, right? Do you have any desires or any ideas of how what's going to look like? Well, so usually we try, we aim for two things. First one is sustainable. And right now, sustainable, the only thing that we really can aim for is low energy. So what we want is to consume as little energy as possible. And that's where we have to rethink the pumps. That's where we have to think about even how we produce electricity. That's why renewables are becoming bigger and bigger and bigger. So low energy is number one. The second one is resilient. And resilient, we're still really defining what that means. So, and I know you had a guest last week. You had Sarah Mirro, who told you all about resilience. She knows way more than me about it. But that's really where we're going. And that's really where we're defining things. So you can, you know, I can even suggest you as someone else, you can talk in the future about resilience of infrastructure. And there's Mike Chester at ASU. It's just really, really great for that. And he's going to talk about infrastructure has to be agile. Infrastructure has to be flexible. And whatever exactly that means, I think we're still defining. But definitely decentralizes is one thing we want to go. So decentralizes because for the all throughout the 20th century, we've been centralized. So centralized means we have one big water turbine plant, one big power plant. We realize that now that even though you might have some economy of scale, because you only have one, you're just not as resilient. You're just more vulnerable because if something happens to that one plant, the whole system can fail. So decentralized is one. And the other one is also integrated. So where we start to integrate infrastructure systems together, so that we can, well, you know, enabling factors. They're interdependent, but we can make them enabling factors. And so one form that we can integrate infrastructure is by making infrastructure more multifunctional. So multifunctionality. And that has, and that we're really at the tip of the iceberg, really what that means. But that really is part of the future because a lot of the infrastructure that we have now requires tremendous amount of funding, of money, of people, of effort to be able to be built. So from spending all that effort for one thing, really doesn't make sense. What if we can tell two words with one stone? What if we can do two things, you know, with one system, with one infrastructure system? I have one famous example for that is the smart tunnel in Kuala Lumpur in Malaysia. Malaysia, tropical weather, gets a lot of rain. All that water has to go somewhere. If you talk to people in Chicago, they would say build a massive underground tunnel, dump some water there. Exactly. And then they were like, well, this is not a bad idea. Maybe we want to do that. But you know what, it won't have a lot of traffic. What if when it's not raining, we use those tunnels that, you know, driving cars in it so that we leave your traffic a little bit. And then when it rains a lot, we just close down the tunnel and we flood it. More capacity, do we really need more roads? I don't know. Are there better ways? Maybe, but that's one way to integrate infrastructure. That's a good example of multifunctional infrastructure. My favorite by far, my favorite type of infrastructure in the whole world, what I think is the future that's going to change cities is green infrastructure. I'm a huge fan of green infrastructure. Let's go back. What is green infrastructure? It's raining a lot, water has to go somewhere for the longest time, we just build those ditches, those underground drains, even though 2000, 5000 years ago, cities that just had those ditches, water falls, it goes in the ditch and it just goes away. So we want to get rid of it as soon as possible. In the past 15, 20 years, we've completely changed the way we think about it. Now instead of, because cities are so big instead of having it go away as soon as possible, we want to keep it there as much as possible. So we build those green infrastructure, very good example is a rain garden, you dig a hole, you put some rocks, the rocks are there because they leave some voids, so there's some space that's there. Then you put some plants, the plants are there because they have roots and because they have roots, they keep the water in. So when it rains a lot, water falls, it goes there, there's a lot of voids, so it stays there and the roots keep the water there. So we're really retaining the water in that space. And if that overflows, then we have biofuels, if it flows, we have other things. So, but all of that is there and it's got really tremendous advantages, completely different way of thinking from an engineering perspective. And on top of that, just having more greenery is known to be good for aesthetic, for people's happiness, you know, you don't walk as fast, economic development, biodiversity, et cetera, et cetera. All of these things are framed within a term that I really don't like, which is ecosystem benefits or ecosystem services. But really it's all good. So green infrastructure, as far as I can say, plus green infrastructure, we can put that everywhere in a city. So it's very decentralized, you integrate it usually with the transport because you can have also pavers on the road. So for me, green infrastructure is the golden child of future infrastructure. We should really try to get inspired by what we do with green infrastructure for everything else. And it's really got a lot, right? Getting rid of the wastewater as soon as possible. No, no, let's keep it there. With traffic, what we do with traffic now is, let's get rid of traffic as soon as possible. We wanna make sure that people can go wherever they want, whenever they want, to wanna go, again, we want them to go as fast as possible. Now we're changing. Now we're thinking about accessibility. It's not about going wherever you want, whenever you want, it's about making sure that what you want is not too far. And if it's not too far, then you're not gonna have to be stuck in traffic for two hours, right? So really completely changing the way we think. And green infrastructure is the golden child of that. And I love the fact that it's all about stormwater because stormwater is what really people don't care about. You care about getting your water, you care about not having to travel too long to go somewhere, you care about getting a electricity. Stormwater is what's outside, don't care about it. But this is really transforming cities right now, everywhere in the world. As far as I know, everywhere in the Americas, in Asia and Europe and Africa, city of rain, especially with climate change is happening. So dealing with that city of rain is something that we wanna do. And so all of it globally, cities, through stormwater infrastructure is getting changed by the next generation infrastructure, which is a green infrastructure, I know. So you can tell, right? I know it's boring. You look at it and you see a little patch of green. I'm passionate about it. I think it's been, I really think it's fantastic, phenomenal, I love it. Well, I mean, plus it's probably cheaper, plus it's good for everything, right? I mean, so why haven't we thought this earlier? Anyhow, it's good that I'm very glad that we're finishing with this point. I want to ask you, okay, so is this your plan for 2021 or what is your, you edited one book, I think, in 2019? So the textbook, then in 2020, you had the other book with Mike Chester on 21st century infrastructure. And then 2021, what do you finish or what do you have on the oven? Yeah, so I am writing another book. So the textbook is a big book. It's a textbook for students, got a lot of equations. I'm trying to write a way shorter version for the general public. I'm hoping to finish that this year, where I think I might want to go in the future and as I've applied for funding for that, but I don't know whether it's coming or not. Where I think I might want to go is in a completely different direction because again, I'm just a very, very curious person and I really want to go into philosophy and moral philosophy because what I see now is that engineers, we consider ourselves as spectators to the world. We're not, there are things happening, climate change, like last matter, me too, all those movements, we're here, we're not supposed to be swayed by that, we just build our infrastructure in there. And clearly it's not enough. So moral philosophy is about should we build it? Should we build it? We're working on a piece of infrastructure, we have all the technical knowledge to build it. Pretty much now we can build anything. We have the technical capacity to build really pretty much everything, not necessarily the financial resources, but technically we can build just about everything. But should we build it from a moral philosophy point of view? Is it the right thing to do? All the interstate highways in the US are really aging. Should we rebuild them or should we tear them apart? And these are not technical engineering questions. I think these are moral philosophy questions. Even when you think about Facebook, Twitter, all those apps that are known to enable connections between people in the world, but they're also known to make people more depressed, more anxious. What if the coders at the beginning, when they coded them, we all had some moral philosophy tools to help them decide whether they wanted to work on them or not. In the US, in last year, we had a big discussion about the Keystone Excel pipeline that's a big pipeline to go from Canada all the way to the Gulf of Mexico. Engineers instead of saying, it's not my role to decide whether we should build it or not. This is public policy. It's like, no, no, you're going to design it in the end. So you have some responsibility toward it. If I ask you right now what you want to do, you're just going to respond to me with your opinion. I don't want you to have the opinion. I want you to have the right tools to be able to think whether you want to work and design the system or not. And so moral philosophy is where, if I can, if I have the time, if I have some funding, that's really where I want to go. Because again, from a technical point of view, we really have already a lot of tools. So it's those moral philosophy questions that would help us. So I don't think that is an answer. No, no, right? I mean, I don't know what it was I was expecting, but probably green infrastructure because you were so excited about it. But now, okay, I'm still waiting, like reading something from you on this realm. I'm very curious on that topic. What would you recommend, perhaps either in the moral philosophy or in the infrastructure realm? Do you, would you recommend any books, any articles, any videos, movies that relate to all of these topics? I know, that's a good question. I mean, you know, you recommend books and then it takes a lot of time to read. I'll tell you, my favorite book recently is not a book, it's a short story. It takes about 15 minutes to read the whole thing. So it's very, very short. And it's called The Men Who Planted Trees. By the French author Jean Junot, Junot is G-I-O-N-O, The Men Who Planted Trees. I have a PDF copy in French. It's originally French, but in French and in English on my website, CSUN, the U.S. and the EU, you can just get it for free. I don't know if I'm supposed to do that, but it's there anyway. It's 15 minutes and I love it because it's almost anti-modern. It's really about taking more time, taking a step back, seeing how things evolve over time. And I don't want to ruin the whole story. And I don't think I've done a really good job at that right now. So just, yeah, The Men Who Planted Trees, I do think that we, it is. I do think that we are overly focusing on solving problems right now, as opposed to changing the way that we think. So reading more fiction, trying to be a bit more creative, I think is the way to go. Otherwise, you just stress because you want to solve a problem. And when you want to solve a problem, you use the tools that you already have. And I'm not sure that's what we want to do now. Now we just want to disconnect from that world that exists a little bit. Try to go back to a world that's more human, you know, that slower pace. And so by reading fiction, and that's what you can do so that when you come back to your first problem, you just see it in a completely different way. So there you go. It's not a technical book at all, The Men Who Planted Trees. 15 minutes to read very, very quickly. And yeah, and you can get it for free on my website. I'm gonna read that to them tonight. Well, thanks a lot, Sibyl, for all of your time. Thanks a lot as well, everyone, for listening until the end. Please make sure to share it around with your colleagues, your friends that also thought that infrastructure was boring, but now they're gonna definitely see the stakes out of it. And yeah, thanks again, Sibyl. Thanks for having me. Thanks a lot.