Esteban A. Rossi-Hansberg:
Thanks, Glenn, it’s great to be here. This is the, this seminar is a great idea, and I’m very excited to share with you the ongoing work that you’re doing with Jose Luis Cruz who is also here. The student at Princeton and we title the economic geography of global warming. This is like I said, ongoing work. We are still in process finalizing the first draft. So, you know, stay tuned for that. But your comments of course are very much appreciated and particularly at this stage. So, you know, as Glenn mentioned, the global warming and climate change more generally with global warming also in particular is a phenomenon that, you know, of course it’s very important. We will understand its importance and its gravity. And that’s, I guess why many of us are here, but it also has very particular characteristics and three of, three characteristics that I listed there in the first Bulletproof, the point is that it’s very protracted first, in the sense that, you know, you’re going to see it evolving and happening over long periods of time. And so we want to try to think about it’s implications et cetera. We need to think about its implications over time, and over potentially long periods of time. It’s global in the sense that, you know, whatever individuals do or whatever in particular regions do is going to affect the whole world. And in particular, the emissions or CO2 emissions that happen in a particular location, you know, make it’s very quickly and the atmosphere and therefore, you know, lead to global impacts on climate. But at the same time, you know, those changes in climate and changes in particular and the rises in temperature in particular have very heterogeneous local effects, affect regions very differently, partly because one degrees in change in the world, you know, leads to different degree changes in the different locations. But also because of course it’s very different to face a warmer climate if you are in a very cold region, that gives to a very warm one. And so, you know, this, these effects are going to be extremely heterogeneous. And so, you know, we want, in some sense, when we think about trying to build assessment models that can talk about this phenomenon, we need to take these characteristics into account. And so that really calls for models that are global, that are dynamic, but they’re also have this, you know, local a detail. And so putting all of that together, it’s important. Then we want to kind of try to do that in a way that it’s incorporates modern economics. And what I mean by that is that, you know, modern economics is all about incorporating behavioral responses of individuals and firms, namely modeling the micro-behaviors of these agents. And in particular, how they react to what happens in the economy to particular strongs or trends, et cetera, that are happening to the economy. And that of course, leads these agents to adapt to whatever is happening, to react to this what’s happening. And we want to kind of model that other patient. And that addition in some sense is potentially very important that have matters also for the type of policies that we put in place. And so it’s very important to have models that are not just mechanical models of losses, but that actually model those behavioral responses. And we want to do it in a world that has, you know, all these hitters you need is spatial heterogeneity that I talked about. And so that’s the goal that we have in front of us. And that’s you know, what we’re trying to do in this paper, trying to propose such a structure. And in particular, what we’re going to emphasize, you know, is in these kinds of spatial dynamic assessment model is these economic adaptation. And we going to introduce three main channels for these economic adaptation. One is going to be migration. And we’re going to allow people to move across locations, subject to cost is going to be costly to move, but they are going to be able to walk. There’s a, there’s going to be trade. So locations are going to be, and individuals are going to be able to trade with each other and there’s going to be then innovation and innovation is kind of key here as well. And it’s key in the following sense. You know, when certain areas become warmer and therefore perhaps less productive as a location, then it’s natural that people are going to move somewhere else. Now, when economic activity moves somewhere else, then they could move to regions where there’s nothing today where there’s potentially no economic activity today, particularly over long periods of time. And so, you know, we want to give those new regions a chance just to develop as well, right? A chance to, you know, for people to invest in them and to make them, you know, thriving reason, regions as some other regions are today. And so, you know, incorporating the ability to build them up in that way. That’s the, you know, the importance of innovation and in what we’re doing. There’s a long literature not going, going to talk too much about the literature on this. You many of you probably know the literature better than me. Let me just kind of separate in four bins here. So there’s of course, you know, empirical studies of climate damages, and these is a partial list of that. Then there is another band that is closer to what we’re doing here with just trying to build economic models of climate changing and to try to evolution climate damages. Of course, you know, the models by North house, et cetera, very much kind of here, but in general, like I said, these malls don’t have, you know, a lot of spatial richness or many of these behavioral responses that I was talking about some due to different extents, et cetera, but you know, there’s definitely room need to incorporate these mechanisms more fully. And it has proven difficult in the literature to build models that have both spatial detail and dynamics. And so this is kind of an agenda that I’ve worked in several papers with multiple coauthors, most notably Dez, Matt, but also David Naji and others, and sooner to try to kind of build a methodology to do this and be able to solve these models, in a tractable way. But there’s others like that have also proposed, you know, alternative ways of writing these malls. And of course they all have a lots of similar features. And so we’re going to definitely use a lot of that machinery here. And, you know, in some sense, apply it to this problem of global warming. And then there is a literature in that mean this intersection. So Claire about Bonnie had a nice paper on flooding recently, the users that can handle the work in pero set up that I talked about, and we have some work with Oppenheimer and Kirk and others that tries to also use some of these machines to think about flooding and the cost of flaunting Crucell and Smith, and have incorporated some of this into their framework, even though they don’t have mobility, but they do have capital accumulation over time in some special detail. And so, you know, there’s a literature there that is emerging and we’re trying to contribute there and we hope others contribute as well. We think that’s in some sense, a good way to go when we in building this assessment malls that are, you know, incorporate these behavioral responses. Okay, so here is, you know, like what we’re doing kind of the model in a diagram. So, like I said, the core of this model is going to be these work with a close Desmet’s and then David Nageen the JPE, which sets up like the economic module, if you will, or the economic model as we going to be using here. And we’re going to add like a climate component to that. And so, you know, everything that is in black is what was already there in that model. And so there’s agents that live in particular location, that location has some amenities. They work, they consume, they can migrate to different regions, they supply labor firms, use labor and land to produce, they trade with other regions. They can innovate, improve their technology by spending some resources and they pay their workers, regions and rents. And so that leads to, you know, and that’s different.
So the different regions are different in terms of their productivity and their amenities, and they, and firms innovate. And so that leads to dynamics, or it leads to a growth model that is spatial, right? Now, we’re going to take that core of the model and do it for a couple of things. We’re going to, first of all, add another input, which is energy, and there’s going to be two types of energy. So, clean energy here and fossil fuel energy. So that we can think about it substitution between these two types of energy, fossil fuels lead to CO2 emissions, would need to increase in the car, and there’s talk of carbon, which lead to changes in room temperature. Then there is a down-scaling into local temperature and that local temperature that then it’s going to have an effect on the local economy in terms of affecting the local productivity as well as the firms that operate in that location, and it’s also going to effect, there are many of these how nice is that region to live in and not your rates or the Dota, the kind of the net fertility rate, if you will, in that location, because we, there’s so many, then the temperature can affect that in particular mortality rates, okay? And so it’s going to matter where agents, it certainly matters, you know, how many, you know, the total fertility rate, but it also matters where that happens because there’s migration cost. Okay, so that’s like the modeling and not shell. And then we’re going to quantify this more in a one degree, by one degree, every solution, the baseline year is going to be 2000 and we’re going to choose fire meters. So that’d be a trade in mobility, friction. So everything is costly. All these interactions sometimes are costly much, you know what we know about the gravity equation in trade and match net migration flows between 2000 and 2005. I’ll talk more about the quantification in a second of course. So let me give you a few details of the model. So there’s agents, so these agents are going to have a utility that depend on where they are in general. I’m going to call that location R, is where the agent is. And these R bar minus is like the history of where these agent has been. And this agent is going to derive utility out of consumption. That’s the end. This is like a CS aggregate of the consumption of the different types of goods in the economy is different varieties for mega. And we tell them, unless this utility that depends on the perimeter row, then there’s going to be these amenities. And these are the key, that’s why I highlighted them in green, right? There’s so many using chronic preferences for the location. You may like it because your grandmother lives there or not. And then there’s some moving costs, right? And that’s why the history matters, you know, it matters and where you’ve been. And so if I moved from Mexico to the US, United States, you know what, the way this is modeled is, I’m going to be paying kind of a flow cost while I’m in the United States. And so this is the flow. This is, this represents that flow cost of having moved from some other location. Okay, so these are preferences. The key component is this B, which tells you how attractive is this particular location R at some period T. And we’re going to allow that. I mean, we’re going to allow for some congestion cost. So if there’s more people that’s, these L is the number of residents in that location. Then, you know, these B is going to be smaller in the sense that there’s, you know, I don’t like big, you know, being crowded and crowded with other people. And then more important than for our purposes here is the next equation here that tells you, well, these B bar that then you know, determines the B is going to be determined by this expression here. And the green part in this expression, that’s where the climate plays a role. And what is he saying is, well, you know, temperature and temperature which is denoted by cap T here, temperature in particular, the change in temperature, that’s the Delta T affects amenities in that location and how it affects amenities in that location depends also on the level of temperature in that location, which is the second term in this equation, okay? So if temperature goes out, you know, whether that’s good or bad for amenities, depends on your temperature in the past, in the previous period, and so we’re, these function we’re going to ask me and that in turn, then is going to determine these amenities. Okay, so there’s going to be these damage function, which is these Lambda cap Lambda that determines how temperature in Purdue changes in temperature affect the amenities of a given location, okay? So that component is very, very important. Then there’s these natality rates. I’ll talk more about natality rates, but for now just think about natality rates in the sense that this is how much, if you end up with a certain population in a particular point in a particular place today, you know, the natality rate is going to tell you, you know, how many what’s the growth rate of that population between today and tomorrow, okay? And we’re, and I’ll explain exactly how we permit tries that in a minute. More involved than perhaps interested is the determination of technology. So, this is the production function. And the first equation here is that for production function. And so this is all per unit of land. So this is all normalized if you will, by the units of land. So think about it like constant returns to scale technology in labor, energy, and land, and I already divided everything units of land. So, that’s why you have these exponents here, which is mu where one mine is mu is the share of land in production. So there’s some decreasing returns here coming from the fact that, you know, land is an input in production, and you put a lot of firms in hiring a lot of workers in a particular location that creates some congestion as well. Okay, so then you, you know, these firms are going to hire a laborer here. They’re going to use some energy, which is the, these terms here. And then there’s a productivity. And the productivity depends on some idiosyncratic component, which is the Z. And the level of that distribution, the level of a, that big credit component is where temperature is going to play a role. And then there’s a, the firm can decide to innovate. And so it can choose these fee at some cost, which would, you’ve got to improve its productivity, okay? So that’s the endogenous part into that. That’s why this is going to at the end of the day, going to be an endogenous growth, okay? So the keys did and this distribution Z of work from which these firms drawn this reducing chronic productivity, the level of that distribution, I’m going to call a, okay, and this a is going to have some agglomeration of facts. This is going to depend on the number of people or the number of workers in that locations raised to some power alpha, which is going to, you know, that’s this classic guy, glomeration effects is good to be New York because in New York, you know, you are more productive because you talk to others or because of all the Marsha Dalian at type of a collaboration of facts. Again, more important for our purposes here is that it’s going to also be determined by these AAV bars is going to be also determined by temperature. And so we’re going to have another Lambda, cap Lambda function here, cap Lambda a that tells you that productivity is going to be a function of the change in temperature and the level of temperature. So same logic that I described. And with many of these is also going to depend on these other terms here in particular, the first term here is going to be about the fusion. Maybe the local technology depends on what, you know, what’s invented in other places. And any of those are depends on innovations in the past. So innovations kind of shift this distribution overtime. So you’re drawing from better and better technologies as you move as the economy kind of grows, okay? But again, key for us here is these damage function. These Gamma functions, cap Lambda a, that we’re going to have to determine in a little while, okay? Now energy, these input energy that
went into the production function that I mentioned before, and it’s going to be form out of two types of fuels. There’s going to be fossil fuels, which is the first form and then there’s going to be clean energy sources. And, you know, there’s an elasticity of substitution between the two that is going to be determined by these permanent disease, or is this barometer Epsilon. And so Epsilon is of course going to be crucial in what we’re going to do, because it’s going to determine how easy it is for producers to substitute fossil fuels for clean sources. For example, if you put a caramel, okay, so, you know, pardon perimeter absent on it? We’ll come back to it of course later. And then there is the unit, cost of extracting energy, or getting a unit of energy in a particular location. And so how are we going to model that? Well, we’re going to have first this function F which is like the extraction costs, if you will, how costly it is to extract fossil fuels. And that’s going to depend on home, you know, the community amount off CO2 that we’ve extracted in the past. And so, you know, at some point we’re going to, we’re going deeper and deeper into the mails. I get to get the coal or to get the, or wells to get the oil, and it’s becoming more and more expensive. And so we’re going to kind of have a curve there that tells us, you know, over time it gets more expensive to extract fossil fuels, but then we’re also going to allow this cost to evolve according to the growth rate of the economy. So if the economy, you know, fades and technology becomes better in everything else, we’re also going to allow the technology to extract both clean energy or to generate both clean energy or fossil fuels. We’re going to make them increase the productivity in that process, okay? And so the growth in that productivity is going to be the same as the, or it’s going to be related to the growth in the world, outward, raised to some power that is going to be technology dependent, namely, it’s going to depend on whether you’re talking about fossil fuels or clean energy, okay? So, and we’re getting these new J, I think this is new, new J parameter is, we’re going to have to determine it in effect. Okay, but that’s function is, is going to be crucial. And I’ll come to that back to that in a second, this cost.
We’ve got a couple of questions, maybe go back to the last slide, I think, and you may cover this later, Michael Greenstone wanted to pin down sort of what is the source of the substitution? Easy substitution between clean and fossil fuel sources? Is it more than the differences in the cost per unit of energy? So substitution between clean and fossil fuels, is that, what is the, what’s driving that, do you think, is it more than just a, is it differences in the cost per unit of energy?
I mean, the way we’re going to mall is a, so there’s the cost, there’s the relative price. And then we should reach at the end of the day, we’re going to get from, at the local level, the relative use of clean versus fossil fuels at the local level. And then there’s some studies done that. And then we’re going to grab that Epsilon, that elasticity of substitution, which is the perimeter that, from the literature, what exactly the, is the source off of that substitution and how easy it is to substitute one to the other. It’s, we’re taking it that as a technological feature of this economy. I don’t know if I can say something more precise than that.
Okay, we can say that elasticity of substitution is bigger than one, but not because clean energy tends to be a little bit intermitted, so they are not perfect substitutes. So we considered that they are subsequent to some degree, but not at all. So we understand that we take these parameter of today, but also we understand that as the cost of storage of blink services, 90 degrees, then the sources of energy may be more substitutable. So this elasticity might change in the future. But as for now, we are considering elasticity and a forgiving parameter. It is substitutable, but not completable.
I think we’re going to use a number that is on the high side for that Epsilon, but, when we go to the numbers, maybe we can have this discussion and I can show you the functions that we’re using. And maybe that discussion is going to be a little bit more informative there, but I mean, happy to going through more questions now, if people want to.
I think there’s also a couple of questions from Maximilian Cots and Clara Gauze Galeazzi, about the calibration of temperature dynamics. So one question is, have you calibrated your temperature dynamics against models from the climate community or against historical observations?
You’ll get to all of that, that’s all right, okay.
I’m just setting up the model and then I’m going to show you the quantification of the model in a second, okay?
So, okay. Now then there is the, you know, in general, this leads to a complicated dynamic problem. But you know, that JP Baker, et cetera, is exactly a way to simplify this because there’s local diffusion of technology and competition for land, that it implies that essentially the innovation problem becomes like a static problem. And so you can talk them all forward. So that’s kind of what makes these all trackable. And then there’s straight balance region by region in iceberg trades costs, which lead to gravity equations for bilateral trade flows, as we kind of know. So what’s the climate component. So they’re sealed two emissions, aren’t going to rise global temperature. So the use of fossil fuels needs to CO2 emissions. Those CO2 emissions then imply that, you know, that’s going to be all endogenous depending on how who produces and how much they produce and what they prevented to do the use. And then there’s going to be an exogenous piece, which is the, you’re going to come from forestry and non CO2 greenhouse gas emissions. And we’re going to take that from the literature. And so at the end of the day, we’re going to have that global temperature, you know, or the carbon cycle we’re essentially taking from the IPCC. So it’s kind of a standard module out of that, and I’m sure you have well with fit that, will fit the different scenarios in a second. Then there is the downscaling of that temperature. How do we map global temperature into local temperature? And that wasn’t going to just do simply with the equation that you see here, where the keys, these G Ryan, or these local scalar, if you will, that we’re going to take us as not evolving over time. So the only thing that evolves over time in terms of temperature is the global temperature, but that of course affects the local temperatures differently according to these G in these G is going to, we’re going to, you know, use historical data to try to estimate, and we’re going to try to be, you know, very flexible. And we try to, you know, be as flexible as possible in some sense, to try to have the best possible match of local temperatures. And so if you do that, this is what you get. So this is one degree, one, all right, what’s the effect of a one degree increase in global temperature, you know, whatever is this color here is the light blue that gives you exactly one degree. If you’re, you know, the dark is red, then you get about 2.5. And in the bluest regions, you get quite a bit less than that. And so this is again important because that’s going to lead to exactly that spatial distribution of a fact, and you seek out of how that the spatial distribution is fairly rich by the way, there’s some countries that the North house data sets, e.g doesn’t have. And so we’re not going to have drought like Libya and some other countries here, parts of, and there’s some squares here that we also want to have. So I’m not going to have those in a bunch of, in general in these, in these calculations, okay. So there’s many far meters. I’m not going to go through all of them. You know, many, we estimated in some detail in that other GB papers. So we’re just kind of borrowing from there. The key one says, well, these are the stages of substitution that I talked about. We’re going to follow some of the studies there and set at 1.6. If people feel that that’s offensive in some sense. That we would definitely like to know that we think it’s a reasonable value given what we know. And I’ll talk in more detail about some of the rest. Okay, so one of the important aspect of this is going to be this extraction cost. So this extraction cost is this half curve that depends on cumulative emissions, right? And so it tells you how much more costly is it to extract carbon, if you have already extracted a certain amount of carbon, right? So in terms of CO2, and so these blue dots are, you know, the estimates of Bauer at all, and four different levels of cumulative emissions. And then we’re going to fit this curve, the black curve that you see there, which is the one I have a written down here. And so then this is going to be our F function. And this F function is kind of important in what we’re doing. Why, because, you know, there’s this natural mechanism by which, you know, if you’re using a lot of carbon, carbon becomes more expensive. And so, you know, people are going to say substitute away from carbon in the future, because this cost is rising. And of course at the end of the day, there’s going to be like a total amount amount of carbon on earth that we can use. We’re going to set that at $19 or 19,500 Gigatons. And that’s it, that’s all the carbon there is. And so after that, you know in some sense, this is not a problem anymore because there’s no carbon. And so we have to use that clean energy anyway. There’s some other details about these energy productivities and how we get them, et cetera. Like I said, the main information, there is the relative use of fossil versus clean energy in different countries that we get from this ed guard dataset. And so let me leave it at that. And if there’s questions later on, I can go into more details. Otherwise I’m going to run badly over time. Okay, so that’s the, that’s that function. Then the other key functions are these damaged functions, these lambda function. And these kind of lambda functions, we have two of them. We have one for amenities, one for our productivities. And we’re going to estimate them like this. So the first step here is to run the model, And if you invert the model. So as to obtain the productivities and amenities, that rationalize according to the model, the location of people and the production, in the different squares, right. Into different locations. And so we’re going to obtain those. So now once we have those amenities and those productivities, then we’re going to relate those to local climber, and we’re going to do that and using it, these expressions here, so we’re going to have some fixed effects on you know. Location, time, fixed effects, and then we’re going to estimate it by bins. So of different temperatures, okay? And so what we’re going to be getting is these deltas here that are going to tell us, well, once the effect on amenities or one degree increasing local temperature condition on being in a particular bin and the same for amenities. And so that’s the estimates that we’re doing. There’s some choices in terms of exactly what type of fixed effects of not do you, was there that, you know, this is what given what I do showed you. This is what we get. And so what is the saying, what this saying is, so these are the, this is, these are the deltas for the different bins, right? So what this is telling you is if you’re in, these are January temperature. So if you’re, you know, one of the coldest places in the world and, you know, temperature increases by one degree Celsius, your amenities go up by 2.5%. That’s what these numbers is saying. And so, you know, and this is like positive. So that increases in temperature are good. If you’re very cold. And then if you are very hot, right? And the opposite is true, increases in temperature are negative for you, they decrease your amenities, right. As you can see here. And so the same is true for productivity. The numbers are actually a little bit larger for productivities here. So one degree here implies about 10% increase in productivity but also about a little bit more than a 10% decrease in productivity at the upper hand here. So if you’re in the Sahara there or something. Now, as you can see, you know, this is not very precisely estimated. And so there, so these bands are quite big. And so that’s, of course, plays a role in our certainty about any of these numbers, the bands are quite big. And that’s partly because, you know, the amount of variation that we seem to climb over the period where we’re doing this is also not huge, right? So, it’s effect is not that easy to piss out, so but of course we can do it for the different limits, et cetera. Okay, and then the last part is these natality. So we’re going to allow this to depend on income of co
urse. So, you know, as countries grow in terms of their income or in terms of their income, natality rates go down, eventually we’re going to make them converge to zero so that, you know, we have a stable global population. So there have these shapes here, and then we’re going to also follow in Greenstone following, have some of these effects where, you know, there’s like an optimal temperature for natality rates. So that’s, we are going to do that. Okay, so that’s the model and that’s the way we’re going to more or less quantify it. I mean, I understand that I had missed a lot of details, but again, we can come back to some of those details later on, if people are interested. And so this is what we get. So here is the model we’re running the model from 2000 and we’re running them all forward. In this graph focus on the left hand side, your left hind side block first. So what you see is the model in blue. And then I have two scenarios, the IPCC RCP 8.5 and RCP 6 there for comparison purpose. So the RCPA 8.5 is what people call like business as usual kind of scenario. And now, you know, and so you can see the model kind of that it’s kind of leads to something that is quite similar to that increase a little bit further up here, but then it comes down, you know, and it comes down a little bit faster than the IPCC. Now, the IPCC pretty much all just in both says this decline, right? In the model, it comes naturally just because of this F function, right? So is that F function of the fact that extracting carbon becomes increasingly more and more costly. And so I see, and you can see, so you can see how that evolution kind of fits that scenario fairly well. So someone was asking that, and this is the implied changes in temperature. And so again, compared to the RCP 8.5, it also exactly, almost exactly matches that, is, you know, certainly higher than the RCP 6, but of course, there’s all these endogenous components, you know, who’s using energy and how, and where they took this, rather that isn’t the background. That means that, you know, leading to these graphs. Okay, so this is just given that evolution, what would be the, or what will be the amenities or the productivities in 2200 say compared do the ones that you would have absent globally warming. So that’s what they shows here. And so everything that is in red is, are places that, you know, are going to suffer from this, sorry, I was going to benefit from this. So in particular, if you’re hearing Siberia, you know, your amenities are going to be about 30% higher. And if you’re in some of these parts in Africa here, they’re going to be, you know, about the same percent lower. With productivity, this is even more dramatic. So some of these areas, you know, gain substantially productivities, almost dominoes with respect to no warming, but some of these areas, you know, productivity halfs, and you particular look at the region here. That is a fact that obviously Africa, South America, importantly from Chille and then Australia a lot. And, you know, to some extent, India as well. So all that, all the, you know, Southern part of the world is, it’s affected both in terms of amenities and both in terms of warming. And so these map of course is going to, or these type of a spatial distribution is going to come all over again, you know, throughout this presentation. There is then global population and the evolution of global population. So again, the blue is the model. I’m comparing that with the, UN forecast up to 2100. So it kind of fares well relative to that. And, then, I have warming versus no warming there. So both groups are there. I know it’s hard to see because the effect is small. So the difference between the two is maybe about 30 million people. So not huge in terms of, as a share of total population, but there’s a little bit about an effect of a warming on reducing global population. And this is where people will be in 2200, according to this in the baseline case. And again, you know, moving to the Northern regions from regions in the South Africa, South America, Australia, parts of Mexico, India, okay? And so, you know, again, moving out of those areas where many, these are lower productivity will be lower again, and you can see kind of the specific geography of that, and this is going to be, we put it all together and we calculate welfare. So firstly, the discounted value of welfare, I’ll talk about the discounting later on discounting, as we all know in all these calculations is essential. This is a little treatment, a little bit even trickier because you have this growth, endogenous growth in the model as well. And so how you discount is you know, clearly open for debate, but here we chose a discount that is, I think, 96.5 so 3.5%, but there is 3% growth in this economy. So, you know, we’re not discounting very heavily and these are the welfare implications. And again, you know, the same type of map that we just discussed emerged. So some, and look at the numbers that this implies to some of these places in Africa, South America, I expected or lose about 10% in terms of their welfare, you know, present discounted while some other places in Siberia gain about 10%. And this is the distribution population we waited, and what you see is like these two modes in the distribution, right? So there’s a bunch of countries and a bunch of population that lose substantially. That’s the first peak. And there’s a bunch of other countries that in some sense, don’t care that much. And there’s a few that gain, but those places that gained, you know, don’t have a lot of population. And so not that many people there gain that much, but, you know, and so the average is there, which leads to about 3%, okay?
– You’ve got about 10 minutes left. I was going to let Richard Toll ask a question.
Okay, so two points by the way, shows that everybody’s going to move to Siberia and Alaska. And certainly that makes intuitive sense, but my, a prominent friends would tell me that there’s no soil there at, it’s been cold and barren for so long that we simply can’t grow any stuff there. So I wonder if that take me to counseling to the model. And secondly, it’s far, far away from everything. So you would need to build up quite a bit of infrastructure for those places to be actually pleasant, even in the absence of soils. And I wonder how that is taken into account into the mall.
Great questions. So first of all, let’s do, let me do the, be a little bit careful with the statement. Everyone is moving to Siberia because it’s true that population in Siberia is going to increase quite a bit in percentage terms, but it’s quite low now. So it’s certainly not the case that, you know, the whole world is going to move to Siberia or anything, right, even though the relative, you know, the percentage increase is going to be high there. And the second thing is, you know, so if you just started with low Brooklyn DVD, and so, you know, you have to invest a lot in improving that place. And so that’s going to happen over time and it’s happening in the mall, ask more people go into that region. So that’s why, in some sense it picks up, moving to Siberia picks up slowly because you exactly, you have to make all those investments in local productive capabilities. We don’t have, you know, soil quality, particular, and the issue that you raised a kind of single down bond. But we do have these, a fact that you start from a low level of productivity that you have to build up and in order to build it up, you need those investments. So that part is there. And then the infrastructure. So all of this is based on a trade network in the world that where the cost of trading locations is given by the current infrastructure and the current modes of transport by sea, et cetera. And so we’re, it’s all, we’re optimally routing conditional on that infrastructure. In principle, of course you could also model the building of new infrastructure, perhaps new canoes, perhaps new better rail, et cetera. We haven’t done that. So this is all conditional on, you know, flows today. And one of what’s implied by flows today. And of course, then there’s the older issues of whether you can have open the other routes of transportation because parts of the sea, you know, get warmer, et cetera. None of that is there, but that would, we could incorporate that because it’s all based on these optimal routing algorithms. And so, you know, whatever we can incorporate those changes in infrastructure, we just haven’t done that. And of course you have to kind of make sure that you guide that with some, you know, proper quantification. So we haven’t really gotten into that problem, but that’s an interesting product so far is think about fixed infrastructure. And so if you think about fixed infrastructure, anything you’re understating it because you would also invest in better infrastructure right, in to the extent that that makes it easier to trade. You would do a little bit more than what this mall tells you.
Great, if you could wrap up in, under 10 minutes, that’d be great.
Sounds great, okay. So you know, all of these, of course. So I gave you some welfare number here in particular at this point 93%. Now, you know, this is, I mean, these graphics are a little ridiculous perhaps, but this is incredibly uncertain, right? And the reason it’s so uncertain in particular, in for this graph is that the, these are these damage functions for many of these in productivity, we’re estimating those with big standard. There’s some significant effects there for sure, right? Now we’re estimating, but there’s a lot of uncertainty. And so this gives you, you know, a sense of this uncertainty for the world average. And so in particular, there’s lots of uncertainty for the world average. I think there’s a lot less uncertainty across these different scenarios in terms of the distribution of the losses and gains. That is a lot more stable, but when we want to kind of one baseline number of what’s going to happen to the world, at least according to this, there’s a lot of uncertainty. And so, you know, of course this should be taking all with a grain of salt, the specific numbers for world averages. Now, you know, these outcomes that are showing you, these aggregate outcomes that I’m showing you, they already incorporate these other patient mechanisms that I mentioned at the beginning of the talk, namely this mobility trade and innovation, right? So they all include them. And so one aspect is, well, we can try to kind of tease out to what extent is different adaptation mechanisms are important or not. And so this is trying to do that for migration. So one, how does the welfare implications of climate change? How do they change if we increase migration cost, if we have a world and with migration costs are 50% larger than what we estimate in the model. And so these are these are, these the outcomes. So who would, who benefits in the current world relative to that other world from the effects of climate change? So this is the different diff is the map. And so again, you can see how the Northern latitudes of course benefit from that. And some of the countries here benefit relative to, you know, their position in terms of the total losses. So they don’t care that much about, or sorry, they care a lot about having a good migration, some other countries like Australia on the other hand, don’t, right? So Australia is not so much better off because migration costs are lower. In fact, it’s a little bit worse off. So here you can see the evolution over time on the other graph. And the baseline is the darker curve. You can see the, and you can see the effect of increasing migration costs by 12.5% or 50%, which is the one that it’s in the map. And you can see, like the bigger fact that migration costs have on the total cost of global warming. So migration is definitely one of the key components in adaptation it’s kind of been very important to incorporate. Their story is quite different for trade. So this is the same exercise for trade, in the map I’m doubling trade costs. This is again the different diff, you know, who gains and who loses, it’s kind of similar than for migration. But if you look at the graph on the right, the effects are much smaller, right? Yes, a little bit more a trade implies that in the far future, you know, the costs are going to be a little bit larger but the effect is relatively small. So not a lot of adaptation is happening to true trade. Trade is not that important. And part of it is because trading, of course, it’s costly and it’s kind of local already. And climate is very especially correlated. And so that those two things kind of play together to make these numbers relatively small. And this is innovation. So innovation is tricky. If you make innovation cheaper, on one hand, of course you facilitate moving to Siberia, kind of when we were discussing with Richard, et cetera. So that process is made easier, but the world also grows faster, which implies it uses more carbon and that affects the things negative and affects productivity negatively. And so those two effects kind of fight with each other. And they’re just going to get another complication, which is that if you make innovation cheaper, you move people into regions of the world today that are going to get warm in the future. And so that is costly as well. And so, or people naturally do that. They move to something in particular, China and India. So you have more movement to lose readers in the short run, but then you invest in those regions and then eventually you leave those regions and that’s constantly for the world economy. So innovation is another patient mechanism, but cheaper innovation in general tends to imply larger losses, not smaller losses from changes in climate. Okay, so my last three minutes, maybe Glenn is, let me just mention one quick thing about policy. So we’ve tried two policies, carbon taxes that are uniform, locally rebated. So same tax for everyone in the world and in the revenues of the tax are locally rebated. Of course, we can do all sorts of things or different designs, et cetera, we just haven’t done it. And so here you see the CO2 emissions. There’s the baseline that I show you in light blue, and then two different cases, which are carbon tax is a 5,000 and 200%. And so, you know, and this is, kind of an important phenomenon that is happening here in the model at least, which is that doing polls, this carbon taxes and you of course, lower emissions, right? In the short run. And essentially what you see is you shift this curve, right? You shift the curve of emissions. And that of course has an effect on temperature in the sense that you lower the temperature in the short run. But then there is this phenomenon, which is that if you’re not using the carbon today, because of the tax, then extraction of carbon doesn’t get that more, much more expensive because that depends on cumulative emissions. And so in the future, that makes carbon relatively cheaper, right? And so you’re kind of playing this race where yes, I can no work with the carbon tax, the use of carbon today, but then I’m kind of, because there’s more left on earth to exploit that makes it in the future easier to extract. And so, you know, people will extract it later on. And so what that implies is, I mean, of course we’re not, there may be a complete technological breakthrough that, these all may, you know, break down in terms of mechanisms, but ups and downs that means that, you know, you’re going to use it later. And so, yeah, it’s kind have a decrease in temperature, but, you know, eventually you kind of use the carbon. And so you end up with rises in temperature that are not that different. And so it’s like you’re flattening the curve to use like a popular term these days, you’re flattening the temperature curve, but you’re not really eliminating the use of carbon. And this is where he said the elasticity of 1.6 kind of matters, right? So it is quite, I mean, it is quite elastic, but you still have that. And so you post that, then you have costs, you’d say in real GDP here on the left, you have costs in the short run, gains in the long run from the improvement in climber, right? And then of course the net depends on how you value the present relative to the future, right? And so what the optimal carbon taxes, et cetera, is going to obviously depend on how you value this piece versus that piece. But, you know, you get the, you get some important increases in real GDP relative to the economy with no carbon by using carbon taxes in the future, okay? But again, you get some costs in the present and here is the distribution of the welfare. So again, who benefits from imposing this tax? You know, the usual soft specs, again, less so Australia, Australia it’s going to be damaged by climate benefits, less from this because it’s a, it uses, is an economy that, kind of an industrialized economy that I guess uses carbon. And so that hits the, its economy a little bit harder, but, and, but these area here clearly benefits. Final word, clean energy subsidies. Clean energy subsidies in this world, in our world, do very little in term
s of temperatures. And the reason they do very little is because yes, they, people substitute but at the end of the day, they also want to use more energy. And so at the end of the day, the effect on total emissions and temperatures are small. The effects on the economy on the other hand are quite big because subsidizing production in this world is a good thing because it also generates more innovation, et cetera. And so there’s some benefit that comes from that, but it’s not really truly the climate channel. And the reason again, is that you do get some substitution, but you also get some general increase in the total use of energy and growth, which at the end of the day, leads to a very small effects on climate. And you can see kind of the distribution of the gains from employer pointing, putting the subsidy is a way to transfer resources to areas that are going to be hardly hit by temperature increases. But again, not because you’re reducing temperatures, but mostly because you’re encouraging production in South America, Brazil in particular per of Y and some of the central African region. But I mean, in this world, it is a good policy. It’s just not for the reasons that one might think in terms of reducing climate. So I’m a little bit or I’m completely out of time. And so what did we do? We’re proposing a model, an assessment model. And the key to remember is that, you know, we are introducing this adaptation to trade migration and innovation. We’ve quantified, we think there’s a lot to learn and do to improve that quantification. So that’s the online process that we’re doing in this paper and probably in the future, thank you.