In this week’s episode, host Daniel Raimi talks with Hannah Druckenmiller, who studies the value of healthy ecosystems and the causes of long-run environmental changes. She joined Resources for the Future as a fellow earlier this month. Elaborating on her various research projects, Druckenmiller describes one study that assesses the economic value of trees, based on how tree mortality shapes property values, air quality, wildfire risk, and more. Druckenmiller also describes an ongoing project, which uses photographs taken by British aircraft during the twentieth century to approximate modern satellite imagery and estimate how environmental resources in Africa have changed over time.
Listen to the Podcast
- The challenge of assessing the economic value of nature: “We think that forests provide a whole different array of ecosystem services, but we don’t have a really good idea of how much they’re worth in terms of dollar value … I think, in general, the challenge with valuing natural resources is that many environmental goods and services—including trees—don’t have a clear price in the market.” (7:19)
- Lessons from tree mortality and property values: “A pretty significant mortality event (you can think of that as like 10 percent of trees in a forest dying) would reduce local property values by 1 to 2 percent and would reduce the value of timber tracts by about $2,500 per acre. These are pretty economically meaningful effects.” (14:53)
- Developing new data sets to answer age-old questions: “Climate scientists often think that the Sahel droughts will be a very close analog for the types of droughts that we’ll see under climate change. It would be really useful to understand how they affected migrations, so we can use that historical knowledge to inform what we think might happen in the future … We plan to pair data on the droughts—this is environmental data that we already have access to—with newly created data on where people were located and how land was used to try and understand some of the social implications that these droughts had in the 1960s and 1970s.” (25:31)
Top of the Stack
- “Estimating an Economic and Social Value for Healthy Forests: Evidence from Tree Mortality in the American West” by Hannah Druckenmiller
- Migrations by Charlotte McConaghy
- “The Lost Canyon Under Lake Powell” by Elizabeth Kolbert
The Full Transcript
Daniel Raimi: Hello and welcome to Resources Radio, a weekly podcast from Resources for the Future. I'm your host, Daniel Raimi. Today, we talk with Hannah Druckenmiller, a new fellow at RFF. This is the second part of our two-part series introducing new RFF fellows, and we couldn't be more thrilled to welcome Hannah to RFF and to Resources Radio. Hannah will tell us about her fascinating paper that estimates the value of forests, not just in the marketplace, but for society. We'll also talk about a project that she's involved in that's using millions of photographs from the 1950s through the 1990s to construct what are essentially satellite images of the developing world from before we had images from satellites. Both projects are really fascinating, so stay with us.
All right, Hannah Druckenmiller, my new colleague here at Resources for the Future, welcome to RFF and welcome to Resources Radio.
Hannah Druckenmiller: Thanks Daniel, and thanks for having me on the show.
Daniel Raimi: Hannah, just like the episode we did recently with our other new colleague, Penny Liao, we're going to ask you a couple of questions about your background and how you got interested in environmental issues and then we're going to talk about a couple different areas of your research. Let's start off by going all the way back to when you were a kid. When you were young, growing up, were you interested in environmental issues and did you have sort of experiences with the natural world that were important for you?
Hannah Druckenmiller: Yeah, absolutely. I've been interested in environmental issues for probably as long as I can remember. I grew up in New York City, but my parents exposed me to the outdoors constantly, and probably the place we spent the most time was the beach, so I always really loved the ocean. We would go swimming and fishing. Really anything to get me out on the water, I wanted to do.
When I was in high school, I was lucky enough to go to a place called the Island School, which is this really unique school that's located on an outer island of the Bahamas. And it's in the middle of nowhere, but it's in an absolutely beautiful location. It's right on the water next to a mangrove. The whole concept behind the school was that learning should be experiential, so instead of learning biology from a textbook, you actually go out and survey the mangrove or snorkel in the coral reefs, and then you come back into the classroom and talk about what you saw and how everything there was interacting.
It ended up being a really, really formative experience for me. It exposed me to all sorts of environmental issues, especially regarding sustainability and around how human and natural systems are interwoven, and I think that's probably the biggest reason why I decided to pursue those topics when I went to college and then to my PhD program.
Daniel Raimi: Wow, that sounds amazing. I want to go back to high school and go snorkeling among the mangroves. It sounds really great. There's two tracks that we often find environmental economists have taken. Some of them kind of start with an interest in the environment and then choose economics as a tool to work on that issue. Then some folks start by wanting to be an economist and then discovering environmental issues, and going that way. Which kind of led the way for you? Was it the environmental angle or the econ angle?
Hannah Druckenmiller: It was the environmental angle. In college, I started out as an environmental science major and I had a focus on oceans, so I got to take all of these amazing classes in marine biology and ocean chemistry, things like the physics of waves, but I also took some courses in marine resource management and I got pretty interested in fisheries.
One of the things I liked the most was that it was at the intersection of a bunch of different disciplines, so you had to understand the underlying biology, but you also had to understand social and political factors in order to manage these resources effectively. I took a class called World Food Economy at Stanford, which is taught by Roz Naylor. She just so powerfully conveyed to me what a useful tool economics was for understanding how systems work and also for effecting change in those systems. That really led me down this path.
My senior year, I started taking a bunch of classes in economics and decided to eventually pursue a PhD in economics. I never ended up doing any research on fisheries, but I think I maintained the general idea that economics is a framework through which you can think about how to optimally manage resources.
Daniel Raimi: Yeah, that's really interesting. We're going to talk about some other research projects in a couple of minutes, but I'm curious given your background whether you've thought about pursuing ocean issues, fishery issues, ocean resource management stuff. Is that something you're hoping to get into at some point?
Hannah Druckenmiller: It absolutely is. I keep having projects on the back burner that are focused on oceans, but one of the things I found really challenging is that it's much harder to find high quality data to work within that sphere. I'm hoping that I can not only contribute to answering some of those questions, but also to produce data sets that help others answer those questions too.
Daniel Raimi: That's interesting. Is the reason that there's so little data because everything's underwater?
Hannah Druckenmiller: Yeah. We can get fairly good counts of natural resources that are on land through surveys. We can do ground surveys or aerial surveys, but trying to count the number of fish that are in a stock in the ocean is hard. We have ways to estimate that; a lot of them are based on how many fish we are pulling out of the ocean, but we have very limited ability to observe what's going on there. It's part of the reason that I've always been interested in oceans. They are so unexplored and unexplained. It's really a unique part of the earth that covers more than 70 percent of surface area, but we have very little idea of what's happening in most of it.
Daniel Raimi: That's so interesting. Well, that's a conversation for another day. Let's move on now and talk about a couple of research projects that you've got going. Instead of talking about oceans, we're going to talk first about forests and then second about the air. Starting with forests, the first question I want to ask you sounds like a really simple question, but it's actually the basis for your job market paper and it's really fascinating. The question is, how much is a tree worth?
Hannah Druckenmiller: That question is what motivated my job market paper, which is focused on estimating the social and economic value of healthy forests. A lot of my work is motivated by the idea that we need to be able to quantify the value of natural resources so that we can know how to manage them, and so that we can weigh the benefits they provide against the cost of environmental protection. For my job market paper, I decided to focus on forests because they're one of our largest sources of natural capital. That's true both in the United States and around the world. We think that forests provide a whole different array of ecosystem services, but we don't have a really good idea of how much they're worth in terms of dollar value.
Unfortunately, we've seen large declines in forest health around the world over the last several decades. Tree mortality rates have doubled in the last 20 years. And so, my paper really tries to understand the consequences of those declines in forest health for human well-being.
I think, in general, the challenge with valuing natural resources is that many environmental goods and services—including trees—don't have a clear price in the market. We could go out there and try to estimate the value of a tree by looking at how much timber costs, and that would give us part of the picture, but we know that trees also provide other benefits. They provide aesthetic value and air purification, and healthy trees protect us against floods and fires. We want to capture all of those benefits—which in economics, we call nonmarket benefits—when we're thinking about the total value of a tree. That's what I try to do in the paper. I try to take into account both the market value of trees and their nonmarket value, so that when we're thinking about how to manage forests, we can weigh that dollar value benefit against the cost of investments and forest health.
Daniel Raimi: That's great. And you do that in lots of different really interesting, technically challenging ways, and we're not going to get into all the details of the methods, but can you give us a thumbnail sketch for the wonks out there how you estimate some of those market and nonmarket benefits?
Hannah Druckenmiller: I won't get too far into the weeds, but I can provide you with a sense of how I measure forest health, which is not straightforward; how I measure economic value; and then how I try to create a causal link between those two things. For forest health, I basically use tree mortality as a summary statistic. I do so for a couple reasons. The first is that we have pretty good data in the United States on tree mortality over time. The Forest Service actually runs a pretty cool survey where they fly planes over almost all forested areas in the western United States and they circle areas where they observe dead trees. So we have these nice annual maps of where tree mortality is occurring and how severe it is. I also chose to focus on tree mortality because it's a pretty stark indicator of forest health, and it's been increasing a lot over the last several decades, so it's something that scientists are interested in understanding the consequences of.
To get at the economic value of trees, again, I'm really focused on capturing both market value and nonmarket benefits. Market value is pretty straightforward because we can just go out there and see what price people and firms are willing to pay for timber tracts. Nonmarket benefits are more challenging. Luckily, the field of environmental economics has spent a lot of time developing methods to estimate nonmarket benefits, and one of the most popular approaches is called hedonics, which is based on the idea that environmental goods and services should capitalize into property values.
You can think of this as the idea that I would probably be more willing to pay more money for a home in an area with lower levels of air pollution because I value my air quality. Similarly, the concept that I would be willing to pay more money for a home in a healthy forest than one in a degraded forest because I think that healthy trees provide me and my family with some sort of benefits. What we can do is we can look at the price premium that homeowners are willing to pay for a higher quality environment, and then that is the dollar value that we assign to that resource. That's what I do in the paper to get at those two different types of benefits. Then the last step is to establish a causal link between forest health and the value that we place on trees.
Daniel Raimi: And then here comes the beetle.
Hannah Druckenmiller: Yes. We really want that link to be causal because we're using this information to hopefully guide policy decisions and so, we don't just want a correlation, which means that we need some sort of random variation in forest health. What I do is I rely on a natural experiment that's based on bark beetles. If you're not familiar with beetles, they're the leading cause of tree mortality in the American West. They're these tiny bugs that burrow in the bark of trees and when they breed, they can cause mortality events.
Something that's really neat about beetles is that their survival is heavily dependent on temperature. In particular, there are temperature thresholds at very low temperatures where we see mass mortality rates in bark beetles because their tissue freezes. So what we can do is we can look at years that had days just above and below these thresholds—those years are pretty comparable in terms of the rest of the weather distribution, but just one additional day below the thresholds causes large differences in beetle survival—and therefore tree mortality. That gives us a way to compare forests that should be similar along many dimensions, but one has very high rates of tree mortality and one has very low rates of tree mortality.
Daniel Raimi: That's great. It's such a clever way to look at it. I remember when I was reading this paper, I was just like having a blast, thinking about these different parts of the West in particular where beetles were surviving or not and how that can tell us so much about the value of these forests. What are some of the key results that you came up with?
Hannah Druckenmiller: The first part of the paper is really focused on establishing this relationship between temperature, beetle survival, and tree mortality. This was not a new idea that I came up with. This theory that temperature should affect beetles, which should affect mortality, is based on modeling exercises that the Forest Service has done for decades. But a lot of these models haven't really been stress tested in the data or we didn’t have a clear mapping of exactly how temperature translates into survival translates into tree mortality. The first part of the paper does that.
Unsurprisingly, I find that beetle population sizes are sensitive to cold temperatures, and that tree mortality is very sensitive to beetle survival, so there's the strong link between very cold days and tree mortality the following summer. I think that's interesting in its own right because with climate change, we're expecting increases in winter temperatures, which would lead to higher rates of beetle survival and higher rates of tree mortality. This is just another thing we need to think about when we're thinking about managing forests in a changing climate. The bulk of the paper is really focused on understanding the consequences of that tree mortality for human well-being. I find that tree mortality greatly reduces the value of timber tracts, so that's the market value of forests, and it also has pretty big impacts on local property values, which again are intended to capture some of these nonmarket benefits.
To give you a sense of magnitude, I find that a pretty significant mortality event (so, you can think of that as like 10 percent of trees in your forest dying) would reduce local property values by 1 to 2 percent and would reduce the value of timber tracts by about $2,500 per acre. These are pretty economically meaningful effects.
I'm also able to look directly at the effect of tree mortality on some specific environmental services. I look at what happens to air quality, wildfire risk, and flood damages when we see mortality events, and I find that tree mortality is actually a strong driver of all three of those natural hazards. This gives us some intuition for why people are willing to pay more for a home in an area with healthier trees, because we have a sense that healthy trees not only provide us with aesthetic value, but might also provide us with hazard protection.
When you add all those things together, I estimate that a tree in my sample is worth about $40 to get at your original question, but it's worth noting that there's huge variety in this value over space. As you might expect, there's much higher value for trees that are located in timber-producing regions and trees that are in areas with high population densities, because more people are exposed to the benefits that those trees provide.
Daniel Raimi: Absolutely. It's so cool to have all of these pieces, these complex moving pieces go into the meat grinder of the analysis and then you come out with $40 for a tree, and I know that's an oversimplification, but I love thinking about it that way. That's one really fascinating area of your research, Hannah. I want to ask you now about another one, which also captured my imagination when I learned about it. This project is all about using millions of photographs taken from aircraft in the middle of the twentieth century of what were then 60 different British colonies, I think mostly in sub-Saharan Africa. What are you doing with all these pictures taken from airplanes and what information are you trying to gather?
Hannah Druckenmiller: Sure. This is a big project that's actually a collaboration with researchers at Stockholm University and the University of California, Berkeley. The main goal is to extend our understanding of where people were located, where infrastructure was and where resources were back in time before data was collected in large-scale or in any sort of systematic way. Part of the motivation is that the research community has really benefited from access to satellite imagery. Starting in around 2000, really high-resolution imagery became widely available, and we were able to take that imagery and make it into maps of human development, of environmental resources, of things that we care about at global scale. Researchers have used these maps to understand relationships between society and the environment. We can look at how deforestation rates have changed over time or we can look at what happens when you add a road to an area, and whether natural resources decline.
This has really kind of transformed our understanding of global change, but a big limitation of this data is that it only dates back a couple of decades. A lot of the questions that researchers are interested in studying span a longer time period than that. The idea of this project is to try and take advantage of these large archives of aerial photography that were taken over the course of the twentieth century to essentially extend the satellite timeline backwards to the 1940s or ’50s. We like to think of it as providing a window back in time to let us see what was happening on the ground before we had really good data collection in a lot of these developing countries.
What we're doing in the project specifically is we have this archive of photos that was taken during the process of mapping the British Empire. Essentially, what the British wanted to understand is where people were located and where resources were located, and how these things were changing over time. We want to understand something very similar. What we're doing is we're taking all of the old images and we're using them to generate data products that map out the location of natural and built capital. One of the really cool things about the archive is that the countries are not only visited once, they're visited multiple times between 1940 and 1990, so we can actually create data sets that span different decades and understand how the locations of people and resources were changing over this time period.
It has turned out to be a really big undertaking. The photos are in boxes in the United Kingdom, as physical prints. The first thing we need to do is get them onto our computer, so there's a whole scanning operation to digitize the archive. Then, the pictures come to us as black-and-white prints of a location in space, but unlike a satellite image that you might download, they don't have any information embedded as to where that image is located in geographic space. That's something that we have to learn from the information contained in the image. We've created this whole machine-learning pipeline that essentially takes these millions of individual images and stitches them together into seamless maps, something like you might see if you opened Google Maps and looked at the satellite layer.
Then the last step is that we want these images to be helpful to researchers and so, instead of just handing them a picture of Kenya in 1950, we want to give them data that they can actually use in their analysis. We need to extract structured information from the imagery. We're applying out-of-the-box machine-learning tools like convolutional neural networks that input the images and output data on road networks or building footprints. We're making maps of land use so we can measure forest cover, croplands, and urban extent. These are data sets that we're lucky enough to currently have access to from about 2000 to 2020, but we think the research community will really benefit from having access to them going much further back in time.
Daniel Raimi: It's so fascinating, this project. I know we won't have time to get into all the details, but it's astonishing to me that you can develop an algorithm to learn where photos were taken without any of that kind of metadata or contextual information. Is it possible to describe, in a general way, how the machine-learning algorithm can ultimately figure out where the photo was taken in space?
Hannah Druckenmiller: I'll note at the beginning that there is still some human input, and we do have some information about where the images are located. What we get is a box full of images, they might be hundreds of images if you're looking at a small country like Barbados or thousands of images if you're looking at a larger country like Kenya. Along with these images, we get a hand drawn map of where the plane flew. You can think of this as a map of Kenya, and then there's a bunch of lines across it that show where the plane was flying, so we know the order of images. What that allows us to do is to use algorithms that can take two images that have overlap and identify common points between them. It's more complicated than this with computer vision, but intuitively, it's like if the computer sees the same road intersection in two adjacent photos, it's going to align those images, so that road intersection is overlapping. We do this for every pair of images in the sample.
But unfortunately, it's not as easy as that, because if you just lay down one image and then sequentially add images on top of that, these small errors in matching really propagate to make something that looks unrecognizable. Our team developed this procedure that essentially optimizes the location of all images jointly, so you can kind of think of this as a person that's trying to align multiple images into a mosaic on their desk and you have to shift each image a little bit and when you shift one, you have to shift another so that it matches, and you do that enough times that finally you get something that you're happy with.
That’s the intuition of what the computer is doing in this case. It builds us a mosaic of the whole country and then we do have to have a person go and place it in geographic space, so they identify points that haven't changed over time—for example, a coastline or a major highway intersection. By finding just a few of those points, we're able to locate the entire image on the modern map.
Daniel Raimi: Wow, that's so cool. You and your colleagues are putting all this time into assembling this information. You've already hinted at some of the research questions that you or others might be able to answer using these data, but for you, are there particular applications that you have in mind?
Hannah Druckenmiller: We do hope that the data will be used across a wide range of disciplines, but personally, I'm most interested in understanding the long-term impact of climate shocks. A nice thing about this data is that it allows us to look at how impacts persist over time, so not just in the next 5 or 10 years, but over half a century. It also allows us to look at climatic events that happened before we had good information on social and economic outcomes.
One event that I'm really interested in studying and that my team plans to work on is the effect of the Sahel droughts on human migration in Africa. These were these decade-long droughts, very severe, that happened in the region between the late 1960s and early 1980s. It’s widely believed that they caused massive famine and displacement of people, but unfortunately, we haven't been able to really study the impact that they had because we don't have good data on where people were located during that period.
Climate scientists often think that the Sahel droughts will be a very close analog for the types of droughts that we'll see under climate change. It would be really useful to understand how they affected migrations, so that we can use that historical knowledge to inform what we think might happen in the future. But again, we just haven't had the data to be able to see what it did to populations on the ground. One thing that we can see in these images is human settlement. We plan to pair data on the droughts—this is environmental data that we already have access to—with newly created data on where people were located and how land was used to try and understand some of the social implications that these droughts had in the 1960s and 1970s.
Daniel Raimi: That's so interesting, and as you say, so important to understand how people are going to move and we know that's going to happen, but we have very little quantitative evidence about exactly how it might happen and trying to tease that out from the past is just so interesting and important.
Hannah Druckenmiller: One thing that we're interested in studying is not just how many people were displaced, but where they went. It's very different policy implications if displaced people go and integrate into the formal sector of cities versus if it leads to growth in slums. Just trying to understand how all of that has played out in the past will hopefully inform how we manage these types of crises in the future.
Daniel Raimi: Absolutely. One more question, Hannah, before we go to our Top of the Stack segment. This is just kind of a nuts and bolts question, which is how did you or your colleagues get access to all these photos and where did the idea come from to do this project? It's so different from most of the other kinds of research projects that we talk about on the show. I'd love to learn a little bit more about its origins.
Hannah Druckenmiller: All credit for that goes to my coauthors at Stockholm University, Andreas Madestam and Anna Tompsett. They discovered the archive and realized the similarities between it and satellite imagery, and had the idea that we can apply all of this infrastructure that the research community has developed to extract information from satellite images to these aerial photographs. They not only found the archive, but raised the funding to digitize it and built a team that is able to assemble the data and also has the economics expertise to examine questions we care about. Most of the team is interested in some way in sustainable development, but people have different areas of expertise. Some people are development economists or focused on infrastructure. We have environmental economists. We're hoping that together we can push forward the fields of sustainable development using this new data set.
Daniel Raimi: That's so cool. Well, thank you so much, Hannah, for coming on the show and introducing us to some of your research. Really looking forward to getting to know it in more detail as we get to work with you, or as I get to work with you, in the months and years ahead.
Let's close it out now with our question that we ask everyone who comes on the show, which is to recommend something that you've watched or read or heard related to the environment or not related to the environment—we're not too picky—but just something that you think is really interesting that you think our listeners would enjoy. I'll start with the latest article in the New Yorker from Elizabeth Kolbert called “The Lost Canyon Under Lake Powell.” It’s in the August 16 edition of the New Yorker. It's all about what's happening in Glen Canyon, which I want to say is in Utah.
As listeners will know, the Colorado River Basin is experiencing a really big drought. It might be a mega-drought, and reservoirs like Lake Powell and Lake Mead are sinking rapidly and that has all sorts of negative implications, but one of the cool things about it is that it is revealing all these wonders in Glen Canyon that have been submerged for decades. In the article, Elizabeth rides a boat around this canyon and explores all these alcoves and talks about them, and it's really fascinating. I'd really recommend that for those of you who are interested in history and geology and drought. But how about you, Hannah? What's at the top of your stack?
Hannah Druckenmiller: That's a great recommendation. I'm looking forward to reading it. One thing I recently read was Migrations by Charlotte McConaghy. It's a book, a work of fiction that takes place in the not-too-distant future, and it takes place during a mass extinction event caused by climate change.
The story is about a researcher who is determined to follow the last Arctic terns on their migration from Greenland to Antarctica. These are these really fascinating birds that make a pole-to-pole migration every year in order to breed. But the book is really about this woman who's a bit broken, she's had a tragic past, and she's navigating a world, which is also sort of broken both ecologically and politically, but somehow it ends up having a pretty hopeful message. I think a lot of it boiled down to resilience, both human resilience and the resilience of the natural world. And so, I found it both a very emotional, but also uplifting book, and it just has these beautiful descriptions of natural landscapes and of all the different animals on Earth, and it makes you think pretty deeply about our role as humans and about the responsibility that we have to other species.
Daniel Raimi: That sounds really interesting. I'll have to check that out. Great. Well, thank you so much, Hannah, once again, a new fellow at RFF for coming on the show and telling us about your fascinating work. We really appreciate it.
Hannah Druckenmiller: Thank you for having me.
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Resources Radio is a podcast from Resources for the Future. RFF is an independent, nonprofit research institution in Washington, DC. Our mission is to improve environmental energy and natural resource decisions through impartial economic research and policy engagement. The views expressed on this podcast are solely those of the podcast guests and may differ from those of RFF experts, its officers or its directors. RFF does not take positions on specific legislative proposals. Resources Radio is produced by Elizabeth Wason with music by me, Daniel Raimi. Join us next week for another episode.