Visions of Sustainable Futures: The Need for a (Multi-)Spatial Lens | CAS Inquire

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09/21/2023 Added

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Patrick Bitterman of the School of Global Integrative Studies gives the CAS Inquire talk in September 2023 around the theme "Sustainable Futures".

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[00:00:01.680]Well, good evening, everyone.
[00:00:03.600]Great to see you all. Thank you so much
[00:00:05.600]for being here and joining us for this presentation this evening.
[00:00:08.800]Whether you're with us here in person or you're joining us by Zoom.
[00:00:13.040]I want to welcome you all to the fourth year
[00:00:16.040]of the College of Arts and Sciences Inquire lecture series.
[00:00:19.880]The organizing theme for the CAS Inquire series
[00:00:22.640]this year is Sustainable Futures.
[00:00:25.680]Our theme, I hope you all will agree, is one
[00:00:29.200]that is both urgent and complex,
[00:00:33.040]but it also expresses optimism and optimism
[00:00:36.720]that I think is inherent to collective agency and our shared abilities
[00:00:40.640]to imagine and to enact a future that will sustain life in all of its diversity
[00:00:46.880]and in keeping with the culture
[00:00:48.600]and really the core identity of the College of Arts and Sciences,
[00:00:52.440]as well as the tradition of this particular lecture series.
[00:00:56.000]This year's theme of Sustainable Futures will be explored through the convergence
[00:01:00.960]of a host of different intellectual orientations and disciplinary
[00:01:05.000]perspectives, including from physics, philosophy, languages and literature,
[00:01:10.800]French in particular, and geology.
[00:01:14.400]And this evening, we have the distinct pleasure to kick off
[00:01:17.080]this important college conversation that's selected by students from the college.
[00:01:21.920]With the presentation from Dr.
[00:01:23.360]Patrick Bitterman an assistant professor of geography
[00:01:26.520]in the School of Global Integrative Studies, who earned his Ph.D.
[00:01:30.880]from the University of Iowa.
[00:01:32.400]And joining us in 2019, after serving a postdoctoral fellow
[00:01:36.120]at the University of Vermont.
[00:01:37.440]Dr. Bitterman is research investigates complex adaptive systems,
[00:01:42.600]geographic information science and environmental governance
[00:01:47.000]with funding support from the NSF,
[00:01:49.400]as well as the National Socio Environmental Synthesis Center.
[00:01:52.960]Dr. Bitterman has published his research in journals such as socio
[00:01:56.120]Environmental Systems Modeling, Ecology and Society, plus one
[00:02:00.920]an Environmental science and policy, just to name a few.
[00:02:05.160]Dr. Bitterman's
[00:02:06.120]interest in understanding how adaptive governance
[00:02:09.960]arrangements affect the trajectory of social and ecological systems
[00:02:15.360]I think will be a wonderful starting point
[00:02:17.880]for our year long conversation in sustainable Futures.
[00:02:21.880]And as you can see here, the title of his talk today
[00:02:25.240]is Visions of Sustainable Futures The Need for a Multi Spatial Lens.
[00:02:30.800]Dr. Bitterman will be speaking for roughly 40 minutes
[00:02:34.080]and then we will reserve the remainder of our time together
[00:02:36.720]to have conversation, discussion, questions and answers.
[00:02:40.320]So please join me in welcoming Dr.
[00:02:41.920]Bitterman.
[00:02:43.200]Thank you, Dean Button and thank you everybody for coming.
[00:02:46.280]How are we all doing?
[00:02:47.680]Good. Excellent.
[00:02:48.720]I see a lot of my students here, which makes me really happy.
[00:02:50.720]The extra credit assignment totally worked.
[00:02:53.640]So, yeah, let me actually advance to the next slide.
[00:02:56.480]Actually.
[00:02:59.800]There we go.
[00:03:00.560]So we don't have a quicker that works.
[00:03:02.040]I'm going to be carrying around a mouse if it looks weird.
[00:03:04.120]So I always have a hard time figuring out how to start talks like this.
[00:03:08.160]So I was thinking about this this morning and trying to figure out
[00:03:11.240]exactly how I'm going to intro this and what I was doing.
[00:03:13.760]I was reflecting on
[00:03:15.240]simply my time since graduate school, thinking back on all the projects
[00:03:18.440]and now what might be that really cool hook that gets everybody super excited.
[00:03:22.080]And as I was counting the projects, I'd realized that
[00:03:24.880]since I started in 2011, my Ph.D.
[00:03:28.440]at the University of Iowa, through my postdoc,
[00:03:31.000]through my first four years, that, you know, I've been funded by NSF
[00:03:35.240]on seven different interdisciplinary projects
[00:03:37.640]that have been explicitly interdisciplinary.
[00:03:39.960]And I come to realize that
[00:03:40.960]I really never learned how to do strictly disciplinary work.
[00:03:44.320]All I've ever done is interdisciplinary work, which I think is really useful.
[00:03:48.960]Maybe it makes me a
[00:03:49.800]little bit less of a geographer, but geography is a big tent, so we're okay.
[00:03:53.600]So what I'm going to talk about today is how we can imagine sustainable futures,
[00:03:57.240]how we can use a multi spatial lens, go sort of beyond the traditional
[00:04:01.800]geographic structures to think about how we might actually improve
[00:04:05.720]our social ecological systems and lead us toward something more sustainable,
[00:04:09.200]more resilient, or just all around more desirable.
[00:04:13.520]So let's see if this is actually going to work.
[00:04:16.680]Okay?
[00:04:17.960]But what I want to actually start with is somewhere a little bit different.
[00:04:21.360]So this is an image from the Working Group
[00:04:24.600]two six assessment report from the IPCC, which came out last year.
[00:04:29.240]And this is like one of my all time favorite scientific figures.
[00:04:32.240]Right.
[00:04:32.720]And the reason why is it shows us that we are right here.
[00:04:37.400]Let's imagine we were right there where I drew that big red arrow
[00:04:40.200]and that there are many, many possible alternative futures.
[00:04:44.080]And if we're going to talk about taking ourselves
[00:04:46.200]towards more sustainable futures or what the IPCC called
[00:04:49.400]more climate resilient futures, which is in the top,
[00:04:52.000]we also have to acknowledge that some of those alternative futures
[00:04:54.960]are, in fact, worse off for us.
[00:04:56.920]They're less sustainable, they are less resilient.
[00:04:59.840]They are less desirable. Right.
[00:05:01.800]But we also need to acknowledge that decisions we made in the past.
[00:05:05.200]So going backwards in time have impacted our opportunities for going forward.
[00:05:10.680]Bad decisions, good decisions have closed off opportunities.
[00:05:14.200]So when we think about going forward, when we identify windows of opportunity,
[00:05:18.560]places of action, we need to be ready to be adaptable
[00:05:22.040]and take those actions such that we can go in the direction we want to go, right?
[00:05:26.960]So that's the way I tend to think about these types of problems,
[00:05:30.280]which means we need a unit of analysis.
[00:05:32.640]And this unit of analysis I'm going to use is over there on the left.
[00:05:35.400]It is what is commonly called the social ecological system.
[00:05:39.280]You may also have heard about this as a social environmental system,
[00:05:42.720]a couple of human and natural system.
[00:05:44.480]They all have different terms, but generally speaking,
[00:05:46.440]the framework is the same.
[00:05:47.840]You have a human component or a social component where all of
[00:05:51.640]our sort of activities are going on.
[00:05:53.720]We have an ecological system
[00:05:55.240]or an ecosystem where we have all the sort of natural processes going on
[00:05:59.080]and then we have connections between the two.
[00:06:00.960]Humans impact the environments, environment impacts
[00:06:03.960]human well-being, right?
[00:06:05.560]So we have to be able to think in this way.
[00:06:07.320]And what that means is when we think about global environmental change,
[00:06:10.600]whether that's climate change, land use change, water quality, degradation,
[00:06:15.160]whatever it is, we have to acknowledge that global change is an inherently social
[00:06:19.080]process, right?
[00:06:20.320]Yes, there are the natural things going on, but the rapidity
[00:06:23.520]of the more recent change is in fact because of us,
[00:06:26.760]which means if it's because of us and if we change our behavior,
[00:06:29.720]we can in some cases at least begin to make the situation better.
[00:06:34.640]But this also means is that we need to engage in multiple disappoints, right?
[00:06:38.880]If we're going to talk to people, engage with policymakers, yes,
[00:06:41.800]we need the natural scientists, the physicists, the hydrologists,
[00:06:45.000]but we need the geographers.
[00:06:46.560]We need people in communications.
[00:06:48.440]We need people in psychology to be able
[00:06:50.280]to make the case and lead us towards these more sustainable futures right.
[00:06:54.040]And I would argue that human and social science cannot be secondary,
[00:06:58.520]it can't be ancillary, it can't be supplementary.
[00:07:00.640]It has to be embedded in what we do from the start.
[00:07:03.840]If I had a nickel for every time I got an email
[00:07:06.520]or a phone call from a natural scientist that says We have this grant proposal
[00:07:10.000]and we figure it out, we have to talk to people.
[00:07:12.000]Do you want to join the team?
[00:07:13.240]I wouldn't be a rich man, but I have a heck of a lot of nickels.
[00:07:15.880]Right.
[00:07:16.440]So we have to engage with people very, very early on.
[00:07:19.640]Right.
[00:07:20.360]Which also means that if we're going to engage, we can use our tools,
[00:07:24.120]our spatial science tools, our engagement with policymakers to identify
[00:07:28.360]where we are getting it wrong, where we have disconnects, or what
[00:07:31.760]sometimes people call mismatch is issues where we are mismatch spatially
[00:07:36.120]mismatch in scale or even mismatch in function,
[00:07:38.640]where we don't have the resources to actually get the job done.
[00:07:43.320]So this was supposed to be a transition, but it's not going to be enough.
[00:07:46.600]Okay.
[00:07:47.200]So traditionally what we think about
[00:07:48.880]when you think about geographic information science
[00:07:51.120]or how we are going to integrate different conceptualization of space
[00:07:54.840]into our models, we tend to think of what's on the top row.
[00:07:57.080]Right? We think of different types of data.
[00:07:58.880]We think of raster data sets that model continuous processes like precipitation
[00:08:03.240]or temperature or something going on in a way,
[00:08:06.120]or we think of vector datasets that delineate administrative
[00:08:09.400]areas like counties or cities or things like that.
[00:08:12.600]Countries.
[00:08:13.480]Or we can think about point cloud data where we have wider that we can do any
[00:08:17.960]like digital elevation models or tree canopy or things like that.
[00:08:21.720]And that is all encapsulated in what I teach our geographic information science.
[00:08:26.600]But I would argue we're going to actually step out of that
[00:08:29.320]and become policy relevant and do real spatial science.
[00:08:32.440]We need to expand the types of space that we integrate into our system,
[00:08:36.160]including network science, figuring out how people and places are connected,
[00:08:40.000]and how our energy and information and goods and services
[00:08:43.040]flow from one place to another.
[00:08:44.720]We need to think about an attribute space where we can map tradeoffs
[00:08:48.360]about different types of functionality inside of our social ecological system
[00:08:52.080]and compare different curves and multidimensional outputs.
[00:08:55.320]But we also need to be able to span a space that includes
[00:08:58.680]public and private entities.
[00:09:00.840]And because, you know, having access and information to different groups
[00:09:04.760]changes quite a bit.
[00:09:05.960]But we also have to be able to bridge that sort of academic ivory
[00:09:08.920]tower, the science based design with policymakers.
[00:09:12.000]Right.
[00:09:12.360]If we're going to actually have some sort of impact.
[00:09:16.320]So I'm going to lead you through
[00:09:17.800]essentially three short cases about how I have done this in the past.
[00:09:21.840]So this the overall themes draw on my work
[00:09:25.480]in the Chesapeake Bay, in India, in California.
[00:09:28.400]But the case is I'm going to show you today are from the Lake Champlain Basin.
[00:09:32.200]So this is the Lake Champlain based in here on the right.
[00:09:34.920]It's quite large
[00:09:36.480]and it encompasses a good chunk of Vermont and a little bit of eastern New York
[00:09:41.560]and a tiny little bit of Quebec,
[00:09:43.280]which actually ends up being quite substantial
[00:09:45.200]because essentially it's highly, highly intensive agriculture.
[00:09:49.680]So the case is here, we're going to look at three things.
[00:09:51.520]The first, we're going to look about how we actually put different types of models
[00:09:54.960]together to try to answer complex questions.
[00:09:58.320]Think about space in terms of a lot of those different types of
[00:10:02.160]those different spatial datasets.
[00:10:03.560]We integrate and think about how we can identify
[00:10:07.200]so that policymakers can try to solve temporal disconnects
[00:10:10.640]or mismatches in the functional functional mismatches as well.
[00:10:14.200]The second one, I'm going to sort of transition
[00:10:16.040]into a different type of modeling
[00:10:17.880]where we think about how we can model interactions and collaborations
[00:10:21.480]among different municipalities, different towns and cities
[00:10:23.960]across Vermont that have been charged
[00:10:26.040]with actually addressing a lot of the water quality issues.
[00:10:28.840]And then lastly, I'm going to get into a third type of space
[00:10:31.880]in network space, figuring out how different individuals, organizations,
[00:10:36.920]institutions, why collectively term actors work together, or don't work together
[00:10:41.400]more commonly to try to address some of these problems, too.
[00:10:44.720]Right.
[00:10:44.920]And here what we can identify through this other use of space is a disconnect
[00:10:49.080]in how we share information and how these different actors work together.
[00:10:54.680]So here I'm a little washed out on the screen here,
[00:10:57.960]but this is what is called a harmful cyanobacteria.
[00:11:01.800]BLOOM And this is sort of at the center of a lot of these social
[00:11:05.320]ecological problems in Lake Champlain, in the Lake Champlain Basin.
[00:11:09.040]Right. So harmful cyanobacteria blooms.
[00:11:11.200]You might also hear this call, that group blue green algae or a hab.
[00:11:16.640]We sort of collectively call these C.I.A. Habs.
[00:11:18.760]And this is a picture of one in Mrs.
[00:11:20.600]Koi Bay, which is in the far northeastern corner of Lake Champlain.
[00:11:23.920]It's a particularly shallow bay.
[00:11:26.040]Cyanobacteria are fueled by increases in temperature,
[00:11:29.520]but also increases in nutrient load
[00:11:31.800]that come primarily from over application of nutrients from agriculture.
[00:11:35.920]In this case,
[00:11:38.160]which champlain's sign of Habs are phosphorus limited,
[00:11:41.320]which means they are more responsive
[00:11:42.920]to changes in phosphorus than they are to other nutrients like nitrogen,
[00:11:46.800]which would be the case in more of the Midwest.
[00:11:51.320]And science hubs are a problem for a lot of different reasons.
[00:11:54.040]First, they degrade the aquatic ecosystem.
[00:11:56.640]They reduce the amount of dissolved oxygen in the water column, which makes it hard
[00:12:00.320]for a lot of aquatic life to continue living.
[00:12:03.200]It's also a public health issue.
[00:12:05.040]People can get sick, essentially.
[00:12:07.440]As far as I know, nobody's actually died from cyanobacteria,
[00:12:11.000]but they do produce a toxin called microbe system
[00:12:13.920]that is highly toxic to some animals every year.
[00:12:16.320]There's always a report about some dogs that went swimming at a beach
[00:12:20.040]where there was a sign of have they ingested the cyanobacteria?
[00:12:23.720]They got persistent poisoning and unfortunately, they died.
[00:12:26.880]So there's a health component to it.
[00:12:28.840]But also there is an economic component.
[00:12:30.920]So when you have a sign, a bacteria tab like this,
[00:12:34.280]what happens is you close the beaches, you can't go fishing.
[00:12:38.320]So all of a sudden, the tourism that Vermont relies on
[00:12:41.720]so much that money goes away or at least decreases substantially.
[00:12:45.280]But there's also been work.
[00:12:46.520]I've had some colleagues that have modeled
[00:12:48.080]the impact of these types of seasonal Habs on home prices
[00:12:52.440]because those big fancy houses on a bluff overlooking the lake.
[00:12:56.160]The people there don't want to pay to look at a green lake.
[00:12:58.920]They want to pay to look at a nice clear water appropriately.
[00:13:03.240]So what that means is their property values go down, which means there's fewer
[00:13:07.000]tax receipts, which again is an economic problem for the state.
[00:13:10.040]So all of these things are connected around this one particular phenomenon.
[00:13:15.120]But it's not just in Vermont.
[00:13:16.400]So last week, I actually grabbed the
[00:13:21.720]the map here of all the Habs across the US and the shaded states.
[00:13:25.200]It's not like they don't have harmful algal blooms going on right now.
[00:13:28.200]They just don't publicly report their data.
[00:13:30.320]But we do here in Nebraska, and it's actually changed since I last looked.
[00:13:34.560]But this morning I actually grabbed a screenshot
[00:13:36.960]and we have one tab with micro in here.
[00:13:40.800]We got 18 parts per billion, which doesn't sound like a lot,
[00:13:44.760]but the the threshold for health impacts is eight parts per billion.
[00:13:48.600]So we're over twice as toxic here.
[00:13:51.720]I think this is up near Norfolk.
[00:13:53.600]Willow Creek is so it's a problem everywhere.
[00:13:56.640]And it's not even just here in the US.
[00:13:58.880]This is a global problem as well.
[00:14:00.200]And it's getting worse as we're going to see here in a little bit.
[00:14:03.440]Right.
[00:14:03.920]So let's actually look at what this looks like.
[00:14:06.120]So what I've done here is I've mapped the Vermont portion
[00:14:08.880]of the Lake Champlain Basin on the left and on the right.
[00:14:11.480]I've zoomed in to Mrs.
[00:14:13.360]Québec, and it's actually a little hard to see
[00:14:16.960]because the color of this is quite day
[00:14:20.320]is the same color as the land, and it's not supposed to be right.
[00:14:24.720]So what we have is we have an ongoing bloom here.
[00:14:28.080]Right?
[00:14:28.480]What we're looking at here is an image from the ultra sensor
[00:14:32.200]on top of the Sentinel three platform, which is way up above our heads right now.
[00:14:35.920]So this is a true color image.
[00:14:37.320]So, again, one type of spatial data, but working with Rick Stump's team at NOA,
[00:14:43.600]we got access to a bunch of different types of datasets
[00:14:46.760]where they actually processed a lot of their a lot of their images,
[00:14:50.000]and they were able to develop
[00:14:51.160]a cyanobacteria index that maps the extent and the severity.
[00:14:55.440]So we can actually look at how the bloom has changed over time, right?
[00:14:59.440]So there's a lot of processing going on.
[00:15:02.560]I obviously I didn't write the index.
[00:15:04.000]I definitely don't run the satellite, but we've done some analysis with the extent
[00:15:07.640]and what happens is we can actually see a temporal pattern.
[00:15:10.680]So over on the left side here is what we tend to see
[00:15:13.000]is a small signal in the late spring months.
[00:15:16.920]So what tends to happen is a bunch of farmers illegally go ahead
[00:15:21.240]and they spread manure on frozen farm fields in the winter.
[00:15:25.120]Right.
[00:15:25.360]Because they have to get rid of all their pig manure and cows, too.
[00:15:29.440]And what they do is they want to use it as fertilizer.
[00:15:31.560]But what happens is when that frozen land melts, when it thaws in
[00:15:36.160]in the spring, what happens is all that frozen pig stuff goes ahead
[00:15:39.920]and runs off and ends up in the rivers and streams and ends up in the way.
[00:15:43.320]So we get a lot of nutrients and we get this blip right.
[00:15:45.960]We get harmful
[00:15:46.680]algal blooms in the spring, but that's actually kind of the baby boom
[00:15:50.600]because what tends to happen is much more significant in July,
[00:15:54.040]August, September is as the week gets warmer, the lake itself stratified
[00:15:59.320]and we don't get as much mixing and we end up getting these very, very
[00:16:02.520]large blooms fed in part by the existing
[00:16:06.680]the existing nutrients, but also by the warmer water.
[00:16:09.120]So we get this big spike, this very large extent in in the late summer as well.
[00:16:14.880]And because we have a Time series, we can actually plot this over time.
[00:16:18.720]So we see right all the way from 2016 to 2023.
[00:16:21.560]Obviously our year is not quite done yet, but there is an ongoing bloom today.
[00:16:26.720]2019, pretty good year. 2020. Okay.
[00:16:29.800]But we generally see the same pattern, right?
[00:16:32.520]So we understand the ecology.
[00:16:34.560]Now we have to understand the actual the policy,
[00:16:37.720]the political response to this, this problem.
[00:16:40.800]So I have a similar timeline and I'm not going to go through every single bit here.
[00:16:44.960]But just to show you
[00:16:45.760]that there has been a lot of active effort to try to address this problem.
[00:16:49.720]And the big key actually started in 2002.
[00:16:52.360]So the Environmental Protection Agency, the EPA, they approved a regulation
[00:16:56.400]commonly called the MDL, which stands for a total maximum daily load
[00:17:00.960]and MDO regulations, essentially a cap.
[00:17:03.760]They put a limit that says there can only be a certain amount
[00:17:06.840]of different types of criteria pollutants going into waterways.
[00:17:09.840]There's two deals for phosphorus, for nitrogen, for sediment,
[00:17:13.200]for other types of pollutants.
[00:17:14.840]In here, we're concerned about the phosphorus one,
[00:17:17.520]and everything was going okay for a while.
[00:17:19.720]And then in 2008, there was a legal challenge to the term deal.
[00:17:23.240]And usually when we see legal challenges to environmental policy,
[00:17:26.680]we see some organizations saying this is going to be too much of a burden.
[00:17:30.320]This is going to have an economic cost.
[00:17:32.320]You know, this is government overreach.
[00:17:33.800]This is a problem right.
[00:17:36.600]In Vermont, it was different.
[00:17:37.760]The Conservation Law Foundation sued and they said your TMD was not strong enough.
[00:17:42.520]It's not going to do its job.
[00:17:44.800]So what happened then is the EPA and the COP, they got together
[00:17:48.440]and they started working it out and they agreed to a new process in 2010.
[00:17:53.640]And then in 2011, everything stopped, right?
[00:17:56.360]Hurricane Irene came up the East Coast and very suddenly Vermont
[00:18:00.040]did not have a water quality problem.
[00:18:01.680]They had a water quantity problem
[00:18:04.120]and a lot of infrastructure got washed away.
[00:18:06.560]All the focus in a very small state with a very small budget
[00:18:09.720]was focused on making sure they were recovering from the flood.
[00:18:12.960]So it took them four years to fully recover and some would argue
[00:18:16.800]they're not recovered yet.
[00:18:17.640]In fact, they just had a very big flood this summer
[00:18:20.560]and they established working with EPA, working with the state,
[00:18:24.200]they established what's called Act 64, which is the Vermont Clean Water Act.
[00:18:28.880]And then the Vermont Clean Water Act went into
[00:18:31.200]a went into effect in 2015.
[00:18:34.360]The TMN deal from EPA went into effect in 2016,
[00:18:38.560]and then a focus that we're going to get into later.
[00:18:40.720]There was a new piece of legislation in 2019 as well.
[00:18:44.080]So this is the sort of time frame, all of the other stuff is important,
[00:18:46.800]right?
[00:18:46.920]Because if we want to think about futures,
[00:18:48.600]we also have to understand the context of where we came from right.
[00:18:52.560]The sort of the social, the political setting policy
[00:18:55.680]setting that preceded this that led us to where we are now.
[00:18:59.840]So real quick, when you look at our first case
[00:19:02.040]of integrated modeling to understand the dynamics inside of Mrs.
[00:19:05.080]Quebec, we're going to use continuous data, continuous processes,
[00:19:08.640]we're going to use raster
[00:19:09.360]datasets, we're going to integrate 3D models of the aquatic ecosystem,
[00:19:13.320]and we're going to get into
[00:19:14.120]that attribute space to sort of understand some of the tradeoffs
[00:19:17.160]and some of the changes that are going to happen over time.
[00:19:20.080]So this is my beautiful cartoon.
[00:19:22.200]I'm sorry for my artistic ability or the lack thereof,
[00:19:25.120]but this is like a cross section of the lake.
[00:19:27.400]Okay.
[00:19:28.200]So what we see here is we have in normal conditions what I would call
[00:19:31.280]measured trophic conditions or sort of middle level conditions.
[00:19:34.680]We have, you know, relatively cool air.
[00:19:36.960]We have a decent amount of precipitation and we don't have that much discharge
[00:19:40.960]of that much nutrients coming into the system.
[00:19:43.960]So in the sort of the normal case,
[00:19:46.120]what we have is we have relatively low concentrations of nutrients.
[00:19:49.520]It's cold.
[00:19:50.240]So we get this nice sort of mixing system going on here.
[00:19:54.040]So the water is very well-mixed
[00:19:56.400]and if we have low nutrients, that means we don't get that many say cyanobacteria.
[00:20:00.680]So the core failed, which is how we measure the cyanobacteria is low.
[00:20:05.000]And then what that means is we have higher dissolved oxygen.
[00:20:08.080]So we have a decently healthy aquatic ecosystem, right?
[00:20:11.040]That's how things should be.
[00:20:14.480]But things change, right?
[00:20:16.080]So when we get into the summer and as climate change is driving
[00:20:19.720]temperatures, the mean temperature higher and higher and higher,
[00:20:22.600]things get different.
[00:20:23.640]So when we get more nutrients with the discharge entry into the system,
[00:20:28.240]right, the amount of available phosphorus goes up
[00:20:30.960]or when it gets warmer, we tend to get sort of bands
[00:20:34.600]of different temperatures throughout the week.
[00:20:36.360]So the top area is very warm, very hot, and then it gets warmer
[00:20:40.120]and then it gets a little bit cooler
[00:20:41.080]and a little bit cooler and a little bit cooler.
[00:20:42.360]And so you get to the bottom.
[00:20:44.040]But overall, the lakes are getting warmer.
[00:20:46.400]And the real tricky thing here is as the lake gets warmer,
[00:20:49.560]the way in which iron binds to phosphorus changes
[00:20:53.240]and iron is more likely to give up the phosphorus.
[00:20:55.840]So all the phosphorus that's already been trapped
[00:20:57.880]in the sediment from the west,
[00:20:59.160]100 years of pollution, more of that gets put back into the water column,
[00:21:03.440]which feeds the blooms, which increases the amount of chlorophyl
[00:21:07.560]which dissolved excuse me, what was the amount of D.O.
[00:21:10.800]which causes more harm to the aquatic ecosystem?
[00:21:13.400]Right.
[00:21:13.560]All of these things are connected.
[00:21:17.480]So in the TMD, out in the regulation, EPA
[00:21:21.400]working with the state, working with an environmental consulting group,
[00:21:25.400]they actually did some modeling and they said to get down
[00:21:28.200]to a standard of 0.0 to 5 milligrams per liter,
[00:21:31.560]we need to reduce the amount of phosphorus going into this Quebec.
[00:21:35.160]They broke it down by sector.
[00:21:36.360]But the biggest, the most important number over there is 65.6.
[00:21:40.960]If I could talk 64.3%.
[00:21:43.400]They said we have to reduce phosphorus by 64.3% in order to hit our goals.
[00:21:48.720]All right.
[00:21:49.360]So all of the efforts of the state from Act 64 through the time deal
[00:21:54.960]always about reducing the amount of inputs into the lake,
[00:21:57.680]all targeted at one number based on surface inputs.
[00:22:01.880]And that's going to be important A second.
[00:22:04.720]So right
[00:22:06.200]when I was working at UVM at the University of Vermont, we had a big team,
[00:22:10.000]we had climate scientists, we had hydrologists, we had geographers,
[00:22:14.480]we had me, we had
[00:22:17.480]Wendy scientists, we had this big team and we had a series of models, right?
[00:22:20.880]And we had this big complex system
[00:22:22.960]which requires a big series of complex models to begin to answer the question.
[00:22:26.640]All right.
[00:22:27.000]So we stuck them all together, and the details of that aren't
[00:22:29.400]super important, but what we end up with is a computer system, right?
[00:22:33.280]And we have a bunch of Nile dials and knobs and levers
[00:22:36.280]that we can pull to simulate different conditions.
[00:22:39.760]And the big conditions are the big question is, well, how will climate change
[00:22:44.360]interface with these proposed changes and loads to the surface,
[00:22:48.720]but also all the phosphorus that's already trapped in the sediment
[00:22:51.640]that we know is there.
[00:22:52.880]That is more problematic as the climate gets warmer.
[00:22:55.840]Right.
[00:22:56.400]And how is that actually going to have outputs relative
[00:22:59.040]to our water quality targets?
[00:23:01.800]So a whole bunch of data up here.
[00:23:03.960]But the key here, what I want to show you is what we have here on the x axis
[00:23:07.720]is we have a series of scenarios, scenarios based on
[00:23:11.440]because we have the knobs, proposed reductions in phosphorus.
[00:23:14.840]These proposed reductions in phosphorus are based on different land use scenarios.
[00:23:18.320]So changes in forest, changes in agriculture, changes in urban lands.
[00:23:22.560]All right.
[00:23:22.960]So we have zero 20, 40% reduction.
[00:23:25.880]We have that magic number of 64.3.
[00:23:29.120]We have 80%.
[00:23:30.040]And let's just crank that knob all the way and turn off all of the phosphorus
[00:23:33.960]artificially. Let's see what's going to happen right now.
[00:23:36.880]What we found here is because the TM DL is a total maximum
[00:23:40.800]daily load, we can actually measure what the average concentration is daily.
[00:23:45.720]So where we want to be is down here at 0.0 to 5 milligrams per liter.
[00:23:51.880]But what our simulation system, our big integrated assessment model says
[00:23:55.680]is that almost all the time, right, Almost all the time,
[00:23:59.080]and especially here with this team.
[00:24:00.360]MDL we are above our threshold about 95% of the time.
[00:24:04.600]We are above our threshold.
[00:24:06.560]And even if we took all of the phosphorus down to nothing coming out of the surface
[00:24:11.360]water, we are still almost three quarters of the days
[00:24:14.640]over the summer we are over our threshold.
[00:24:17.640]That's a huge problem, right?
[00:24:19.200]Because we've set our policy.
[00:24:20.440]We've taken the entire apparatus of EPA, of the Agency of Natural Resources,
[00:24:25.480]the AFN, which is the agricultural agency in Vermont,
[00:24:28.680]and pointed it at towards hitting that goal.
[00:24:32.880]And the reason why I don't want to overwhelm you
[00:24:34.680]with too many graphs here is because even as we reduce the amount
[00:24:38.880]of the surface inputs, which is the white or green earth can be white or blue,
[00:24:43.960]what we still have is we have that long legacy.
[00:24:46.720]We have literal decades of pollution that have accumulated in the sediment,
[00:24:51.920]and as it gets warmer, they are going to more easily release that
[00:24:55.400]phosphorus, which are going to continue to fuel blooms.
[00:24:58.560]Even if we hit our policy goals
[00:25:02.280]from now.
[00:25:03.440]Last graph for this one.
[00:25:05.160]That's, you know, this goal of 0.0
[00:25:07.840]to 5 that's grounded in science, but that's not like that's
[00:25:11.080]what's hosted on some sign or some website all that often what people
[00:25:15.360]what policymakers with the public really see are signs like this
[00:25:19.000]beach closed due to cyanobacteria, right?
[00:25:21.480]That is the public signal of this problem.
[00:25:24.840]Right.
[00:25:25.480]So what we were able to do is four different climate scenarios,
[00:25:28.400]and I'm only showing one of them there,
[00:25:29.680]which is the moderate warming scenario, RCP 4.5.
[00:25:33.280]We were actually able to
[00:25:35.640]simulate how many additional
[00:25:37.680]days would beaches be closed
[00:25:41.480]over some baseline.
[00:25:43.000]And what we found here is that the orange bar here,
[00:25:45.760]which is the TMD, well, essentially there's no meaningful statistical change.
[00:25:50.160]The interval contains zero, right?
[00:25:52.800]So even if we were to hit that goal of 64.3%,
[00:25:57.640]there would be no obvious like public facing change the beaches would be closed
[00:26:01.600]just as often because the chlorophyl concentrations
[00:26:04.320]would be just as high as they otherwise would be.
[00:26:07.240]Even if we. Yeah, well, not if we did nothing.
[00:26:09.960]If we did nothing.
[00:26:10.600]You're talking about almost two weeks of additional days, right?
[00:26:14.040]So it's not nothing, but it's not getting us anywhere near our goal.
[00:26:18.720]So the story here from my first case, right,
[00:26:21.560]is that we can build these big fancy models.
[00:26:24.640]Right.
[00:26:25.560]But what it tells us here is that our policies
[00:26:29.120]that are solely focused on external loads, right?
[00:26:31.800]If we have a single target, they are insufficient.
[00:26:35.160]And all of this work we're doing only approximately offsets climate change.
[00:26:39.000]Right.
[00:26:39.800]And even if we were to get rid of everything, we would only achieve
[00:26:43.240]a moderate gain and really only in the summer months.
[00:26:46.480]So we talk about mismatches and disconnects.
[00:26:48.920]We have policy aimed at the problem.
[00:26:53.160]We have a policy aimed at what is perceived to be the problem,
[00:26:56.040]but we are totally missing the root cause.
[00:27:00.400]Right?
[00:27:00.960]So the challenge here is because we know we have this long legacy
[00:27:04.560]and we know that even if we do an exceptional amount of work,
[00:27:08.040]we're not necessarily going to see the impacts of that.
[00:27:11.760]Right. That's a big problem, right?
[00:27:13.800]How does effective public policy and just the general public cope
[00:27:17.880]with the fact
[00:27:18.680]that we're going to do all this work, spend all this money, increase
[00:27:21.560]taxes, probably,
[00:27:22.920]and we're not going to see the impacts because it's not like
[00:27:25.680]we're doing anything wrong.
[00:27:26.840]It's just that we're not doing enough
[00:27:28.080]because we have to fight against these increased impacts of climate change.
[00:27:31.960]We have to be able to consider all these systems simultaneously.
[00:27:36.480]So let's think about how we might do that.
[00:27:38.800]So the second case here is integrating a different type of space.
[00:27:42.560]So we have network space, which essentially is
[00:27:45.200]how do we understand how different things are connected, right?
[00:27:48.360]In this case, I'm going to be talking about
[00:27:50.040]how different towns and cities or sort of collectively municipalities
[00:27:53.640]are connected and choose to collaborate over space.
[00:27:56.880]We have an attribute space because again, we have to think about tradeoffs.
[00:28:00.520]We're going to different domains.
[00:28:01.800]So we got academics interfacing with policymakers.
[00:28:04.680]I was the academic in this case,
[00:28:06.640]and then we have to try to minimize spatial mismatch
[00:28:09.080]to try to recognize the fact that we have drawn ad hoc lines that say
[00:28:12.960]this town is here, that totally mismatch with the underlying
[00:28:16.080]environmental processes.
[00:28:17.160]The hydrology.
[00:28:19.920]So let's zoom out for Mrs.
[00:28:22.400]Quite Bay and think about the basin scale.
[00:28:24.720]So on the left, what I've done is I've mapped
[00:28:27.000]the phosphorus inputs that end up in the bay.
[00:28:29.880]The darker shades are where
[00:28:31.160]the phosphorus concentration, in this case the phosphorus yield is greater.
[00:28:35.280]So there is a spatial pattern.
[00:28:36.480]So we need to be able to think sort of geographically, but like I said,
[00:28:40.360]we have to be able to bridge the academic space and the policy domains.
[00:28:44.520]And here the policy domain is actually trying really hard.
[00:28:48.000]So 26, 2016, the state of Vermont has invested
[00:28:51.880]almost $350 million in this problem.
[00:28:55.520]Right.
[00:28:56.080]Which for a small state is a significant amount of money.
[00:28:59.120]There's more people in Omaha than there are in the state of Vermont.
[00:29:01.560]Right.
[00:29:02.200]It's a relatively small tax base, but yet they're doing a lot of work.
[00:29:06.520]So when I say it's not enough, it's
[00:29:07.960]not because they're not trying hard enough, it's
[00:29:09.520]because the scope of the problem is so big.
[00:29:12.080]All right.
[00:29:13.280]So one of the things I really appreciated about working in the state of Vermont
[00:29:17.160]is because it's so small, I had a lot of access to policymakers
[00:29:20.320]and a lot of the policymakers were scientists
[00:29:22.280]themselves, physical scientists, social scientists. Right.
[00:29:25.760]In fact, the secretary
[00:29:26.840]of the at the Agency of Natural Resources is an environmental engineer.
[00:29:30.600]Right.
[00:29:30.840]So she got the science.
[00:29:32.040]She understood what was going on.
[00:29:34.080]And we could have conversations based on empiricism, which is a treat.
[00:29:39.320]Right.
[00:29:40.280]So what they recognized at the time, so this was back in 2018
[00:29:44.920]that the efforts under their current regulatory authority were insufficient.
[00:29:49.040]They knew they weren't going to get it done,
[00:29:50.720]and the way in which they were funding projects wouldn't scale right.
[00:29:54.480]The policy system was not set up to fix this type of problem.
[00:29:59.080]So and Secretary Moore is the one of the top of the cartoon.
[00:30:03.240]She's a very, very nice person. Great.
[00:30:06.200]So let's think about how things work, right?
[00:30:08.680]So that current model that was not scaled, it looks something like this.
[00:30:12.560]So each municipality was charged with reducing phosphorus load in there
[00:30:18.400]within their boundary boundaries that do not fit actual watersheds.
[00:30:22.560]They don't actually fit the hydrology.
[00:30:24.120]So Richmond, Stowe, Montpelier, these are three sort of
[00:30:28.440]examples of them.
[00:30:29.320]They're all within the Winooski watershed, right?
[00:30:32.160]They all have a goal, each and every one of them.
[00:30:34.880]And the other 123 too.
[00:30:37.200]What that means is the state has said
[00:30:39.600]so you have to figure it out in your own backyard.
[00:30:42.000]Richmond You got to figure it out in your own backyard.
[00:30:44.120]Montpelier You got to figure it out in your own backyard, right?
[00:30:46.680]So they, with limited resources, apply.
[00:30:49.800]Essentially, they figure out
[00:30:50.680]the project that they want to do and they apply to the state for funding.
[00:30:54.520]So essentially they're writing a grants.
[00:30:56.720]The state then takes all of these different competing
[00:31:01.320]proposals and says, let's do this one, let's do this one.
[00:31:04.440]So they have a prioritization less right,
[00:31:06.560]and then they go back to the state and they said, you first, here's
[00:31:09.240]your money next, here's your money, things like that.
[00:31:12.520]And that does okay.
[00:31:14.520]So the thing is, a lot of these towns are super, super small.
[00:31:16.920]We're talking, you know, a couple thousand people at most.
[00:31:19.920]They don't have a lot of resources.
[00:31:21.520]They don't have the staff, they don't have the money.
[00:31:23.760]They don't have all the equipment necessary.
[00:31:26.920]So in working with the state government, there was a question,
[00:31:31.120]what if we did something different?
[00:31:32.680]What if we tried to change our policy future in pursuit of changing our social
[00:31:37.600]and our environmental future right to try to get out of the trap
[00:31:41.120]that we're in with respect to phosphorus in the lake?
[00:31:44.200]So the question is, is it what if we actually create
[00:31:47.240]a different type of geography, a different type of space,
[00:31:50.240]or what if we regionalize collaborative governance and legally allow
[00:31:55.440]Richmond, Stowe, Montpelier and whatever other city that makes sense,
[00:31:59.080]to work together, to share resources, to plan together,
[00:32:02.760]to try to solve the problem collectively rather than everybody go, Oh, well,
[00:32:08.200]all right.
[00:32:08.880]So we built a different type of model.
[00:32:10.760]It was what's called an agent based model.
[00:32:12.640]I'm going to spare you the details of exactly how that works.
[00:32:15.960]But the idea there is we can actually, inside of a computer simulation,
[00:32:19.800]represent each of those individual municipalities as a computer actor.
[00:32:23.800]Right.
[00:32:24.480]And we can give them their own decision rules.
[00:32:26.320]We can give them their own levels of resources.
[00:32:29.160]We can do all kinds of different sort of analysis with them. Okay.
[00:32:32.080]And then we can tell them, go find a buddy,
[00:32:34.840]figure out how to work together under prescribed types of policy.
[00:32:39.480]So the big takeaway here is that because we're working so closely with the state,
[00:32:42.960]because we have successfully bridged the academic and the policy domains,
[00:32:47.040]we get access to a lot of data that we would not otherwise get right.
[00:32:50.800]So we had essentially data on every single project
[00:32:54.440]which had been funded by the state.
[00:32:55.720]To that point, we knew the financial resources, we knew the human resources,
[00:33:00.480]we knew how they were prioritizing which projects to favor,
[00:33:03.960]which ones not to favor.
[00:33:05.640]So we were able to build that into our computer model.
[00:33:08.240]Right.
[00:33:09.040]And so what we did is we actually did the simulation.
[00:33:11.080]We looked at different scenarios and we said, how is this going to impact
[00:33:14.320]the amount of load?
[00:33:15.560]The amount of phosphorous
[00:33:17.160]reduced to the landscape first can be reduced to the lake, right?
[00:33:21.320]So we had scenarios, right?
[00:33:22.560]Ah, no case is essentially business as usual, right?
[00:33:25.880]So everybody doing everything for themselves.
[00:33:28.360]So what I have here is two big categories.
[00:33:30.000]The main activities when you're doing
[00:33:31.440]one of these project is first you have to planning.
[00:33:33.840]So you have to get those city planners, you got to get the engineers, everybody
[00:33:37.160]in a room or after covered everybody on Zoom, right,
[00:33:40.120]and talking to each other to figure out what they're going to do.
[00:33:42.800]But that's the first stage.
[00:33:44.280]The second stage is you got to do the actual work.
[00:33:46.200]So you got to get your dump trucks and your backhoes
[00:33:48.960]and you actually physically have to do that.
[00:33:50.880]And for a lot of these small towns, they don't have the human resources
[00:33:54.360]or they're relying on consultants and then they don't have a lot
[00:33:57.480]of the actual physical infrastructure to do a lot of this work.
[00:34:00.600]So the idea here is like, what if we enabled them to plan
[00:34:03.960]or to do the implementation together?
[00:34:06.680]So are other scenarios or let's allow them to plan.
[00:34:09.240]But because of legal reasons and insurance reasons,
[00:34:12.600]let's worry about let's let them simply do their own implementation, right?
[00:34:15.720]So I would call this like an incomplete or a partial district.
[00:34:19.560]And then the other scenario was a complete district.
[00:34:21.760]What if they were able to do everything together?
[00:34:25.680]What if they were able to plan together?
[00:34:27.120]What if they were able
[00:34:27.720]to use their backhoes and their dump trucks all together?
[00:34:30.440]Right.
[00:34:31.080]So that was the basis of the policy scenario.
[00:34:33.480]But because we're working with the state
[00:34:34.920]and they have a limited budget, they also wanted to know
[00:34:37.160]how much money do I need to spend and how many people are we going to hire?
[00:34:40.600]So you can think about that
[00:34:41.560]as essentially dollars to fund things, but also throughput.
[00:34:45.080]Right?
[00:34:45.800]So let's get into what the actual results are.
[00:34:49.160]So again, there's going to be a lot of data up here,
[00:34:51.800]but what I want to show you here is essentially
[00:34:53.320]we have different types of scenarios.
[00:34:55.040]So on the x axis, we have that measure of throughput, which is like state capacity.
[00:34:59.400]How many projects can we actually get through this bottleneck a year?
[00:35:03.560]And on the Y axis we have money, right?
[00:35:06.120]So how much are we going to spend?
[00:35:07.880]And then the values in the middle, which are going to change two
[00:35:10.400]or three dimensional graph here in a second are how much or what
[00:35:14.080]was the two kilograms of phosphorus that we reduce.
[00:35:17.360]So what we want is those numbers to be higher.
[00:35:20.600]And if we're going to have our scenario, where do where we are doing a planning
[00:35:23.880]only district so we're not sharing the dump trucks and backhoes,
[00:35:27.000]what we see is that this is essentially a linear it's a flat relationship, right?
[00:35:32.240]Our capacity or semi are the amount
[00:35:35.400]that we reduce increases as we increase our capacity.
[00:35:38.440]So as we throw more people at it, but it's effectively
[00:35:41.800]not very sensitive to how much money we throw at it so we can hire more people.
[00:35:45.600]But if you start throwing money at the problem,
[00:35:47.240]the money is just going to go to waste.
[00:35:49.080]So this allows us to sort of visualize these tradeoffs and communicate
[00:35:52.640]with the policymakers.
[00:35:54.680]The other
[00:35:54.960]thing is if we actually think about the other policy scenario
[00:35:57.960]and build out those complete districts where all these actors are allowed,
[00:36:02.040]these municipalities are allowed to share all their resources,
[00:36:04.880]we get a very different response.
[00:36:06.280]It gets non-linear, which is,
[00:36:07.400]in this case, good, because now we're seeing if we have our capacity
[00:36:11.560]throwing more money at the problem gets you more bang for the buck, right,
[00:36:16.240]Even if you're down at the bottom. Okay.
[00:36:17.800]Yeah, maybe throwing money at it gets you about a 1% increase.
[00:36:20.600]But if you increase that capacity, the way in which the prioritization
[00:36:24.440]algorithm works, it actually gets up to almost a 50% increase.
[00:36:29.080]So the idea here is like, let's let people work together.
[00:36:32.280]And we would not have figured this out if we were
[00:36:34.200]if we were strictly looking at geographic space.
[00:36:35.960]We had to sort of look at the network. Right.
[00:36:38.840]And here's the comparison. Obviously, the one on the right is better.
[00:36:41.480]We want the numbers to be higher.
[00:36:44.880]So this is success, I think. Right.
[00:36:47.840]So what happened here? Right. We figured out a goal.
[00:36:50.440]We worked with the state agency and what's a little scary is
[00:36:54.920]they actually wrote and passed a law based on our work, which on the one hand cool.
[00:36:59.280]The other hand hopefully we were right.
[00:37:01.440]But what ultimately happened is they broke this new law called Act 76.
[00:37:05.760]It's the Clean Water Service Delivery Act, and it mandates that we
[00:37:09.720]they form these districts.
[00:37:10.920]They are calling them clean water service providers
[00:37:13.280]that says you all have to work together to solve the problem.
[00:37:16.680]But what happened is this sort of what sounds like a successful story
[00:37:21.880]maybe wasn't super successful because what happened is
[00:37:24.880]they decided to fund all these clean water efforts with two main things.
[00:37:28.880]First, a hotel tax.
[00:37:31.280]So essentially taxing tourists
[00:37:34.480]to increase the amount of money that's available in the Clean Water Fund.
[00:37:38.200]And the other thing they did is they said we're going to
[00:37:41.280]essentially reclaim the waste recycling deposit.
[00:37:44.160]So if you throw away like a beer can or a Diet Coke,
[00:37:48.080]you put it in the trash, you know, somebody is sorting that out.
[00:37:50.240]And eventually those nickels get added up and they get put in a fund to, okay,
[00:37:54.320]now here's the problem.
[00:37:55.360]This was passed in 2019 and what happened in 2020
[00:37:59.600]for tourism go right.
[00:38:00.720]Went straight down with COVID. Right.
[00:38:03.000]So everything locked down and now all of a sudden we have all this
[00:38:06.640]goodwill, we have all this momentum that just goes nowhere.
[00:38:10.480]So it's actually taken them a very long time to spin up
[00:38:13.440]these clean water service providers
[00:38:15.120]and actually get the clean water fund back where it needs to be to address
[00:38:18.400]all these new projects that they want to do.
[00:38:20.160]Again, that's not necessarily a flaw in what was
[00:38:23.880]in the policy itself, but we have to be able
[00:38:26.480]to think about more than one thing at a time.
[00:38:29.680]All right.
[00:38:30.360]So real quick, I got one last case.
[00:38:32.360]And again, we're going to move into a slightly different space again. Right.
[00:38:35.200]This is a multi spatial wants to think about our futures.
[00:38:39.120]So we're going to talk about how people actually work together.
[00:38:42.720]So if we're going to ask through Act 76 that municipalities begin
[00:38:46.360]to work together, well, what might be useful to know is how different
[00:38:50.120]organizations, agencies, individuals are already doing that work, right?
[00:38:55.120]Who's already working together.
[00:38:56.440]Because if we're going to have to pull a bunch of people together to work together,
[00:38:59.680]well, it's probably a good idea to know who define right.
[00:39:02.520]So we're already working in a network space.
[00:39:05.200]We're thinking about geographic space because people can work
[00:39:07.800]across the entire state, across the entire watershed.
[00:39:10.880]They could work hyper locally, right?
[00:39:12.360]They might be
[00:39:12.720]focused on a particular stream or a trout stream or something like that.
[00:39:16.760]There's a big variety.
[00:39:17.760]It's a very heterogeneous group.
[00:39:19.800]But then we also need to think about
[00:39:21.600]where these people are actually working together.
[00:39:23.880]Are they working together sort of one on one,
[00:39:26.280]or are they doing a bigger collaborative effort
[00:39:28.360]that they're getting together in some sort of public space?
[00:39:30.480]So we are bridging, again, the public and the private.
[00:39:34.040]So what we did here is we got this big survey
[00:39:36.520]and we sent it to a whole bunch of people
[00:39:38.560]on budget organizations, institutions across the state and collective.
[00:39:42.240]We called them actors, and we ended up getting responses from 203 of them
[00:39:47.760]private actors, nonprofits, state agencies, federal agencies.
[00:39:52.440]And we asked them, essentially, who are you?
[00:39:54.520]And we knew who they were, but we asked them,
[00:39:56.520]how much capacity do you have, How many resources do you have,
[00:39:59.680]What type of issues are you interested in, But also who do you work with?
[00:40:04.080]And by asking them who they work with, we can build out a network
[00:40:06.640]that essentially maps those relationships.
[00:40:09.160]And we did it across five different dimensions
[00:40:11.880]who they share information with, who they provide technical assistance to,
[00:40:15.640]who they coordinate the projects with, who they report to,
[00:40:18.600]and who do they have some sort of financial relationship with.
[00:40:21.960]And this gives us an idea of the shape of the governance system
[00:40:25.440]because we're about ready to go in and modify the governance system.
[00:40:28.080]It would be a good idea to know where we start.
[00:40:31.800]So one of the ways we do this is to actually use a different type
[00:40:35.600]of analysis, right? So we have our network structure here.
[00:40:38.240]And because Act 76 is concerned about the regional scale,
[00:40:42.000]what would formerly be called the hockey scale,
[00:40:44.160]that's where we did our analysis as well.
[00:40:46.080]And we were able to map
[00:40:47.840]the network for who's coordinating with each other to build project,
[00:40:51.440]who also has who is sharing information with each other.
[00:40:54.600]And there's an analysis tool tool called an exponential random graph model
[00:40:58.560]or called an Ergon that actually is able to predict the relative likelihood
[00:41:02.720]of an edge or a linkage between any one of these actors in the state
[00:41:07.880]and an Ergon
[00:41:09.600]is a lot like a logistic regression model, except for all the ways.
[00:41:12.960]It's nothing like a logistic regression model.
[00:41:14.880]But what it does do is actually gives us an estimate
[00:41:18.240]of the different influences on who is or who is not working together.
[00:41:22.920]All right.
[00:41:23.560]And again, I know this is a big table of numbers,
[00:41:25.440]but I'm just going to point out a couple of findings.
[00:41:27.640]What we found is that these actors are
[00:41:30.240]more likely to work with people that are physically close to each other.
[00:41:34.000]Right. So we see this geographic signal.
[00:41:36.920]It's a small state. It doesn't take very long to drive across.
[00:41:39.400]But we still see, you know, everybody tends to work with their neighbor.
[00:41:42.600]They're not tending to work with people that are a couple hours away.
[00:41:46.440]But because we also ask them what they care about
[00:41:49.480]and what domains they work in, we were able to map
[00:41:52.080]what's called homophily, which is essentially,
[00:41:54.680]do you like to work with people that are like you or in this case, do?
[00:41:57.960]Are more likely to work with people that are engaged in the same problems as you?
[00:42:02.600]So what we found here is that for wastewater issues,
[00:42:05.640]if, for example, Dean Button and I are both engaged
[00:42:08.040]in wastewater issues, that that's the stuff we care about,
[00:42:10.560]we're actually less likely to work together.
[00:42:12.800]Right?
[00:42:13.520]But if we are both engaged
[00:42:15.040]in issues around forestry, that means we are more likely to work together.
[00:42:19.200]So now if we want to target foresters or we want to target wastewater,
[00:42:23.120]we know exactly the type of groups that we might want to focus on, right.
[00:42:26.400]Or we might have to harder to try to bring into the fall.
[00:42:30.120]And the other thing here is we also found out we have clustering.
[00:42:33.200]So back to Dean Button here.
[00:42:34.880]If Dean Button works with Jason and I work with Jason,
[00:42:38.760]sorry to put you on the spot. He's one of my students.
[00:42:41.160]That means Dean Button and I are more likely to work together, right?
[00:42:44.440]So we see this sort of friend of friend type of relationship.
[00:42:50.720]So that's the sort of private how people work together in the sort of 1 to 1.
[00:42:54.600]Right.
[00:42:55.320]But we also know a lot of this happens in public.
[00:42:58.160]So we ask them about their eye contact,
[00:43:00.960]their participation in public forums, and we have them all up there.
[00:43:04.680]And what I've done is I've scaled the size of the edge
[00:43:07.920]based on how many people participate.
[00:43:10.920]So this big, thick edge here between green Infrastructure Collaborative
[00:43:15.480]and the municipal municipal stormwater group means there's
[00:43:18.080]a lot of people that engage in both of that, right?
[00:43:21.360]So the question is what actually leads to that sort of joint collaboration, right?
[00:43:25.600]How do we find people that are collectively engaged
[00:43:28.400]with these issues so we can bring them into the fold
[00:43:30.600]and we can integrate them into the new governance system?
[00:43:33.120]And what we found here is we can do a similar type of analysis, right?
[00:43:36.360]In this case, participation at the watershed scale
[00:43:39.960]is actually really, really high, and it's the best predictor that we have
[00:43:43.280]for such a who wants to work together.
[00:43:45.040]Right?
[00:43:45.240]It's not people across the entire state, it's people that are working together
[00:43:48.360]locally.
[00:43:48.960]They are much more likely to engage with each other.
[00:43:51.760]But again, we asked about domains and we know what these groups
[00:43:54.760]are actually about.
[00:43:55.880]So what we found is that if you care about development,
[00:43:58.680]so if you care about the new apartment
[00:44:00.040]building or the new Costco going in or whatever it is,
[00:44:03.080]those actors are actually more likely to participate publicly.
[00:44:06.520]So we know where to find those.
[00:44:07.640]Right. But that makes a lot of sense
[00:44:08.640]because you have public hearings for new development.
[00:44:11.000]Right? There's a permitting process.
[00:44:12.960]All that makes a ton.
[00:44:15.960]But what we also found is that if you're engaged in agriculture,
[00:44:20.800]you are significantly less likely to engage in a public forum.
[00:44:24.080]Right.
[00:44:24.400]Which tracks for how we know agricultural actors work across across the US, Right.
[00:44:29.760]This is much more engaging with places like there. See
[00:44:34.400]there, see dealers or the
[00:44:35.840]Farm Bureau or talking in the diner at four in the morning.
[00:44:39.000]Right. All the things that farmers tend to do. Right.
[00:44:41.520]So again, if we want to target different groups,
[00:44:43.720]this can help us understand where we want to target.
[00:44:46.200]So if we're going to imagine more sustainable futures,
[00:44:48.720]well, we have to think about where to look.
[00:44:52.080]All right.
[00:44:52.760]So what this has shown us is that, yeah, we can reach those urban actors.
[00:44:56.040]We're going to have a hard time with the agricultural actors,
[00:44:59.280]at least going through the sort of official public channels.
[00:45:02.440]But we can't rely on existing actor actor networks to go and find people.
[00:45:06.680]We actually have to go look for them.
[00:45:09.760]So that's an information disconnect.
[00:45:11.920]So I've been going for a while here, but the big picture takeaway is that,
[00:45:15.960]you know, we have all these different convergent policy, relevant research.
[00:45:19.360]And I think in the whole it's been relatively effective, right?
[00:45:22.760]We've integrated multiple types of spatial data, We've engaged
[00:45:26.040]with stakeholders and we've been able to effectively inform decisions.
[00:45:30.520]But the whole big takeaway is like, are we actually meeting our goals?
[00:45:33.880]We're actually not.
[00:45:35.160]The TMD is going to fail the work.
[00:45:38.040]I have an NSF funded grant to do similar work in the Chesapeake
[00:45:42.360]Bay watershed that TMD is going to fail, right?
[00:45:46.440]They already know that in Vermont, in the Chesapeake, right.
[00:45:50.840]Our efforts are mismatched.
[00:45:52.360]We have a functional mismatch.
[00:45:53.760]We are if we're going to imagine more sustainable futures, we have to,
[00:45:57.000]however, significantly more resources and we got to do a lot more work.
[00:46:00.680]So we have a lot of headwinds against right
[00:46:03.960]now. As I close, there's a couple of other things I want to point out.
[00:46:06.640]All right.
[00:46:07.040]So I've been doing all kinds of different types of modeling for a decade now.
[00:46:10.920]And some of the pitfalls I found right, is that when we put together
[00:46:14.520]these type of models, what we tend to do is we build them linearly, right?
[00:46:18.720]We have a climate model that drives the hydrological model
[00:46:22.080]that might connect to a land use model,
[00:46:24.000]and then that has
[00:46:24.600]some sort of environmental output in a lake or an estuary or whatever it is.
[00:46:28.320]But all of these processes go on simultaneously.
[00:46:31.480]But the way in which we've constructed our models is all like A
[00:46:34.440]to B, to C, to D, You write output to input, output to input.
[00:46:38.280]And that's a problem for actually addressing
[00:46:40.080]all the complex dynamics in the systems.
[00:46:43.040]But what I
[00:46:43.440]think is actually a bigger problem is that we build all these tools NSF
[00:46:48.120]and USDA and EPA, they invest all this money, right?
[00:46:52.320]And yeah, we train some postdocs and we got the papers published.
[00:46:55.720]But ultimately all of that infrastructure, all of that modeling
[00:46:59.040]is sort of necessary abandon, right?
[00:47:01.880]But it's not translated to a new setting very easily.
[00:47:05.040]Right.
[00:47:05.200]We hope that maybe one of the scientists we train goes somewhere else
[00:47:08.480]and takes their model.
[00:47:09.520]And that's what I've done when I go to the Chesapeake.
[00:47:11.760]But ultimately, we are not reusing.
[00:47:13.480]We're spending all this money to build technology and then it goes away
[00:47:16.960]or primarily goes away at the end of the grants.
[00:47:20.400]So this is a significant problem.
[00:47:23.360]So my
[00:47:24.240]last slide here is I try to pitch something new.
[00:47:27.240]I think what we need is a spatial data science
[00:47:30.080]that is inclusive of traditional geographic information, science
[00:47:33.800]that has all the data types, right, but includes
[00:47:36.840]network science, includes the ability to engage across the public and policy
[00:47:41.560]and figure out how to more accurately communicate
[00:47:44.240]or more effectively communicate through attribute space and, you know,
[00:47:47.240]talk about those tradeoffs, but also is explicitly social, right?
[00:47:51.360]That doesn't start with the hydrologist and the physicist.
[00:47:54.080]They're all great people. Absolutely.
[00:47:55.280]And I'm sure they bring in all kinds of money. Right.
[00:47:56.840]But these are social problems we need to start with humans
[00:48:01.320]and the ability to communicate and engage with policymakers.
[00:48:04.920]So here's where I actually pitch and sort of brag a little bit that
[00:48:08.880]with collaborators at Virginia Tech, at Dartmouth and the University
[00:48:11.920]of Maryland Center for Environmental Sciences,
[00:48:13.920]we actually just got awarded a big $3.5 million NSF grant
[00:48:17.480]to do exactly this right.
[00:48:19.560]So we developing a new convergence paradigm
[00:48:22.440]that actually takes a systems of systems approach and tries to integrate
[00:48:25.560]a lot of these problems simultaneously, because we would argue
[00:48:28.560]we actually need a bigger shift in the way in which we do this type of science.
[00:48:31.800]It's less about can we build a better model?
[00:48:34.080]How can we change and transform the way we do the science right?
[00:48:37.320]So we are actually building on this essentially we're developing
[00:48:41.160]essentially a common ontology or a way of understanding across disciplines.
[00:48:45.480]We have a computational framework that's built on machine learning
[00:48:48.920]and hetero functional graph theory, which I'm still learning about.
[00:48:52.120]We have a decision support tool so we can engage with the policymakers,
[00:48:56.280]but fundamentally all of these things are great,
[00:48:58.920]but they're sort of meaningless if we don't actually do pedagogy,
[00:49:02.480]if we don't train scientists, if we don't train people,
[00:49:05.880]the policymakers, if we don't train the public to understand
[00:49:08.560]and think in more complex ways. Right?
[00:49:10.760]Think about these.
[00:49:12.000]Now, at the same time, I'm not naive, right?
[00:49:14.120]It's all this new science is great.
[00:49:16.360]All these ways to think about new data are great.
[00:49:19.400]But ultimately, if we're thinking about sustainable
[00:49:22.080]futures, it just can't be about the shiny new thing or the fancy new model
[00:49:25.960]or the new paper or the new grant.
[00:49:28.080]Because we can do a whole bunch of science.
[00:49:29.600]But if it's not actionable, if we don't engage with the policymakers
[00:49:32.800]and actually get people to get up and do stuff and try to understand the scope
[00:49:37.360]and the urgency of these problems, and it doesn't really matter.
[00:49:40.640]Right?
[00:49:41.160]So I don't mean to be a bit of a bummer at the end. Right.
[00:49:43.680]But we do have to do this work to engage with policymakers.
[00:49:47.280]All right.
[00:49:48.000]So with that, I think I'm probably a little bit over,
[00:49:49.680]but thank you very much and I'll take any questions if there's time.
[00:50:14.240]No, thank you. Nice to talk to. So there's
[00:50:19.440]a discussion
[00:50:20.560]in there about the sustainability of the approach in a sense.
[00:50:23.560]Right.
[00:50:23.800]Does it is it going to get pigeonholed in, you know, relatively narrowly
[00:50:27.480]focused studies and then lose its impact over time?
[00:50:31.440]So is there a role in industry in this?
[00:50:33.120]And I don't mean industry in terms of like polluting
[00:50:34.880]industries in manufacturing, but but like for you and your
[00:50:38.640]against the kind of pathways that SBIR and the AI core kind of thing,
[00:50:42.840]I mean, will there be companies one day that would occupy?
[00:50:45.840]I mean I'm,
[00:50:46.520]I'm curious because it's got environmental regulation and environmental modeling.
[00:50:49.440]So is there space for industry to be a good player here versus
[00:50:53.200]where those roles are often
[00:50:54.240]usually played by like state departments of natural resources, things like that?
[00:50:58.240]Yeah.
[00:50:58.560]So I mean, that's not really my domain, but I would imagine
[00:51:02.680]that there absolutely could be a but I think there it's
[00:51:05.640]we really need to think about how we structure those partnerships.
[00:51:08.440]Right.
[00:51:08.920]And how we ensure that whatever
[00:51:11.400]new technologies or subsidies that we are sort of enabling or helping
[00:51:14.640]to give to these private entities
[00:51:16.560]that they are actually in the pursuit of the public good. Right.
[00:51:18.960]And it's not just about,
[00:51:20.360]you know, profitability or stock buybacks or things like that, right?
[00:51:23.400]So as long as we're serving the public good, whether it's a B Corp,
[00:51:26.720]whatever, I think that would be a good idea. Jim,
[00:51:39.600]thank you for the great talk. Dr.
[00:51:40.880]Biderman, I was just thinking about your models
[00:51:43.920]based on your hydrologic datasets and wondering
[00:51:47.120]if you think that there are certain things
[00:51:48.720]that are inherently about water and how people understand problems
[00:51:52.080]around water that wouldn't translate to things around atmospheric phenomenon
[00:51:55.840]or natural disasters like hurricanes or fires or things like that.
[00:52:02.160]Do you think there's lessons that people just inherently get about everything
[00:52:06.120]flows downhill or collects in a spot that they might not understand
[00:52:09.800]about other things that might be challenging?
[00:52:12.960]I think it's less about water
[00:52:15.840]versus fire versus warming or whatever it is.
[00:52:19.480]It's more about what's my experience and what's also in the literature.
[00:52:23.400]It's more about like personal lived experiences, right?
[00:52:25.920]Have I been exposed to it? Have I felt the real impacts?
[00:52:28.320]Have I noticed that, you know, it's significantly warmer
[00:52:31.880]or that the area that I go canoeing, you know, usually has this type of bloom.
[00:52:36.280]It's less about, you know, is it water, is it fire?
[00:52:39.280]You know, it's does the does my granddaughter
[00:52:42.440]who lives outside of San Diego does she experience a fire?
[00:52:45.240]Well, now she did. So climate change must be real, right?
[00:52:48.000]Is that type of lived personal experience more than I think it is.
[00:52:50.520]The domain.
[00:53:01.560]I am part of two geoscience majors.
[00:53:04.920]And so I was wondering if you all benefited at all
[00:53:06.600]from like open science and if you all
[00:53:08.200]were trying to move towards open science with a lot of your research.
[00:53:11.440]Yeah.
[00:53:11.760]So I mean, all of the stuff that we do, I mean, it's tough for some of
[00:53:14.920]the big proprietary models or even if you go ahead and parameter
[00:53:19.000]wise a model that is open source that's sometimes hard to share.
[00:53:22.440]But in terms of data sharing agreements, everything is, you know, eventually,
[00:53:27.000]generally speaking,
[00:53:27.720]after we publish it posted to GitHub or some sort of institutional repository,
[00:53:31.800]see how we absolutely benefit from open science, We benefit from public datasets.
[00:53:35.760]So we have a responsibility to make sure that the things that we're doing
[00:53:40.080]become public and get into the public domain so other people can use them.
[00:53:42.760]Absolutely. And
[00:53:51.120]June Got it.
[00:54:07.360]Well, thank you all. It's been a lot of fun.
[00:54:08.880]I'll stick around if anybody wants to talk.
[00:54:15.520]Thank you again, everyone, for being here.
[00:54:17.000]I just also wanted to announce
[00:54:18.240]that October 10th is our next lecture as a part of the series.
[00:54:22.320]Professor Mark Benjamin here
[00:54:24.240]from the Department of Philosophy will be guiding us through
[00:54:26.800]using relatively basic knowledge to filter through climate controversy.
[00:54:30.600]So that will be something to look forward. October 10th.
[00:54:32.720]We hope to see you all here again and again.
[00:54:34.760]Thank you very much, Dr. Biderman. Wonderful part.

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Visions of Sustainable Futures: The Need for a (Multi-)Spatial Lens | CAS Inquire

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