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

CAS MarComm Author

09/21/2023 Added

21 Plays

Description

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

Searchable Transcript

Search:

[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.

The screen size you are trying to search captions on is too small!

You can always jump over to MediaHub and check it out there.

Comments

0 Comments

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

Description

Searchable Transcript

Comments icon comment

Related Channels

Comments