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The Across Scales Panel Discussion

Published onDec 24, 2019
The Across Scales Panel Discussion

The Across Scales Panel discussion started with a discussion of how the DOE and NSF ecosystems could create better synergies in terms of investments and the sustainable urban systems agenda. There are new DOE programs emerging for academic institutions. In the fall, DOE opens calls to National Labs and at least a few offices allow university support. In the spring—typically around March—DOE opens calls to university-led teams that allow National Lab support. This provides opportunities for additional cooperation on some of these investments in the future. One of the issues inhibiting collaboration across academia, National Labs, and other communities is the fact that the research of Lab researchers is not immediately visible, not even on their websites. While National Labs and their researchers offer a wide range of capabilities and expertise, they don't necessarily have knowledge that addresses the “down scale” needs of communities. Here, academia and others can step in and fill the gap. There is an opportunity to bring these different levels of expertise together and to collaborate on projects. Currently, there is no well-documented and communicated way to go about it.

Craig Colten then lead the conversation to the term “scale jumping” used in the critical social theory as an effective technique for actually going from one scale to the next. The question was raised whether the same analysis can be used at the micro scale and the macro scale? How do we seamlessly transition from one scale to the next and connect all three scales?

Sarah Rimer responded that good computer modeling faces the issue that the sensitivities of the different timescales and the spatial scales need to be well understood. Thus, in order to connect something from a large scale to a smaller scale, there has to be an understanding of the issues and the uncertainties that occur at both scales. With a good model it is actually possible to at least try to quantify what these uncertainties are, so that we have an estimate of what uncertainties still exists between the two scales.

Mark Imerman argued that it is crucial to have enough information to determine how things work at each individual scale to facilitate jumping across scales. For example, one of the problems with controlling flooding across the whole Mississippi River basin is that fact that nobody has a full accounting of how much money is spent at different levels and how it is summed up as a system. It would be a difficult endeavor to find out how much is spent in local government budgets in Iowa on flooding. Money is spent by the Davenport Department of Public Works, by the Guthrie County road department among others, and none of their budgets shows this as flood money. Across state levels, everyone uses different accounting rules. Twenty-five years ago I was actually asked to come back to Iowa State to help build a local database to facilitate local development work. At that time, it was impossible to get the granularity needed from the federal data that were consistent across states.

While the team started building that database with Iowa State administrative data, the data did not reflect what was happening in Missouri or in Minnesota. So, one of the first things that enables jumping across scales or consolidating scales is to look at how money is spent across state, county, and local jurisdictions and to track where federal money goes. When investigating levels below the regional level, it is very hard to actually determine where the money is spent.

Josh Sperling noted different types of benchmarking indicators, such as emissions. When considering per capita emissions, we are able to go from a household to a neighborhood to city. Evaluating global GDP can be useful when analyzing cities or settlements or more production oriented versus consumption oriented resources. “Follow the money” can be a way to compare across scales using money as a numerator and denominator.

Kimberly Zarecor—as moderator—took the opportunity to reflect on what people need to know, when do they need to know it, and what is going to be useful for them in the future.

Referring to Anderson, who is working with young elementary school students and exemplifies the hope that they are smarter in the future on these issues. They will understand more about the science and the effects of choices, and in general the clarity of the science. We know what the problems are, but people are not acting on these issues as a community goal. The question is: how do we encourage better decisions in the future? Do we start with education? Matt was very clear that communication itself is not the answer - that we have to account for the prevailing power structures.

Claire Anderson responded and noted that there definitely is this undercurrent of we are doing this work, posing these research questions, gathering and sharing these data so that we can affect change in the world - or at least can say we defined the problem and it is not a technical problem, it is a social problem. So, if this is a social problem, what is required is broad public buy-in and public pressure to dismantle systems in which economic and political power is concentrated in certain places and not spread evenly.

When we look at 4th-graders, all the way through 12th grade, these students not that far from being voters - they are not that far from being scientists. Before long, they could be undergrad or graduate students. From my point of view, the quality of the question asked determines the quality of the answer received.

Chris Jones’ work is very Iowa-centric. When looking at the history of how voters have reacted to, e.g., water quality in Iowa, a fairly large percentage of the people believe that we need better water quality. In 2010, the Iowa Water and Land Legacy Act was passed by 61 % of voters, yet the legislature still has not funded it. It is apparent that the legislature will not fund it until the formula on how the money will be spent is changed.

Recently, Linn County (where Cedar Rapids is) passed a $40 million bond issue with 74 % of votes, and a large portion of this money will go to water quality projects. It is apparent that people in Iowa do want clean water - so why are things so paralyzed in our legislature? We have this entrenched power infrastructure that prevents improvements even though the vast majority of people want change. In a recent poll in the Des Moines Register of Trump voters, the #1 environmental issue was water quality. So even people considered fairly conservative believe that we need to improve water quality. The questions becomes: what can science do to push this forward? By science I don't mean just engineers and scientists, but rather social scientists and artists who do work to try to affect change. As scientists, we spend way too much time talking to each other and not enough time talking to the people who really care about what we do. It is up to us to do a better job, and at least here in Iowa this has thea potential to move the ball a little bit.

Sarah Rimer responded directly to Claire: Water is exciting because everybody interacts with water on a day to day basis. It is more accessible, and for the general public to realize that they can getting involved. The information and knowledge about water is accessible and it should not be an elusive thing. However, the major limitation is getting that information out. People know how water impacts them on a day-to-day basis, and usually, once people are studying or working in the water realm, they are usually very passionate about the field.

Kimberly Zarecor made a general comment: We have to return to the point that Matt made, which is that there is so much information out there already. There is a disconnect between people stating that they care about water quality, but to actually getting legislature act on it does not require more information. A lot of data science work reflects a positivist idea that, if the information is clear and is modeled more precisely, it is more convincing.

In a broad sense across the whole public, we have discovered that this is not true. In fact, people resist information for many reasons.. Scientists talk too much to each other (because we love that stuff), trying to be more precise and to get more models. But, there are so many deeper issues underlying this – think of Claire's students as the receivers of what we are doing here with farming.

Sarah Rimer noted that a model will convince the public. Sensors may be more accessible and timely at this point. What gets people excited in particular is the idea that you can build a sensor and gain knowledge about your environment. This sort of technology is far more accessible and exciting for people who may not have considered getting involved in issues such as water quality.

This is the same reason scientists sound like they enjoy modeling and use models to test their assumptions. It is lowering the stakes before you have to implement your results in the real world, and the same thing is so true for students. They have to practice difficult decision-making. They have to practice understanding vulnerability, weighing risk, realizing that there are some scenarios in which not everyone will win. They can start doing that in kindergarten. It does not mean that we are scaring them every day, but it is a matter of learning to transform: when learning feels real and when it feels like it has something to do with people's lives. There is certainly a story component but the story is the package; it is the vehicle that makes the science. It makes it matter whether you are a kid or an adult, and you need a time and a place to test your knowledge and to have the strength and the political will when the time comes to make the real decision.

Qi Li noted a famous statistician’s quote “All models are wrong, but some are useful”. Speaking from the modeling engineering perspective, models are approximations of reality. Scientists are able to obtain useful information about reality. The way we convey this information to the next generation really matters, and we need people like Claire to work together with the modelers who are may be producing incorrect results.

Mark Imerman mentioned the issue again that society is not putting values on things. Many of the problems we talk about are externalities, things that a farmer does upstream. They push water off their ground, which may be polluted, which may cause a flood—but they do it to make crops to grow.

People worry about water quality in Des Moines, but they do no worry about floods in St. Louis. Earlier today we talked about Minnesota where people in urban areas can buy the right to have run off. They can use this money to eliminate runoff that is causing problems. Chris talked about taxing nitrogen: when taxing a pollutant, the money collected can be used to solve the problems they cause. Another example are manure markets: Iowa grows 38 million pigs and will not get rid of them because they consume much of the corn produced in Iowa. The problem is that pig manure is treated as a waste; it is thrown away and put on land that does not need it. Land is actually bought to serve as pig manure dumps around the state, a fact that ruins my own value equation for people that want to build environmental quality into land sales. At some level we need to find ways to use manure—something between taxing nitrogen and doing a better job of building a manure market. A lot of our problems are rooted in the fact that we don't put values on the things that we want to control.

Much of the conversation to this point just seems to focus on money, manure, and water. In other words, we are just moving things around. Why would a young person, or anyone, care about this kind of research. We talk about communication as a problem—not a communication problem but a problem of systems.

There is an inherent justice issue at the root of a lot of our talking points. Crucial questions are: What has that to do with our concepts of land and water? Who owns them? Who can control them? What are they for? What are they valued as? And one of the most exciting things that I have encountered with young people in the course of collaborative projects is when they talk about STEM justice and the idea that science can be a tool for dismantling systems. It is not just about gathering information about how things work.

The final discussion point questioned the use of more data across scale and what the possible avenues are to propagate across scales. This is a challenge because data in the same scale sometimes the easier to control. But if we model across scale down, any error will propagate and we do not know any more where the error came from. This important question lead into the breakout session discussion.

This event is supported by the National Science Foundation, Award #1929601. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


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