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Impacts of Deglobalization on the
Sustainability of Regional Food, Energy, Water Systems

Spring 2021 Updates

Monitoring Sustainability and Targeting Interventions: Indicators, Planetary Boundaries, Benefits and Costs

Pros and Cons of Downscaled Planetary Boundaries for Regional Sustainability

A new paper by team member Alan Randall was recently selected as the feature paper for Sustainability journal’s next issue. “Monitoring Sustainability and Targeting Interventions: Indicators, Planetary Boundaries, Benefits and Costs” examines the utility and dangers of using downscaled planetary boundaries for regional sustainability goals.

Take for example another recent Ohio State sustainability study from The Ohio State University’s Fisher College of Business, which found that multinational companies headquartered in countries with tougher environmental policies tend to locate their polluting factories in countries with more lax regulations. Regionally, it may look like the company is an eco-champion, but lowering regional emissions in one region, while increasing them in others, does nothing to lower overall planetary emissions. For sustainability indicators like carbon emissions, the planetary boundary is critical. 

In his paper, Randall notes that dividing up a planetary sustainability goal can be useful to seek lower overall abatement costs, address hot spots, and set regional abatement targets. However, the paper points out the need for carefully selecting science-based and informative sustainability indicators (for instance, using ratio-scale indicators that allow quantitative assessment and targeting). Randall also outlines the need for equity considerations in weighing the costs-benefit approach to abatement in industrialized vs. less wealthy nations.

These are issues our modeling team has grappled with in our regional sustainability indicators. We are using life cycle assessment or a sustainability accounting matrix to trace sustainability impacts of major imported goods in our model, such as fertilizers.

Other Brief Project Updates

Land Use Projections

If you haven’t watched it yet, we shared a brief update video in November that explains how we are treating land use and land use changes in our model. Watch it here (8.5 minutes).  Since this fall, we’ve made amazing progress on our Land Use submodel. Here’s a brief summary:

Phase 1 Document Past Trends
Combining millions of 30mx30m land use/ land cover observations from the Cropland Data Layer with USDA field boundary data, our land use team has been able to examine county-level land use change from 2008-2018. This information provides an historical baseline and allows us to model how these land use transitions were influenced by various economic and geographic factors.

Phase 2 Identify Influential Factors
The land use team then spent considerable time researching the conditions that lead to land use changes and factors that influence those conditions, including those that influence farmer decisions based on the results from our survey. The team has created and is testing equations to calculate the likelihood of land use transitions under different scenarios. These conversion equations will focus on converting land to/from cropland, pasture, and forest; from non-urban to urban use; and from wetland to other land uses.

Phase 3 Use for Future Projections
Once these land use transition equations have been determined, they will be used to project land use changes for each scenario. Some variables that influence land use change are determined as part of our scenarios, such as conservation payments and population growth, while other changes, like crop prices, land rents, and energy demand, are determined by the regional economic model. The outputs from the economic model are inputs into the land use change model, which is how we integrate the two models.

How to Train Your Model

Our land use team continues to train the Soil and Water Assessment Tool (or SWAT) software to make longer-term predictions for our entire region, creating what we’re calling a “surrogate SWAT model.” Our talented team of graduate students has acquired datasets for several watersheds in the Great Lakes region. We use about 75% of this new data to train our model, and the remaining 25% to test its accuracy. So far, this training/testing procedure has allowed us to accurately predict water quality impacts for the Maumee River and River Rasin watersheds. We are also working with data from the Huron watershed, but still honing our approach for predicting total phosphorus and sediments in this watershed. Once the surrogate SWAT model is ready, it will use data from our economic and land use models to create vital inputs for our regional sustainability assessment.

FEWS Curriculum Development

We’re encouraging high school and undergraduate students to train models too!

DRFEWS team members are working with an independent contractor to develop a Food Energy Water Systems curriculum for high school and introductory-level college courses. Proposed lessons would introduce systems thinking, allow students to compare different systems models, think through cause-and-effect relationships between connected systems, and see how models can be used to compare different scenarios. The curriculum should be ready for testing this summer and we hope it will become a useful resource for courses on economics and environmental science.

Diagram

Description automatically generated with low confidenceOne of the tools students will use with the curriculum is a free program called Loopy that allows students to design their own integrated models. Follow this link (https://tinyurl.com/2ewecu7d) to see an example of a Loopy diagram that our team set up to reflect our DRFEWS model. You can see how trade policy and sustainability measures (like BMPs, conservation programs, etc.) are connected to land use and other aspects of agricultural commodity production in the Great Lakes region. You can move model components or click on various factors to increase or decrease them and see how the model reacts. (Your changes won’t affect the original diagram.)