From the food on our plates to the phones in our hands, all of the things we use have a life. From their conception to their disposal, they leave behind an ecological footprint. In environmental management studies, researchers created a methodology to assess the environmental impacts associated with products throughout their life cycle. But is this assessment completely accurate?
Considering both the successes and drawbacks of the modern agricultural systems, like climate change, air pollution and water quality deterioration, it’s essential to look at how to create a sustainable food system. Eating healthy foods is a strategy that has emerged in response to this issue, but the environmental benefits of this change are unclear. So researchers across UC Santa Barbara’s Bren School, UC Berkeley, Chongqing University, Dartmouth College and Leiden University decided to analyze how to improve the technique that measures these environmental benefits and drawbacks.
Life Cycle Assessment (LCA) is a way of measuring the environmental impacts in the stages of a product’s life: raw material extraction, materials processing, manufacture, distribution and use. LCAs are the main way a food’s environmental performance is measured. They can be used to infer environmental costs, benefits and trade-offs between different dietary choices. LCA ultimately can be used to inform public policy and improve product design.
“Most of the LCA studies, they used past data, which is based on the average emissions, which may have been collected 10 years ago. If we use those past data, or the average data, it won’t reflect a change in the future,” Yuwei Qin, a postdoctoral scholar at UC Berkeley who started this project when she was a doctoral student at the Bren School, explained.
Due to complexities in human-environment interactions, there are economic, social and political factors that influence different outcomes for the future. Qin said, “If we want to know what would happen, for example, if people will eat more vegetables, if they will have a healthy diet in the future, we cannot use those passive data to estimate what the future will be like.”
Qin studied environmental policy in undergrad and continued into environmental management for her master’s and doctor’s degrees. In total, she has been in the environmental field for 12 years now. She became very interested in the LCA area during her master’s study, where she then decided to contribute to life cycle research and related projects, focusing on the uncertainty of the life cycle assessment. This paper examining the non-linearity of LCA has been a side project and is about five to six years in the making.
To estimate the future as accurately as possible, Qin and her colleagues created models that were able to measure the different outcomes of future scenarios in which more potatoes need to be produced in the United States. The first, based on the way the market currently works, looks at taking the lowest production costs. The second looks at a high fuel price scenario, if there are high fuel taxes in the future. The third is based on a high water price scenario. This is more focused on the environment, where a shortage in water supply could result in higher water costs.
Qin reflected that the challenges in modeling these scenarios came from “first building the model and then the data part.” Picking the right models and equations to optimize the runtime of data processing required Qin to do intensive literature reviews to find different economic strategies that addressed the scenarios explored. Qin also found it difficult to find irrigation and transportation data, so she decided to collaborate with experts in those fields to find the right sources.
Their results found that different environmental outcomes will come about depending on how economic constraints, social factors and policy measures play out. The current LCA model of the economic system as it stands doesn’t adequately address the question of dietary shift and what decisions should be made. Therefore, the model they developed can be extrapolated to other products and used to understand the impacts from changes in demands.
Regarding where this work can be taken, Qin stated, “First, we can apply this technology more to other products.” The model isn’t limited to just vegetables but can also be used for technology as well. “Second, we can build a more comprehensive database that includes not only the average emissions but also the marginal emissions, so that we can have a better estimate of what the environmental impacts would be in the future.”