New method created to look at farm production's environmental impacts

Source:Xinhua Published: 2017/4/22 10:34:36

A team of researchers has developed a new kind of life-cycle assessments (LCA) to integrate land, water and biodiversity impacts in a more detailed way, so as to figure out the potential consequences of new designs and sourcing.

The new method, known as Land Use Change Improved Life Cycle Assessment, or LUCI-LCA, was designed to help researchers or companies more accurately predict how their decisions affect the environment, was the result of a partnership called the Natural Capital Project, with the participation of researchers from Stanford University and the University of Minnesota, along with researchers from Unilever's Safety and Environmental Assurance Centre.

"The size and reach of multinational companies is stunning, on par with that of many nations," said Gretchen Daily, professor of biology at Stanford and senior author of a paper published Friday in Nature Communications. "When we think about how to bring human activities into balance with what Earth can sustain, corporations have a major role to play in decoupling economic growth from environmental impact."

LCA offers a systematic way of determining potential environmental impacts of a product from source materials to disposal. Results from these assessments often inform decisions companies make about product design, material and technology choices and sourcing strategies. An incomplete or inaccurate assessment could lead to well intentioned but environmentally damaging decisions.

The researchers tested their new LCA by evaluating the potential environmental impacts of two bio-plastic products that could be produced from sugarcane grown in Mato Grosso, Brazil, or from corn grown in Iowa, the United States. With more accurate data about the regional land composition than the traditional LCA, their approach came to different conclusions about which option would be more environmentally responsible.

One problem with a standard LCA is that it represents the average land composition of the country from which materials will be sourced. In this case, it assumes that Mato Grosso contains the same proportion of rainforest as all of Brazil. The researchers made improvements that allow for more refined assessment using data relevant to the exact regions from which materials would likely be sourced, taking into account predictions about future impacts to the environment.

"In reality, from the modeling that we did, it looked like most of the expansion of agriculture in Mato Grosso would happen in the savannah," rather than in the Amazon forest, said Rebecca Chaplin-Kramer, research associate at the Stanford Woods Institute for the Environment and lead author of the study. "Whereas in Iowa, if any expansion happens, it will likely mean expanding into forest."

While the standard LCA showed that the Mato Grosso sugarcane would lead to more carbon dioxide (CO2) in the atmosphere, the more spatially sensitive LCA found that the carbon footprint of the Iowan corn was larger. In addition, while the traditional LCA found that the corn would result in more water use than the sugarcane, the new LCA found that the sugarcane would use more -- 900 percent more.

"This work has major implications for anybody involved in product innovation, commodity sourcing or policy setting for new land development," Ryan Noe, a researcher with the National Capital Project at University of Minnesota and co-author of the paper, was quoted as saying in a news release from Stanford. "Where that sourcing comes from matters and it' s not really being captured with the approaches being used."

As the researchers hope that the stark and significant differences between the results of the two LCAs will encourage companies and policymakers to adopt the new approach for decision-making, Chaplin-Kramer stated that "our ultimate mission is to get this kind of information -- this spatially explicit value of nature -- to people and to have the impact on natural capital included in as many different kinds of decisions as possible."

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