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Natural Capital Project finds a better way to predict the environmental impacts of agricultural prod


A team of researchers from Stanford University and the University of Minnesota, along with researchers from Unilever’s Safety and Environmental Assurance Centre, claim to have developed a better way to measure in detail how products affect natural resources such as land, water and biodiversity. The approach, called the Land Use Change Improved Life Cycle Assessment, or LUCI-LCA is designed to help researchers or companies more accurately predict impacts of new designs and sourcing. Standard LCA represents the average land composition of the country from which the materials will be sourced. So it assumes that sourcing sugarcane from any region of Brazil, regardless of the specific proportion of rainforest in that region, will lead to deforestation in the Amazon. The new system allows for a more refined assessment using data relevant to the exact region from which materials will be sourced. Researchers compared standard LCA results and LUCI-LCA results to compare the environmental impact of sugarcane from the Mato Grosso region of Brazil and corn from Iowa. Standard LCA showed that the Mato Grosso sugarcane would lead to more CO2 in the atmosphere, while the more spatially sensitive LUCI-LCA found the carbon footprint of Iowan corn was larger. Traditional LCA found that the corn would result in more water us than sugarcar, while the new LCA found that sugarcane used 900% more than the corn. Substantial work is required to pull together the data necessary for this type of study. The research team believe that with industry interest and investment they will be able to develop a more streamlined tool to facilitate this new type of LCA with little manual work. Source: https://www.nature.com/articles/ncomms15065


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Message from the ALCAS President

December 20, 2023. I was honoured to be elected as ALCAS President at the ALCAS AGM in late October, being handed the baton from Rob Rouwette. The ALCAS Board is very grateful for Rob’s consistent ser

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