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Multi-objective optimization of nutritional, environmental and economic aspects of diets applied to the Spanish context
(MDPI, 2020-11-01)
Current food consumption patterns must be revised in order to improve their sustainability. The nutritional, environmental, and economic consequences of these dietary patterns must be taken into consideration when diet guidelines are proposed. This study applied a systematic optimization methodology to define sustainable dietary patterns complying with nutritional, environmental, and economic issues. The methodology was based on a multi-objective optimization model that considered a distance-to-target approach. Although the three simultaneous objectives (maximal nutritional contribution, minimal greenhouse gas emissions, and minimal costs) could be divergent, the proposed model identified the optimal intake of each food product to achieve the maximal level of nutritional, environmental, and economic diets. This model was applied to six different eating patterns within the Spanish context: one based on current food consumption and five alternative diets. The results revealed that dietary patterns with improved nutritional profiles and reduced environmental impacts could be defined without additional costs just by increasing the consumption of vegetables, fruits, and legumes, while reducing the intake of meat and fish.
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Exploring machine learning techniques to predict deforestation to enhance the decision-making of road construction projects
(Wiley, 2021-08-24)
Land use changes (LUCs), which are defined as the modification in the use of land due to anthropogenic activities, are important sources of GHG emissions. In this context, understanding future trends of LUCs, such as deforestation, in a spatial manner is relevant. The main objective of this study is to generate a deforestation prediction model for a given period of time (i.e., 2002–2017 and 2010–2017) to estimate the potential carbon emissions associated with different anthropogenic variables in the Peruvian Amazon using machine learning (ML) algorithms. This study was motivated in the analysis of a road project previously studied using life cycle assessment (LCA). Models using neural networks and random forest algorithms were trained and evaluated in a fully cloud-based environment using Google Earth Engine. ML-related results demonstrated that random forest is a quicker and straightforward response to model the system under study, especially considering that data do not require additional processing during the modeling and prediction stages. Predicted results suggest that expected road expansion may be related to considerable carbon emissions in the future. Calculated values are relevant especially if the mitigation efforts that Peru has complied with in the Paris Agreement are considered. The increased complexity of the framework is justified since it allows identifying the location of hotspots and may potentially complement the utility of LCA in policy support in the areas of territorial planning and tropical road expansion.
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