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Item type:Publication, Regionalized strategies for food loss and waste management in Spain under a life cycle thinking approach(MDPI, 2020-12-01)Food loss and waste (FLW) has become a central concern in the social and political debate. Simultaneously, using FLW as a bioenergy source could significantly contribute to closing the carbon cycle by reintroducing energy into the food supply chain. This study aims to identify best strategies for FLW management in each of the 17 regions in Spain, through the application of a Life Cycle Assessment. To this end, an evaluation of the environmental performance over time between 2015 and 2040 of five different FLW management scenarios implemented in a framework of (i) compliance and (ii) non-compliance with the targets of the Paris Agreement was performed. Results revealed savings in the consumption of abiotic resources in those regions in which thermal treatment has a strong presence, although their greenhouse gas (GHG) emissions in a scenario of compliance with climate change targets are higher. In contrast, scenarios that include anaerobic digestion and, to a lesser extent those applying aerobic composting, present lower impacts, including climate change, suggesting improvements of 20–60% in non-compliance and 20–80% in compliance with Paris Agreement targets, compared to the current scenarios.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Introducing a degrowth approach to the circular economy policies of food production, and food loss and waste management: towards a circular bioeconomy(MDPI, 2021-03-02)There is a growing debate surrounding the contradiction between an unremitting increase in the use of resources and the search for environmental sustainability. Therefore, the concept of sustainable degrowth is emerging aiming to introduce in our societies new social values and new policies, capable of satisfying human requirements whilst reducing environmental impacts and consumption of resources. In this framework, circular economy strategies for food production and food loss and waste management systems, following the Sustainable Development Goals agenda, are being developed based on a search for circularity, but without setting limits to the continual increase in environmental impacts and resource use. This work presents a methodology for determining the percentage of degrowth needed in any food supply chain, by analyzing four scenarios in a life cycle assessment approach over time between 2020 and 2040. Results for the Spanish case study sug-gested a degrowth need of 26.8% in 2015 and 58.9% in 2040 in order to achieve compliance with the Paris Agreement targets, highlighting the reduction of meat and fish and seafood consumption as the most useful path.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Embedding circularity into the transition towards sustainable agroforestry systems in Peru(Elsevier B.V., 2022-09-10)Peru is promoting the adoption of agroforestry systems with the aim to halt the deforestation of tropical forests caused by smallholder farmers. However, deficient soil conservation practices and nutrient management are common among the targeted smallholders, hampering the success of this strategy. In this study, we explore the potential of valorizing municipal biowaste as compost to be used as soil amendment in coffee agroforestry systems and in silvopastoral systems. The analysis was concentrated in four Peruvian regions and the most populous city in each of them. For lands with coffee production, it was assumed that 90 kg N ha−1 (i.e., 50% of the N requirements) should come from compost, while for pastures, the requirement was 40 kg P ha−1. We found that composting could lead to large greenhouse gas (GHG) reductions compared with the current waste disposal methods (i.e., deep dumping and landfilling), as it only emits 5–10% of the GHG emissions produced with the other methods. Nonetheless, the area of agroforestry and silvopastoral systems that could be fertilized with compost obtained from the main city of each region is limited and insufficient. If all compost were to be used for the coffee agroforestry system, less than 3% of the coffee agroforestry area could be fertilized, while in the case of pastures, only 4% would be attained. Large amounts of compost could be obtained from Lima, the most populated city; however, its transportation to the agroforestry areas would increase compost GHG emissions by 15–60%. Although composting municipal food waste and loss may bring GHG benefits and should be promoted, its use as a fertilizer requires mixing with N-rich sources to improve its nutrient quality.4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying random forest to forecast municipal solid waste generation from household fuel consumption(Elsevier BV, 2025-09-01)Accurately forecasting municipal solid waste (MSW) generation is essential for designing efficient waste management systems and promoting sustainable urban development. As cities expand and consumption patterns shift, reliable data-driven approaches are increasingly necessary to address the complexities of MSW generation. This study applied the random forest (RF) algorithm, a machine learning technique, to predict MSW generation at the household level. RF was selected for its capacity to handle non-linear relationships, imbalanced datasets, and outliers. The analysis focused on data from year 2019, avoiding distortions associated with the COVID-19 pandemic. The model integrated per capita MSW data with household fuel consumption indicators (i.e., natural gas, electricity, and liquefied petroleum gas) and demographic variables such as age, education level, and monthly expenditure. The case study focused on the city of Lima, Peru, using 80% of the data for training and 20% for testing, with hyperparameters optimized via 5-fold cross-validation. The final model explained 55% of the variance in MSW generation (R² = 0.55). This result reflects the model's ability to capture significant drivers of variability, although it leaves room for refinement due to factors not included in the analysis, such as cultural practices or seasonality. Among the predictors, household monthly expenditure on cooking fuels emerged as the most influential variable, reinforcing the connection between resource consumption and waste generation. These findings highlight the potential of integrating socioeconomic indicators into predictive models to enhance their reliability. By improving forecasting capabilities, this study supports targeted policies for urban waste management and sustainable resource use.1