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Item type:Publication, Unveiling the energy consumption–food waste nexus in households: A focus on key predictors of food waste generation(Springer, 2024-07-01)In most cities worldwide, household food waste constitutes a significant portion of municipal solid waste (MSW). However, its management often proves inadequate due to the insufficient resources allocated to waste management systems, the omission of the resource potential in MSW, and the lack of recognition of household food waste drivers for forecasting generation in specific geographical contexts. This research aims to identify social, economic, and environmental variables serving as proxies to forecast household food waste generation. To achieve this, a multiple linear regression model was proposed to assess the relationship between cooking fuel type (i.e., liquefied petroleum gas, natural gas, and electricity), land use categories (i.e., commercial, industrial, and residential), population density, expenditure on in-house food consumption, and household food waste generation. Three alternate modeling scenarios were considered based on available data, with Lima, Peru, serving as a case study. The results indicate that the combined consumption of liquefied petroleum gas and natural gas, and electricity consumption, along with residential land use, were the most influential variables. Finally, for a comprehensive understanding of the studied phenomenon, it is crucial to analyze and consider the intricate dynamics of societal consumption patterns. Graphical (Figure presented.). - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Integrating the water-energy-food nexus and LCA + DEA methodology for sustainable fisheries management: A case study of Cantabrian fishing fleets(Elsevier B.V., 2024-11-01)The fishing sector constitutes an important source of economic revenue in northern Spain. In this context, various research studies have focused on the application of the five-step Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) methodology to quantify environmental impacts of fishing systems. However, some of them have used environmental indicators that focus on individual environmental issues, hindering the goal of achieving integrated resource management. Therefore, in this study, the Water-Energy-Food (WEF) Nexus is employed as an integrative perspective that considers the synergies and trade-offs between carbon footprint, energy requirements, and water demand. The main objective of this study is to evaluate the operational efficiency and environmental impacts of Cantabrian fishing fleets. To this end, the combined use of LCA and DEA, along with the WEF Nexus, was applied to the Cantabrian purse seine fleet. DEA matrices were generated using the LCA-derived WEF nexus values as inputs to calculate efficiency scores for each vessel. Subsequently, based on the efficiency projections provided by the DEA model, a new impact assessment was performed to understand the eco-efficiency and potential environmental benefits of operating at higher levels of efficiency within this fleet. The average efficiency of the fleet was above 60 %. Inefficient units demonstrated a greater potential to reduce their environmental impacts (up to 65 %) by operating according to efficiency projections. Furthermore, the results revealed a strong dependence of environmental impacts on one of the operational inputs, i.e., fuel consumption. These findings highlight the significance of embracing holistic approaches that combine technical, economic, and social factors to achieve a sustainable balance in fisheries systems. In this regard, the five-step LCA + DEA method applied in conjunction with the WEF Nexus emerged as a suitable tool for measuring operational and environmental objectives. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identifying environmental impacts linked to the production of plant-based spreads in Peru using life cycle assessment(Elsevier B.V., 2024-06-01)Plant-based spread products, such as margarine, are made up of a combination of diverse ingredients, many times arriving from different parts of the world. This makes their environmental impact challenging to compute. In Latin America, despite efforts in recent years to enlarge the number of food items that have been analyzed from an environmental perspective, many processed products remain unexplored. In this context, the main objective of the current study was to determine the environmental impacts of a set of five plant-based spread products in Peru using life cycle assessment. For this, primary data were collected from the main margarine producer up to the gate of the agroindustrial plant ready for distribution. Methodological choices, such as allocation, the computation of land use changes (LUCs) or agricultural management variability, were an important subset of variables to be considered in the life cycle modeling and accounted for through scenario and sensitivity analyses. Results demonstrated that greenhouse gas (GHG) emissions related to margarine production in Peru range from 1.66 to 6.00 kg CO2eq per kilogram of product, in a similar range to other studies in the literature. LUCs accounted for the highest contribution to GHG emissions, whereas crude oil extraction, as well as on field fertilizer emissions were the other main contributors. In other impact categories, plant protection agents were relevant in toxicity indicators, fertilization in eutrophication and transport in air quality-related categories. These results constitute a benchmark for the production of plant-based products in Latin America and are useful for attaining cleaner production, as well as for the optimization of ingredients and packaging design. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Are Peruvians moving toward healthier diets with lower environmental burden? Household consumption trends for the period 2008–2021(John Wiley and Sons Inc, 2024-10-01)Peru is one of the most diverse countries in the world in terms of food production, but also suffers a wide range of food security challenges, including malnutrition, the impact of natural hazards, and rising food prices. People living in poverty conditions are the main victims of these problems, which trigger undernutrition, obesity, and diet-related non-transmittable diseases. Despite these challenges, Peru lacks historical food intake data. Therefore, in the current study, we assess the diet quality evolution in the period 2008–2021 based on apparent household purchases extracted from the National Household Survey. The results reveal significant variations in the consumption of certain food items and groups, and the consequences of these changes are discussed in environmental and human health terms. The consumption of lower environmental impact animal protein, such as chicken, eggs, and marine fish, has increased by 37%, 69%, and 29%, respectively; whereas the consumption of high environmental impact foods, such as beef and other red meat, has decreased. Moreover, consumption of less processed carbohydrate sources (e.g., legumes, fruits, and vegetables) has risen, while refined sugar and sugar-sweetened beverages have decreased significantly (almost 45%). Regional differences were also visible; hence, cities on the Northern coast and the Amazon basin had similar consumption habits, whereas Central/Southern coastal and Andean cities had closer consumption patterns. On average, this improvement was reflected in the increase in calories (9.9%) and macronutrient intake (up to 15%), but at the socioeconomic level, food inequality persists, with consumption of many food groups below minimum thresholds in lower socioeconomic strata. This article met the requirements for a gold/gold JIE data openness badge described at http://jie.click/badges. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review(Elsevier B.V., 2024-10-01)The presence of macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs' abundance is crucial for assessing the issue and formulating effective waste management strategies. In this context, the main objective of this critical review is to analyze the use of machine learning (ML) techniques, with a particular interest in deep learning (DL) approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. The search methodology aimed to categorize the scientific contributions through temporal and spatial criteria for bibliometric analysis, whereas the critical review was based on generating homogeneous groups according to the complexity of ML and DL methods, as well as the type of dataset. In light of the review carried out, classical ML techniques, such as random forest or support vector machines, showed robustness in MPs detection. However, it seems that achieving optimal efficiencies in multiclass classification is a limitation for these methods. Consequently, more advanced techniques such as DL approaches are taking the lead for the detection and multiclass classification of MPs. A series of architectures based on convolutional neural networks, and the use of complex pre-trained models through the transfer learning, are currently being explored (e.g., VGG16 and YOLO models), although currently the computational expense is high due to the need for processing large volumes of data. Additionally, there seems to be a trend towards detecting smaller plastic, which need higher resolution images. Finally, it is important to stress that since 2020 there has been a significant increase in scientific research focusing on transformer-based architectures for object detection. Although this can be considered the current state of the art, no studies have been identified that utilize these architectures for MP detection.