Examinando por Autor "Alonso Vicario, Ainhoa"

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    Advanced food waste quantification at municipal level to strengthen the assessment of prevention actions
    (Nature Research, 2025-08-12) Olano Oteiza, Begoña Untzizu; Amador Cervera, Manuel; Vargas Viedma, Virginia; Alonso Vicario, Ainhoa
    Food loss and waste (FLW) demands urgent attention: over 58 million tonnes wasted annually in the EU, while 828 million people face hunger worldwide. SDG 12.3 targets a 50% reduction in FLW by 2030, requiring rigorous quantification. Governments must quantify FLW, especially at the consumption stage, where most of FLW is generated. However, the limited granularity of existing data emphasises the need for improved quantification methods that enable reliable comparisons and set solid basis to measure the impact of future FLW prevention actions. This paper proposes a FLW quantification methodology at municipal and regional scales aligned with the existing urban waste management requirements. The methodology involved experts and stakeholders to define the optimal FLW quantification framework and measurement method. It was validated in 6 Basque municipalities with a common waste characterisation matrix to compare waste fractions, generator types and waste collection systems. A cartographic analysis, from regional to street level, demonstrates how data granularity shapes FLW pattern interpretation. As in the case of Zamudio, where data at container level permits detailed insights. This approach enables improved waste collection and the design of ad-hoc FLW prevention actions (like economic instruments) according to the generator profile and type of FLW.
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    Carbon and water footprint of food loss and waste prevention actions: cradle-to-grave life cycle assessment of a prepared salad
    (Elsevier Ltd, 2025-02-06) Amador Cervera, Manuel; Scherhaufer, Silvia; Gollnow, Sebastian; Alonso Vicario, Ainhoa
    Food loss and waste (FLW) represents a major challenge for sustainable development. FLW prevention actions are proliferating, but key gaps persist in assessing their actual net environmental benefits, particularly in prevention at the point of generation. Additionally, data transparency is still a key issue. This study aims to assess the environmental impacts avoided by 5 groups of FLW prevention actions implemented in a prepared salad case study in Spain. Additionally, 4 different salad recipes are analysed, so that the influence of ingredient composition on the effectiveness of FLW prevention actions is accounted for as well. A cradle-to-grave life cycle assessment (LCA) was conducted to have a holistic view of the real impacts across the entire system. The methodology involves an identification and characterisation of the points of FLW generation, enabling a more precise estimation of the FLW that can be reduced through prevention actions. Results indicate that innovative governance solutions yield the most positive impacts, avoiding climate change impacts by 9.8 and water use by 8.80 %. The salad with higher proportions of animal-based ingredients experienced the largest impact reductions. These findings highlight areas for targeted prevention efforts for decision makers. Primary production generated the most environmental impacts coming from FLW. However, the holistic perspective taken demonstrated that effective prevention should focus on downstream points of generation. In conclusion, the presented LCA approach accurately identifies the most promising FLW prevention opportunities to improve the environmental performance of food products. The enhanced data transparency improves accuracy and promotes openness and accountability in LCA and FLW research
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    E-Mobility in positive energy districts
    (MDPI, 2022-02-24) Castillo Calzadilla, Tony; Alonso Vicario, Ainhoa; Borges Hernández, Cruz E.; Martín Andonegui, Cristina
    A rise in the number of EVs (electric vehicles) in Europe is putting pressure on power grids. At an urban scale, Positive Energy Districts (PEDs) are devised as archetypes of (small) urban districts managing a set of interconnected buildings and district elements (lighting system, vehicles, smart grid, etc.). This paper offers a comprehensive analysis of the impact of e-mobility in a PED, simulated using MATLAB-Simulink software. The PED, a small district in northern Spain, is assessed in five scenarios representing varying requirements in terms of energy efficiency of buildings, type of street lighting and number of EVs. The results suggest that the energy rating of the buildings (ranging from A for the most efficient to E) conditions the annual energy balance. A PED with six interconnected buildings (3 residential and 3 of public use) and 405 EVs (as a baseline) only achieves positivity when the buildings have a high energy rating (certificate A or B). In the most efficient case (A-rated buildings), simulation results show that the PED can support 695 EVs; in other words, it can provide nearly 9 million green kilometres. This result represents a potential 71% saving in carbon emissions from e-mobility alone (as compared to the use of fossil-fuel vehicles), thus contributing a reduction in the carbon footprint of the district and the city as a whole.
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    Enhancing the missing data imputation of primary substation load demand records
    (Elsevier Ltd, 2020-06-23) Borges Hernández, Cruz E.; Kamara Esteban, Oihane; Castillo Calzadilla, Tony; Martín Andonegui, Cristina; Alonso Vicario, Ainhoa
    The daily analysis of loads is one of the most important activities for power utilities in order to be able to meet the energy demand. This analysis not only includes short-term forecasting but it also encompasses the completion of missing load data, known as imputation. In this work we show that adding information of attached or bordering primary substation helps to improve the prediction accuracy in a single substation, since its neighbours may share common weather-related (e.g. temperature, humidity, wind direction, etc.) and human-related (e.g. work-calendar, holidays, cultural consumption patterns, etc.) data. In order to validate these approaches, we test the forecasting and imputation neighbouring methodology on a wide variety of datasets. Results confirm that, given a primary substation, the addition of information from surrounding substations does improve the forecasting and imputation errors.
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    The FOODRUS index: assessing suitability for effective food loss and waste prevention management under an integral perspective
    (Elsevier Ltd, 2024-04) Amador Cervera, Manuel; Angarita Zapata, Juan S.; Calle, Alberto de la; Alonso Vicario, Ainhoa
    The impact of food loss and waste (FLW) generation on food supply chains' (FSC) sustainability represents a challenge embodied in the Sustainable Development Goal (SDG) 12.3. This problem requires a methodology to measure such an impact in a rigorous, holistic, and standardized way that can be applied to any FSC. This paper aims to develop and validate a single index to assess the readiness of FSCs to implement FLW prevention strategies and measure their impact: the so-called FOODRUS index. The co-creation methodology followed incorporates experts and FSC stakeholders feedback. The index has been validated in 3 FSCs: The Slovak pilot scored 74.35%, the Spanish pilot reached 68.79%, and the Danish pilot was rated 61.14%. Its calculation, eased by the FOODRUS index self-assessment tool (described in the Appendix), allows quick diagnosis of the FSC capability to implement FLW prevention strategies considering both the knowledge provided by experts and the experience of the FSC stakeholders that participated in its co-creation process. In this way the FSC can assess its FLW prevention performance at a strategic and management level, with the aim of improving its sustainability impact.
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    Green space assessment and management in Biscay Province, Spain using remote sensing technology
    (AGH University of Science and Technology Press, 2021-10-20) Makinde, Esther O.; Martín Andonegui, Cristina; Alonso Vicario, Ainhoa
    Our ecosystem, particularly forest lands, contains huge amounts of carbon storage in the world today. This study estimated the above ground biomass and carbon stock in the green space of Bilbao Spain using remote sensing tech-nology. Landsat ETM+ and OLI satellite images for year 1999, 2009 and 2019 were used to assess its land use land cover (LULC), change detection, spectral indices and model biomass based on linear regression. The result of the LULC showed that there was an increase in forest vegetation by 12.5% from 1999 to 2009 and a further increase by 2.3% in 2019. However, plantation cover had decreased by 3.5% from 1999–2009; while wetlands had also decreased by 9% within the same period. There was, however, an increase in plantation cover from 2009 to 2019 by 2.1% but a further decrease in wetlands of 4.3%. Further results revealed a positive correlation across the three decades between the widely used Normalized Differential Vegetation Index (NDVI) with other spectral indices such as Enhance Vegetation Index (EVI) and Normalized Differential Moisture Index (NDMI) for biomass were: for 1999 EVI (R2 = 0.1826), NDMI (R2 = 0.0117), for 2009 EVI (R2 = 0.2192), NDMI (R2 = 0.3322), for 2019 EVI (R2 = 0.1258), NDMI (R2 = 0.8148). A reduction in the total carbon stock from 14,221.94 megatons in 1999 to 10,342.44 megatons 2019 was observed. This study concluded that there has been a reduction in the amount of carbon which the Biscay Forest can sequester.
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    A taxonomy of food supply chain problems from a computational intelligence perspective
    (MDPI, 2021-10-18) Angarita Zapata, Juan S. ; Alonso Vicario, Ainhoa; Masegosa Arredondo, Antonio David ; Legarda Macon, Jon
    In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a datadriven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.