Search

Social life cycle assessment (S-LCA) is a tool to address social impacts from a life cycle perspective. This work presents the first S-LCA of the Portuguese pulp and paper sector. It presents the social risks and the positive social impacts of the sector, which have not been extensively covered in the scientific literature. Two different approaches were used to compile the social life cycle inventory based on input-output analysis. Approach 1 was based on impact analysis of changes in added value. Approach 2 was based on structural path analysis, supporting the disaggregation by production levels (or tiers). The functional unit was the production of 1 United States Dollar in the Portuguese pulp and paper sector. In the social life cycle impact assessment, the social risk assessment was conducted using the Social Hotspots Database. The positive impacts were addressed considering indicators of employment and remuneration, namely total employment, number of employees, labor compensation, and compensation of employees. Based on Approach 1, the results of the social risks showed the Portuguese pulp and paper industry, commerce and forestry sectors, and the Spanish forestry sector as the critical sectors. Based on Approach 2, Portugal and Spain continued to be the countries having the greatest social risks. Other contributions of countries were identified at different production levels, particularly China in tier 3. In both approaches, the stakeholder category 'worker' was the most critical. Regarding the positive social impacts, Approach 1 showed that Portugal and Spain contribute to the highest employment level linked to the pulp and paper, commerce, and forestry sectors. Approach 2 supported the differentiation of employment and remuneration at different production levels. Tier 2 (direct suppliers) presented the highest employment level and persons engaged and the lowest remuneration level. The results showed that Approach 1 provides a faster and more aggregated assessment of social risks and positive social impacts. Furthermore, Approach 2 allowed a more detailed evaluation, providing a better understanding of the social risks and positive social impacts on the supply chain. Thus, Approach 2 is encouraged for different applications since it produces a better understanding of where social risks and positive social impacts occur, providing better guidance for corrective measures and decision-making. [ABSTRACT FROM AUTHOR]

The operation of solar photovoltaic installations for many years may lead to problems such as reduced energy efficiency and aging of components. Under this framework, replacing certain components in the facility to extend the service life of the power plant is viewed, in some cases, as a feasible strategy. However, despite its enormous strategic interest for the solar PV industry, we find that revamping PV installations has been the focus of very few scientific publications, and even less so when it comes to analyzing real facilities. Thus, this paper performs a techno-economic analysis of three revamping strategies at a real PV power plant located in the south-east of Spain. The strategies consist of substituting, in each case, the PV modules, the inverter, and both components. The simulated results show that the highest production value by installed power is obtained when both the modules and the inverter are replaced. In the same vein, the economic analysis reveals this scenario as the most favorable in terms of profitability. Based on the results obtained, an interesting future line of research would be focused on the extension of the analysis at country level to study the joint impact of numerous revamping actions. • Techno-economic analysis of revamping projects of a real solar PV power plant. • Replacing modules and inverter emerges as the most feasible strategy. • Study's applicability extends to other revamping initiatives and geographical regions. • Economic outcomes allow the most cost-effective alternatives to be determined. • This research can contribute to efficient and sustainable energy planning. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

Efficiency is a fundamental element of the transition towards a sustainable use of natural resources among which water has a prominent role. Spain is among the European countries facing considerable water shortage problems, particularly within its southern regions. The current paper introduces an efficiency evaluation model based on Data Envelopment Analysis (DEA). The model incorporates a subset of outputs as undesirable inputs to account for the circularity of the production process by means of treated and reused water. The main differences existing between the circular approach proposed, a standard two-stage DEA model and a dynamic Slacks-Based Measure process will be carefully illustrated. In particular, the drawbacks faced by these latter techniques to consistently incorporate circularity into their optimization structures will be demonstrated both formally and numerically. The malleability of the current approach and its capacity to generate consistent identifiable categories among the regions evaluated within a circular production setting will be studied in detail. The analysis performed demonstrates significant structural differences in production capacity and efficiency across regions, with the inefficient ones displaying a considerable subpar use of resources through their production processes. The prevalence of inefficiency in several northern regions is partially compensated through their treatment and reutilization of water. However, this is not the case for the southern ones, which face stricter constraints and more inefficient production processes. The subsequent implications for efficiency are examined and policies aimed at enhancing the structural quality of the corresponding production systems suggested. [Display omitted] • We define a circular Data Envelopment Analysis (DEA) efficiency evaluation model. • Undesirable inputs implement the production circularity generated by treated water. • Two-stage and dynamic Slacks-Based Measure DEA are inapplicable to our setting. • Significant differences in production efficiency are found across Spanish regions. • Southern regions remain inefficient even with water treatment and reutilization. [ABSTRACT FROM AUTHOR]

Finding alternative phosphorus sources is imperative to address negative environmental and societal impacts caused by its current inefficient use. However, the direct use of phosphorus in sewage sludge in agriculture is controversial. This paper uses Denmark, Germany, and Spain as case examples to assess relevant legislation and boundary conditions in agricultural production to identify opportunities and barriers for the utilisation of recycled phosphorus from wastewater in agriculture on a regional level. Only five out of 22 phosphorus recycling technologies considered were in full compliance with legislation across all three countries, and these five were then assessed for their potential to supply phosphorus to major crops within countries. We considered the application of technologies across four scenarios: 1) struvite; 2) vivianite as iron supply; 3) vivianite for calcium phosphate precipitation; and 4) ashes for calcium phosphate precipitation. The most suitable scenario identified for Denmark was vivianite for calcium phosphate precipitation, whereas in Spain vivianite as iron supply was identified as most suitable, and ashes for calcium phosphate in Germany. We found that in 2018, the potential phosphorus supply from recycling technologies was on average 0.38, 0.29 and 0.05 kg of phosphorus per capita for Danish, German, and Spanish regions. These quantities could meet 9.1, 21.7, and 10.0 percent of the phosphorus required to produce major cereals in each country (specifically wheat, barley, and rye). Given current legal constraints, wastewater treatment plant connections and agronomic context, the potential contribution of recycled phosphorus is non-negligible in many sub-national regions. Still, to access the full potential of phosphorus circularity clear product specifications and transport and logistics among regions will be necessary. [ABSTRACT FROM AUTHOR]

Despite the relevant potential of offshore resource to substantially mitigate the effects of climate change through the generation of renewable electricity, the full exploitation of such wind resource currently remains incomplete. To promote offshore wind energy projects, it is crucial to engage in comprehensive planning processes that encompass technical, social, environmental, stakeholder, and political considerations. Within this framework, this paper presents a combined approach based on Geographical Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methodology to optimize the selection of offshore wind farm locations. The proposed methodology includes annual net electricity production as a critical factor in the decision-making process, which has been previously optimized with regard to the wake effect. Subsequently, the determination of potential locations involves key criteria such as technical feasibility, environmental impact, economic viability, and power generation potential. This MCDM-GIS approach was applied and evaluated in a case study along the Spanish coastline, considering 92 initial alternatives. The results indicate that the offshore wind energy targets established for Spain in 2030 and 2050 should be reevaluated, as the optimal utilization of the available area represents only 16% of the total potential area. Based on the aforementioned criteria, a suitability map was generated by integrating all relevant map layers, including their respective buffer zones. [Display omitted] • Net electricity production criterion included in the alternative decision matrix. • GIS-MCDM synergy methodology at territorial level to optimize offshore wind sites. • Multi-criteria definition includes technical, environmental, and economic factors. • 92 potential offshore wind plant alternatives in a Spanish case study are considered. • Results give different ranking of alternatives depending on each MCDM approach. [ABSTRACT FROM AUTHOR]

The consumption of plastic raw materials has been exponentially growing throughout the world in the last decade and so has, in the same proportion, their associated waste. Recycling through the mechanical treatment of the heavy fraction of these plastics is the most economic and the safest alternative to use this waste and to recirculate it for reuse within the same industry or for other related industries located near the place where the waste is generated. In this paper, a study of characterization and post-processing of recycled high density polyethylene received by a hazardous waste manager in southern Spain is presented, an area in which containers, Intermediate Bulk Containers, and drums, originally used for oil products from refineries and petrochemical industries nearby, are employed. These high density polyethylene materials are validated to meet the requirements of the harmonized standards to be used as self-supply in the production of manhole covers. EN 124–6:2015 standard allows the use of plastic materials such as PE or PP, even using material externally reprocessed, such as material derived from thermoplastic products that have not been previously used as lids or manhole covers. Class A-15 and B-125 of the standard are oriented for the construction of these types of elements to cover or close in certain urban spaces such as pedestrian areas, parking areas or multi-story car parks for vehicles. The study shows that the recycled material fully complies with the density; it keeps unalterable during fire test after application of the coating, there are no adverse effects on artificial ageing; the decrease of the average value of the tensile impact strength by 21.84%; and achieves 6 mm of maximum deflection against the test load Fp (45.3 MPa). Therefore, it can be considered a valid scenario for the effective use of solid plastic waste in products that make up part of the urban or industrial areas, as an example of complete circularity material and offers a valid alternative to the end-of-waste condition. [Display omitted] [ABSTRACT FROM AUTHOR]

Depopulation is a major challenge for many rural inland areas, as is the case in a considerable number of rural municipalities in Galicia (north-western Spain). The pandemic had a considerable impact on internal migration in 2020, but it was far from being anywhere near high enough to reverse the ongoing trend of population decline, highlighting the need for further action. Thus, this paper has a twofold objective: firstly, to propose an index of rural sustainable development for the rural Galician municipalities, and secondly, to identify the factors related to it. In so doing, a composite index stemming from the Benefit of the Doubt methodology has been proposed in the first stage, an index built from four-dimension factors– economic, demographic, social, and environmental. The empirical evidence of this index has been compared with three additional ones (namely, common weights, super-efficiency, and geometric mean and logistic normalization models). In the second stage, the relationship between the composite index and a set of exogenous variables that affect rural development has been analyzed, the data confirming that the rates of female immigration and occupancy in the hospitality sector favors sustainable development, whereas the distance from the provincial capital hinders it. This paper paves the way for an assessment of the potentialities of each territory, and the evidence found allows actions to be supported that generate sustainable progress in rural Galician municipalities and that are useful for tackling this complex problem. • A new rural sustainable development index based on four dimensions is proposed. • Strengths and weaknesses for each Galician rural municipality are identified. • Environmental factors constitute a driver of rural development in Galicia. • Social aspects (i.e., health, education, services) stand out as main weaknesses. • Policies to boost social care and attract childbearing-age inhabitants are paramount. [ABSTRACT FROM AUTHOR]

In Spain, due to its important mining past, there are many abandoned mine waste facilities. This study evaluates the potential of Spanish mine wastes for the recovery of critical and strategic raw materials and other elements of economic relevance. For this purpose, 20 mine waste facilities have been selected in different parts of Spain based on criteria such as tonnage, element content or metal market price. Surface samples were taken at the facilities, considered representative of these, for subsequent granulometric, mineralogical and chemical analysis. The grain size data were obtained by standard sieving (ASTM 5000), while the mineralogy was obtained by X-ray Diffraction (XRD). For chemical characterization, Wavelength Dispersion X-ray Fluorescence Spectrometry (WDXRF) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) were used to analyze 38 chemical elements (Be, Sc, V, Cr, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ag, Cd, Sn, Sb, I, Cs, Ba, La, Ce, Nd, Sm, Hf, Ta, W, Tl, Pb, Bi, Th and U), many of them included in the European Union's list of Critical Raw Materials (CRMs). In addition, leachate data from mine wastes in the Iberian Pyritic Belt were analyzed to assess their bioleaching potential. The calculated enrichment factors (EF) indicate that some critical elements, as well as others with strategic and economic interest, are highly enriched in the wastes, including Sb, Bi, As, Pb, Cu, Ag, Zn, Cd, Sn, Se and Th. The most promising facilities with the highest recovery potential are located in the historic Riotinto and Tharsis mines (both in Huelva, Iberian Pyrite Belt) and the Rubiais mine in Lugo, whose tailings could contribute up to 3.2 billion dollars gross, according to the current market metal prices. This paper aims to provide a first step to promote mine waste recycling and circular mining in Spain, an initiative that would also help to mitigate the environmental damage derived from abandoned mine wastes. • First national-scale assessment of Critical Raw Materials from mine waste facilities in Spain. • Mine waste facilities enriched with critical and strategic metals in terms of enrichment factors. • Most promising facilities evaluated in terms of metal tonnages and estimated gross benefit. [ABSTRACT FROM AUTHOR]

The rapid expansion of photovoltaics within the global energy market requires increasing use of land, a limited resource where different uses compete. In this respect, photovoltaic systems that combine electricity generation with land use for crops are becoming increasingly important. This article proposes an analytical model validated in an operating bifacial photovoltaic plant during the summer months. This analytical model considers view factors and heat transfer calculations, as well as an electrical model of the photovoltaic modules. A study was carried out on a commercial solar power plant (11.24 MWp) in Seville, southern Spain, where different vegetal species were planted in two strings, and string performance was monitored. Nine crop species with different irrigation and light requirements, offering different albedo parameters and thermal characteristics. The results show advantages from the point of view of module temperature and power gain. This paper provides regressions for the characterisation of the photovoltaic agricultural plant, with R2 higher than 85%, both for module temperature and energy exported in the string with crops. [Display omitted] [ABSTRACT FROM AUTHOR]

Studies in the agro-industry sector have shown the importance to evaluate the feasibility of an IBLC (a multi-product transformation plant). This paper develops a model to simulate the performance of an IBLC in Spain, using a combined approach: AHP, TOPSIS, and a hybrid simulation model. Results show that profits can be incremented by about 55.7% and environmental emissions of KgCO2e per tonne of bio-commodity produced, reduced by 24.2%. The scientific contribution covers the development of a simulation model that dynamically select suppliers based on a combination of social, environmental, and economic criteria. In addition, the model can be used to evaluate the economic and environmental feasibility of the IBLC by integrating the suppliers selection with the operations of a multi-product transformation plant. [Display omitted] • We provide a list of criteria that are relevant to choose suppliers of wheat. • We introduce the concept of ÍBLC and test its economic feasibility. • We develop an approach to integrate AHP and TOPSIS in a simulation environment. • We show, based on a case study in Spain, that the IBLC can be profitably run. • Profits increase by 55% while KgCO2e emissions reduces by 24.2%. [ABSTRACT FROM AUTHOR]

Wine is the main economic resource in the 'Area of Jerez', located in the south of Spain. It is one of the most important wine tourism destinations in the world. However, its sustainable production process, so strongly influenced by climatic conditions, can be clearly affected by the phenomenon of global warming. The ageing process of "Sherry wine" takes place in buildings known as 'Cathedral warehouses', an example of Nearly-Zero Energy Buildings (NZEBs). For centuries, they have been used to provide specific ecological conditions for biological ageing without any type of mechanical heating/cooling system. The purpose of this paper is to assess the impact of climate change on this production model. For this, the annual thermal behavior of a representative winery was monitored during several years. The first energy simulation model of this type of buildings was developed and validated (daily R2 = 99%; hourly R2 = 97%). Finally, the model was simulated by modifying the outdoor climatic conditions (9 years training data sets) for a climate change scenario, applying the 'morphing' method for the transformation of the weather data. The results show that, by the middle of this century, the average outdoor temperature will increase by 2.3 °C (in summer up to 3.6 °C). As a consequence, the temperature in the ageing zone would exceed the comfort limit for 19% of the year, (up to 34% in hot years). For 2080, the prediction is even worse (average value of 27%). As a result, the microorganisms responsible for this unique process could not maintain their activity. This fact will force the sector to face a harsh reconversion through the adoption of new mechanisms and strategies, which endangers the economic sustainability of an entire region dedicated to this practice. Beyond the specific case, the work highlights the need to evaluate the future behavior of NZEBs through simulation. [Display omitted] • Assess the impact of climate change in a Near Zero Energy Building. • "Cathedral warehouses" will lose their usefulness by the middle of this century. • Comfort limits will be exceeded 19% of the year, interrupting biological ageing. • The economic sustainability of an entire region would be endangered. • The need to evaluate the future behavior of Near Zero Energy Building is highlighted. [ABSTRACT FROM AUTHOR]

The evaluation and comparative analyses of energy conversion technologies are carried out with well-established concepts, like the concept of efficiency. Today, however, new technologies based on renewable energy sources challenge conventional approaches. Accounting for fuel diversity and other inequalities can ensure that comparative analyses result in trustworthy results. This paper aims to address this issue by updating conventional efficiency for more accurate comparative evaluations of fossil fuel energy systems. Specifically, the concept of total efficiency is introduced to account for extraction, processing and transportation of fossil fuels, aspects that are not currently considered in the conventional definition of efficiency. The total efficiency is determined by studying the environmental impacts of these processes and quantifying their energy requirements in terms of additional fossil fuel use. Two case studies in Spain are presented to demonstrate the impact of using the developed method on efficiency estimates. The total efficiency is found to be lower than the convectional efficiency by 21–25% to up to 41%, in the most unfavorable cases. This shows the significant impact of the preparation steps of fossil fuels and represents a fairer comparison between fossil fuels and renewable conversion processes. • Introducing total efficiency for more accurate comparative evaluations of energy systems. • Total efficiency accounts for extraction, processing, and transportation of fossil fuels. • The developed method links environmental impacts to additional fuel input. • Fuel processing decreases the efficiencies of fossil power plants significantly. • Updated tools, like total efficiency, are necessary to achieve future energy sustainability. [ABSTRACT FROM AUTHOR]

Livestock farming has been traditionally assessed by its economic figures, whereas the impact caused by their production models on the environment and on society have been largely disregarded. This paper analyses various organic cattle farming systems from the point of view of their technical-economic management, but also on the environmental impact caused by their greenhouse gas (GHG) emissions. Within this context, 34 organic meat cattle dehesa farms located in the southwest of Spain were studied. On the one hand, a principal component analysis devised four factors that explained the technical-economic management model of these farms in terms of their level of dependence on subsidies, production intensification, feeding practices and productivity. The farms were then divided into three clusters whose main differences came from the size of the farms, the intensification level and the joint rearing of cattle with other livestock species. On the other hand, GHG emissions and carbon sequestration levels for each of these clusters were calculated using the life cycle assessment methodology. The average GHG net emissions from the farms were 461.98 kg of CO 2 eq per ha (or 8.86 kg of CO 2 eq per kg of live weight of sold animal), with the largest and more extensive farms reporting 262.05 kg of CO 2 eq per ha (or 6.02 kg of CO 2 eq per kg of live weight of sold animal) and the smallest and more intensive farms resulting in as much as 697.49 kg of CO 2 eq per ha (or 11.18 kg of CO 2 eq per kg of live weight of sold animal). One of the most significant aspects of the study was the carbon sequestration capacity of dehesa extensive farms, which allowed these livestock systems to be in line with the preservation of the natural resources, animal welfare and the provision of ecosystem services. [Display omitted] • Sustainability of organic livestock farms is at risk due to low profitability. • C storage is a key ecosystem service of livestock systems to fight climate change. • Carbon sequestration in LCA reduces net emissions on extensive organic beef farms. • Livestock management on extensive organic farms improves climate change adaptation. [ABSTRACT FROM AUTHOR]

Circular Economy (CE) implementation is considered an instrumental strategy to help reaching the world's resource, energy, and climate mitigation targets, including the Sustainable Development Goals (SDGs) 2030. However, major barriers must be overcome to facilitate CE implementation in industry such as cultural, market, regulatory and technological considerantions (Kirchherr et al., 2018). Improving management systems within industrial companies is crucial when implementing effective CE solutions in compliance with existing industrial standards such as quality and environmental management systems. However, it is not yet common practice yet due to the lack of standardized, suitable, and easy-to-use supporting analytical tools. This paper attempts to fill this gap by presenting Industrial Circular Economy Questionnaire (ICEQ) , a self-diagnosis questionnaire built upon the revision of industrial harmonized standards and, academic literature on business-level CE assessment tools. ICEQ , compiling a set of 165 questions for company self-CE assessment, was applied to 30 industrial companies belonging to 17 economic sectors (automotive, machining and metalworking transformation, waste management, machine tool, etc.) in the Basque Autonomous Community (BAC) in Spain. The automotive sector stands out with 70% of circularity while Machining and metalworking transformation only has 34%, showing that the automotive sector is more conscious of CE since they must comply with End-of-Life Vehicles (EoLV) legislation. [ABSTRACT FROM AUTHOR]

The efficient integration of high levels of industrial waste heat in low temperature district-heating networks is a promising technique that requires specific methodologies for its satisfactory implementation. This paper presents a novel methodology for assessing the energy and economic feasibility of new district-heating networks in existing urban areas for the integration of industrial waste heat sources. The methodology consists in an innovative multistep procedure using geographic information systems and data analysis tools, combining georeferenced data about buildings, industries and roads. The spatial distribution of the analysis area is divided into smaller buffers and grids, as a result, the routing design of the pipelines that makes up the district-heating topology is obtained under several assumptions. The methodology provides the most suitable area choice for the deployment of a district-heating, also implemented with a multi-step algorithm for routing the pipelines of the network. This methodology is applied to a particular case study located in Vitoria-Gasteiz (northern Spain). Different configurations for the district heating network are obtained with lengths of the network varying from 8 to 27 km. Payback values near to six years are achieved in most of the district-heating network configurations. The maximum payback period obtained within the configurations is 8.5 years. An economic sensitivity analysis is presented for the proposed optimal district-heating network configuration. The proposed methodology could be replicated for different case studies as long as the input data is available to the user. • GIS and data analytics are combined for the DH network design in already built areas. • Economically optimal areas and buildings are returned by the grid analysis. • Graph theory is used as routing algorithms, matching up the existing road network. • This methodology is applied to a case study in Spain with PB values around 6 years. • LiDAR data is used for the estimation of the heating demand in residential buildings. [ABSTRACT FROM AUTHOR]

The production of berry, a fruit highly appreciated by consumers, has considerably increased in the last years. Europe Union has 1.55 millions of smallholdings dedicated to blueberry farming. Actually, it is an important sector on Spain agrifood exports, especially in Asturias. In order to increase sustainability in crops, it is necessary to identify the key environmental aspects. Blueberry cultivation methods are well documented in bibliography, but Life Cycle Assessment (LCA) researches are virtually inexistent in Europe. For this reason, this work has studied from an environmental perspective four blueberry orchards sit in Asturias (Northern Spain) and the field agricultural practises used. LCA results showed that the main environmental impacts of blueberry production were originated by the use of fertilisers, followed by the use of fossil fuels, plastic and paper materials and incineration of pruning. Significant differences between conventional and organic crops have not been found. The carbon footprints (CF) obtained in this study were between 0.32 kg and 1.66 kg CO 2 -eq/kg blueberry. CF values obtained in two of the systems analysed were within the literature ranges, between 0.20 and 0.80 kg CO 2 -eq. per kg of fruit. However, higher values were found in the other two orchards. Environment improvements have been proposed for each particular case. Results here obtained could be used to support policy changes, such as the implementation of sustainable practices in order to lead to a more sustainable blueberry production with lower GHG emissions. [Display omitted] • Blueberry orchards at small scale in Europe were analysed by LCA. • Realistic improvement actions were proposed to reduce the environmental impacts. • Fertilisers, fossil fuels and wastes were the most relevant sources of impacts. • Carbon footprint decreased by employing good agricultural and environmental conditions (GAEC). • Small orchards and moderate productivities entails low environmental impacts and GAEC. [ABSTRACT FROM AUTHOR]

The literature on Sustainable Business Models (SBMs) has burgeoned, identifying different archetypes to capture the variety of business models applied. Little is known, however, regarding to what extent such SBMs are effectively driving sustainable performance. This paper addresses this gap by exploring how SBMs relate to sustainability performance, considering both overall sustainable performance and the balance across the three dimensions – environmental, social, and economic (integrated performance). Based on original survey data on B Corps located in Italy, Spain and the United Kingdom, our findings suggest that the implementation of most SBMs results in the prioritization of one sustainability dimension over the others, especially when it comes to economically-oriented SBMs. Furthermore, our study suggests that none of the SBM archetypes considered is associated with a balanced sustainable performance, that is, none of them are inherently better able to overcome tensions across the Triple Bottom Line. • We study how SBMs archetypes relate to sustainability performance (SP). • Most SBMs prioritise a triple bottom line (TBL) axes over the others. • Economically-oriented SBMs are not connected with superior SP. • No SBM archetype drives a balanced performance across TBL axes. • A means-end divide emerges. [ABSTRACT FROM AUTHOR]