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The transition to energy systems with a high share of renewable energy depends on the availability of technologies that can connect the physical distances or bridge the time differences between the energy supply and demand points. This study focuses on energy storage technologies due to their expected role in liberating the energy sector from fossil fuels and facilitating the penetration of intermittent renewable sources. The performance of 27 energy storage alternatives is compared considering sustainability aspects by means of data envelopment analysis. To this end, storage alternatives are first classified into two clusters: fast-response and long-term. The levelized cost of energy, energy and water consumption, global warming potential, and employment are common indicators considered for both clusters, while energy density is used only for fast-response technologies, where it plays a key role in technology selection. Flywheel reveals the highest efficiency between all the fast-response technologies, while green ammonia powered with solar energy ranks first for long-term energy storage. An uncertainty analysis is incorporated to discuss the reliability of the results. Overall, results obtained, and guidelines provided can be helpful for both decision-making and research and development purposes. For the former, we identify the most appealing energy storage options to be promoted, while for the latter, we report quantitative improvement targets that would make inefficient technologies competitive if attained. This contribution paves the way for more comprehensive studies in the context of energy storage by presenting a powerful framework for comparing options according to multiple sustainability indicators.

Within the Erasmus+ project RRIL, the empirical studies on the degree of implementation of RRI in universities Finland, Poland and Spain showed that a) RRI has a low implementation degree in all three countries as a concept. But in all three countries, the basic principles of RRI as public engagement, gender equality, science education, open access, and ethics are present in the public discourse on science and are in different degree integrated in the structures and processes of the university as such. However, their integration in the concrete research and innovation activities is still a gap. b) RRI has not become a general guiding vision of university policies. In the recent years, the main guiding vision has become the Sustainable Development Goals (SDGs) similar as for the European Union and its member states. As the basic principles of RRI as also present in the SDGs, the decision was taken to integrate both in one concept called Responsible & Sustainable Innovation. Similar to RRI also the SDGs runs the risk to be only present in the official discourses without an effective integration in the research and innovation practice and to become a buzzword as so much others. Considering the big sustainable challenges which the humanity is facing actually, our project sustains that sustainability must be the reference point of all responsible innovation activities. To contribute to the anchorage of responsibility and sustainability in innovation based on scientific research, RRIL has developed and offers a training programme for Responsible & Sustainable Innovation for researchers, technicians of local authorities and university staff. It is expected that the training programme will support the orientation of university processes to the SDGs and to responsible innovation. In this article we will not present the learning programme, but the basics of the concept of Responsible & Sustainable Innovation (ReSI).

Tasks such as classification, clustering, and regression require the identification and elimination of outliers as part of the preprocessing of data. Without an adequate processing of outliers the results of data analysis will be biased and inexact. This article proposes a distance-based method for the detection of outliers in multivariate datasets. The proposed method takes advantage of the principal components for avoiding problems of collinearity in datasets and the high concentration of variance is used to increase the separation between outliers and inliers. The proposed method was compared against four outlier detection methods from the literature, two deterministic and two stochastic. The datasets used in the comparison were generated according to different works in the literature. These datasets follows different distributions and contains different amount of outliers and inliers, and different number of variables and instances. Per each distribution, low and high dimensionality were considered. Unlike to other methods in the state-of-the-art, the proposed method does not require apriori the definition of any value for its operation. Also, the calculation of distances of elements in the dataset takes lower processing time. According to the experiments, the proposed method is suitable to deal with dataset contaminated with low and high proportions of outliers, low and high dimensions, and simmetric and assimetric distributions. Also supports colineality in data.

This work shows a new approach to the parameter-fitting problem useful in the solutions thermodynamic field, providing a more objective framework to obtain better empirical/semiempirical models for...

Purpose Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA. Methods Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts. Results and discussion According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N2O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner. Conclusions Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.

The circular economy can be promoted as a solution to support the sustainability market position of renewable energy systems. To design a circular and sustainable system, a structured approach is needed. The present study develops a methodology framework for sustainable circular system design (SCSD), aiming to assess thermal energy storage (TES) technologies from a sustainable perspective. To this end, a composite indicator, namely, environmental sustainability and circularity indicator (ESC) is provided. This indicator combines the environmental impacts of the TES system via the conduction of a life cycle assessment and its circulatory performance using the product-level material circularity indicator (MCI). The developed methodology is applied to a case study of high-temperature TES using molten salts as a part of a concentrated solar power plant. The SCSD embraces the analysis for the most relevant processes through proposing different ecological scenarios including, increasing the recycling rates (Modest Scenario), increasing the reuse rates (Medium Scenario), and a combination of both (Optimistic scenario). The circularity analysis showed that for the Modest, Medium and optimistic scenarios, the MCI moves from 20.6% for the current situation to 30.3%, 38.6%, and 46.4%, respectively. Accordingly, the optimistic scenario showed the most environmentally sustainable and circular scenario with ESC of 7.89%, whereas the Modest and Medium scenarios exhibited ESCs of 1.20% and 2.16%, respectively. A major obstacle for substantial improvement of the circulatory and ESC is the high share of unrecyclable molten salts in the system and therefore, any effort to improve the circulatory and the environmental benefits of this system can be reached by using more environmentally friendly alternative materials. The study concludes that the integration of reusing and recycling at the initial design should be sought in order to achieve a more environmentally sustainable and circular outcome. The authors would like to acknowledge financial support from the Spanish Ministry of Economy and Competitiveness RTI2018-093849-B-C31 and RTI2018-093849-B-C33 (MCIU/AEI/FEDER, UE), and thank the Catalan Government (2019 FI-B-00762). The authors would like to thank the Catalan Government for the quality accreditation given to their research group (GREiA - 2017 SGR 1537, AGACAPE - 2017 SGR 1409 and 2019 FI-B-00762). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme. This work is partially funded by the Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación (AEI) (RED2018-102431-T). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713679 and from the Universitat Rovira i Virgili (URV).

The global agenda is undoubtedly determined by the success of achieving the Sustainable Development Goals (SDGs). Both public and private institutions show great efforts towards the full integration of the SDGs in their own agendas. Ultimately, national governments are responsible for the effective budget allocation for sustainable development. The lack of open, discussed and widely accepted general guidelines related to how to link national public spending (based on the classification of the functions of government) to the achievement of the SDGs is reported in the literature. Thus, the aim of this paper is to propose an initial mapping between them, as well as to assess, through data envelopment analysis (DEA), the national public spending efficiency where government expenditure is consumed (inputs) to attain a certain progress in indicators specific to all 17 SDGs (outputs). On the one hand, results were analyzed for each SDG by income groups, unraveling inefficient spending strategies, thus identifying potential weaknesses that should be overcome before some countries can achieve the same level of progress on SDGs as the best performing nations. On the other hand, it was demonstrated that low income and high income countries deliver higher average public spending efficiency. Countries of these two groups are more often deemed efficient, being displayed alongside the efficient frontiers of the DEA. This finding highlights that low middle-income and upper middle-income countries exhibit the major room for improvement in public spending. One of the authors (J. Cristóbal) acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities through the "Beatriz Galindo" grant (BEAGAL18/00035)