A Management Framework for Municipal Solid Waste Systems and its Application to Food Waste prevention

Waste management is a complex task involving numerous waste fractions, a range of technological treatment options, and many outputs that are circulated back to society. A systematic, interdisciplinary systems management framework was developed to facilitate the planning, implementation, and maintenance of sustainable waste systems. It aims not to replace existing decision-making approaches, but rather to enable their integration to allow for inclusion of overall sustainability concerns and address the complexity of solid waste management. The framework defines key considerations for system design, steps for performance monitoring, and approaches for facilitating continual system improvements. It was developed by critically examining the literature to determine what aspects of a management framework would be most effective at improving systems management for complex waste systems. The framework was applied to food waste management as a theoretical case study to exemplify how it can serve as a systems model for complex waste systems, as well as address obstacles typically faced in the field. Its benefits include the integration of existing waste assessment models; the inclusion of environmental, economic, and social priorities; efficient performance monitoring; and a structure to continuously define, review, and improve systems. This framework may have broader implications for addressing sustainability in other disciplines

VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain

[EN] Many-objective optimization methods have proven successful in the integration of research attributes demanded for urban vulnerability assessment models. However, these techniques suffer from the curse of the dimensionality problem, producing an excessive burden in the decision-making process by compelling decision-makers to select alternatives among a large number of candidates. In other fields, this problem has been alleviated through cluster analysis, but there is still a lack in the application of such methods for urban vulnerability assessment purposes. This work addresses this gap by a novel combination of visual analytics and cluster analysis, enabling the decision-maker to select the set of indicators best representing urban vulnerability accordingly to three criteria: expert¿s preferences, goodness of fit, and robustness. Based on an assessment framework previously developed, VisualUVAM affords an evaluation of urban vulnerability in Spain at regional, provincial, and municipal scales, whose results demonstrate the effect of the governmental structure of a territory over the vulnerability of the assessed entities.This research was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER, grant number Project: BIA2017-85098-R".Salas, J.; Yepes, V. (2019). VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability. 11(8):2191-01-2191-17. https://doi.org/10.3390/su11082191S2191-012191-17118Rigillo, M., & Cervelli, E. (2014). Mapping Urban Vulnerability: The Case Study of Gran Santo Domingo, Dominican Republic. Advanced Engineering Forum, 11, 142-148. doi:10.4028/www.scientific.net/aef.11.142Malekpour, S., Brown, R. R., & de Haan, F. J. (2015). Strategic planning of urban infrastructure for environmental sustainability: Understanding the past to intervene for the future. Cities, 46, 67-75. doi:10.1016/j.cities.2015.05.003Salas, J., & Yepes, V. (2018). Urban vulnerability assessment: Advances from the strategic planning outlook. Journal of Cleaner Production, 179, 544-558. doi:10.1016/j.jclepro.2018.01.088Moraci, F., Errigo, M., Fazia, C., Burgio, G., & Foresta, S. (2018). Making Less Vulnerable Cities: Resilience as a New Paradigm of Smart Planning. Sustainability, 10(3), 755. doi:10.3390/su10030755De Gregorio Hurtado, S. (2017). Is EU urban policy transforming urban regeneration in Spain? Answers from an analysis of the Iniciativa Urbana (2007–2013). Cities, 60, 402-414. doi:10.1016/j.cities.2016.10.015Salas, J., & Yepes, V. (2019). MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios’ risks and opportunities on multi-scale infrastructure systems. Journal of Cleaner Production, 216, 607-623. doi:10.1016/j.jclepro.2018.12.083Dor, A., & Kissinger, M. (2017). A multi-year, multi-scale analysis of urban sustainability. Environmental Impact Assessment Review, 62, 115-121. doi:10.1016/j.eiar.2016.05.004Rega, C., Singer, J. P., & Geneletti, D. (2018). Investigating the substantive effectiveness of Strategic Environmental Assessment of urban planning: Evidence from Italy and Spain. Environmental Impact Assessment Review, 73, 60-69. doi:10.1016/j.eiar.2018.07.004Salas, J., & Yepes, V. (2018). A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models. Journal of Cleaner Production, 176, 1231-1244. doi:10.1016/j.jclepro.2017.11.249Penadés-Plà, V., García-Segura, T., Martí, J., & Yepes, V. (2016). A Review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design. Sustainability, 8(12), 1295. doi:10.3390/su8121295Zio, E., & Bazzo, R. (2011). A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems. European Journal of Operational Research, 210(3), 624-634. doi:10.1016/j.ejor.2010.10.021Ishibuchi, H., Akedo, N., & Nojima, Y. (2015). Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems. IEEE Transactions on Evolutionary Computation, 19(2), 264-283. doi:10.1109/tevc.2014.2315442A fast and effective method for pruning of non-dominated solutions in many-objective problems https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750253049&partnerID=40&md5=f46109796025a884fd054d73e71c308eTaboada, H. A., Baheranwala, F., Coit, D. W., & Wattanapongsakorn, N. (2007). Practical solutions for multi-objective optimization: An application to system reliability design problems. Reliability Engineering & System Safety, 92(3), 314-322. doi:10.1016/j.ress.2006.04.014Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281. doi:10.1016/j.gloenvcha.2006.02.006A new decision sciences for complex systems http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L1/Material/Lempert02.pdfThomas, J., & Kielman, J. (2009). Challenges for Visual Analytics. Information Visualization, 8(4), 309-314. doi:10.1057/ivs.2009.26Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., … Tominski, C. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24(10), 1577-1600. doi:10.1080/13658816.2010.508043Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380-1393. doi:10.1016/j.jclepro.2017.07.027Análisis urbanístico de barrios vulnerables https://www.fomento.gob.es/MFOM/LANG_CASTELLANO/DIRECCIONES_GENERALES/ARQ_VIVIENDA/SUELO_Y_POLITICAS/OBSERVATORIO/Analisis_urba_Barrios_Vulnerables/Informes_CCAA.htmBirkmann, J., Garschagen, M., & Setiadi, N. (2014). New challenges for adaptive urban governance in highly dynamic environments: Revisiting planning systems and tools for adaptive and strategic planning. Urban Climate, 7, 115-133. doi:10.1016/j.uclim.2014.01.006Besagni, G., & Borgarello, M. (2019). The socio-demographic and geographical dimensions of fuel poverty in Italy. Energy Research & Social Science, 49, 192-203. doi:10.1016/j.erss.2018.11.007Khalil, N., Kamaruzzaman, S. N., & Baharum, M. R. (2016). Ranking the indicators of building performance and the users’ risk via Analytical Hierarchy Process (AHP): Case of Malaysia. Ecological Indicators, 71, 567-576. doi:10.1016/j.ecolind.2016.07.032Pellicer, E., Sierra, L. A., & Yepes, V. (2016). Appraisal of infrastructure sustainability by graduate students using an active-learning method. Journal of Cleaner Production, 113, 884-896. doi:10.1016/j.jclepro.2015.11.010Sierra, L. A., Yepes, V., & Pellicer, E. (2018). A review of multi-criteria assessment of the social sustainability of infrastructures. Journal of Cleaner Production, 187, 496-513. doi:10.1016/j.jclepro.2018.03.022Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. doi:10.1016/0377-2217(90)90057-

Systems Thinking for Life Cycle Sustainability Assessment: A Review of Recent Developments, Applications, and Future Perspectives

abstract: Tracking the environmental impacts of production, use, and disposal of products (e.g., goods, and services) have been an important issue in the global economy. Although Life Cycle Assessment (LCA) is a widely applied method to track these environmental impacts and support policies, it has certain limitations and an isolated way of evaluating the environmental impacts with no consideration of social and economic impacts and mechanisms. To overcome the limits of current LCA, three mechanisms have been proposed in the literature: (1) broadening the indicators by including social and economic indicators in addition to the environmental impacts; (2) broadening the scope of analysis from product-level assessment to national and global levels; (3) deepening the assessment by inclusion of more mechanisms to account for interrelations among the system elements, uncertainty analysis, stakeholder involvement, etc. With these developments, LCA has been evolving into a new framework called Life Cycle Sustainability Assessment (LCSA). Practical application of LCSA requires integration of various methods, tools, and disciplines. In this study, a comprehensive literature review is conducted to investigate recent developments, current challenges, and future perspectives in the LCSA literature. According to the review, a high number (40%) of LCSA studies are from the environmental science discipline, while contributions from other disciplines such as economics (3%) and social sciences (9%) are very low. On broadening the scope of analysis, 58% of the studies are product-level works, while 37% quantified the impacts at national level and achieved an economy-wide analysis, and only 5% of the studies were able to quantify the global impacts of products using LCSA framework. Furthermore, current applications of LCSA have not considered the rebound effects, feedback mechanisms, and interrelations of the system of interest sufficiently. To address these challenges, we present a complete discussion about the overarching role of systems thinking to bring tools, methods and disciplines together, and provide practical examples from the earlier studies that have employed various system-based methods. We discuss the importance of integrated system-based methods for advancement of LCSA framework in the following directions: (1) regional and global level LCSA models using multi-region input-output analysis that is capable of quantitatively capturing macro-level social, environmental, and economic impacts; (2) dealing with uncertainties in LCSA during multi-criteria decision-making process and expert judgments in weighting of LCSA indicators; and (3) integration of system dynamics modeling to reveal complex interconnections, dependencies, and causal relationships between sustainability indicators.The final version of this article, as published in Sustainability, can be viewed online at: http://www.mdpi.com/2071-1050/9/5/70

Quality and Environmental Management Linkage: A Review of the Literature

This article presents a conceptual and empirical review of the literature related to the link between the different perspectives, models, and tools associated with Quality Management and Environmental Management. Several academic works identified in the literature that aimed to establish conceptual similarities between QM and EM are reviewed and discussed. In general, terms, the scholarly literature suggests that the main quality practices and programs associated with the Quality Management paradigmsuch as ISO 9001 and Total Quality Managementfacilitate the adoption of environmental practices associated with corporate Environmental Management. However, there is evidence of certain limitations driven by different biases, whether or not they are recognized in the reviewed publications. The concentration on some avenues of research focused on very detailed aspects of the linkage between QM and EM is discussed. Conversely, lines that have been overlooked and are in need for more research were also identified. The implications for scholars, such as suggestions for further research, are included as a contribution of the article.This research was funded by the Basque Autonomous Government (Grupos de Investigacion del Sistema Universitario Vasco; GIC 15/176) and the Chaire de recherche du Canada sur l'internalisation du developpement durable et la responsabilisation des organisations

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Eco‐Holonic 4.0 Circular Business Model to Conceptualize Sustainable Value Chain Towards Digital Transition

The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects

Improving Food Supply Chain Management by a Sustainable Approach to Supplier Evaluation

[EN] Increasing food supply chain sustainability means having to deal with many conflicting aspects and involves producers, several departments in distribution companies, and consumers. The objectives of this research are to develop models to solve real-world supplier evaluation problems and validate them with real data on fresh fruits in a supermarket chain. Literature review and results from a survey with managers from purchasing, logistics, and quality departments of a food distribution company are used to establish criteria, to first model the assessment of products and, second, to model supplier evaluation. A multicriteria hybrid approach is proposed, using multi-attribute utility theory (MAUT) to assess the quality of products and Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) to complete their evaluation with strategic criteria to be included in the second phase. The results allow companies to rank suppliers by product and classify them according to the main criteria categories, such as product strategy, food safety, economic, logistic, commercial, green image and corporate social responsibility. A sorting approach is also applied to obtain ordered groups of suppliers. Finally, the models proposed can form the core of a decision support system in order to create and monitor the supplier base in food distribution companies, as well as to inform sustainable decision making.This research was funded by the Regional Ministry of Education, Research, Culture and Sport of the Autonomous Government of the Valencian Region, Spain, grant number AICO/2017/066.Segura Maroto, M.; Maroto Álvarez, MC.; Segura García Del Río, B.; Casas-Rosal, JC. (2020). Improving Food Supply Chain Management by a Sustainable Approach to Supplier Evaluation. Mathematics. 8(11):1-23. https://doi.org/10.3390/math8111952S123811Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16-24. doi:10.1016/j.ejor.2009.05.009Zimmer, K., Fröhling, M., & Schultmann, F. (2015). Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412-1442. doi:10.1080/00207543.2015.1079340Aouadni, S., Aouadni, I., & Rebaï, A. (2019). A systematic review on supplier selection and order allocation problems. Journal of Industrial Engineering International, 15(S1), 267-289. doi:10.1007/s40092-019-00334-yChai, J., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872-3885. doi:10.1016/j.eswa.2012.12.040Chai, J., & Ngai, E. W. T. (2020). Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead. Expert Systems with Applications, 140, 112903. doi:10.1016/j.eswa.2019.112903Wetzstein, A., Hartmann, E., Benton jr., W. C., & Hohenstein, N.-O. (2016). A systematic assessment of supplier selection literature – State-of-the-art and future scope. International Journal of Production Economics, 182, 304-323. doi:10.1016/j.ijpe.2016.06.022Ansari, Z. N., & Kant, R. (2017). A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management. Journal of Cleaner Production, 142, 2524-2543. doi:10.1016/j.jclepro.2016.11.023Schramm, V. B., Cabral, L. P. B., & Schramm, F. (2020). Approaches for supporting sustainable supplier selection - A literature review. Journal of Cleaner Production, 273, 123089. doi:10.1016/j.jclepro.2020.123089Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98, 66-83. doi:10.1016/j.jclepro.2013.06.046Rajeev, A., Pati, R. K., Padhi, S. S., & Govindan, K. (2017). Evolution of sustainability in supply chain management: A literature review. Journal of Cleaner Production, 162, 299-314. doi:10.1016/j.jclepro.2017.05.026Demir, L., Akpınar, M. E., Araz, C., & Ilgın, M. A. (2018). A green supplier evaluation system based on a new multi-criteria sorting method: VIKORSORT. Expert Systems with Applications, 114, 479-487. doi:10.1016/j.eswa.2018.07.071Diaz-Balteiro, L., González-Pachón, J., & Romero, C. (2017). Measuring systems sustainability with multi-criteria methods: A critical review. European Journal of Operational Research, 258(2), 607-616. doi:10.1016/j.ejor.2016.08.075Thies, C., Kieckhäfer, K., Spengler, T. S., & Sodhi, M. S. (2019). Operations research for sustainability assessment of products: A review. European Journal of Operational Research, 274(1), 1-21. doi:10.1016/j.ejor.2018.04.039Konys. (2019). Green Supplier Selection Criteria: From a Literature Review to a Comprehensive Knowledge Base. Sustainability, 11(15), 4208. doi:10.3390/su11154208Segura, M., Maroto, C., & Segura, B. (2019). Quantifying the Sustainability of Products and Suppliers in Food Distribution Companies. Sustainability, 11(21), 5875. doi:10.3390/su11215875Memari, A., Dargi, A., Akbari Jokar, M. R., Ahmad, R., & Abdul Rahim, A. R. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24. doi:10.1016/j.jmsy.2018.11.002Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. doi:10.1016/j.eswa.2016.06.030Chang, L., Ouzrout, Y., Nongaillard, A., Bouras, A., & Jiliu, Z. (2014). Multi-criteria decision making based on trust and reputation in supply chain. International Journal of Production Economics, 147, 362-372. doi:10.1016/j.ijpe.2013.04.014Ekici, A. (2013). An improved model for supplier selection under capacity constraint and multiple criteria. International Journal of Production Economics, 141(2), 574-581. doi:10.1016/j.ijpe.2012.09.013Lin, R.-H. (2012). An integrated model for supplier selection under a fuzzy situation. International Journal of Production Economics, 138(1), 55-61. doi:10.1016/j.ijpe.2012.02.024Amid, A., Ghodsypour, S. H., & O’Brien, C. (2011). A weighted max–min model for fuzzy multi-objective supplier selection in a supply chain. International Journal of Production Economics, 131(1), 139-145. doi:10.1016/j.ijpe.2010.04.044Chen, Y.-J. (2011). Structured methodology for supplier selection and evaluation in a supply chain. Information Sciences, 181(9), 1651-1670. doi:10.1016/j.ins.2010.07.026Zeydan, M., Çolpan, C., & Çobanoğlu, C. (2011). A combined methodology for supplier selection and performance evaluation. Expert Systems with Applications, 38(3), 2741-2751. doi:10.1016/j.eswa.2010.08.064Şen, C. G., Baraçlı, H., Şen, S., & Başlıgil, H. (2009). An integrated decision support system dealing with qualitative and quantitative objectives for enterprise software selection. Expert Systems with Applications, 36(3), 5272-5283. doi:10.1016/j.eswa.2008.06.070Bottani, E., & Rizzi, A. (2008). An adapted multi-criteria approach to suppliers and products selection—An application oriented to lead-time reduction. International Journal of Production Economics, 111(2), 763-781. doi:10.1016/j.ijpe.2007.03.012Segura, M., & Maroto, C. (2017). A multiple criteria supplier segmentation using outranking and value function methods. Expert Systems with Applications, 69, 87-100. doi:10.1016/j.eswa.2016.10.031Trapp, A. C., & Sarkis, J. (2016). Identifying Robust portfolios of suppliers: a sustainability selection and development perspective. Journal of Cleaner Production, 112, 2088-2100. doi:10.1016/j.jclepro.2014.09.062Araz, C., & Ozkarahan, I. (2007). Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure. International Journal of Production Economics, 106(2), 585-606. doi:10.1016/j.ijpe.2006.08.008Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363-11368. doi:10.1016/j.eswa.2009.03.039Zopounidis, C., & Doumpos, M. (2002). Multicriteria classification and sorting methods: A literature review. European Journal of Operational Research, 138(2), 229-246. doi:10.1016/s0377-2217(01)00243-0Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The Promethee method. European Journal of Operational Research, 24(2), 228-238. doi:10.1016/0377-2217(86)90044-5Nemery, P., & Lamboray, C. (2007). ℱlow $\mathcal{S}$ ort: a flow-based sorting method with limiting or central profiles. TOP, 16(1), 90-113. doi:10.1007/s11750-007-0036-xLau, H., Nakandala, D., & Shum, P. K. (2018). A business process decision model for fresh-food supplier evaluation. Business Process Management Journal, 24(3), 716-744. doi:10.1108/bpmj-01-2016-0015D-Sight CDM http://www.d-sight.com/solutions/d-sight-cdmNemery, P., Lidouh, K., & Mareschal, B. (2011). On the usefulness of taking the weights into account in the GAIA visualisations. International Journal of Information and Decision Sciences, 3(3), 228. doi:10.1504/ijids.2011.041585Nemery, P., Ishizaka, A., Camargo, M., & Morel, L. (2012). Enriching descriptive information in ranking and sorting problems with visualizations techniques. Journal of Modelling in Management, 7(2), 130-147. doi:10.1108/17465661211242778Xu, Z. (2000). On consistency of the weighted geometric mean complex judgement matrix in AHP. European Journal of Operational Research, 126(3), 683-687. doi:10.1016/s0377-2217(99)00082-xOrtiz‐Barrios, M., Miranda‐De la Hoz, C., López‐Meza, P., Petrillo, A., & De Felice, F. (2019). A case of food supply chain management with AHP, DEMATEL, and TOPSIS. Journal of Multi-Criteria Decision Analysis, 27(1-2), 104-128. doi:10.1002/mcda.169

Standardization Framework for Sustainability from Circular Economy 4.0

The circular economy (CE) is widely known as a way to implement and achieve sustainability, mainly due to its contribution towards the separation of biological and technical nutrients under cyclic industrial metabolism. The incorporation of the principles of the CE in the links of the value chain of the various sectors of the economy strives to ensure circularity, safety, and efficiency. The framework proposed is aligned with the goals of the 2030 Agenda for Sustainable Development regarding the orientation towards the mitigation and regeneration of the metabolic rift by considering a double perspective. Firstly, it strives to conceptualize the CE as a paradigm of sustainability. Its principles are established, and its techniques and tools are organized into two frameworks oriented towards causes (cradle to cradle) and effects (life cycle assessment), and these are structured under the three pillars of sustainability, for their projection within the proposed framework. Secondly, a framework is established to facilitate the implementation of the CE with the use of standards, which constitute the requirements, tools, and indicators to control each life cycle phase, and of key enabling technologies (KETs) that add circular value 4.0 to the socio-ecological transition

Normative, systemic and procedural aspects: a review of indicator‐based sustainability assessments in agriculture

Several methods for assessing the sustainability of agricultural systems have been developed. These methods do not fully: (i) take into account the multi‐functionality of agriculture; (ii) include multidimensionality; (iii) utilize and implement the assessment knowledge; and (iv) identify conflicting goals and trade‐offs. This paper reviews seven recently developed multidisciplinary indicator‐based assessment methods with respect to their contribution to these shortcomings. All approaches include (1) normative aspects such as goal setting, (2) systemic aspects such as a specification of scale of analysis, (3) a reproducible structure of the approach. The approaches can be categorized into three typologies. The top‐down farm assessments focus on field or farm assessment. They have a clear procedure for measuring the indicators and assessing the sustainability of the system, which allows for benchmarking across farms. The degree of participation is low, potentially affecting the implementation of the results negatively. The top‐down regional assessment assesses the on‐farm and the regional effects. They include some participation to increase acceptance of the results. However, they miss the analysis of potential trade‐offs. The bottom‐up, integrated participatory or transdisciplinary approaches focus on a regional scale. Stakeholders are included throughout the whole process assuring the acceptance of the results and increasing the probability of implementation of developed measures. As they include the interaction between the indicators in their system representation, they allow for performing a trade‐off analysis. The bottom‐up, integrated participatory or transdisciplinary approaches seem to better overcome the four shortcomings mentioned above