Productivity, Digital Footprint and Sustainability in the Textile and Clothing Industry

[EN] In recent years, there has been a shift from the linear economic model on which the textile and clothing industry is based to a more sustainable model. However, to date, limited research on the relationship between sustainability commitment and firm productivity has focused on the textile and clothing industry. This study addresses this gap and aims to explore whether the digital footprint of small and medium-sized textile companies in terms of their sustainable performance is related to their productivity. To this end, the paper proposes an innovative model to monitor the companies’ commitment to sustainable issues by analyzing online data retrieved from their corporate websites. This information is merged with balance sheet data to examine the impact of sustainability practices, capital and human capital on productivity. The estimated firm’s total factor productivity is explained as a function of the sustainability digital footprint measures and additional control variables for a sample of 315 textile firms located in the region of Comunidad Valenciana, Spain.This work was partially funded by MCIN/AEI/10.13039/501100011033 under grant PID2019-107765RB-I00.Domenech, J.; Garcia-Bernabeu, A.; Diaz-Garcia, P. (2023). Productivity, Digital Footprint and Sustainability in the Textile and Clothing Industry. Editorial Universitat Politècnica de València. 319-326. https://doi.org/10.4995/CARMA2023.2023.1644631932

A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain

[EN] The evaluation of regional innovation performance through composite innovation indices can serve as a valuable tool for policy-making. While discussion on the best methodology to construct composite innovation indices continues, we are interested in deepening the use of reference levels and the aggregation issue. So far, additive aggregation methods are, largely, the most widespread aggregation rule, thus allowing for full compensability among single indicators. In this paper, we present an integrated assessment methodology to evaluate regional innovation performance using the Multi-Reference Point based Weak and Strong Composite Indicator (MRP-WSCI) approach, which allows defining reference levels and different degrees of compensability. As an example of application to the Regional Innovation Scoreboard, the proposed technique is developed to measure the innovation performance of Spain¿s regions taking into account Spanish and European reference levels. The main features of the proposed approach are: (i) absolute or relative reference levels could be previously defined by the decision maker; (ii) by establishing the reference levels, the resulting composite innovation index is an easy-to-interpret measure; and (iii) the non-compensatory strong composite indicator provides an additional layer of information for policy-making (iv) a visualization tool called Light-Diagram is proposed to track the specific strengths and weaknesses of the regions' innovation performance.This research has been partially supported by the Spanish Ministry of Economy and Competitiveness (Project ECO2016-76567-C4-4-R), by the Regional Government of Andalucia (research group SEJ-417), and by the ERDF funds (Project UMA18-FEDERJA-065).Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2020). A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain. Mathematics. 8(5):1-21. https://doi.org/10.3390/math8050797S12185Hauser, C., Siller, M., Schatzer, T., Walde, J., & Tappeiner, G. (2018). Measuring regional innovation: A critical inspection of the ability of single indicators to shape technological change. Technological Forecasting and Social Change, 129, 43-55. doi:10.1016/j.techfore.2017.10.019Makkonen, T., & van der Have, R. P. (2012). Benchmarking regional innovative performance: composite measures and direct innovation counts. Scientometrics, 94(1), 247-262. doi:10.1007/s11192-012-0753-2Asheim, B. T., Smith, H. L., & Oughton, C. (2011). Regional Innovation Systems: Theory, Empirics and Policy. Regional Studies, 45(7), 875-891. doi:10.1080/00343404.2011.596701Buesa, M., Heijs, J., & Baumert, T. (2010). The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach. Research Policy, 39(6), 722-735. doi:10.1016/j.respol.2010.02.016Di Cagno, D., Fabrizi, A., Meliciani, V., & Wanzenböck, I. (2016). The impact of relational spillovers from joint research projects on knowledge creation across European regions. Technological Forecasting and Social Change, 108, 83-94. doi:10.1016/j.techfore.2016.04.021Capello, R., & Lenzi, C. (2012). Territorial patterns of innovation: a taxonomy of innovative regions in Europe. The Annals of Regional Science, 51(1), 119-154. doi:10.1007/s00168-012-0539-8Navarro, M., Gibaja, J. J., Bilbao-Osorio, B., & Aguado, R. (2009). Patterns of Innovation in EU-25 Regions: A Typology and Policy Recommendations. Environment and Planning C: Government and Policy, 27(5), 815-840. doi:10.1068/c0884rPinto, H. (2009). The Diversity of Innovation in the European Union: Mapping Latent Dimensions and Regional Profiles. European Planning Studies, 17(2), 303-326. doi:10.1080/09654310802553571Ruiz, F., El Gibari, S., Cabello, J. M., & Gómez, T. (2020). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega, 95, 102060. doi:10.1016/j.omega.2019.04.003Hollenstein, H. (1996). A composite indicator of a firm’s innovativeness. An empirical analysis based on survey data for Swiss manufacturing. Research Policy, 25(4), 633-645. doi:10.1016/0048-7333(95)00874-8Gu *, W., & Tang, J. (2004). Link between innovation and productivity in Canadian manufacturing industries. Economics of Innovation and New Technology, 13(7), 671-686. doi:10.1080/1043890410001686806Tang, J., & Le, C. D. (2007). Multidimensional Innovation and Productivity. Economics of Innovation and New Technology, 16(7), 501-516. doi:10.1080/10438590600914585Kumar, S., Haleem, A., & Sushil. (2019). Assessing innovativeness of manufacturing firms using an intuitionistic fuzzy based MCDM framework. Benchmarking: An International Journal, 26(6), 1823-1844. doi:10.1108/bij-12-2017-0343Grupp, H., & Mogee, M. E. (2004). Indicators for national science and technology policy: how robust are composite indicators? Research Policy, 33(9), 1373-1384. doi:10.1016/j.respol.2004.09.007Schibany, A., & Streicher, G. (2008). The European Innovation Scoreboard: drowning by numbers? Science and Public Policy, 35(10), 717-732. doi:10.3152/030234208x398512Kozłowski, J. (2015). Innovation indices: the need for positioning them where they properly belong. Scientometrics, 104(3), 609-628. doi:10.1007/s11192-015-1632-4Carayannis, E. G., Goletsis, Y., & Grigoroudis, E. (2018). Composite innovation metrics: MCDA and the Quadruple Innovation Helix framework. Technological Forecasting and Social Change, 131, 4-17. doi:10.1016/j.techfore.2017.03.008Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61-94. doi:10.1007/s11205-017-1832-9El Gibari, S., Gómez, T., & Ruiz, F. (2018). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1-24. doi:10.1007/s11573-018-0902-zRuiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189-197. doi:10.1057/jors.2009.187Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313-325. doi:10.1016/j.ejor.2013.11.031Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building Ease-of-Doing-Business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130-140. doi:10.1016/j.techfore.2017.06.005El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235-1250. doi:10.1016/j.joi.2018.10.003Mazziotta, M., & Pareto, A. (2017). Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices. Social Indicators Research, 136(3), 967-976. doi:10.1007/s11205-017-1577-5Munda, G., & Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting. Applied Economics, 41(12), 1513-1523. doi:10.1080/00036840601019364Cabello, J. M., Navarro, E., Prieto, F., Rodríguez, B., & Ruiz, F. (2014). Multicriteria development of synthetic indicators of the environmental profile of the Spanish regions. Ecological Indicators, 39, 10-23. doi:10.1016/j.ecolind.2013.11.01

A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators

[EN] There is a growing interest in research on the role that space plays in defining and measuring well-being or quality of life. In this paper, we propose to evaluate the regional quality of life using the Multi-Reference Point based Weak Strong Composite Indicator approach, to further enhance the quality of the sub-national analysis. The major motivation is to facilitate assessing the regional quality of life performance at different geographical scales and compensability levels. As an example of application, we compute the composite indicators for 19 Spanish regions to paint a comprehensive picture of the regional quality of life using two different geographical scales: the Spanish and the European ones. Moreover, we provide warning signals to regional, national and European policy-makers on the quality of life dimensions in which each region needs further improvements.This research was partially funded by the Spanish Ministry of Economy and Competitiveness (Project PID2019-104263RB-C42), from the Regional Government of Andalucía (Project P18-RT-1566), and by the EU ERDF operative program (Project UMA18-FEDERJA-065)Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2021). A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators. Social Indicators Research (Online). 1-20. https://doi.org/10.1007/s11205-021-02818-0S120Blancas, F., Caballero, R., González, M., Lozano-Oyola, M., & Pérez, F. (2010). Goal programming synthetic indicators: An application for sustainable tourism in andalusian coastal counties. Ecological Economics, 69(11), 2158–2172.Boggia, A., Massei, G., Pace, E., Rocchi, L., Paolotti, L., & Attard, M. (2018). Spatial multicriteria analysis for sustainability assessment: A new model for decision making. Land Use Policy, 71, 281–292.Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59(2), 115–151.Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual fund’s environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313–325.Costa, D. S. (2015). Reflective, causal, and composite indicators of quality of life: A conceptual or an empirical distinction? Quality of Life Research, 24(9), 2057–2065.Durand, M. (2015). The OCDE better life initiative: How’s life? and the measurement of well-being. Review of Income and Wealth, 61(1), 4–17.El Gibari, S., Cabello, J. M., Gómez, T., & Ruiz, F. (2021). Composite indicators as decision making tools: The joint use of compensatory and non-compensatory schemes. International Journal of Information Technology and Decision Making, 20(3), 847–879.El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235–1250.El Gibari, S., Gómez, T., & Ruiz, F. (2019). Building composite indicators using multicriteria methods: A review. Journal of Business Economics, 89(1), 1–24.European Commission: Eurostat quality of life database. (2020). url http://ec.europa.eu/eurostat/data/database.Freudenberg, M. (2003). Composite indicators of country performance.Garcia-Bernabeu, A., Cabello, J. M., & Ruiz, F. (2020). A multi-criteria reference point based approach for assessing regional innovation performance in Spain. Mathematics, 8(5), 797.Goerlich, F. J., & Reig, E. (2021). Quality of life ranking of spanish cities: A non-compensatory approach. Cities, 109, 102979.Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141, 61–94.Greyling, T., & Tregenna, F. (2017). Construction and analysis of a composite quality of life index for a region of South Africa. Social Indicators Research, 131(3), 887–930.Hagerty, M. R., Cummins, R., Ferriss, A. L., Land, K., Michalos, A. C., Peterson, M., et al. (2001). Quality of life indexes for national policy: Review and agenda for research. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 71(1), 58–78.INE: Indicadores de calidad de vida. (2020). url https://cutt.ly/Zj0L0qX.Ivaldi, E., Bonatti, G., Soliani, R., et al. (2014). Composite index for quality of life in italian cities: An application to urbes indicators. Review of Economics and Finance, 4(4)Karagiannis, R., & Karagiannis, G. (2020). Constructing composite indicators with shannon entropy: The case of human development index. Socio-Economic Planning Sciences, 70, 100701.Lagas, P., van Dongen, F., van Rijn, F., & Visser, H. (2015). Regional quality of living in Europe. Region, 2(2), 1–26.Malkina-Pykh, I. G., & Pykh, Y. A. (2008). Quality-of-life indicators at different scales: Theoretical background. Ecological Indicators, 8(6), 854–862.Marchante, A. J., & Ortega, B. (2006). Quality of life and economic convergence across Spanish regions, 1980–2001. Regional Studies, 40(5), 471–483.Mazziotta, M., & Pareto, A. (2016). On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social Indicators Research, 127(3), 983–1003.Mazziotta, M., & Pareto, A. (2020). Composite indices construction: The performance interval approach. Social Indicators Research pp. 1–11.Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2008). Handbook on constructing composite indicators.OECD: Handbook on constructing composite indicators: methodology and user guide. (2008). Paris: OECD publishing.Patil, G.R., & Sharma, G. (2020). Urban quality of life: An assessment and ranking for indian cities. Transport Policy.Royuela, V., Suriñach, J., & Reyes, M. (2003). Measuring quality of life in small areas over different periods of time. Social Indicators Research, 64(1), 51–74.Ruiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189–197.Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building ease-of-doing-business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130–140.Ruiz, F., El Gibari, S., Cabello, J.M., & Gómez, T. (2019). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega.Saisana, M., & Tarantola, S. (2002). State-of-the-art report on current methodologies and practices for composite indicator development. Ispra: Joint Research Centre.Stiglitz, J.E., Sen, A., Fitoussi, J.P., et al. (2009). Report by the commission on the measurement of economic performance and social progress

Extended Fuzzy Analytic Hierarchy Process (E-FAHP): A General Approach

[EN] Fuzzy analytic hierarchy process (FAHP) methodologies have witnessed a growing development from the late 1980s until now, and countless FAHP based applications have been published in many fields including economics, finance, environment or engineering. In this context, the FAHP methodologies have been generally restricted to fuzzy numbers with linear type of membership functions (triangular numbers-TN-and trapezoidal numbers-TrN). This paper proposes an extended FAHP model (E-FAHP) where pairwise fuzzy comparison matrices are represented by a special type of fuzzy numbers referred to as (m,n)-trapezoidal numbers (TrN (m,n)) with nonlinear membership functions. It is then demonstrated that there are a significant number of FAHP approaches that can be reduced to the proposed E-FAHP structure. A comparative analysis of E-FAHP and Mikhailov's model is illustrated with a case study showing that E-FAHP includes linear and nonlinear fuzzy numbers.Reig-Mullor, J.; Pla Santamaría, D.; Garcia-Bernabeu, A. (2020). Extended Fuzzy Analytic Hierarchy Process (E-FAHP): A General Approach. Mathematics. 8(11):1-14. https://doi.org/10.3390/math8112014S114811Chai, 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.2012.12.040Tavana, M., Zareinejad, M., Di Caprio, D., & Kaviani, M. A. (2016). An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Applied Soft Computing, 40, 544-557. doi:10.1016/j.asoc.2015.12.005Medasani, S., Kim, J., & Krishnapuram, R. (1998). An overview of membership function generation techniques for pattern recognition. International Journal of Approximate Reasoning, 19(3-4), 391-417. doi:10.1016/s0888-613x(98)10017-8Medaglia, A. L., Fang, S.-C., Nuttle, H. L. W., & Wilson, J. R. (2002). An efficient and flexible mechanism for constructing membership functions. European Journal of Operational Research, 139(1), 84-95. doi:10.1016/s0377-2217(01)00157-6Mikhailov, L. (2003). Deriving priorities from fuzzy pairwise comparison judgements. Fuzzy Sets and Systems, 134(3), 365-385. doi:10.1016/s0165-0114(02)00383-4Appadoo, S. S. (2014). Possibilistic Fuzzy Net Present Value Model and Application. Mathematical Problems in Engineering, 2014, 1-11. doi:10.1155/2014/865968Mikhailov, L., & Tsvetinov, P. (2004). Evaluation of services using a fuzzy analytic hierarchy process. Applied Soft Computing, 5(1), 23-33. doi:10.1016/j.asoc.2004.04.001Hepu Deng. (1999). Multicriteria analysis with fuzzy pairwise comparison. FUZZ-IEEE’99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315). doi:10.1109/fuzzy.1999.793038Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1-3), 229-241. doi:10.1016/s0165-0114(83)80082-7Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. doi:10.1016/0165-0114(85)90090-9Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. doi:10.1016/0377-2217(95)00300-2Enea, M., & Piazza, T. (2004). Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making, 3(1), 39-62. doi:10.1023/b:fodm.0000013071.63614.3dKrejčí, J., Pavlačka, O., & Talašová, J. (2016). A fuzzy extension of Analytic Hierarchy Process based on the constrained fuzzy arithmetic. Fuzzy Optimization and Decision Making, 16(1), 89-110. doi:10.1007/s10700-016-9241-0Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378. doi:10.1016/j.eswa.2007.08.041Isaai, M. T., Kanani, A., Tootoonchi, M., & Afzali, H. R. (2011). Intelligent timetable evaluation using fuzzy AHP. Expert Systems with Applications, 38(4), 3718-3723. doi:10.1016/j.eswa.2010.09.030Büyüközkan, G., & Güleryüz, S. (2016). A new integrated intuitionistic fuzzy group decision making approach for product development partner selection. Computers & Industrial Engineering, 102, 383-395. doi:10.1016/j.cie.2016.05.038Zheng, G., Zhu, N., Tian, Z., Chen, Y., & Sun, B. (2012). Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Safety Science, 50(2), 228-239. doi:10.1016/j.ssci.2011.08.042Calabrese, A., Costa, R., & Menichini, T. (2013). Using Fuzzy AHP to manage Intellectual Capital assets: An application to the ICT service industry. Expert Systems with Applications, 40(9), 3747-3755. doi:10.1016/j.eswa.2012.12.081Ishizaka, A., & Nguyen, N. H. (2013). Calibrated fuzzy AHP for current bank account selection. Expert Systems with Applications, 40(9), 3775-3783. doi:10.1016/j.eswa.2012.12.089Somsuk, N., & Laosirihongthong, T. (2014). A fuzzy AHP to prioritize enabling factors for strategic management of university business incubators: Resource-based view. Technological Forecasting and Social Change, 85, 198-210. doi:10.1016/j.techfore.2013.08.007Chan, H. K., Wang, X., & Raffoni, A. (2014). An integrated approach for green design: Life-cycle, fuzzy AHP and environmental management accounting. The British Accounting Review, 46(4), 344-360. doi:10.1016/j.bar.2014.10.004Tan, R. R., Aviso, K. B., Huelgas, A. P., & Promentilla, M. A. B. (2014). Fuzzy AHP approach to selection problems in process engineering involving quantitative and qualitative aspects. Process Safety and Environmental Protection, 92(5), 467-475. doi:10.1016/j.psep.2013.11.005Rezaei, J., Fahim, P. B. M., & Tavasszy, L. (2014). Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP. Expert Systems with Applications, 41(18), 8165-8179. doi:10.1016/j.eswa.2014.07.005Song, Z., Zhu, H., Jia, G., & He, C. (2014). Comprehensive evaluation on self-ignition risks of coal stockpiles using fuzzy AHP approaches. Journal of Loss Prevention in the Process Industries, 32, 78-94. doi:10.1016/j.jlp.2014.08.002Dong, M., Li, S., & Zhang, H. (2015). Approaches to group decision making with incomplete information based on power geometric operators and triangular fuzzy AHP. Expert Systems with Applications, 42(21), 7846-7857. doi:10.1016/j.eswa.2015.06.007Mangla, S. K., Kumar, P., & Barua, M. K. (2015). Risk analysis in green supply chain using fuzzy AHP approach: A case study. Resources, Conservation and Recycling, 104, 375-390. doi:10.1016/j.resconrec.2015.01.001Mosadeghi, R., Warnken, J., Tomlinson, R., & Mirfenderesk, H. (2015). Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision making model for urban land-use planning. Computers, Environment and Urban Systems, 49, 54-65. doi:10.1016/j.compenvurbsys.2014.10.001Lupo, T. (2016). A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily. Applied Soft Computing, 40, 468-478. doi:10.1016/j.asoc.2015.12.010Tuljak-Suban, D., & Bajec, P. (2018). The Influence of Defuzzification Methods to Decision Support Systems Based on Fuzzy AHP with Scattered Comparison Matrix: Application to 3PLP Selection as a Case Study. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 26(03), 475-491. doi:10.1142/s021848851850023xAkbar, M. A., Shameem, M., Mahmood, S., Alsanad, A., & Gumaei, A. (2020). Prioritization based Taxonomy of Cloud-based Outsource Software Development Challenges: Fuzzy AHP analysis. Applied Soft Computing, 95, 106557. doi:10.1016/j.asoc.2020.106557Jung, H. (2011). A fuzzy AHP–GP approach for integrated production-planning considering manufacturing partners. Expert Systems with Applications, 38(5), 5833-5840. doi:10.1016/j.eswa.2010.11.039Shaw, K., Shankar, R., Yadav, S. S., & Thakur, L. S. (2012). Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications, 39(9), 8182-8192. doi:10.1016/j.eswa.2012.01.149Abdullah, L., & Zulkifli, N. (2015). Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management. Expert Systems with Applications, 42(9), 4397-4409. doi:10.1016/j.eswa.2015.01.021Akkaya, G., Turanoğlu, B., & Öztaş, S. (2015). An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Systems with Applications, 42(24), 9565-9573. doi:10.1016/j.eswa.2015.07.061Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61-67. doi:10.1016/j.eswa.2011.06.044Büyüközkan, G., & Çifçi, G. (2012). A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Systems with Applications, 39(3), 2341-2354. doi:10.1016/j.eswa.2011.08.061Taylan, O., Bafail, A. O., Abdulaal, R. M. S., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116. doi:10.1016/j.asoc.2014.01.003Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Systems with Applications, 41(2), 679-693. doi:10.1016/j.eswa.2013.07.093Sun, L., Ma, J., Zhang, Y., Dong, H., & Hussain, F. K. (2016). Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection. Future Generation Computer Systems, 57, 42-55. doi:10.1016/j.future.2015.11.025Ar, I. M., Erol, I., Peker, I., Ozdemir, A. I., Medeni, T. D., & Medeni, I. T. (2020). Evaluating the feasibility of blockchain in logistics operations: A decision framework. Expert Systems with Applications, 158, 113543. doi:10.1016/j.eswa.2020.113543Yalcin, N., Bayrakdaroglu, A., & Kahraman, C. (2012). Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39(1), 350-364. doi:10.1016/j.eswa.2011.07.024Chang, S.-C., Tsai, P.-H., & Chang, S.-C. (2015). A hybrid fuzzy model for selecting and evaluating the e-book business model: A case study on Taiwan e-book firms. Applied Soft Computing, 34, 194-204. doi:10.1016/j.asoc.2015.05.011Li, N., & Zhao, H. (2016). Performance evaluation of eco-industrial thermal power plants by using fuzzy GRA-VIKOR and combination weighting techniques. Journal of Cleaner Production, 135, 169-183. doi:10.1016/j.jclepro.2016.06.113Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30-37. doi:10.1016/j.econmod.2014.07.036Li, Y., Liu, X., & Chen, Y. (2012). Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management. Fuzzy Optimization and Decision Making, 11(2), 147-176. doi:10.1007/s10700-012-9117-xKaya, Ö., Alemdar, K. D., & Çodur, M. Y. (2020). A novel two stage approach for electric taxis charging station site selection. Sustainable Cities and Society, 62, 102396. doi:10.1016/j.scs.2020.102396Chen, J.-F., Hsieh, H.-N., & Do, Q. H. (2015). Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Applied Soft Computing, 28, 100-108. doi:10.1016/j.asoc.2014.11.050Javanbarg, M. B., Scawthorn, C., Kiyono, J., & Shahbodaghkhan, B. (2012). Fuzzy AHP-based multicriteria decision making systems using particle swarm optimization. Expert Systems with Applications, 39(1), 960-966. doi:10.1016/j.eswa.2011.07.095Che, Z. H., Wang, H. S., & Chuang, C.-L. (2010). A fuzzy AHP and DEA approach for making bank loan decisions for small and medium enterprises in Taiwan. Expert Systems with Applications, 37(10), 7189-7199. doi:10.1016/j.eswa.2010.04.010Krejčí, J. (2015). Additively reciprocal fuzzy pairwise comparison matrices and multiplicative fuzzy priorities. Soft Computing, 21(12), 3177-3192. doi:10.1007/s00500-015-2000-2Xu, Z., & Liao, H. (2014). Intuitionistic Fuzzy Analytic Hierarchy Process. IEEE Transactions on Fuzzy Systems, 22(4), 749-761. doi:10.1109/tfuzz.2013.2272585Mikhailov, L. (2000). A fuzzy programming method for deriving priorities in the analytic hierarchy process. Journal of the Operational Research Society, 51(3), 341-349. doi:10.1057/palgrave.jors.260089

Monitoring multidimensional phenomena with a multicriteria composite performance interval approach

[EN] In the last two decades, the construction of composite indicators to measure and compare multidimensional phenomena in a broad spectrum of domains has increased considerably. Different methodological approaches are used to summarise huge datasets of information in a single figure. This paper proposes a new approach that consists in computing a multicriteria composite performance interval based on different aggregation rules. The suggested approach provides an additional layer of information as the performance interval displays a lower bound from a non-compensability perspective, and an upper bound allowing for full-compensability. The outstanding features of this proposal are: 1) a distance-based multicriteria technique is taken as the baseline to construct the multicriteria performance interval; 2) the aggregation of distances/separation measures is made using particular cases of Minkowski Lp metric; 3) the span of the multicriteria performance interval can be considered as a sign of the dimensions or indicators balance.Garcia-Bernabeu, A.; Hilario Caballero, A.; Pla Santamaría, D.; Salas-Molina, F. (2021). Monitoring multidimensional phenomena with a multicriteria composite performance interval approach. International Journal of Multicriteria Decision Making (Online). 8(4):368-385. https://doi.org/10.1504/IJMCDM.2021.120760S3683858

A Compact Representation of Preferences in Multiple Criteria Optimization Problems

[EN] A critical step in multiple criteria optimization is setting the preferences for all the criteria under consideration. Several methodologies have been proposed to compute the relative priority of criteria when preference relations can be expressed either by ordinal or by cardinal information. The analytic hierarchy process introduces relative priority levels and cardinal preferences. Lexicographical orders combine both ordinal and cardinal preferences and present the additional difficulty of establishing strict priority levels. To enhance the process of setting preferences, we propose a compact representation that subsumes the most common preference schemes in a single algebraic object. We use this representation to discuss the main properties of preferences within the context of multiple criteria optimization.Salas-Molina, F.; Pla Santamaría, D.; Garcia-Bernabeu, A.; Reig-Mullor, J. (2019). A Compact Representation of Preferences in Multiple Criteria Optimization Problems. Mathematics. 7(11):1-16. https://doi.org/10.3390/math7111092S116711Ahmadi, A., Ahmadi, M. R., & Nezhad, A. E. (2014). A Lexicographic Optimization and Augmented ϵ-constraint Technique for Short-term Environmental/Economic Combined Heat and Power Scheduling. Electric Power Components and Systems, 42(9), 945-958. doi:10.1080/15325008.2014.903542González-Arteaga, T., Alcantud, J. C. R., & de Andrés Calle, R. (2016). A new consensus ranking approach for correlated ordinal information based on Mahalanobis distance. Information Sciences, 372, 546-564. doi:10.1016/j.ins.2016.08.071Miettinen, K., & M�kel�, M. M. (2002). On scalarizing functions in multiobjective optimization. OR Spectrum, 24(2), 193-213. doi:10.1007/s00291-001-0092-9Ignizio, J. P. (1983). Generalized goal programming An overview. Computers & Operations Research, 10(4), 277-289. doi:10.1016/0305-0548(83)90003-5Sitorus, F., Cilliers, J. J., & Brito-Parada, P. R. (2019). Multi-criteria decision making for the choice problem in mining and mineral processing: Applications and trends. Expert Systems with Applications, 121, 393-417. doi:10.1016/j.eswa.2018.12.001Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158-181. doi:10.1016/j.eswa.2017.02.016Erdoğan, M., & Kaya, İ. (2016). A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey. Applied Soft Computing, 39, 84-93. doi:10.1016/j.asoc.2015.11.013Chen, Y., Liu, R., Barrett, D., Gao, L., Zhou, M., Renzullo, L., & Emelyanova, I. (2015). A spatial assessment framework for evaluating flood risk under extreme climates. Science of The Total Environment, 538, 512-523. doi:10.1016/j.scitotenv.2015.08.094Zammori, F. (2010). The analytic hierarchy and network processes: Applications to the US presidential election and to the market share of ski equipment in Italy. Applied Soft Computing, 10(4), 1001-1012. doi:10.1016/j.asoc.2009.07.013Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360-387. doi:10.1108/09600030810882816Ignizio, J. P. (1976). An Approach to the Capital Budgeting Problem with Multiple Objectives. The Engineering Economist, 21(4), 259-272. doi:10.1080/00137917608902798Lonergan, S. C., & Cocklin, C. (1988). The use of lexicographic goal programming in economic/ecolocical conflict analysis. Socio-Economic Planning Sciences, 22(2), 83-92. doi:10.1016/0038-0121(88)90020-1González-Pachón, J., & Romero, C. (2014). Properties underlying a preference aggregator based on satisficing logic. International Transactions in Operational Research, 22(2), 205-215. doi:10.1111/itor.1211

Multiple-criteria cash-management policies with particular liquidity terms

[EN] Eliciting policies for cash management systems with multiple assets is by no means straightforward. Both the particular relationship between alternative assets and time delays from control decisions to availability of cash introduce additional difficulties. Here we propose a cash management model to derive short-term finance policies when considering multiple assets with different expected returns and particular liquidity terms for each alternative asset. In order to deal with the inherent uncertainty about the near future introduced by cash flows, we use forecasts as a key input to the model. We express uncertainty as lack of predictive accuracy and we derive a deterministic equivalent problem that depends on forecasting errors and preferences of cash managers. Since the assessment of the quality of forecasts is recommended, we describe a method to evaluate the impact of predictive accuracy in cash management policies. We illustrate this method through several numerical examples.Salas-Molina, F.; Pla Santamaría, D.; Garcia-Bernabeu, A.; Mayor-Vitoria, F. (2020). Multiple-criteria cash-management policies with particular liquidity terms. IMA Journal of Management Mathematics. 31(2):217-231. https://doi.org/10.1093/imaman/dpz010S217231312Abdelaziz, F. B., Aouni, B., & Fayedh, R. E. (2007). Multi-objective stochastic programming for portfolio selection. European Journal of Operational Research, 177(3), 1811-1823. doi:10.1016/j.ejor.2005.10.021Aouni, B., Ben Abdelaziz, F., & La Torre, D. (2012). The Stochastic Goal Programming Model: Theory and Applications. Journal of Multi-Criteria Decision Analysis, 19(5-6), 185-200. doi:10.1002/mcda.1466Aouni, B., Colapinto, C., & La Torre, D. (2014). Financial portfolio management through the goal programming model: Current state-of-the-art. European Journal of Operational Research, 234(2), 536-545. doi:10.1016/j.ejor.2013.09.040Baccarin, S. (2009). Optimal impulse control for a multidimensional cash management system with generalized cost functions. European Journal of Operational Research, 196(1), 198-206. doi:10.1016/j.ejor.2008.02.040Ballestero, E. (2001). Stochastic goal programming: A mean–variance approach. European Journal of Operational Research, 131(3), 476-481. doi:10.1016/s0377-2217(00)00084-9Ballestero, E., & Romero, C. (1998). Multiple Criteria Decision Making and its Applications to Economic Problems. doi:10.1007/978-1-4757-2827-9Bemporad, A., & Morari, M. (1999). Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), 407-427. doi:10.1016/s0005-1098(98)00178-2Cabello, J. G. (2013). Cash efficiency for bank branches. SpringerPlus, 2(1). doi:10.1186/2193-1801-2-334García Cabello, J., & Lobillo, F. J. (2017). Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand. Omega, 70, 118-134. doi:10.1016/j.omega.2016.09.005Charnes, A., & Cooper, W. W. (1959). Chance-Constrained Programming. Management Science, 6(1), 73-79. doi:10.1287/mnsc.6.1.73Charnes, A., & Cooper, W. W. (1977). Goal programming and multiple objective optimizations. European Journal of Operational Research, 1(1), 39-54. doi:10.1016/s0377-2217(77)81007-2Constantinides, G. M., & Richard, S. F. (1978). Existence of Optimal Simple Policies for Discounted-Cost Inventory and Cash Management in Continuous Time. Operations Research, 26(4), 620-636. doi:10.1287/opre.26.4.620Moraes, M. B. da C., & Nagano, M. S. (2014). Evolutionary models in cash management policies with multiple assets. Economic Modelling, 39, 1-7. doi:10.1016/j.econmod.2014.02.010Da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic Cash Flow Management Models: A Literature Review Since the 1980s. Decision Engineering, 11-28. doi:10.1007/978-3-319-11949-6_2Eppen, G. D., & Fama, E. F. (1969). Cash Balance and Simple Dynamic Portfolio Problems with Proportional Costs. International Economic Review, 10(2), 119. doi:10.2307/2525547Gormley, F. M., & Meade, N. (2007). The utility of cash flow forecasts in the management of corporate cash balances. European Journal of Operational Research, 182(2), 923-935. doi:10.1016/j.ejor.2006.07.041Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643-656. doi:10.1016/0305-0483(76)90092-xHerrera-Cáceres, C. A., & Ibeas, A. (2016). Model predictive control of cash balance in a cash concentration and disbursements system. Journal of the Franklin Institute, 353(18), 4885-4923. doi:10.1016/j.jfranklin.2016.09.007Higson, A., Yoshikatsu, S., & Tippett, M. (2009). Organization size and the optimal investment in cash. IMA Journal of Management Mathematics, 21(1), 27-38. doi:10.1093/imaman/dpp015Miller, M. H., & Orr, D. (1966). A Model of the Demand for Money by Firms. The Quarterly Journal of Economics, 80(3), 413. doi:10.2307/1880728Miller, T. W., & Stone, B. K. (1985). Daily Cash Forecasting and Seasonal Resolution: Alternative Models and Techniques for Using the Distribution Approach. The Journal of Financial and Quantitative Analysis, 20(3), 335. doi:10.2307/2331034Penttinen, M. J. (1991). Myopic and stationary solutions for stochastic cash balance problems. European Journal of Operational Research, 52(2), 155-166. doi:10.1016/0377-2217(91)90077-9Prékopa, A. (1995). Stochastic Programming. doi:10.1007/978-94-017-3087-7Salas-Molina, F. (2017). Risk-sensitive control of cash management systems. Operational Research, 20(2), 1159-1176. doi:10.1007/s12351-017-0371-0Salas-Molina, F., Martin, F. J., Rodríguez-Aguilar, J. A., Serrà, J., & Arcos, J. L. (2017). Empowering cash managers to achieve cost savings by improving predictive accuracy. International Journal of Forecasting, 33(2), 403-415. doi:10.1016/j.ijforecast.2016.11.002Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1-2), 515-529. doi:10.1007/s10479-016-2359-1Salas-Molina, F., Pla-Santamaria, D., & Rodríguez-Aguilar, J. A. (2017). Empowering Cash Managers Through Compromise Programming. Financial Decision Aid Using Multiple Criteria, 149-173. doi:10.1007/978-3-319-68876-3_7Salas-Molina, F., Rodríguez-Aguilar, J. A., & Pla-Santamaria, D. (2018). Boundless multiobjective models for cash management. The Engineering Economist, 63(4), 363-381. doi:10.1080/0013791x.2018.1456596Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145-166. doi:10.1016/0305-0483(86)90017-4Stone, B. K. (1972). The Use of Forecasts and Smoothing in Control-Limit Models for Cash Management. Financial Management, 1(1), 72. doi:10.2307/3664955Stone, B. K., & Miller, T. W. (1987). Daily Cash Forecasting with Multiplicative Models of Cash Flow Patterns. Financial Management, 16(4), 45. doi:10.2307/366610

A Process Oriented MCDM Approach to Construct a Circular Economy Composite Index

[EN] The purpose of this contribution is to develop a Circular Economy Composite indicator to benchmark EU countries performance. Europe is at the forefront of the global transition towards a sustainable and circular economy. To this end, the European Commission has launched in 2015 a Circular Economy Action Plan including a monitoring framework to measure progress and to assess the effectiveness of initiatives towards the circular economy in the European Union (EU) and Member States. Still, this monitoring framework lacks a composite indicator at the national level to aggregate the circular economy dimensions into a single summary indicator. Although there is a wide range of sustainability composite indicators, no aggregate circular economy index exits to this date. We use a multi-criteria approach to construct a circular economy composite index based on TOPSIS (Technique for Order Preferences by Similarity to Ideal Solutions) methodology. In addition, we introduce a novel aggregation methodology for building a composite indicator where different levels of compensability for the distances to the ideal and anti-ideal (or negative-ideal) values of each indicator are considered. In order to illustrate the advantages of this proposal, we have applied it to evaluate the Circular Economy performance of EU Member States for the year 2016. This proposal can be a valuable tool for identifying areas in which the countries need to concentrate their efforts to boost their circular economy performance.Garcia-Bernabeu, A.; Hilario Caballero, A.; Pla Santamaría, D.; Salas-Molina, F. (2020). A Process Oriented MCDM Approach to Construct a Circular Economy Composite Index. Sustainability. 12(2):1-14. https://doi.org/10.3390/su12020618S114122Genovese, A., Acquaye, A. A., Figueroa, A., & Koh, S. C. L. (2017). Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications. Omega, 66, 344-357. doi:10.1016/j.omega.2015.05.015Di Maio, F., & Rem, P. C. (2015). A Robust Indicator for Promoting Circular Economy through Recycling. Journal of Environmental Protection, 06(10), 1095-1104. doi:10.4236/jep.2015.610096Geng, Y., Sarkis, J., Ulgiati, S., & Zhang, P. (2013). Measuring China’s Circular Economy. Science, 339(6127), 1526-1527. doi:10.1126/science.1227059Geng, Y., Fu, J., Sarkis, J., & Xue, B. (2012). Towards a national circular economy indicator system in China: an evaluation and critical analysis. Journal of Cleaner Production, 23(1), 216-224. doi:10.1016/j.jclepro.2011.07.005Elia, V., Gnoni, M. G., & Tornese, F. (2017). Measuring circular economy strategies through index methods: A critical analysis. Journal of Cleaner Production, 142, 2741-2751. doi:10.1016/j.jclepro.2016.10.196Huijbregts, M. A. J., Rombouts, L. J. A., Hellweg, S., Frischknecht, R., Hendriks, A. J., van de Meent, D., … Struijs, J. (2006). Is Cumulative Fossil Energy Demand a Useful Indicator for the Environmental Performance of Products? Environmental Science & Technology, 40(3), 641-648. doi:10.1021/es051689gBrown, M. T., & Ulgiati, S. (2004). Energy quality, emergy, and transformity: H.T. Odum’s contributions to quantifying and understanding systems. Ecological Modelling, 178(1-2), 201-213. doi:10.1016/j.ecolmodel.2004.03.002Rees, W. E. (1992). Ecological footprints and appropriated carrying capacity: what urban economics leaves out. Environment and Urbanization, 4(2), 121-130. doi:10.1177/095624789200400212Wiedmann, T., & Barrett, J. (2010). A Review of the Ecological Footprint Indicator—Perceptions and Methods. Sustainability, 2(6), 1645-1693. doi:10.3390/su2061645Narodoslawsky, M., & Krotscheck, C. (1995). The sustainable process index (SPI): evaluating processes according to environmental compatibility. Journal of Hazardous Materials, 41(2-3), 383-397. doi:10.1016/0304-3894(94)00114-vMunda, G. (2005). «Measuring Sustainability»: A Multi-Criterion Framework. Environment, Development and Sustainability, 7(1), 117-134. doi:10.1007/s10668-003-4713-0Janeiro, L., & Patel, M. K. (2015). Choosing sustainable technologies. Implications of the underlying sustainability paradigm in the decision-making process. Journal of Cleaner Production, 105, 438-446. doi:10.1016/j.jclepro.2014.01.029Diaz-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.2016.08.075Wilson, M. C., & Wu, J. (2016). The problems of weak sustainability and associated indicators. International Journal of Sustainable Development & World Ecology, 24(1), 44-51. doi:10.1080/13504509.2015.1136360Arrow, K. J., Chenery, H. B., Minhas, B. S., & Solow, R. M. (1961). Capital-Labor Substitution and Economic Efficiency. The Review of Economics and Statistics, 43(3), 225. doi:10.2307/1927286Blackorby, C., Donaldson, D., & Weymark, J. A. (1982). A normative approach to industrial-performance evaluation and concentration indices. European Economic Review, 19(1), 89-121. doi:10.1016/0014-2921(82)90007-1Rennings, K., Ludwig Brockmann, K., & Bergmann, H. (1997). Voluntary agreements in environmental protection: experiences in Germany and future perspectives. Business Strategy and the Environment, 6(5), 245-263. doi:10.1002/(sici)1099-0836(199711)6:53.0.co;2-fMathews, J. A., & Tan, H. (2016). Circular economy: Lessons from China. Nature, 531(7595), 440-442. doi:10.1038/531440aCherchye, L., Moesen, W., Rogge, N., Van Puyenbroeck, T., Saisana, M., Saltelli, A., … Tarantola, S. (2008). Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index. Journal of the Operational Research Society, 59(2), 239-251. doi:10.1057/palgrave.jors.2602445Giannetti, B. F., Bonilla, S. H., Silva, C. C., & Almeida, C. M. V. B. (2009). The reliability of experts’ opinions in constructing a composite environmental index: The case of ESI 2005. Journal of Environmental Management, 90(8), 2448-2459. doi:10.1016/j.jenvman.2008.12.018Makkonen, T., & van der Have, R. P. (2012). Benchmarking regional innovative performance: composite measures and direct innovation counts. Scientometrics, 94(1), 247-262. doi:10.1007/s11192-012-0753-2Mazziotta, M., & Pareto, A. (2015). On a Generalized Non-compensatory Composite Index for Measuring Socio-economic Phenomena. Social Indicators Research, 127(3), 983-1003. doi:10.1007/s11205-015-0998-2Greco, M., Mazziotta, M., & Pareto, A. (2016). A Composite Index to Measure the Italian «Enological Vocation». Agriculture and Agricultural Science Procedia, 8, 691-697. doi:10.1016/j.aaspro.2016.02.045Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61-94. doi:10.1007/s11205-017-1832-9Attardi, R., Cerreta, M., Sannicandro, V., & Torre, C. M. (2018). Non-compensatory composite indicators for the evaluation of urban planning policy: The Land-Use Policy Efficiency Index (LUPEI). European Journal of Operational Research, 264(2), 491-507. doi:10.1016/j.ejor.2017.07.064Angilella, S., Catalfo, P., Corrente, S., Giarlotta, A., Greco, S., & Rizzo, M. (2018). Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach. Knowledge-Based Systems, 158, 136-153. doi:10.1016/j.knosys.2018.05.041Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators. European Journal of Operational Research, 278(3), 942-960. doi:10.1016/j.ejor.2019.04.012Ruiz, F., El Gibari, S., Cabello, J. M., & Gómez, T. (2020). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega, 95, 102060. doi:10.1016/j.omega.2019.04.003Sands, G. R., & Podmore, T. H. (2000). A generalized environmental sustainability index for agricultural systems. Agriculture, Ecosystems & Environment, 79(1), 29-41. doi:10.1016/s0167-8809(99)00147-4Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. doi:10.1016/0022-2496(77)90033-5Singh, R. K., Murty, H. R., Gupta, S. K., & Dikshit, A. K. (2007). Development of composite sustainability performance index for steel industry. Ecological Indicators, 7(3), 565-588. doi:10.1016/j.ecolind.2006.06.004Ülengin, B., Ülengin, F., & Güvenç, Ü. (2001). A multidimensional approach to urban quality of life: The case of Istanbul. European Journal of Operational Research, 130(2), 361-374. doi:10.1016/s0377-2217(00)00047-3Buckland, S. T., Studeny, A. C., Magurran, A. E., Illian, J. B., & Newson, S. E. (2011). The geometric mean of relative abundance indices: a biodiversity measure with a difference. Ecosphere, 2(9), art100. doi:10.1890/es11-00186.1El Gibari, S., Gómez, T., & Ruiz, F. (2018). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1-24. doi:10.1007/s11573-018-0902-zGan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., & Wu, J. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491-502. doi:10.1016/j.ecolind.2017.05.068Li, H., Bao, W., Xiu, C., Zhang, Y., & Xu, H. (2010). Energy conservation and circular economy in China’s process industries. Energy, 35(11), 4273-4281. doi:10.1016/j.energy.2009.04.02

Financial risk management in renewable energy projects: A multicriteria approach

[EN] The problem of financing renewable energy (RE) projects has become a crucial issue for private and public decision-makers worldwide. Based on the European experience, the lack of credit makes it difficult for commercial banks to fund RE projects with traditional loans. In recent years, Project Finance has been widely used as a mechanism for funding RE projects. Project Finance decisions from lenders are made in a traditional way including quantitative financial criteria and qualitative risk assessment for non-financial criteria. In this paper, we show how the VIKOR method is applied to the selection of RE projects to be funded by commercial banks when using Project Finance. The method is combined with the AHP method for weighting the importance of the different criteria. We propose a multicriteria approach to select RE projects in which the decision maker takes into consideration financial criteria and a set of non-financial criteria regarding the technological, political-legal and socio-environmental risks of RE projects. In order to gain insight into the RE financial decision-making process, the paper makes a contribution to research in the financial field of RE investments and proposes some suggestions for managerial and practical decision making.Garcia-Bernabeu, A.; Mayor-Vitoria, F.; Bravo Selles, M.; Pla Santamaría, D. (2019). Financial risk management in renewable energy projects: A multicriteria approach. Journal of Management Information and Decision Sciences. 22(4):360-371. http://hdl.handle.net/10251/164156S36037122