Sustainability performance assessment with intuitionistic fuzzy composite metrics and its application to the motor industry

The performance assessment of companies in terms of sustainability requires to find a balance between multiple and possibly conflicting criteria. We here rely on composite metrics to rank a set of companies within an industry considering environmental, social and corporate governance criteria. To this end, we connect intuitionistic fuzzy sets and composite programming to propose novel composite metrics. These metrics allow to integrate important environmental, social and governance principles with the gradual membership functions of fuzzy set theory. The main result of this paper is a sustainability assessment method to rank companies within a given industry. In addition to consider multiple objectives, this method integrates two important social principles such as maximum utility and fairness. A real-world example is provided to describe the application of our sustainability assessment method within the motor industry. A further contribution of this paper is a multicriteria generalization of the concept of magnitude of a fuzzy number

Inverse Malthusianism and Recycling Economics: The Case of the Textile Industry

[EN] The current use of natural resources in the textile industry leads us to introduce a new economic concept called inverse Malthusianism describing a context in which population grows linearly and resource consumption grows exponentially. Inverse Malthusianism implies an exponential increase in environmental impact that recycling may contribute to reduce. Our main goal is to extend the analysis of materials selection under the principle of equimarginality proposed by Jevons. As a first result, we show the particular circumstances under which policies excluding recycled supplies are never optimal. We also aim to overcome the difficulties of reducing environmental aspects to monetary units. To this end, we propose a multicriteria approach to solve the conventional-recycled materials dilemma considering not only economic but also environmental criteria. Then, we allow producers to enrich their decision-making process with relevant information about the environmental impact of materials selection. Although we use examples of the textile industry to illustrate our results, most of the insights in this paper can be extended to other industries.Salas-Molina, F.; Pla Santamaría, D.; Vercher-Ferrandiz, ML.; Reig-Mullor, J. (2020). Inverse Malthusianism and Recycling Economics: The Case of the Textile Industry. Sustainability. 12(14):1-20. https://doi.org/10.3390/su12145861S1201214Chapagain, A. K., Hoekstra, A. Y., Savenije, H. H. G., & Gautam, R. (2006). The water footprint of cotton consumption: An assessment of the impact of worldwide consumption of cotton products on the water resources in the cotton producing countries. Ecological Economics, 60(1), 186-203. doi:10.1016/j.ecolecon.2005.11.027Esteve-Turrillas, F. A., & de la Guardia, M. (2017). Environmental impact of Recover cotton in textile industry. Resources, Conservation and Recycling, 116, 107-115. doi:10.1016/j.resconrec.2016.09.034McInerney, J. (1976). THE SIMPLE ANALYTICS OF NATURAL RESOURCE ECONOMICS. Journal of Agricultural Economics, 27(1), 31-52. doi:10.1111/j.1477-9552.1976.tb00964.xRomero, C. (2012). Short communication. Economics of natural resources: in search of a unified theoretical framework. Spanish Journal of Agricultural Research, 10(1), 29. doi:10.5424/sjar/2012101-329-11Sandin, G., & Peters, G. M. (2018). Environmental impact of textile reuse and recycling – A review. Journal of Cleaner Production, 184, 353-365. doi:10.1016/j.jclepro.2018.02.266Leal Filho, W., Ellams, D., Han, S., Tyler, D., Boiten, V. J., Paço, A., … Balogun, A.-L. (2019). A review of the socio-economic advantages of textile recycling. Journal of Cleaner Production, 218, 10-20. doi:10.1016/j.jclepro.2019.01.210Hotelling, H. (1931). The Economics of Exhaustible Resources. Journal of Political Economy, 39(2), 137-175. doi:10.1086/254195Solow, R. M. (1974). Intergenerational Equity and Exhaustible Resources. The Review of Economic Studies, 41, 29. doi:10.2307/2296370Thampapillai, D. J. (1985). Trade-offs for conflicting social objectives in the extraction of finite energy resources. International Journal of Energy Research, 9(2), 179-192. doi:10.1002/er.4440090209Stahel, W. R. (2016). The circular economy. Nature, 531(7595), 435-438. doi:10.1038/531435aGeissdoerfer, M., Savaget, P., Bocken, N. M. P., & Hultink, E. J. (2017). The Circular Economy – A new sustainability paradigm? Journal of Cleaner Production, 143, 757-768. doi:10.1016/j.jclepro.2016.12.048Ayres, R. U. (1997). Metals recycling: economic and environmental implications. Resources, Conservation and Recycling, 21(3), 145-173. doi:10.1016/s0921-3449(97)00033-5Ljungberg, L. Y. (2007). Materials selection and design for development of sustainable products. Materials & Design, 28(2), 466-479. doi:10.1016/j.matdes.2005.09.006Garcia-Bernabeu, A., Hilario-Caballero, A., Pla-Santamaria, D., & Salas-Molina, F. (2020). A Process Oriented MCDM Approach to Construct a Circular Economy Composite Index. Sustainability, 12(2), 618. doi:10.3390/su12020618Scott, A. D. (1953). Notes on User Cost. The Economic Journal, 63(250), 368. doi:10.2307/2227129Romero, C. (1997). Multicriteria decision analysis and environmental economics: An approximation. European Journal of Operational Research, 96(1), 81-89. doi:10.1016/s0377-2217(96)00118-xLaitala, K., Klepp, I., & Henry, B. (2018). Does Use Matter? Comparison of Environmental Impacts of Clothing Based on Fiber Type. Sustainability, 10(7), 2524. doi:10.3390/su10072524Materials Sustainability Indexhttps://msi.higg.orgAlcott, B. (2005). Jevons’ paradox. Ecological Economics, 54(1), 9-21. doi:10.1016/j.ecolecon.2005.03.020Roy, J. (2000). The rebound effect: some empirical evidence from India. Energy Policy, 28(6-7), 433-438. doi:10.1016/s0301-4215(00)00027-6Cambra‐Fierro, J., & Ruiz‐Benitez, R. (2009). Advantages of intermodal logistics platforms: insights from a Spanish platform. Supply Chain Management: An International Journal, 14(6), 418-421. doi:10.1108/1359854091099518

New decision rules under strict uncertainty and a general distance-based approach

Strict uncertainty implies a complete lack of knowledge about the probabilities of possible future states of the world. However, there is complete information about the set of alternatives under consideration, the set of future states, and the scalar evaluation of choosing every alternative if a given state occurs. The principle of insufficient reason by Laplace, the maximin rule by Wald, the Hurwicz criterion, or the minimax regret criterion by Savage are examples of decision rules under strict uncertainty. Within the context of strict uncertainty, moderate pessimism implies the existence of a decision-maker who cautiously assumes that the most favorable state will not occur when the action has been taken with no conjecture being made about the other states. The criterion of moderate pessimism proposed by Ballestero implies the use of the inverse of the range of evaluation for each state as a weight system. In this paper, we extend the notion of moderate pessimism under strict uncertainty to solve some of its limitations. First, we propose a new domination analysis that avoids removing dominated alternatives that are still relevant in the final ranking of alternatives. Second, we propose additional score functions using the inverse of the standard deviation and the mean absolute deviation instead of the range of evaluations for each future state to reduce the impact of the possible existence of outliers in the decision table. This partial result is later generalized through the concept of average deviation of a given order. Finally, we show that all the mentioned decision rules are special cases of a general ranking method based on the Minkowski distance function. We illustrate the use of distance-based decision rules through an application in the context of portfolio selection

Geometric compromise programming: application in portfolio selection

[EN] Compromise programming (CP) aims to find solutions by minimising distances to an ideal point with maximum achievement which is usually infeasible. A common assumption in CP is that it is highly unlikely that the optimum decision will lie out of the bounds of the compromise set given by metrics p=1$p=1$ and p=infinity$p=\infty$ of the Minkowski distance function. This assumption excludes the use of multiplicative functions as a measure of achievement. We propose geometric CP (GCP) to provide alternative solutions based on multiplicative functions to overcome this limitation. This methodology is an extension of CP that allows to incorporate the principle of limited compensability. An additional interesting feature of GCP is that, under reasonable assumptions, characterises extreme seekers' behaviour with non-concave utility functions (expressing no preference for any of the extremes). We discuss the practical implications of our approach and present three numerical illustrations in the context portfolio selection.Salas-Molina, F.; Pla Santamaría, D.; Vercher Ferrandiz, ML.; Garcia-Bernabeu, A. (2023). Geometric compromise programming: application in portfolio selection. International Transactions in Operational Research. 30(5):2571-2594. https://doi.org/10.1111/itor.131782571259430