×

Evaluation of social factors within the circular economy concept for European countries. (English) Zbl 07700323

Summary: The circular economy (CE) is a rapidly growing theme, particularly in the European Union (EU), that encourages the responsible and circular use of resources in the field of contributing to long-term development. The environmental and economic magnitude of development are frequently debated in the subject of CE whereas, social aspects have been only rarely and sporadically combined into the CE. Therefore, this study involves a multidisciplinary holistic framework of clustering methods and Multi-Criteria Decision Making (MCDM) for evaluating the CE paradigm in relation to EU countries’ social growth. In terms of the social impact of CE strategies, the k-means cluster analysis was used to group the 27 EU members with similar social impact levels. Subsequently, a novel integrated CRITIC (The criteria importance through intercriteria correlation) and MEREC (Method based on the Removal Effects of Criteria) methods were employed for determining the weights of social indicators in order to balance the two weighting methods. In order to select the best country in each cluster, a novel extension of the hybrid MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method combined with Power averaging and Heronian operator was developed. According to final solutions, Western European countries in the first cluster have the lowest unemployment and corruption rates, with the Netherlands having the best performance in this cluster. The second cluster includes countries with the lowest employment rates following university graduates between the ages of 20 and 64. Accordingly, Croatia is the best social performance in this cluster. Countries with the highest income distribution and unemployment rate are in the 3rd cluster, the best country in this group is Lithuania. Finally, the results obtained from the innovative MCDM methods have validated in order to demonstrate the proposed methodology’s applicability.

MSC:

90Bxx Operations research and management science

Software:

Silhouettes
Full Text: DOI

References:

[1] Abdel-Basset, M.; Mohamed, R., A novel Plithogenic TOPSIS-CRITIC model for sustainable supply chain risk management, J Clean Prod, 247 (2020) · doi:10.1016/j.jclepro.2019.119586
[2] Ali, Z.; Mahmood, T.; Ullah, K.; Khan, Q., Einstein geometric aggregation operators using a novel complex interval-valued pythagorean fuzzy setting with application in green supplier chain management, Rep Mech Eng, 2, 1, 105-134 (2021) · doi:10.31181/rme2001020105t
[3] Alliance G (2015) The social benefits of a circular economy: lessons from the UK. Green Alliance, London
[4] Arsu, T.; Ayçin, E., Evaluation of OECD countries with multi-criteria decision-making methods in terms of economic, social and environmental aspects, Oper Res Eng Sci Theory Appl, 4, 2, 55-78 (2021) · doi:10.31181/oresta20402055a
[5] Ayçin, E., Using CRITIC and MAIRCA methods in personnel selection process, Bus J, 1, 1, 1-12 (2020)
[6] Aytaç, E., Unsupervised learning approach in defining the similarity of catchments: hydrological response unit-based k-means clustering, a demonstration on Western Black Sea Region of Turkey, Int Soil Water Conserv Res, 8, 321-331 (2020) · doi:10.1016/j.iswcr.2020.05.002
[7] Badi, I.; Pamucar, D., Supplier selection for steelmaking company by using combined Grey-MARCOS methods, Decis Mak Appl Manag Eng, 3, 2, 37-48 (2020) · doi:10.31181/dmame2003037b
[8] Bain, KK; Firli, I.; Tri, S., Genetic algorithm for optimized initial centers K-means clustering in SMEs, J Theoret Appl Inf Technol (JATIT), 90, 23 (2016)
[9] Bakır, M.; Akan, Ş.; Özdemir, E., Regional aircraft selection with fuzzy PIPRECIA and fuzzy MARCOS: A case study of the turkish airline industry, Facta Universitatis Ser Mech Eng, 19, 3, 423-445 (2021) · doi:10.22190/FUME210505053B
[10] Chakraborty, S.; Chattopadhyay, R.; Chakraborty, S., An integrated D-MARCOS method for supplier selection in an iron and steel industry, Decis Mak Appl Manag Eng, 3, 2, 49-69 (2020) · doi:10.31181/dmame2003049c
[11] Della Spina, L., Multidimensional assessment for “culture-led” and “community-driven” urban regeneration as driver for trigger economic vitality in urban historic centers, Sustainability, 11, 24, 7237 (2019) · doi:10.3390/su11247237
[12] Diakoulaki, D.; Mavrotas, G.; Papayannakis, L., Determining objective weights in multiple criteria problems: The critic method, Comput Oper Res, 22, 7, 763-770 (1995) · Zbl 0830.90079 · doi:10.1016/0305-0548(94)00059-H
[13] Djordjevic, D.; Stojic, G.; Stevic, Z.; Pamucar, D.; Vulevic, A.; Misic, V., A new model for defining the criteria of service quality in rail transport: the full consistency method based on a rough power heronian aggregator, Symmetry, 11, 8, 992 (2019) · doi:10.3390/sym11080992
[14] Ecer, F.; Pamucar, D., MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services, Appl Soft Comput, 104 (2021) · doi:10.1016/j.asoc.2021.107199
[15] European Commission (2015) Commission adopts ambitious new Circular Economy Package. https://ec.europa.eu/commission/presscorner/detail/en/IP_15_6203
[16] Eurostat (2021) https://ec.europa.eu/eurostat/data/database. Accessed 9.26.21
[17] Geissdoerfer, M.; Savaget, P.; Bocken, NMP; Hultink, EJ, The circular economy – a new sustainability paradigm?, J Clean Prod, 143, 757-768 (2017) · doi:10.1016/j.jclepro.2016.12.048
[18] Ghisellini, P.; Cialani, C.; Ulgiati, S., A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems, J Clea Prod, 114, 11-32 (2016) · doi:10.1016/j.jclepro.2015.09.007
[19] Goswami SS, Mohanty SK, Behera DK (2021) Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. In: Materials today: proceedings.
[20] Hadi, A., Web application system to find best urban hospital location for COVID-19 patients based on internet of things, Bull Electr Eng Inform, 11, 1, 386-395 (2022) · doi:10.11591/eei.v11i1.3214
[21] ILO (2015) Gender equality and green jobs, green jobs Programme, international labour organization). https://www.ilo.org/wcmsp5/groups/public/—ed_emp/—emp_ent/documents/publication/wcms_360572.pdf.
[22] Kayapinar Kaya, S.; Aycin, E., An integrated interval type 2 fuzzy AHP and COPRAS-G methodologies for supplier selection in the era of Industry 4.0, Neural Comput Appl, 33, 10515-10535 (2021) · doi:10.1007/s00521-021-05809-x
[23] Keshavarz Ghorabaee, M.; Amiri, M.; Kazimieras Zavadskas, E.; Antuchevičienė, J., Assessment of third-party logistics providers using a CRITIC-WASPAS approach with interval type-2 fuzzy sets, Transport, 32, 1, 66-78 (2017) · doi:10.3846/16484142.2017.1282381
[24] Keshavarz Ghorabaee, M.; Amiri, M.; Zavadskas, EK; Antucheviciene, J., A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations, Arch Civil Mech Eng, 18, 32-49 (2018) · doi:10.1016/j.acme.2017.04.011
[25] Keshavarz-Ghorabaee, M., Assessment of distribution center locations using a multi-expert subjective objective decision-making approach, Sci Rep (2021) · doi:10.1038/s41598-021-98698-y
[26] Korhonen, J.; Honkasalo, A.; Seppälä, J., Circular economy: the concept and its limitations, Ecol Econ, 143, 37-46 (2018) · doi:10.1016/j.ecolecon.2017.06.041
[27] Kou, G.; Peng, Y.; Wang, G., Evaluation of clustering algorithms for financial risk analysis using MCDM methods, Inf Sci, 275, 1-12 (2014) · doi:10.1016/j.ins.2014.02.137
[28] Ljubljana BL (2021) University of, 2021. Data mining. https://orangedatamining.com/
[29] Lleti, R.; Ortiz, MC; Sarabia, LA; Sánchez, MS, Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes, Anal Chim Acta, 515, 1, 87-100 (2004) · doi:10.1016/j.aca.2003.12.020
[30] Lotfi R, Kargar B, Gharehbaghi A, Weber GW (2021a) Viable medical waste chain network design by considering risk and robustness. Environ Sci Pollut Res 1-16
[31] Lotfi, R.; Kargar, B.; Hoseini, SH; Nazari, S.; Safavi, S.; Weber, GW; Lotfi, R.; Kargar, B.; Hoseini, SH; Nazari, S.; Safavi, S.; Weber, GW, Resilience and sustainable supply chain network design by considering renewable energy, Int J Energy Res, 45, 12, 17749-17766 (2021) · doi:10.1002/er.6943
[32] Lotfi, R.; Mostafaeipour, A.; Mardani, N.; Mardani, S., Investigation of wind farm location planning by considering budget constraints, Int J Sustain Energ, 37, 8, 799-817 (2018) · doi:10.1080/14786451.2018.1437160
[33] Lotfi, R.; Safavi, S.; Gharehbaghi, A.; Ghaboulian Zare, S.; Hazrati, R.; Weber, GW, Viable supply chain network design by considering blockchain technology and cryptocurrency, Math Prob Eng (2021) · doi:10.1155/2021/7347389
[34] Lotfi R, Sheikhi Z, Amra M, AliBakhshi M, Weber GW (2021b) Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize. Int J Logist Res Appl pp 1-41
[35] MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Oakland, CA, USA, 281-297 · Zbl 0214.46201
[36] Milosevic, T.; Pamucar, D.; Chatterjee, P., Model for selecting a route for the transport of hazardous materials using a fuzzy logic system, Military Tech Courier, 69, 2, 355-390 (2021)
[37] Milovanovic, VR; Aleksic, AV; Sokolovic, VS; Milenkov, MA, Uncertainty modeling using intuitionistic fuzzy numbers, Military Tech Courier, 69, 4, 905-929 (2021)
[38] Mukhametzyanov, I., Specific character of objective methods for determining weights of criteria in MCDM problems: entropy, CRITIC and SD, Decis Mak Appl Manag Eng, 4, 2, 76-105 (2021) · doi:10.31181/dmame210402076i
[39] Murray, A.; Skene, K.; Haynes, K., The circular economy: an interdisciplinary exploration of the concept and application in a global context, J Bus Ethics, 140, 3, 369-380 (2017) · doi:10.1007/s10551-015-2693-2
[40] Nikonorova, M.; Imoniana, JO; Stankeviciene, J., Analysis of social dimension and well-being in the context of circular economy, Int J Glob Warm, 21, 299-316 (2020) · doi:10.1504/IJGW.2020.108678
[41] Orhan, M.; Aytekin, M., Comparing The R&D performance of turkey and last members countries of EU using critic weighted maut and saw methods, Bus Manag Stud Int J, 8, 1, 754-778 (2020)
[42] Özdağoğlu, A.; Keleş, MK; Işıldak, B., Cabin crew selection in civil aviation with fuzzy SWARA and fuzzy MARCOS methods, Gümüşhane Univ J Soc Sci Inst, 12, 2, 284-302 (2021)
[43] Özdağoğlu, A.; Keleş, MK; Işildak, B., Evaluation of the world’s busiest airports with Pıprecıa-E, smart and marcos methods, Erciyes Univ J Fac Econ Admin Sci, 58, 333-352 (2021)
[44] Padilla-Rivera, A.; do Carmo, B. B.T., Arcese, G., Merveille, N.,, Social circular economy indicators: selection through fuzzy delphi method, Sustain Prod Consump, 26, 101-110 (2021) · doi:10.1016/j.spc.2020.09.015
[45] Padilla-Rivera, A.; Russo-Garrido, S.; Merveille, N., Addressing the social aspects of a circular economy: a systematic literature review, Sustainability, 12, 7912 (2020) · doi:10.3390/su12197912
[46] Pamucar, D., Normalized weighted geometric Dombi Bonferroni mean operator with interval grey numbers: application in multicriteria decision making, Rep Mech Eng, 1, 1, 44-52 (2020) · doi:10.31181/rme200101044p
[47] Panchal, D.; Chatterjee, P.; Sharma, R.; Garg, RK, Sustainable oil selection for cleaner production in Indian foundry industries: A three phase integrated decision-making framework, J Cleaner Prod, 313 (2021) · doi:10.1016/j.jclepro.2021.127827
[48] Peng, X.; Krishankumar, R.; Ravichandran, KS, A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation, Soft Comput, 25, 6, 4213-4241 (2021) · Zbl 1498.91155 · doi:10.1007/s00500-020-05437-y
[49] Pitkänen K, Karppinen TKM, Kautto P, Turunen S, Judl J, Myllymaa T (2020) Sex, drugs and the circular economy: the social impacts of the circular economy and how to measure them. Handb Circ Econ
[50] Puška, A.; Stojanović, I.; Maksimović, A.; Osmanović, N., Evaluation software of project management used measurement of alternatives and ranking according to compromise solution (MARCOS) method, Oper Res Eng Sci Theory Appl, 3, 1, 89-102 (2020) · doi:10.31181/oresta2001089p
[51] Rani, P.; Mishra, AR; Saha, A.; Hezam, IM; Pamucar, D., Fermatean fuzzy Heronian mean operators and MEREC-based additive ratio assessment method: an application to food waste treatment technology selection, Int J Intell Syst, 37, 3, 2612-2647 (2021) · doi:10.1002/int.22787
[52] Robinson S (2021) Social circular economy. http://www.socialcirculareconomy.com/uploads/7/3/5/2/73522419/social_circular_economy.pdf
[53] Rousseeuw, PJ, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J Comput Appl Math, 20, 53-65 (1987) · Zbl 0636.62059 · doi:10.1016/0377-0427(87)90125-7
[54] Sabaghian, K.; Khamforoosh, K.; Ghaderzadeh, A., Presentation of a new method based on modern multivariate approaches for big data replication in distributed environments, PLoS ONE, 16, 7 (2021) · doi:10.1371/journal.pone.0254210
[55] Schroeder, P.; Anggraeni, K.; Weber, U., The relevance of circular economy practices to the sustainable development goals, J Ind Ecol, 23, 77-95 (2019) · doi:10.1111/jiec.12732
[56] Simic V, Gokasar I, Deveci M, Karakurt A (2021) An integrated CRITIC and MABAC based Type-2 neutrosophic model for public transportation pricing system selection. Soc Econ Plann Sci, 101157
[57] Skvarciany, V.; Lapinskaitė, I.; Volskytė, G., Circular economy as assistance for sustainable development in OECD countries, Oeconomia Copernicana, 12, 1, 11-34 (2021) · doi:10.24136/oc.2021.001
[58] Social Circular Economy (2018) The frank Jackson foundation. https://circulareconomy.europa.eu/platform/en/knowledge/social-circular-economy-opportunities-people-planet-and-profit
[59] Stević, Ž.; Pamučar, D.; Puška, A.; Chatterjee, P., Sustainable supplier selection in healthcare industries using a new MCDM method: measurement of alternatives and ranking according to COmpromise solution (MARCOS), Comput Ind Eng, 140 (2020) · doi:10.1016/j.cie.2019.106231
[60] Tinsley HE, Brown SD (2000) Handbook of applied multivariate statistics and mathematical modeling. Academic Press, Cambridge
[61] Trung, DD; Thinh, HX, A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: a comparative study, Adv Prod Eng Manag, 16, 4, 443-456 (2021)
[62] Ulutaş, A.; Karabasevic, D.; Popovic, G.; Stanujkic, D.; Nguyen, PT; Karaköy, Ç., Development of a novel integrated CCSD-ITARA-MARCOS decision-making approach for stackers selection in a logistics system, Mathematics, 8, 10, 1672 (2020) · doi:10.3390/math8101672
[63] Vujičić, MD; Papić, MZ; Blagojević, MD, Comparative analysis of objective techniques for criteria weighing in two MCDM methods on example of an air conditioner selection, Tehnika, 72, 3, 422-429 (2017) · doi:10.5937/tehnika1703422V
[64] Walker, AM; Opferkuch, K.; Roos Lindgreen, E.; Simboli, A.; Vermeulen, WJV; Raggi, A., Assessing the social sustainability of circular economy practices: industry perspectives from Italy and the Netherlands, Sustain Prod Consump, 27, 831-844 (2021) · doi:10.1016/j.spc.2021.01.030
[65] Wang F, Franco-Penya HH, Kelleher JD, Pugh J, Ross R (2017) An analysis of the application of simplified silhouette to the evaluation of k-means clustering validity. In: International conference on machine learning and data mining in pattern recognition, Springer, Cham, pp 291-305
[66] Wang, Q.; Wang, C.; Feng, Z.; Ye, J., Review of K-means clustering algorithm, Electron Des Eng, 20, 21-24 (2012)
[67] Wei, G.; Lei, F.; Lin, R.; Wang, R.; Wei, Y.; Wu, J.; Wei, C., Algorithms for probabilistic uncertain linguistic multiple attribute group decision making based on the GRA and CRITIC method: application to location planning of electric vehicle charging stations, Econ Res-Ekonomska Istraživanja, 33, 1, 828-846 (2020) · doi:10.1080/1331677X.2020.1734851
[68] WESO (2018) World employment and social outlook 2018: greening with jobs. https://www.ilo.org/weso-greening/documents/WESO_Greening_EN_web2.pdf.
[69] Yager, RR, The power average operator, IEEE Trans Syst Man Cybernet Part Syst Hum, 31, 6, 724-731 (2001) · doi:10.1109/3468.983429
[70] Yu, D., Intuitionistic fuzzy geometric Heronian mean aggregation operators, Appl Soft Comput, 13, 1235-1246 (2013) · doi:10.1109/3468.983429
[71] Zavadskas, EK; Turskis, Z.; Antucheviciene, J.; Zakarevicius, A., Optimization of weighted aggregated sum product assessment, Elektronika Ir Elektrotechnika, 122, 6, 3-6 (2012) · doi:10.5755/j01.eee.122.6.1810
[72] Zhao, H.; Zhao, H.; Guo, S., Evaluating the comprehensive benefit of eco-industrial parks by employing multi criteria decision-making approach for circular economy, J Clean Prod, 142, 2262-2276 (2017) · doi:10.1016/j.jclepro.2016.11.041
[73] Zhao, S.; Wang, D.; Liang, C.; Leng, Y.; Xu, J., Some Single-valued neutrosophic power heronian aggregation operators and their application to multiple-attribute group decision-making, Symmetry, 11, 5, 653 (2019) · doi:10.3390/sym11050653
[74] Zolfani, SF; Yazdani, M.; Pamucar, D.; Zarate, P., A VIKOR and TOPSIS focused reanalysis of the MADM methods based on logarithmic normalization, Facta Univ Ser Mech Eng, 18, 3, 341-355 (2020)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.