Evaluation of OECD Countries with Multi-Criteria Decision-Making Methods in terms of Economic, Social and Environmental Aspects

Authors

  • Talip Arsu Aksaray University, Vocational School of Social Sciences, Department of Tourism and Hotel Management, Turkey
  • Ejder Ayçin Kocaeli University, Faculty of Economics and Administrative Sciences, Department of Business Administration, Turkey

DOI:

https://doi.org/10.31181/oresta20402055a

Keywords:

OECD Countries, economic- social- environmental development, CRITIC, MARCOS

Abstract

Exhausted natural resources and deteriorating ecological balance, together with the social privileges that people expect to have, are proof that the development of countries cannot be reduced to economic development alone. In this respect, this study aimed to evaluate the economic, social and environmental aspects of Organization for Economic Co-operation and Development (OECD) countries. Within this scope, the countries were firstly divided into two groups by performing cluster analysis in order to create more homogeneous country groups. Then, 12 criteria, consisting of four economic, four social and four environmental criteria, were determined by considering the literature and expert opinions. The criteria importance through intercriteria correlation (CRITIC) method was used to weight the determined criteria and using the calculated criterion weights, the countries in each cluster were then evaluated with the measurement of alternatives and ranking according to compromise solution (MARCOS) method. As a result, the most successful countries in the first cluster were determined as Switzerland, Denmark and Ireland with 68.8%, 62.7% and 62.5% performance scores, respectively. Whereas, the most unsuccessful countries were USA, Canada and Australia with 49.8%, 50.0% and 50.1% performance scores, respectively. The most successful countries in the second cluster were found as Slovenia, Spain and Portugal with 65.9%, 65.5% and 64.5% performance scores, while the most unsuccessful countries were Turkey, Chile and Colombia with 45.9%, 55.4% and 55.9% performance scores, respectively. Finally, in order to test the sensitivity of the MARCOS method, the solution was repeated with the MAIRCA, WASPAS, MABAC and CoCoSo methods using the weights obtained by the CRITIC method. A high correlation (greater than 80%) was found between the rankings acquired using the other methods and the rankings obtained by the MARCOS method.

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References

Akbulut, O. Y. (2019). CRITIC ve EDAS yöntemleri ile İş Bankası'nın 2009-2018 yılları arasındaki performansının analizi. Ekonomi Politika ve Finans Araştırmaları Dergisi, 4(2), 249-263. https://doi.org/10.30784/epfad.594762

Antanasijević, D., Pocajt, V., Ristić, M., & Perić-Grujić, A. (2017). A differential multi-criteria analysis for the assessment of sustainability performance of European countries: Beyond country ranking. Journal of cleaner production, 165, 213-220. https://doi.org/10.1016/j.jclepro.2017.07.131

Apan, M., & Öztel, A. (2020). Girişim sermayesi yatırım ortaklıklarının CRITIC-PROMETHEE bütünleşik karar verme yöntemi ile finansal performans değerlendirmesi: Borsa İstanbul’da bir uygulama. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 63, 54-73

Ayçin, E., (2020). Personel seçim sürecinde CRITIC ve MAIRCA yöntemlerinin kullanılması, İşletme, 1(1), 1-12.

Badi, I., & Pamucar, D. (2020). Supplier selection for steelmaking company by using combined Grey-MARCOS methods. Decision Making: Applications in Management and Engineering, 3(2), 37-48. https://doi.org/10.31181/dmame2003037b

Bakır, M., & Atalık, Ö. (2021). Application of Fuzzy AHP and Fuzzy MARCOS Approach for the Evaluation of E-Service Quality in the Airline Industry. Decision Making: Applications in Management and Engineering, 4(1), 127-152. https://doi.org/10.31181/dmame2104127b

Benítez, R., & Liern, V. (2021). Unweighted TOPSIS: a new multi-criteria tool for sustainability analysis. International Journal of Sustainable Development & World Ecology, 28(1), 36-48. https://doi.org/10.1080/13504509.2020.1778583

Charles, V., & D'Alessio, F. A. (2020). An envelopment-based approach to measuring regional social progress. Socio-Economic Planning Sciences, 70, 100713. https://doi.org/10.1016/j.seps.2019.05.004

Chatterjee, P., Panchal, D., & Chakraborty, S. (2020). A developed meta-model for biomaterials selection. Trends in Biomaterials & Artificial Organs, 34(1), 20-32.

Chattopadhyay, R., Chakraborty, S., & Chakraborty, S. (2020). An integrated D-MARCOS method for supplier selection in an iron and steel industry. Decision Making: Applications in Management and Engineering, 3(2), 49-69. https://doi.org/10.31181/dmame2003049c

Costa, A. S., Rui Figueira, J., Vieira, C. R., & Vieira, I. V. (2019). An application of the ELECTRE TRI‐C method to characterize government performance in OECD countries. International Transactions in Operational Research, 26(5), 1935-1955. https://doi.org/10.1111/itor.12394

Cracolici, M. F., Cuffaro, M., & Nijkamp, P. (2010). The measurement of economic, social and environmental performance of countries: A novel approach. Social indicators research, 95(2), 339. https://doi.org/10.1007/s11205-009-9464-3

Đalić, I., Stević, Ž., Ateljević, J., Turskis, Z., Zavadskas, E. K., & Mardani, A. (2021). A novel integrated MCDM-SWOT-TOWS model for the strategic decision analysis in transportation company. Facta Universitatis, Series: Mechanical Engineering.

Đalić, I., Stević, Z., Erceg, Ž., Macura, P., & Terzić S. (2020). Selection of a distribution channel using the integrated FUCOM-MARCOS model. International Review, 3(4), 80-96. https://doi.org/10.5937/intrev2003080Q

Das, I., Panchal, D. and Tyagi, M. (2021), A novel PFMEA-Doubly TOPSIS approach-based decision support system for risk analysis in milk process industry. International Journal of Quality & Reliability Management. Online First. https://doi.org/10.1108/IJQRM-10-2019-0320

Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305-0548(94)00059-H

Ecer, F. (2020). Çok Kriterli Karar Verme, Geçmişten Günümüze Kapsamlı Bir Yaklaşım. Ankara: Seçkin Yayınevi.

Ecer, F., & Pamucar, D. (2021). MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services. Applied Soft Computing, 104, 107199. https://doi.org/10.1016/j.asoc.2021.107199

Ecer, F., Pamucar, D., Zolfani, S. H., & Eshkalag, M. K. (2019). Sustainability assessment of OPEC countries: Application of a multiple attribute decision making tool. Journal of Cleaner Production, 241, 118324. https://doi.org/10.1016/j.jclepro.2019.118324

Environmental Performance Index (EPI). EPI 2020 Report. (2020). https://epi.yale.edu/downloads/epi2020report20210112.pdf Accessed 4 January 2020

Fakher, H. A., & Abedi, Z. (2017). Relationship between environmental quality and economic growth in developing countries (based on environmental performance index). Environmental Energy and Economic Research, 1(3), 299-310. https://doi.org/10.22097/eeer.2017.86464.1001

Giannakitsidou, O., Giannikos, I., & Chondrou, A. (2020). Ranking European countries on the basis of their environmental and circular economy performance: A DEA application in MSW. Waste Management, 109, 181-191. https://doi.org/10.1016/j.wasman.2020.04.055

Global Footprint Network. Footprint data. (2019). https://data.footprintnetwork.org/#/ Accessed 18 December 2020

Gopal, N., & Panchal, D. (2021). A structured framework for reliability and risk evaluation in the milk process industry under fuzzy environment. Facta Universitatis, Series: Mechanical Engineering. Online First, 1-29.

International Labor Organization (ILO). ILO statistics and databases (2019). https://www.ilo.org/global/statistics-and-databases/lang--en/index.htm Accessed 26 December 2020.

Iqbal, W., Altalbe, A., Fatima, A., Ali, A., & Hou, Y. (2019). A DEA approach for assessing the energy, environmental and economic performance of top 20 industrial countries. Processes, 7(12), 902-921. https://doi.org/10.3390/pr7120902

Iram, R., Zhang, J., Erdogan, S., Abbas, Q., & Mohsin, M. (2020). Economics of energy and environmental efficiency: evidence from OECD countries. Environmental Science and Pollution Research, 27(4), 3858-3870. https://doi.org/10.1007/s11356-019-07020-x

Kaklauskas, A., Dias, W. P. S., Binkyte-Veliene, A., Abraham, A., Ubarte, I., Randil, O. P. C., Siriwardana, C. S. A., Lill, I., Milevicius, V., Podviezko, A., & Puust, R. (2020). Are environmental sustainability and happiness the keys to prosperity in Asian nations?. Ecological Indicators, 119, 106562. https://doi.org/10.1016/j.ecolind.2020.106562

Keshavarz Ghorabaee, M., Amiri, M., Kazimieras Zavadskas, E., & Antuchevičienė, J. (2017). Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets. Transport, 32(1), 66-78. https://doi.org/10.3846/16484142.2017.1282381

Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2018). A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering, 18, 32-49. https://doi.org/10.1016/j.acme.2017.04.011

Kılıç Depren, S., & Bağdatlı Kalkan, S. (2018). Determination of countries' position using better life index: the entropy based MULTIMOORA approach. Trakya University Journal of Social Science, 20(2), 353-366. https://doi.org/10.26468/trakyasobed.466902

Madenoğlu, F. S. (2020). Dengeli puan kart-AHP-MARCOS yöntemlerine dayalı tedarikçi seçimi. Economics Business and Organization Research, 2(2), 99-120.

Malul, M., Hadad, Y., & Ben‐Yair, A. (2009). Measuring and ranking of economic, environmental and social efficiency of countries. International Journal of Social Economics, 36(8), 832-843. https://doi.org/10.1108/03068290910967109

Moutinho, V., Fuinhas, J. A., Marques, A. C., & Santiago, R. (2018). Assessing eco-efficiency through the DEA analysis and decoupling index in the Latin America countries. Journal of Cleaner Production, 205, 512-524. https://doi.org/10.1016/j.jclepro.2018.08.322

OECD Better Life Index. Life satisfaction. (2020). http://www.oecdbetterlifeindex.org/topics/life-satisfaction/ Accessed 14 December 2020

OECD. OECD income inequality. (2018). https://data.oecd.org/inequality/income-inequality.htm Accessed 8 January 2021

Omrani, H., Alizadeh, A., & Amini, M. (2020). A new approach based on BWM and MULTIMOORA methods for calculating semi-human development index: An application for provinces of Iran. Socio-Economic Planning Sciences, 70, 100689. https://doi.org/10.1016/j.seps.2019.02.004

Orhan M. & Aytekin M. (2020). Türkiye ile AB’ye Son Katılan Ülkelerin Ar-Ge Performanslarının CRITIC Ağırlıklı MAUT ve SAW Yöntemiyle Kıyaslanması. Business & Management Studies: An International Journal, 8(1), 754-778. https://doi.org/10.15295/bmij.v8i1.1355

Our World in Data. CO2 data explorer. (2019). https://ourworldindata.org/explorers/co2 Accessed 12 December 2020

Panchal, D., Chatterjee, P., Shukla, R. K., Choudhury, T., & Tamosaitiene, J. (2017). Integrated Fuzzy AHP-Codas framework for maintenance decision in urea fertilizer industry. Economic Computation & Economic Cybernetics Studies & Research, 51(3), 179-196.

Panchal, D., Singh, A. K., Chatterjee, P., Zavadskas, E. K., & Keshavarz-Ghorabaee, M. (2019). A new fuzzy methodology-based structured framework for RAM and risk analysis. Applied Soft Computing, 74, 242-254. https://doi.org/10.1016/j.asoc.2018.10.033

Peng, X., Zhang, X., & Luo, Z. (2019). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53, 3813–3847. https://doi.org/10.1007/s10462-019-09780-x

Phillis, Y. A., Grigoroudis, E., & Kouikoglou, V. S. (2011). Sustainability ranking and improvement of countries. Ecological Economics, 70(3), 542-553. https://doi.org/10.1016/j.ecolecon.2010.09.037

Puška, A., Stojanović, I., Maksimović, A., & Osmanović, N. (2020). Evaluation software of project management used measurement of alternatives and ranking according to compromise solution (MARCOS) method. Operational Research in Engineering Sciences: Theory and Applications, 3(1), 89-102. https://doi.org/10.31181/oresta2001089p

Şahin, C., & Öztel, A. (2017). Ülkelerin yaşanabilirlik düzeylerinin COPRAS yöntemiyle karşılaştırmalı analizi: BRICS ülkeleri ve Türkiye. Uluslararası Batı Karadeniz Sosyal ve Beşeri Bilimler Dergisi, 1(1), 75-84. https://doi.org/10.46452/baksoder.370487

Santana, N. B., Aparecida do Nascimento Rebelatto, D., Périco, A. E., & Mariano, E. B. (2014). Sustainable development in the BRICS countries: an efficiency analysis by data envelopment. International Journal of Sustainable Development & World Ecology, 21(3), 259-272. https://doi.org/10.1080/13504509.2014.900831

Şenol, Z., & Ulutaş, A. (2018). Muhasebe temelli performans ölçümleri ile piyasa temelli performans ölçümlerinin CRITIC ve ARAS yöntemleriyle değerlendirilmesi. Finans Politik & Ekonomik Yorumlar, 55(641), 83-102.

Shmelev, S. E., & Rodríguez-Labajos, B. (2009). Dynamic multidimensional assessment of sustainability at the macro level: The case of Austria. Ecological Economics, 68(10), 2560-2573. https://doi.org/10.1016/j.ecolecon.2009.03.019

Simić, V., Soušek, R., & Jovčić, S. (2020). Picture fuzzy MCDM approach for risk assessment of railway infrastructure. Mathematics, 8(12), 2259-2288. https://doi.org/10.3390/math8122259

Skare, M., & Rabar, D. (2017). Measuring sources of economic growth in OECD countries. Engineering Economics, 28(4), 386-400. https://doi.org/10.5755/j01.ee.28.4.18502

Social Progress Imperative. Social progress index. (2020). https://www.socialprogress.org/index/global/results Accessed 13 January 2021

Stanković, M., Stević, Ž., Das, D. K., Subotić, M., & Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8(3), 457. https://doi.org/10.3390/math8030457

Stević, Ž., & Brković, N. (2020). A novel integrated FUCOM-MARCOS model for evaluation of human resources in a transport company. Logistics, 4(1), 4. https://doi.org/10.3390/logistics4010004

Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231

Tajbakhsh, A., & Shamsi, A. (2019). Sustainability performance of countries matters: A non-parametric index. Journal of Cleaner Production, 224, 506-522. https://doi.org/10.1016/j.jclepro.2019.03.189

Torkayesh, A. E., Zolfani, S. H., Kahvand, M., & Khazaelpour, P. (2021). Landfill location selection for healthcare waste of urban areas using hybrid BWM-grey MARCOS model based on GIS. Sustainable Cities and Society, 67, 102712. https://doi.org/10.1016/j.scs.2021.102712

Ulutaş, A., & Karaköy, Ç. (2019). CRITIC ve ROV yöntemleri ile bir kargo firmasının 2011-2017 yılları sırasındaki performansının analiz edilmesi. MANAS Sosyal Araştırmalar Dergisi, 8(1), 223-230. https://doi.org/10.33206/mjss.458643

Ulutaş, A., Karabasevic, D., Popovic, G., Stanujkic, D., Nguyen, P. T., & Karaköy, Ç. (2020). Development of a novel integrated CCSD-ITARA-MARCOS decision-making approach for stackers selection in a logistics system. Mathematics, 8(10), 1672. https://doi.org/10.3390/math8101672

United Nations Development Programme (UNDP). Human development report. (2020). http://hdr.undp.org/en/content/human-development-index-hdi Accessed 1 February 2021

Vujičić, M. D., Papić, M. Z., & Blagojević, M. D. (2017). Comparative analysis of objective techniques for criteria weighing in two MCDM methods on example of an air conditioner selection. Tehnika, 72(3), 422-429. https://doi.org/10.5937/tehnika1703422V

WorldBank, World Bank Open Data. (2019). https://data.worldbank.org/ Accessed 19 December 2020

Yalçın, N., & Karakaş, E. Kurumsal sürdürülebilirlik performans analizinde CRITIC-EDAS yaklaşımı. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 34(4), 147-162. https://doi.org/10.21605/cukurovaummfd.704167

Published

2021-06-26

How to Cite

Arsu, T., & Ayçin, E. (2021). Evaluation of OECD Countries with Multi-Criteria Decision-Making Methods in terms of Economic, Social and Environmental Aspects . Operational Research in Engineering Sciences: Theory and Applications, 4(2), 55–78. https://doi.org/10.31181/oresta20402055a