Evaluating economic freedom via a multi-criteria MEREC-DNMA model-based composite system: case of OPEC countries
Economic freedom indicators create a beneficial and suitable guide and a crucial reference for investors, policymakers, lenders, and market researchers worldwide. In light of these indicators, the economic freedom performances of countries can be determined. The Heritage Foundation annually releases a ranked list of the country based on their performance in terms of fourteen economic freedom criteria with equal weights through a simple aggregation approach. According to an average-based aggregator, equal weight of economic freedom criteria and calculating rank of countries cannot be a completely reliable approach. Thus, this work establishes a composite index system in the form of a decision support system that employs the method based on the removal effects of criteria (MEREC) and the double normalization-based multi-aggregation (DNMA) to specify the economic freedom levels of the OPEC countries. MEREC obtains the importance weights of indicators without the interference of any stakeholder or decision-makers. Afterward, DNMA, as a novel ranking multi-criteria method, is applied to sort countries based on their performance against all economic freedom criteria. This is the first attempt in the literature to calculate the index of economic freedom utilizing an integrated multi-criteria decision support model. Whereas “investment freedom” is the most significant indicator of economic freedom, the UAE is in the best position in terms of economic freedom among OPEC countries. A fourphased sensitivity control is also performed so as to verify the robustness and usefulness of the developed decision tool.
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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
Arbolino, R., De Simone, L., Carlucci, F., Yigitcanlar, T., & Ioppolo, G. (2018). Towards a sustainable industrial ecology: Implementation of a novel approach in the performance evaluation of Italian regions. Journal of Cleaner Production, 178, 220–236. https://doi.org/10.1016/j.jclepro.2017.12.183
Asadzadeh, A., Kötter, T., Salehi, P., & Birkmann, J. (2017). Operationalizing a concept: The systematic review of composite indicator building for measuring community disaster resilience. International Journal of Disaster Risk Reduction, 25, 147–162. https://doi.org/10.1016/j.ijdrr.2017.09.015
Assi, A. F., Isiksal, A. Z., & Tursoy, T. (2020). Highlighting the connection between financial development and consumption of energy in countries with the highest economic freedom. Energy Policy, 147, 111897. https://doi.org/10.1016/j.enpol.2020.111897
Attardi, 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. https://doi.org/10.1016/j.ejor.2017.07.064
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. https://doi.org/10.1016/j.landusepol.2017.11.036
Böyükaslan, A., & Ecer, F. (2021). Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework. Technology in Society, 67, 101745. https://doi.org/10.1016/j.techsoc.2021.101745
Cabello, J. M., Ruiz, F., & Pérez-Gladish, B. (2021). An alternative aggregation process for composite indexes: An application to the Heritage Foundation Economic Freedom Index. Social Indicators Research, 153(2), 443–467. https://doi.org/10.1007/s11205-020-02511-8
Clivillé, V., Berrah, L., & Mauris, G. (2007). Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method. International Journal of Production Economics, 105(1), 171–189. https://doi.org/10.1016/j.ijpe.2006.03.002
Dobos, I., & Vörösmarty, G. (2014). Green supplier selection and evaluation using DEA-type composite indicators. International Journal of Production Economics, 157, 273–278. https://doi.org/10.1016/j.ijpe.2014.09.026
Ecer, F. (2014). A hybrid banking websites quality evaluation model using AHP and COPRAS-G: A Turkey case. Technological and Economic Development of Economy, 20(4), 758–782. https://doi.org/10.3846/20294913.2014.915596
Ecer, F. (2018). Third-party logistics (3PLs) provider selection via Fuzzy AHP and EDAS integrated model. Technological and Economic Development of Economy, 24(2), 615–634. https://doi.org/10.3846/20294913.2016.1213207
Ecer, F. (2021a). Sustainability assessment of existing onshore wind plants in the context of triple bottom line: A best-worst method (BWM) based MCDM framework. Environmental Science and Pollution Research, 28, 19677–19693. https://doi.org/10.1007/s11356-020-11940-4
Ecer, F. (2021b). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143, 110916. https://doi.org/10.1016/j.rser.2021.110916
Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266, 121981. https://doi.org/10.1016/j.jclepro.2020.121981
Ecer, F., & Torkayesh, A. E. (2022). A stratified fuzzy decision-making approach for sustainable circular supplier selection. IEEE Transactions on Engineering Management, 1–15. https://doi.org/10.1109/TEM.2022.3151491
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
El Gibari, S., Gómez, T., & Ruiz, F. (2019). Building composite indicators using multicriteria methods: A review. Journal of Business Economics, 89(1), 1–24. https://doi.org/10.1007/s11573-018-0902-z
Emas, R. (2015). The concept of sustainable development: Definition and defining principles. Brief for GSDR 2015.
Erilli, N. A. (2018). Economic freedom index calculation using FCM. Alphanumeric Journal, 6(1), 93–116. https://doi.org/10.17093/alphanumeric.337322
Galik, A., Bąk, M., Bałandynowicz-Panfil, K., & Cirella, G. T. (2022). Evaluating labour market flexibility using the TOPSIS method: Sustainable industrial relations. Sustainability, 14(1), 526. https://doi.org/10.3390/su14010526
Graafland, J. (2019). Economic freedom and corporate environmental responsibility: The role of small government and freedom from government regulation. Journal of Cleaner Production, 218, 250–258. https://doi.org/10.1016/j.jclepro.2019.01.308
Gropper, D. M., Jahera Jr, J. S., & Park, J. C. (2015). Political power, economic freedom and Congress: Effects on bank performance. Journal of Banking & Finance, 60, 76–92. https://doi.org/10.1016/j.jbankfin.2015.08.005
Gušavac, B. A., & Savić, G. (2021). Operations research problems and data envelopment analysis in agricultural land processing – A review. Management: Journal of Sustainable Business & Management Solutions in Emerging Economies, 26(1), 1–13.
Gwartney, J. (2017). Economic freedom of the world. The Fraser Institute.
Haider, H., Hewage, K., Umer, A., Ruparathna, R., Chhipi-Shrestha, G., Culver, K., Holland, M., Kay, J., & Sadiq, R. (2018). Sustainability assessment framework for small-sized urban neighbourhoods: An application of fuzzy synthetic evaluation. Sustainable Cities and Society, 36, 21–32. https://doi.org/10.1016/j.scs.2017.09.031
Hashemkhani Zolfani, S., Ecer, F., Pamučar, D., & Raslanas, S. (2020). Neighborhood selection for a newcomer via a novel BWM-based revised MAIRCA integrated model: A case from the Coquimbo-La Serena conurbation, Chile. International Journal of Strategic Property Management, 24(2), 102–118. https://doi.org/10.3846/ijspm.2020.11543
Hashemkhani Zolfani, S., Torkayesh, A. E., Ecer, F., Turskis, Z., & Šaparauskas, J. (2021). International market selection: A MABA based EDAS analysis framework. Oeconomia Copernicana, 12(1), 99–124. https://doi.org/10.24136/oc.2021.005
Hernandez-Perdomo, E. A., & Mun, J. (2017). Active management in state-owned energy companies: Integrating a real options approach into multicriteria analysis to make companies sustainable. Applied Energy, 195, 487–502. https://doi.org/10.1016/j.apenergy.2017.03.068
Keshavarz-Ghorabaee, M. (2021). Assessment of distribution center locations using a multi-expert subjective–objective decision-making approach. Scientific Reports, 11(1), 1–19. https://doi.org/10.1038/s41598-021-98698-y
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525. https://doi.org/10.3390/sym13040525
Krajnc, D., & Glavič, P. (2005). How to compare companies on relevant dimensions of sustainability. Ecological economics, 55(4), 551–563. https://doi.org/10.1016/j.ecolecon.2004.12.011
Kropp, W. W., & Lein, J. K. (2012). Assessing the geographic expression of urban sustainability: A scenario based approach incorporating spatial multicriteria decision analysis. Sustainability, 4(9), 2348–2365. https://doi.org/10.3390/su4092348
Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596–609. https://doi.org/10.1016/j.rser.2016.11.191
Lai, H., & Liao, H. (2021). A multi-criteria decision making method based on DNMA and CRITIC with linguistic D numbers for blockchain platform evaluation. Engineering Applications of Artificial Intelligence, 101, 104200. https://doi.org/10.1016/j.engappai.2021.104200
Lai, H., Liao, H., Šaparauskas, J., Banaitis, A., Ferreira, F. A., & Al-Barakati, A. (2020). Sustainable cloud service provider development by a Z-number-based DNMA method with Gini-coefficient-based weight determination. Sustainability, 12(8), 3410. https://doi.org/10.3390/su12083410
Li, W., Yu, S., Pei, H., Zhao, C., & Tian, B. (2017). A hybrid approach based on fuzzy AHP and 2-tuple fuzzy linguistic method for evaluation in-flight service quality. Journal of Air Transport Management, 60, 49–64. https://doi.org/10.1016/j.jairtraman.2017.01.006
Liao, H., & Wu, X. (2020). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega, 94, 102058. https://doi.org/10.1016/j.omega.2019.04.001
Liao, H., Long, Y., Tang, M., Streimikiene, D., & Lev, B. (2019). Early lung cancer screening using double normalization-based multi-aggregation (DNMA) and Delphi methods with hesitant fuzzy information. Computers & Industrial Engineering, 136, 453–463. https://doi.org/10.1016/j.cie.2019.07.047
Liao, H., Xue, J., Nilashi, M., Wu, X., & Antucheviciene, J. (2020). Partner selection for automobile manufacturing enterprises with a q-rung orthopair fuzzy double normalizaion-based multi-aggregation method. Transformations in Business & Economics, 19.
Martí, L., Martín, J. C., & Puertas, R. (2017). A DEA – logistics performance index. Journal of Applied Economics, 20(1), 169–192. https://doi.org/10.1016/S1514-0326(17)30008-9
Miller, T., Kim, A. B., & Roberts, J. M. (2021). Index of economic freedom. The Heritage Foundation. https://www.heritage.org/index
Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Giovannini, E., & Hofman, A. (2008). Handbook on constructing composite indicators: Methodology and user guide. Technical report. OECD, Paris.
Nie, S., Liao, H., Wu, X., Tang, M., & Al-Barakati, A. (2019). Hesitant fuzzy linguistic DNMA method with cardinal consensus reaching process for shopping mall location selection. International Journal of Strategic Property Management, 23(6), 420–434. https://doi.org/10.3846/ijspm.2019.10851
Ott, J. (2018). Measuring economic freedom: Better without size of government. Social Indicators Research, 135(2), 479–498. https://doi.org/10.1007/s11205-016-1508-x
Pamucar, D., Ecer, F., Cirovic, G., & Arlasheedi, M. A. (2020). Application of improved best worst method (BWM) in real-world problems. Mathematics, 8(8), 1342. https://doi.org/10.3390/math8081342
Pamucar, D., Ecer, F., & Deveci, M. (2021). Assessment of alternative fuel vehicles for sustainable road transportation of United States using integrated fuzzy FUCOM and neutrosophic fuzzy MARCOS methodology. Science of the Total Environment, 788, 147763. https://doi.org/10.1016/j.scitotenv.2021.147763
Paruolo, P., Saisana, M., & Saltelli, A. (2013). Ratings and rankings: Voodoo or science? Journal of the Royal Statistical Society: Series A (Statistics in Society), 176(3), 609–634. https://doi.org/10.1111/j.1467-985X.2012.01059.x
Petrović, M., Bojković, N., Anić, I., Stamenković, M., & Tarle, S. P. (2014). An ELECTRE-based decision aid tool for stepwise benchmarking: An application over EU Digital Agenda targets. Decision Support Systems, 59, 230–241. https://doi.org/10.1016/j.dss.2013.12.002
Radovanović, S., Petrović, A., Delibašić, B., & Suknović, M. (2021). Eliminating Disparate Impact in MCDM: The case of TOPSIS. In Central European Conference on Information and Intelligent Systems (pp. 275–282). Faculty of Organization and Informatics. Varazdin.
Rani, P., Mishra, A. R., Saha, A., Hezam, I. M., & Pamucar, D. (2022). Fermatean fuzzy Heronian mean operators and MEREC‐based additive ratio assessment method: An application to food waste treatment technology selection. International Journal of Intelligent Systems, 37(3), 2612–2647. https://doi.org/10.1002/int.22787
Rodrigues, T. C., Montibeller, G., Oliveira, M. D., & e Costa, C. A. B. (2017). Modelling multicriteria value interactions with reasoning maps. European Journal of Operational Research, 258(3), 1054–1071. https://doi.org/10.1016/j.ejor.2016.09.047
Saisana, M., & Tarantola, S. (2002). State-of-the-art report on current methodologies and practices for composite indicator development. European Commission.
Shahnazi, R., & Shabani, Z. D. (2021). The effects of renewable energy, spatial spillover of CO2 emissions and economic freedom on CO2 emissions in the EU. Renewable Energy, 169, 293–307. https://doi.org/10.1016/j.renene.2021.01.016
Sibille, A. D. C. T., Cloquell-Ballester, V. A., Cloquell-Ballester, V. A., & Darton, R. (2009). Development and validation of a multicriteria indicator for the assessment of objective aesthetic impact of wind farms. Renewable and Sustainable Energy Reviews, 13(1), 40–66. https://doi.org/10.1016/j.rser.2007.05.002
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. Socio-Economic Planning Sciences, 101157. https://doi.org/10.1016/j.seps.2021.101157
Singh, R. K., Murty, H. R., Gupta, S. K., & Dikshit, A. K. (2009). An overview of sustainability assessment methodologies. Ecological Indicators, 9(2), 189–212. https://doi.org/10.1016/j.ecolind.2008.05.011
Sousa, M., Almeida, M. F., & Calili, R. (2021). Multiple criteria decision making for the achievement of the UN sustainable development goals: A systematic literature review and a research agenda. Sustainability, 13(8), 4129. https://doi.org/10.3390/su13084129
Strezov, V., Evans, A., & Evans, T. J. (2017). Assessment of the economic, social and environmental dimensions of the indicators for sustainable development. Sustainable Development, 25(3), 242–253. https://doi.org/10.1002/sd.1649
Torkayesh, A. E., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2021b). Comparative assessment of social sustainability performance: Integrated data-driven weighting system and CoCoSo model. Sustainable Cities and Society, 71, 102975. https://doi.org/10.1016/j.scs.2021.102975
Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio-Economic Planning Sciences, 78, 101052. https://doi.org/10.1016/j.seps.2021.101052
Trung, D. D., & Thinh, H. X. (2021). A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study. Advances in Production Engineering & Management, 16(4), 443–456. https://doi.org/10.14743/apem2021.4.412
Tutak, M., & Brodny, J. (2022). Analysis of the level of energy security in the three seas initiative countries. Applied Energy, 311, 118649. https://doi.org/10.1016/j.apenergy.2022.118649
Wang, J., Wang, Z., Yang, C., Wang, N., & Yu, X. (2012). Optimization of the number of components in the mixed model using multi-criteria decision-making. Applied Mathematical Modelling, 36(9), 4227–4240. https://doi.org/10.1016/j.apm.2011.11.053
Wang, L., & Rani, P. (2021). Sustainable supply chains under risk in the manufacturing firms: An extended double normalization-based multiple aggregation approach under an intuitionistic fuzzy environment. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-05-2021-0222
Wang, Q., Dai, H. N., & Wang, H. (2017). A smart MCDM framework to evaluate the impact of air pollution on city sustainability: A case study from China. Sustainability, 9(6), 911. https://doi.org/10.3390/su9060911
Wu, X. L., & Liao, H. C. (2019). Comparison analysis between DNMA method and other MCDM methods. ICSES Transaction on Neural and Fuzzy Computing, 2(1), 4–10.
Wu, X., Nie, S., Liao, H., & Gupta, P. (2020). A large‐scale group decision making method with a consensus reaching process under cognitive linguistic environment. International Transactions in Operational Research, 1–26. https://doi.org/10.1111/itor.12843
Yalcin, N., & Ünlü, U. (2018). A multi-criteria performance analysis of Initial Public Offering (IPO) firms using CRITIC and VIKOR methods. Technological and Economic Development of Economy, 24(2), 534–560. https://doi.org/10.3846/20294913.2016.1213201
Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2019), A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458
Zakeri, S., Ecer, F., Konstantas, D., & Cheikhrouhou, N. (2021). The vital-immaterial-mediocre multi-criteria decision-making method. Kybernetes. https://doi.org/10.1108/K-05-2021-0403
Zhang, H., Liao, H., Wu, X., Zavadskas, E. K., & Al-Barakati, A. (2020). Internet financial investment product selection with Pythagorean fuzzy DNMA method. Engineering Economics-Inzinerine ekonomika, 31(1), 61–71. https://doi.org/10.5755/j01.ee.31.1.23255
Zhang, H., Peng, Y., Tian, G., Wang, D., & Xie, P. (2017). Green material selection for sustainability: A hybrid MCDM approach. PloS ONE, 12(5), e0177578. https://doi.org/10.1371/journal.pone.0177578
Zu, J., Peng, Z., & Chen, F. (2022). Overseeing road safety progress using CV-PROMETHEE II-JSS: A case study in the EU context. Expert Systems with Applications, 195, 116623. https://doi.org/10.1016/j.eswa.2022.116623