71 research outputs found
Analysis of the role of innovation and efficiency in coastal destinations affected by tourism seasonality
This research analyses the relationship between efficiency, innovation and seasonality of the Spanish coasts
for a five-year period (2015−2019). First of all, the nexus between the level of efficiency and changes in productivity,
driven by improvements in innovation and/or efficiency, is determined using Data Envelopment
Analysis and the Malmquist Index. Second, this paper proposes a synthetic index to measure seasonality and
assess its connection with efficiency and innovation, using a cross efficiency approach to do so. Results show
how the intensity of seasonality influences efficiency. In addition, it is observed that innovation can offset
possible decreases in efficiency; as such, policies that promote both aspects are needed in the more seasonal
destinations
European countries' vulnerability to COVID-19: multicriteria decision-making techniques
[EN] COVID-19 has triggered an unprecedented health crisis, crippling economic activity around the world. The aim of this paper is to analyse European countries' vulnerability to the associated consequences. The analysis will focus on three areas that a priori are expected to be most severely affected by the pandemic - health, society and work - examining the possible relationship with countries' wealth. The multicriteria decision-making Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) will be used to generate a ranking of countries based on criteria that define each of these three areas. The findings will provide authorities with quantitative information to guide their aid policies. The results show that Eastern European countries should direct their resources towards addressing health-related and social issues. Conversely, those that have higher GDP per capita and that have been hardest hit by coronavirus will have to make changes to their labour systems in order to minimize the fallout.Martí Selva, ML.; Puertas Medina, RM. (2021). European countries' vulnerability to COVID-19: multicriteria decision-making techniques. Economic Research-Ekonomska Istrazivanja. 34(1):3309-3320. https://doi.org/10.1080/1331677X.2021.1874462S3309332034
Regional analysis of the sustainable development of two Mediterranean countries: Spain and Italy
[EN] The 17 Sustainable Development Goals (SDGs) require the implementation of 167 targets aimed at eradicating poverty, protecting the planet and improving the quality of life of humankind. The United Nations calls for uniform sustainable development
at the global, local and individual levels. This research pursues a twofold objective: first, to obtain evidence on the extent to which the achievement of the (SDGs) may be uniform across territories; second, to identify the socioeconomic characteristics that contribute to sustainable development. The empirical analysis has been carried out using clustering, cross efficiency and contingency tables applied to statistical information from 101 municipalities in Spain and Italy. The results provide evidence of inequalities between territories, revealing that only in the dimensions People (SDGs 1, 2, 3, 4, and 5) and Prosperity (SDGs 7, 8, 9, 10, and 11) has the desired homogeneity been attained. Notable differences are found in the degree of compliance with the other SDGs. Furthermore, it is shown that the socioeconomic characteristics associated with the geographical location contribute substantially to the gap between municipalities. In order to ensure countries' sustainable development, there is a need for environmental policies adapted to the specific features of each region.Puertas Medina, RM.; Martí Selva, ML. (2023). Regional analysis of the sustainable development of two Mediterranean countries: Spain and Italy. Sustainable Development. 31(2):798-811. https://doi.org/10.1002/sd.242079881131
Determinants of tourist arrivals in European Mediterranean countries: Analysis of competitiveness
[EN] This research employs a gravity framework to evaluate the tourism in European Mediterranean countries. The paper analyses the destination competitiveness as a means for tourism attraction and also verifies whether more competitive countries can be used as a point of reference for the development of those lagging behind. The gravity equations are used because of their proven effectiveness in estimating other similar studies fields. The study focuses on the Mediterranean European countries, mainly due essentially, to the wide span of their positions along the TTCI ranking (Spain ranks first, whereas Montenegro is in 67th place). Results reveal that these European destinations are not efficiently exploiting their tourism capacity and they need apply policies to foster this economic activity and enable the transformation of competitiveness into greater numbers of visitors.Martí Selva, ML.; Puertas Medina, RM. (2017). Determinants of tourist arrivals in European Mediterranean countries: Analysis of competitiveness. European Journal of Tourism Research. 15:131-142. http://hdl.handle.net/10251/109185S1311421
Factors determining the trade costs of major European exporters
[EN] The aim of this paper is to analyse the determining factors of trade costs in the top European exporting nations (Germany, United Kingdom, Italy, France, Netherlands, Belgium, Spain and Sweden). For this purpose, we have estimated a trade costs equation to evaluate the importance of logistical performance and other variables that may be key in determining trade costs. Our results reveal the great importance of logistics, even greater than the effect of distance on trade costs, and they also show that in those countries where trade costs are lower, logistics becomes more decisive in international trade. This analysis allows one to draw conclusions on the type of improvements necessary for cost reductions and, therefore, for greater international competitiveness. The research has been conducted for 2 years, thus facilitating the detection of possible changes that can in turn reveal the existence of a trade pattern in these countries.Martí Selva, ML.; Puertas Medina, RM. (2019). Factors determining the trade costs of major European exporters. Maritime Economics & Logistics. 21(3):324-333. https://doi.org/10.1057/s41278-017-0093-5S32433321
Link between structural risk factors for adverse impacts of COVID-19 and food insecurity in developed and developing countries
[EN] COVID-19 has had serious consequences for world food security; lockdowns and social distancing have led to changes in global food value chains, primarily afecting the poorest of the planet. The aim of this research is to analyse the relationship between food insecurity and the structural risk factors for adverse impacts of COVID-19. To that end, 12 contingency tables are constructed to identify the association between the pillars of the food insecurity index and the INFORM COVID-19 Risk Index. We use the Gamma coefcient as a measure of association. In addition, this paper proposes a synthetic index
produced by applying the TOPSIS method, using the pillars of the two aforementioned indices (criteria) to establish a ranking of 112 countries (alternatives) ordered from highest to lowest risk faced in the key year of the pandemic, 2020. The results show that the two
problems are connected, indicating to international organizations that countries with worse food insecurity will sufer more serious consequences from extreme situations such as the one experienced during the pandemic. The ranking established directs international
organizations¿ attention to countries such as Haiti, Zambia and Burundi, highlighting their greater need for an injection of fnancial aid than other emerging economies. Conversely, Switzerland is the country with the lowest combined risk.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by: Grant RTI2018-093791-B-C22 funded by MCIN/AEI/https://doi.org/10.13039/501100011033 and by ERDF A way of making Europe.Martí Selva, ML.; Puertas Medina, RM. (2022). Link between structural risk factors for adverse impacts of COVID-19 and food insecurity in developed and developing countries. Environment, Development and Sustainability (Online). https://doi.org/10.1007/s10668-022-02749-
Relevance of trade facilitation in emerging countries exports
The objective of this article is to analyse trade flows in emerging nations with a maritime boundary, where trade facilitation plays a decisive role in their international development. In order to detect possible patterns in performance, we apply the economic approach of gravity models using the World Bank Logistic Performance Index (LPI) as a good proxy of trade facilitation. The results of the estimation lead to the conclusion that the more complex the transportation of goods is, the more influential the logistics indicator, trade facilitation being most prominent in Middle East exporters.Martí Selva, ML.; Puertas Medina, RM.; García Menéndez, L. (2012). Relevance of trade facilitation in emerging countries exports. Journal of International Trade and Economic Development. 23(2):202-222. doi:10.1080/09638199.2012.698639S202222232Behar, A. and Manner, P. Logistics and exports. African Economics Working Paper Series 293. CSAE WPS/2008-13. Oxford: University of Oxford.Bergstrand, J. H. (1989). The Generalized Gravity Equation, Monopolistic Competition, and the Factor-Proportions Theory in International Trade. The Review of Economics and Statistics, 71(1), 143. doi:10.2307/1928061Bergstrand, J. H. (1985). The Gravity Equation in International Trade: Some Microeconomic Foundations and Empirical Evidence. The Review of Economics and Statistics, 67(3), 474. doi:10.2307/1925976Clark, X., Dollar, D., & Micco, A. (2004). Port efficiency, maritime transport costs, and bilateral trade. Journal of Development Economics, 75(2), 417-450. doi:10.1016/j.jdeveco.2004.06.005Decreux, I. and Fontagne, L. A quantitative assessment of the outcome of the Doha development agenda. CEPII Working Paper 2006–10. CEPIIDennis, A. The impact of regional trade agreements and trade facilitation in the Middle East and North Africa region. World Bank Policy Research Working Paper 3837. Washington, DC: World Bank.Djankov, S., Freund, C. and Pham, C. Trading on time. World Bank Policy Research Working Paper 3909. Washington, DC: World Bank.Freund, C. L., & Weinhold, D. (2004). The effect of the Internet on international trade. Journal of International Economics, 62(1), 171-189. doi:10.1016/s0022-1996(03)00059-xHanson, G. and Xiang, C. The home market effect and bilateral trade patterns. NBER Working Paper Series, Working Paper 9076. Cambridge, MA: 02138. July.Hausman, W., Lee, H L. and Subramanian, U. Global logistic indicators, supply chain metrics, and bilateral trade patterns. World Bank Policy Research Working Paper 3773. Washington, DC: World Bank.Henderson, J. V., Shalizi, Z., & Venables, A. J. (2001). Geography and development. Journal of Economic Geography, 1(1), 81-105. doi:10.1093/jeg/1.1.81Hoekman, B., & Nicita, A. (2010). Assessing the Doha Round: Market access, transactions costs and aid for trade facilitation. The Journal of International Trade & Economic Development, 19(1), 65-79. doi:10.1080/09638190903327476Hoekman, B., & Nicita, A. (2011). Trade Policy, Trade Costs, and Developing Country Trade. World Development, 39(12), 2069-2079. doi:10.1016/j.worlddev.2011.05.013Iwanow, T., & Kirkpatrick, C. (2009). Trade Facilitation and Manufactured Exports: Is Africa Different? World Development, 37(6), 1039-1050. doi:10.1016/j.worlddev.2008.09.014Krugman, P. (1991). Increasing Returns and Economic Geography. Journal of Political Economy, 99(3), 483-499. doi:10.1086/261763Korinek, J. and Sourdin, P. To what extent are high-quality logistics services trade facilitating? OECD Trade Policy Working Papers 108. Paris: OECD Publishing.Limao, N. (2001). Infrastructure, Geographical Disadvantage, Transport Costs, and Trade. The World Bank Economic Review, 15(3), 451-479. doi:10.1093/wber/15.3.451Martínez-Zarzoso, I., Pérez-García, E. M., & Suárez-Burguet, C. (2008). Do transport costs have a differential effect on trade at the sectoral level? Applied Economics, 40(24), 3145-3157. doi:10.1080/00036840600994179Moïsé, E., Orliac, T. and Minor, P. Trade facilitation indicators: The impact on trade costs. OECD Trade Policy Working Papers 118. Paris: OECD Publishing.Nordas, H. and Piermartini, R. Infrastructure and trade. WTO Economic Research and Statistics Division Staff, Working Paper ERSD-2004-04. Geneva: WTO.Portugal-Perez, A. and Wilson, J S. Export performance and trade facilitation reform. Policy Research Working Paper 5261. Washington, DC: World Bank. AprilPrabir, D. Impact of trade costs and trade: Empirical evidence from Asian countries. Working Paper Series 27. Asia-Pacific Research and Training Network on Trade. New York: United Nations. JanuaryShepherd, B. and Wilson, J. Road infrastructure in Europe and Central Asia: Does network quality affect trade? Bank Policy Research Working Paper 4104. Washington, DC: World Bank.Soloaga, I., Wilson, J S. and Mejía, A. Trade facilitation reform and Mexican competitiveness. World Bank Policy Research Working Paper 3953. Washington, DC: World Bank. JuneTang, D. (2005). Effects of the Regional Trading Arrangements on Trade: Evidence from the NAFTA, ANZCER and ASEAN Countries, 1989 – 2000. The Journal of International Trade & Economic Development, 14(2), 241-265. doi:10.1080/09638190500093562Wilmsmeier, G., Hoffmann, J., & Sanchez, R. J. (2006). The Impact of Port Characteristics on International Maritime Transport Costs. Research in Transportation Economics, 16, 117-140. doi:10.1016/s0739-8859(06)16006-0Wilson, J. S., Mann, C. L., & Otsuki, T. (2005). ASSESSING THE POTENTIAL BENEFIT OF TRADE FACILITATION: A GLOBAL PERSPECTIVE. Quantitative Methods for Assessing the Effects of Non-Tariff Measures and Trade Facilitation, 121-160. doi:10.1142/9789812701350_0008Wilson, J S. and Otsuki, T. Regional integration in South Asia: What role for trade facilitation? World Bank Policy Research Working Paper 4423. Washington, DC: World Bank. Decembe
Application of data envelopment analysis to evaluate investments in the modernization of collective management irrigation systems in Valencia (Spain)
[EN] Climate change and increased competition for water resources are generating growing concern about how to improve water-use efficiency in agriculture. In turn, this has prompted substantial investments in the installation of water-saving technologies in irrigation systems. The first aim of this research is to use data envelopment analysis to quantify, in terms of gross water savings (GWS), the local-scale efficiency of the irrigation policies adopted in an area of Spain suffering from a structural water deficit. Second, the cross-efficiency method is used to produce a ranking of the irrigation organizations analysed, in order to identify patterns of water-use efficiency performance that can guide future lines of investment. The results reveal that water-use efficiency prior to modernization is a key determinant of the efficiency achieved in terms of GWS at local scale. However, the investments targeted at irrigation modernization often have objectives other than water savings. These and other aspects should be taken into account when allocating public funds to irrigation modernization.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This
study has been supported by the ADAPTAMED project ((RTI2018-101483-B-I00) and by the former IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and European FEDER funds.García Molla, M.; Puertas Medina, RM.; Sanchis Ibor, C. (2021). Application of data envelopment analysis to evaluate investments in the modernization of collective management irrigation systems in Valencia (Spain). Water Resources Management. 35:5011-5027. https://doi.org/10.1007/s11269-021-02986-1S501150273
The effects on European importers' food safety controls in the time of COVID-19
[EN] COVID-19 has highlighted the fragility of the global economic system. In just a few months, the consequences of the pandemic have left their mark on the affected countries at all levels and without exception. This article analyses the profile of food safety notifications reported by European countries in the first five months of 2020. The aim was to detect possible changes in food safety regulations imposed by control authorities that could aggravate the economic impacts of the pandemic. While COVID-19 does not appear to be a foodborne disease, some outbreaks have been linked to imported food, which might have affected the food control behaviour of importing countries. In this study, contingency tables and clustering were used to assess differences between years and notification characteristics and to detect homogeneous groups to help identify how the reported notifications might have changed. In the period considered in this study, the volume of notifications on most imported foodstuffs decreased considerably. This decrease was a direct consequence of the fall in international trade, which might have increased countries' reliance on domestic sources. The COVID-19 crisis has not caused a substantial change in the profile of European countries¿ in terms of the characteristics of reported notifications (product category and risk decision). However, the worst affected countries have replaced border rejections with alerts, which may indicate greater reliance on intra-EU markets.This research was supported by grant RTI2018-093791-B-C22 funded by Ministry of Science (Spain) and European Regional Development FundMartí Selva, ML.; Puertas Medina, RM.; García Alvarez-Coque, JM. (2021). The effects on European importers' food safety controls in the time of COVID-19. Food Control. 125:1-11. https://doi.org/10.1016/j.foodcont.2021.107952S11112
Food Supply without Risk: Multicriteria Analysis of Institutional Conditions of Exporters
[EN] International trade in food knows no borders, hence the need for prevention systems to avoid the consumption of products that are harmful to health. This paper proposes the use of multicriteria risk prevention tools that consider the socioeconomic and institutional conditions of food exporters. We propose the use of three decision-making methods-Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), Elimination et Choix Traduisant la Realite (ELECTRE), and Cross-Efficiency (CE)-to establish a ranking of countries that export cereals to the European Union, based on structural criteria related to the detection of potential associated risks (notifications, food quality, corruption, environmental sustainability in agriculture, and logistics). In addition, the analysis examines whether the wealth and institutional capacity of supplier countries influence their position in the ranking. The research was carried out biannually over the period from 2012-2016, allowing an assessment to be made of the possible stability of the markets. The results reveal that suppliers' rankings based exclusively on aspects related to food risk differ from importers' actual choices determined by micro/macroeconomic features (price, production volume, and economic growth). The rankings obtained by the three proposed methods are not the same, but present certain similarities, with the ability to discern countries according to their level of food risk. The proposed methodology can be applied to support sourcing strategies. In the future, food safety considerations could have increased influence in importing decisions, which would involve further difficulties for low-income countries.Ministry of Science and Innovation (Spain) and European Commission-ERDF. Project "Strengthening innovation policy in the agri-food sector" (RTI2018-093791-B-C22).Puertas Medina, RM.; Martí Selva, ML.; García Alvarez-Coque, JM. (2020). Food Supply without Risk: Multicriteria Analysis of Institutional Conditions of Exporters. International Journal of Environmental research and Public Health. 17(10):1-21. https://doi.org/10.3390/ijerph17103432S1211710Walker, E., & Jones, N. (2002). An assessment of the value of documenting food safety in small and less developed catering businesses. Food Control, 13(4-5), 307-314. doi:10.1016/s0956-7135(02)00036-1Sun, Y.-M., & Ockerman, H. W. (2005). A review of the needs and current applications of hazard analysis and critical control point (HACCP) system in foodservice areas. Food Control, 16(4), 325-332. doi:10.1016/j.foodcont.2004.03.012Rohr, J. R., Barrett, C. B., Civitello, D. J., Craft, M. E., Delius, B., DeLeo, G. A., … Tilman, D. (2019). Emerging human infectious diseases and the links to global food production. Nature Sustainability, 2(6), 445-456. doi:10.1038/s41893-019-0293-3De Jonge, J., van Trijp, J. C. M., van der Lans, I. A., Renes, R. J., & Frewer, L. J. (2008). How trust in institutions and organizations builds general consumer confidence in the safety of food: A decomposition of effects. Appetite, 51(2), 311-317. doi:10.1016/j.appet.2008.03.008Neill, C. L., & Holcomb, R. B. (2019). Does a food safety label matter? Consumer heterogeneity and fresh produce risk perceptions under the Food Safety Modernization Act. Food Policy, 85, 7-14. doi:10.1016/j.foodpol.2019.04.001Wood, V. R., & Robertson, K. R. (2000). Evaluating international markets. International Marketing Review, 17(1), 34-55. doi:10.1108/02651330010314704Jouanjean, M.-A., Maur, J.-C., & Shepherd, B. (2015). Reputation matters: Spillover effects for developing countries in the enforcement of US food safety measures. Food Policy, 55, 81-91. doi:10.1016/j.foodpol.2015.06.001Van Ruth, S. M., Huisman, W., & Luning, P. A. (2017). Food fraud vulnerability and its key factors. Trends in Food Science & Technology, 67, 70-75. doi:10.1016/j.tifs.2017.06.017Baylis, K., Nogueira, L., & Pace, K. (2010). Food Import Refusals: Evidence from the European Union. American Journal of Agricultural Economics, 93(2), 566-572. doi:10.1093/ajae/aaq149Bouzembrak, Y., & Marvin, H. J. P. (2016). Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling. Food Control, 61, 180-187. doi:10.1016/j.foodcont.2015.09.026Tudela-Marco, L., Garcia-Alvarez-Coque, J. M., & Martí-Selva, L. (2016). Do EU Member States Apply Food Standards Uniformly? A Look at Fruit and Vegetable Safety Notifications. JCMS: Journal of Common Market Studies, 55(2), 387-405. doi:10.1111/jcms.12503Verhaelen, K., Bauer, A., Günther, F., Müller, B., Nist, M., Ülker Celik, B., … Wallner, P. (2018). Anticipation of food safety and fraud issues: ISAR - A new screening tool to monitor food prices and commodity flows. Food Control, 94, 93-101. doi:10.1016/j.foodcont.2018.06.029Garcia‐Alvarez‐Coque, J., Taghouti, I., & Martinez‐Gomez, V. (2020). Changes in Aflatoxin Standards: Implications for EU Border Controls of Nut Imports. Applied Economic Perspectives and Policy, 42(3), 524-541. doi:10.1093/aepp/ppy036Fischer, A. R. H., de Jong, A. E. I., de Jonge, R., Frewer, L. J., & Nauta, M. J. (2005). Improving Food Safety in the Domestic Environment: The Need for a Transdisciplinary Approach. Risk Analysis, 25(3), 503-517. doi:10.1111/j.1539-6924.2005.00618.xHoughton, J. R., Rowe, G., Frewer, L. J., Van Kleef, E., Chryssochoidis, G., Kehagia, O., … Strada, A. (2008). The quality of food risk management in Europe: Perspectives and priorities. Food Policy, 33(1), 13-26. doi:10.1016/j.foodpol.2007.05.001Demortain, D. (2012). Enabling global principle-based regulation: The case of risk analysis in the Codex Alimentarius. Regulation & Governance, 6(2), 207-224. doi:10.1111/j.1748-5991.2012.01144.xFAZIL, A., RAJIC, A., SANCHEZ, J., & MCEWEN, S. (2008). Choices, Choices: The Application of Multi-Criteria Decision Analysis to a Food Safety Decision-Making Problem. Journal of Food Protection, 71(11), 2323-2333. doi:10.4315/0362-028x-71.11.2323Ruzante, J. M., Davidson, V. J., Caswell, J., Fazil, A., Cranfield, J. A. L., Henson, S. J., … Farber, J. M. (2010). A Multifactorial Risk Prioritization Framework for Foodborne Pathogens. Risk Analysis, 30(5), 724-742. doi:10.1111/j.1539-6924.2009.01278.xMazzocchi, M., Ragona, M., & Zanoli, A. (2013). A fuzzy multi-criteria approach for the ex-ante impact assessment of food safety policies. Food Policy, 38, 177-189. doi:10.1016/j.foodpol.2012.11.011Govindan, K., Kadziński, M., & Sivakumar, R. (2017). Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega, 71, 129-145. doi:10.1016/j.omega.2016.10.004Segura, M., Maroto, C., & Segura, B. (2019). Quantifying the Sustainability of Products and Suppliers in Food Distribution Companies. Sustainability, 11(21), 5875. doi:10.3390/su11215875Lau, H., Nakandala, D., & Shum, P. K. (2018). A business process decision model for fresh-food supplier evaluation. Business Process Management Journal, 24(3), 716-744. doi:10.1108/bpmj-01-2016-0015Garcia-Alvarez-Coque, J.-M., Abdullateef, O., Fenollosa, L., Ribal, J., Sanjuan, N., & Soriano, J. M. (2020). Integrating sustainability into the multi-criteria assessment of urban dietary patterns. Renewable Agriculture and Food Systems, 36(1), 69-76. doi:10.1017/s174217051900053xGrant, W. (2012). Economic patriotism in European agriculture. Journal of European Public Policy, 19(3), 420-434. doi:10.1080/13501763.2011.640797Maye, D., & Kirwan, J. (2013). Food security: A fractured consensus. Journal of Rural Studies, 29, 1-6. doi:10.1016/j.jrurstud.2012.12.001Anthony, R. (2011). Taming the Unruly Side of Ethics: Overcoming Challenges of a Bottom-Up Approach to Ethics in the Areas of Food Policy and Climate Change. Journal of Agricultural and Environmental Ethics, 25(6), 813-841. doi:10.1007/s10806-011-9358-7MacMillan, T., & Dowler, E. (2011). Just and Sustainable? Examining the Rhetoric and Potential Realities of UK Food Security. Journal of Agricultural and Environmental Ethics, 25(2), 181-204. doi:10.1007/s10806-011-9304-8Jaud, M., Cadot, O., & Suwa-Eisenmann, A. (2013). Do food scares explain supplier concentration? An analysis of EU agri-food imports. European Review of Agricultural Economics, 40(5), 873-890. doi:10.1093/erae/jbs038Spink, J., Fortin, N. D., Moyer, D. C., Miao, H., & Wu, Y. (2016). Food Fraud Prevention: Policy, Strategy, and Decision-Making – Implementation Steps for a Government Agency or Industry. CHIMIA International Journal for Chemistry, 70(5), 320-328. doi:10.2533/chimia.2016.320Van Ruth, S. M., Luning, P. A., Silvis, I. C. J., Yang, Y., & Huisman, W. (2018). Differences in fraud vulnerability in various food supply chains and their tiers. Food Control, 84, 375-381. doi:10.1016/j.foodcont.2017.08.020Xidonas, P., & Psarras, J. (2009). Equity portfolio management within the MCDM frame: a literature review. International Journal of Banking, Accounting and Finance, 1(3), 285. doi:10.1504/ijbaaf.2009.022717Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196(2), 401-412. doi:10.1016/j.ejor.2008.05.007Mandic, 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.2014.07.036Uygun, Ö., Kaçamak, H., & Kahraman, Ü. A. (2015). An integrated DEMATEL and Fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company. Computers & Industrial Engineering, 86, 137-146. doi:10.1016/j.cie.2014.09.014Wanke, P., Azad, M. D. A. K., & Barros, C. P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485-498. doi:10.1016/j.ribaf.2015.10.002Stojčić, M., Zavadskas, E., Pamučar, D., Stević, Ž., & Mardani, A. (2019). Application of MCDM Methods in Sustainability Engineering: A Literature Review 2008–2018. Symmetry, 11(3), 350. doi:10.3390/sym11030350Xu, L., Shah, S. A. A., Zameer, H., & Solangi, Y. A. (2019). Evaluating renewable energy sources for implementing the hydrogen economy in Pakistan: a two-stage fuzzy MCDM approach. Environmental Science and Pollution Research, 26(32), 33202-33215. doi:10.1007/s11356-019-06431-0Huang, I. B., Keisler, J., & Linkov, I. (2011). Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of The Total Environment, 409(19), 3578-3594. doi:10.1016/j.scitotenv.2011.06.022Pons, O., de la Fuente, A., & Aguado, A. (2016). The Use of MIVES as a Sustainability Assessment MCDM Method for Architecture and Civil Engineering Applications. Sustainability, 8(5), 460. doi:10.3390/su8050460Shishegaran, A., Shishegaran, A., Mazzulla, G., & Forciniti, C. (2020). A Novel Approach for a Sustainability Evaluation of Developing System Interchange: The Case Study of the Sheikhfazolah-Yadegar Interchange, Tehran, Iran. International Journal of Environmental Research and Public Health, 17(2), 435. doi:10.3390/ijerph17020435Wu, H.-Y., Chen, J.-K., Chen, I.-S., & Zhuo, H.-H. (2012). Ranking universities based on performance evaluation by a hybrid MCDM model. Measurement, 45(5), 856-880. doi:10.1016/j.measurement.2012.02.009Shakouri G., H., & Tavassoli N., Y. (2012). Implementation of a hybrid fuzzy system as a decision support process: A FAHP–FMCDM–FIS composition. Expert Systems with Applications, 39(3), 3682-3691. doi:10.1016/j.eswa.2011.09.063Mavi, R. K., Goh, M., & Mavi, N. K. (2016). Supplier Selection with Shannon Entropy and Fuzzy TOPSIS in the Context of Supply Chain Risk Management. Procedia - Social and Behavioral Sciences, 235, 216-225. doi:10.1016/j.sbspro.2016.11.017Montgomery, B., Dragićević, S., Dujmović, J., & Schmidt, M. (2016). A GIS-based Logic Scoring of Preference method for evaluation of land capability and suitability for agriculture. Computers and Electronics in Agriculture, 124, 340-353. doi:10.1016/j.compag.2016.04.013Debnath, A., Roy, J., Kar, S., Zavadskas, E., & Antucheviciene, J. (2017). A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products. Sustainability, 9(8), 1302. doi:10.3390/su9081302Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A. A., Ghorbani, M. A., & Shahbazi, F. (2018). Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops. Geoderma, 310, 178-190. doi:10.1016/j.geoderma.2017.09.012Rostamzadeh, R., Ghorabaee, M. K., Govindan, K., Esmaeili, A., & Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach. Journal of Cleaner Production, 175, 651-669. doi:10.1016/j.jclepro.2017.12.071Raut, R. D., Gardas, B. B., Kharat, M., & Narkhede, B. (2018). Modeling the drivers of post-harvest losses – MCDM approach. Computers and Electronics in Agriculture, 154, 426-433. doi:10.1016/j.compag.2018.09.035Qureshi, M. R. N., Singh, R. K., & Hasan, M. A. (2017). Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM. Environment, Development and Sustainability, 20(2), 641-659. doi:10.1007/s10668-016-9903-7Srinivasa Rao, C., Kareemulla, K., Krishnan, P., Murthy, G. R. K., Ramesh, P., Ananthan, P. S., & Joshi, P. K. (2019). Agro-ecosystem based sustainability indicators for climate resilient agriculture in India: A conceptual framework. Ecological Indicators, 105, 621-633. doi:10.1016/j.ecolind.2018.06.038Paul, M., Negahban-Azar, M., Shirmohammadi, A., & Montas, H. (2020). Assessment of agricultural land suitability for irrigation with reclaimed water using geospatial multi-criteria decision analysis. Agricultural Water Management, 231, 105987. doi:10.1016/j.agwat.2019.105987Balezentis, T., Chen, X., Galnaityte, A., & Namiotko, V. (2020). Optimizing crop mix with respect to economic and environmental constraints: An integrated MCDM approach. Science of The Total Environment, 705, 135896. doi:10.1016/j.scitotenv.2019.135896Jahan, A., & Edwards, K. L. (2013). VIKOR method for material selection problems with interval numbers and target-based criteria. Materials & Design, 47, 759-765. doi:10.1016/j.matdes.2012.12.072Pourhejazy, P., Kwon, O., Chang, Y.-T., & Park, H. (2017). Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach. Sustainability, 9(2), 255. doi:10.3390/su9020255Stewart, T. J. (1996). Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis. Journal of the Operational Research Society, 47(5), 654-665. doi:10.1057/jors.1996.77Li, X.-B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. doi:10.1016/s0377-2217(98)00130-1Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). STATE OF ART SURVEYS OF OVERVIEWS ON MCDM/MADM METHODS. Technological and Economic Development of Economy, 20(1), 165-179. doi:10.3846/20294913.2014.892037Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237-253. doi:10.1016/j.matdes.2017.02.041Bouyssou, D. (1999). Using DEA as a tool for MCDM: some remarks. Journal of the Operational Research Society, 50(9), 974-978. doi:10.1057/palgrave.jors.2600800Özcan, T., Çelebi, N., & Esnaf, Ş. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38(8), 9773-9779. doi:10.1016/j.eswa.2011.02.022LOKEN, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595. doi:10.1016/j.rser.2005.11.005Darji, V. P., & Rao, R. V. (2014). Intelligent Multi Criteria Decision Making Methods for Material Selection in Sugar Industry. Procedia Materials Science, 5, 2585-2594. doi:10.1016/j.mspro.2014.07.519Ceballos, B., Lamata, M. T., & Pelta, D. A. (2016). A comparative analysis of multi-criteria decision-making methods. Progress in Artificial Intelligence, 5(4), 315-322. doi:10.1007/s13748-016-0093-1Sen, B., Bhattacharjee, P., & Mandal, U. K. (2016). A comparative study of some prominent multi criteria decision making methods for connecting rod material selection. Perspectives in Science, 8, 547-549. doi:10.1016/j.pisc.2016.06.016Wu, D. (2006). A note on DEA efficiency assessment using ideal point: An improvement of Wang and Luo’s model. Applied Mathematics and Computation, 183(2), 819-830. doi:10.1016/j.amc.2006.06.030Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. doi:10.1016/j.ins.2014.02.137Roy, B. (1991). The outranking approach and the foundations of electre methods. Theory and Decision, 31(1), 49-73. doi:10.1007/bf00134132YOON, K., & HWANG, C.-L. (1985). Manufacturing plant location analysis by multiple attribute decision making: part I—single-plant strategy. International Journal of Production Research, 23(2), 345-359. doi:10.1080/00207548508904712Doyle, J., & Green, R. (1994). Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45(5), 567-578. doi:10.1057/jors.1994.84Martí, L., Martín, J. C., & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, 20(1), 169-192. doi:10.1016/s1514-0326(17)30008-9Canadá y la UE: Si Quierohttps://www.Euroganadería.euKARABIYIK, C., & KUTLU KARABIYIK, B. (2018). Benchmarking International Trade Performance of OECD Countries: TOPSIS and AHP Approaches. Gaziantep University Journal of Social Sciences. doi:10.21547/jss.267381Lin, M.-C., Wang, C.-C., Chen, M.-S., & Chang, C. A. (2008). Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry, 59(1), 17-31. doi:10.1016/j.compind.2007.05.013Lourenzutti, R., & Krohling, R. A. (2016). A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Information Sciences, 330, 1-18. doi:10.1016/j.ins.2015.10.005Roy, B. (1968). Classement et choix en présence de points de vue multiples. Revue française d’informatique et de recherche opérationnelle, 2(8), 57-75. doi:10.1051/ro/196802v100571Jaini, N., & Utyuzhnikov, S. (2016). Trade-off ranking method for multi-criteria decision analysis. Journal of Multi-Criteria Decision Analysis, 24(3-4), e1600. doi:10.1002/mcda.1600Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi:10.1287/mnsc.30.9.1078Angulo-Meza, L., & Lins, M. P. E. (2002). Annals of Operations Research, 116(1/4), 225-242. doi:10.1023/a:1021340616758Falagario, M., Sciancalepore, F., Costantino, N., & Pietroforte, R. (2012). Using a DEA-cross efficiency approach in public procurement tenders. European Journal of Operational Research, 218(2), 523-529. doi:10.1016/j.ejor.2011.10.031Puertas, R., & Marti, L. (2019). Sustainability in Universities: DEA-GreenMetric. Sustainability, 11(14), 3766. doi:10.3390/su1114376
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