Aggregation of meta-technology ratio in DEA framework using the evidential reasoning approach

References

• Afsharian M, Ahn H, Harms SG. 2019. Performance comparison of management groups under centralised management. Eur J Oper Res. 278(3):845–854. doi: 10.1016/j.ejor.2019.05.005.
   Web of Science ®Google Scholar
• Atici KB, Podinovski VV. 2015. Using data envelopment analysis for the assessment of technical efficiency of units with different specializations: an application to agriculture. Omega. 54:72–83. doi: 10.1016/j.omega.2015.01.015.
   Web of Science ®Google Scholar
• Avilés-Sacoto SV, Cook WD, Güemes-Castorena D, Villarreal-González A. 2017. Setting goals for economic activities in Mexico. INFOR. 55(3):161–187. doi: 10.1080/03155986.2017.1231767.
   Web of Science ®Google Scholar
• Azad M, Kalam A, Munisamy S, Masum AKM, Saona P, Wanke P. 2017. Bank efficiency in Malaysia: a use of Malmquist meta-frontier analysis. Eurasian Bus Rev. 7(2):287–311. doi: 10.1007/s40821-016-0054-4.
   Web of Science ®Google Scholar
• Buchanan BG, Shortliffe EH. 1984. Rule-based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project. (the Addison-Wesley series in artificial intelligence). Addison-Wesley Longman Publishing Co., Inc..
   Google Scholar
• Cao J, Chen G, Khoveyni M, Eslami R, Yang G. 2016. Specification of a performance indicator using the evidential-reasoning approach. Knowledge-Based Syst. 92:138–150. doi: 10.1016/j.knosys.2015.10.023.
   Web of Science ®Google Scholar
• Chang V, Tovar B. 2022. Efficiency drivers for the South Pacific West coast port terminals: a two-stage non-convex metafrontier dea approach. Oper Res Int J. 22(3):2667–2701. doi: 10.1007/s12351-021-00626-5.
   Web of Science ®Google Scholar
• Charnes A, Cooper WW, Rhodes E. 1978. Measuring the efficiency of decision making units. Eur J Oper Res. 2(6):429–444. doi: 10.1016/0377-2217(78)90138-8.
   Web of Science ®Google Scholar
• Chen L, Huang Y, Li MJ, Wang YM. 2020. Meta-frontier analysis using cross-efficiency method for performance evaluation. Eur J Oper Res. 280(1):219–229. doi: 10.1016/j.ejor.2019.06.053.
   Web of Science ®Google Scholar
• Chen Y, Wang M, Feng C, Zhou H, Wang K. 2021. Total factor energy efficiency in Chinese manufacturing industry under industry and regional heterogeneities. Resour Conserv Recycl. 168:105255. doi: 10.1016/j.resconrec.2020.105255.
   Web of Science ®Google Scholar
• Chin KS, Wang YM, Poon GKK, Yang JB. 2009. Failure mode and effects analysis using a group-based evidential reasoning approach. Comput Operat Res. 36(6):1768–1779. doi: 10.1016/j.cor.2008.05.002.
   Web of Science ®Google Scholar
• Chiu YH, Lee JH, Lu CC, Shyu MK, Luo Z. 2012. The technology gap and efficiency measure in WEC countries: application of the hybrid meta frontier model. Energy Policy. 51:349–357. doi: 10.1016/j.enpol.2012.08.026.
   Web of Science ®Google Scholar
• Cook WD, Seiford LM. 2009. Data envelopment analysis (DEA)–Thirty years on. Eur J Oper Res. 192(1):1–17. doi: 10.1016/j.ejor.2008.01.032.
   Web of Science ®Google Scholar
• Couso I, Moral S. 2010. Independence concepts in evidence theory. Int J Approximate Reason. 51(7):748–758. doi: 10.1016/j.ijar.2010.02.004.
   Web of Science ®Google Scholar
• Daniel WW. 1978. Applied nonparametric statistics. Boston (MA): Houghton Mifflin.
   Google Scholar
• Dempster AP. 1967. Upper and lower probabilities induced by a multivalued mapping. Ann Math Statist. 38(2):325–339. doi: 10.1214/aoms/1177698950.
   Google Scholar
• Feng C, Wang M. 2018. Analysis of energy efficiency in China’s transportation sector. Renewable Sustainable Energy Rev. 94:565–575. doi: 10.1016/j.rser.2018.06.037.
   Web of Science ®Google Scholar
• Feng C, Huang JB, Wang M. 2018. Analysis of green total-factor productivity in China’s regional metal industry: a meta-frontier approach. Resour Policy. 58:219–229. doi: 10.1016/j.resourpol.2018.05.008.
   Web of Science ®Google Scholar
• Fishburn PC. 1970. Utility theory for decision making. Research analysis corp. London: McLean.
   Google Scholar
• Fu C, Yang SL. 2010. The group consensus based evidential reasoning approach for multiple attributive group decision analysis. Eur J Oper Res. 206(3):601–608. doi: 10.1016/j.ejor.2010.03.011.
   Web of Science ®Google Scholar
• Fu C, Xu C, Xue M, Liu W, Yang S. 2021. Data-driven decision making based on evidential reasoning approach and machine learning algorithms. Appl Soft Comput. 110:107622. doi: 10.1016/j.asoc.2021.107622.
   Web of Science ®Google Scholar
• Fu C, Yang JB, Yang SL. 2015. A group evidential reasoning approach based on expert reliability. Eur J Oper Res. 246(3):886–893. doi: 10.1016/j.ejor.2015.05.042.
   Web of Science ®Google Scholar
• Fu C, Zhan Q, Liu W. 2021. Evidential reasoning based ensemble classifier for uncertain imbalanced data. Inf Sci. 578:378–400. doi: 10.1016/j.ins.2021.07.027.
   Web of Science ®Google Scholar
• Ganji SS, Rassafi AA. 2019. Measuring the road safety performance of Iranian provinces: a double-frontier DEA model and evidential reasoning approach. Int J Inj Contr Saf Promot. 26(2):156–169. doi: 10.1080/17457300.2018.1535510.
   PubMed Web of Science ®Google Scholar
• Ganji SS, Rassafi AA, Bandari SJ. 2020. Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers. Socio-Econ Plann Sci. 69:100706. doi: 10.1016/j.seps.2019.04.003.
   Web of Science ®Google Scholar
• Guan Y, Shan Y, Huang Q, Chen H, Wang D, Hubacek K. 2021. Assessment to China’s recent emission pattern shifts. Earth’s Future. 9(11):e2021EF002241. doi: 10.1029/2021EF002241.
   Web of Science ®Google Scholar
• Guo H, Xia Y, Jin J, Pan C. 2022. The impact of climate change on the efficiency of agricultural production in the world’s main agricultural regions. Environ Impact Assess Rev. 97:106891. doi: 10.1016/j.eiar.2022.106891.
   Web of Science ®Google Scholar
• Hollander M, Wolfe DA, Chicken E. 2013. Nonparametric statistical methods. Hoboken (NJ): John Wiley & Sons.
   Google Scholar
• Huang MY, Juo JC, Fu TT. 2015. Metafrontier cost Malmquist productivity index: an application to Taiwanese and Chinese commercial banks. Journal of Productivity Analysis. 44:321–335. doi: 10.1007/s11123-014-0411-1.
   Web of Science ®Google Scholar
• Jin Q, Kerstens K, Van de Woestyne I. 2020. Metafrontier productivity indices: questioning the common convexification strategy. Eur J Oper Res. 283(2):737–747. doi: 10.1016/j.ejor.2019.11.019.
   Web of Science ®Google Scholar
• Kalantary M, Saen RF. 2019. Assessing sustainability of supply chains: an inverse network dynamic DEA model. Computers & Industrial Engineering. 135:1224–1238. doi: 10.1016/j.cie.2018.11.009.
   Web of Science ®Google Scholar
• Keeney RL, Raiffa H, Meyer RF. 1993. Decisions with multiple objectives: preferences and value trade-offs. Cambridge: Cambridge University Press.
   Google Scholar
• Kerstens K, O’donnell C, Van de Woestyne I. 2019. Metatechnology frontier and convexity: a restatement. Eur J Oper Res. 275(2):780–792. doi: 10.1016/j.ejor.2018.11.064.
   Web of Science ®Google Scholar
• Li LB, Liu BL, Liu WL, Chiu YH. 2017. Efficiency evaluation of the regional high-tech industry in China: a new framework based on meta-frontier dynamic DEA analysis. Socio-Econ Plann Sci. 60:24–33. doi: 10.1016/j.seps.2017.02.001.
   Web of Science ®Google Scholar
• Li Y, Li J, Gong Y, Wei F, Huang Q. 2020. CO2 emission performance evaluation of Chinese port enterprises: a modified meta-frontier non-radial directional distance function approach. Transport Rest D Transport Environ. 89:102605. doi: 10.1016/j.trd.2020.102605.
   Web of Science ®Google Scholar
• Liang L, Yang F, Cook WD, Zhu J. 2006. DEA models for supply chain efficiency evaluation. Ann Oper Res. 145(1):35–49. doi: 10.1007/s10479-006-0026-7.
   Web of Science ®Google Scholar
• Lim S, Zhu J. 2019. Primal-dual correspondence and frontier projections in two-stage network DEA models. Omega. 83:236–248. doi: 10.1016/j.omega.2018.06.005.
   Web of Science ®Google Scholar
• Lin B, Zhao H. 2016. Technology gap and regional energy efficiency in China’s textile industry: a non-parametric meta-frontier approach. J Cleaner Prod. 137:21–28. doi: 10.1016/j.jclepro.2016.07.055.
   Web of Science ®Google Scholar
• Liu W, Lin B. 2018. Analysis of energy efficiency and its influencing factors in China’s transport sector. J Cleaner Prod. 170:674–682. doi: 10.1016/j.jclepro.2017.09.052.
   Web of Science ®Google Scholar
• Liu X, Peng R, Zhong C, Wang M, Guo P. 2021. What drives the temporal and spatial differences of CO2 emissions in the transport sector? Empirical evidence from municipalities in China. Energy Policy. 159:112607. doi: 10.1016/j.enpol.2021.112607.
   Web of Science ®Google Scholar
• Liu X, Yang F, Wu J. 2020. DEA considering technological heterogeneity and intermediate output target setting: the performance analysis of Chinese commercial banks. Ann Oper Res. 291(1–2):605–626. doi: 10.1007/s10479-019-03413-w.
   Web of Science ®Google Scholar
• Liu Y, Zhang Z. 2022. How does economic policy uncertainty affect CO2 emissions? A regional analysis in China. Environ Sci Pollut Res Int. 29(3):4276–4290. doi: 10.1007/s11356-021-15936-6.
   PubMed Web of Science ®Google Scholar
• Liu Y, Yang S, Liu X, Guo P, Zhang K. 2021. Driving forces of temporal-spatial differences in CO2 emissions at the city level for China’s transport sector. Environ Sci Pollut Res Int. 28(20):25993–26006. doi: 10.1007/s11356-020-12235-4.
   PubMed Web of Science ®Google Scholar
• MacFarland TW, Yates JM. 2016. Mann–whitney u test. Introduction to nonparametric statistics for the biological sciences using R. Cham, Switzerland: Springer; p. 103–132.
   Google Scholar
• Mao W, Koo WW. 1997. Productivity growth, technological progress, and efficiency change in Chinese agriculture after rural economic reforms: a DEA approach. China Econ Rev. 8(2):157–174. doi: 10.1016/S1043-951X(97)90004-3.
   Web of Science ®Google Scholar
• Nguyen MAT, Yu MM, Lirn TC. 2022. Revenue efficiency across airline business models: a bootstrap non-convex meta-frontier approach. Transport Policy. 117:108–117. doi: 10.1016/j.tranpol.2022.01.007.
   Web of Science ®Google Scholar
• O’Donnell CJ, Rao DP, Battese GE. 2008. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics 34:231–255. doi: 10.1007/s00181-007-0119-4
   Web of Science ®Google Scholar
• Omrani H, Oveysi Z, Emrouznejad A, Teplova T. 2022. A mixed-integer network DEA with shared inputs and undesirable outputs for performance evaluation: efficiency measurement of bank branches. J Oper Res Soc. 74(4):1150–1165. doi: 10.1080/01605682.2022.2064783.
   Web of Science ®Google Scholar
• Rahman MT, Nielsen R, Khan MA, Asmild M. 2019. Efficiency and production environmental heterogeneity in aquaculture: a meta-frontier DEA approach. Aquaculture. 509:140–148. doi: 10.1016/j.aquaculture.2019.05.002.
   Web of Science ®Google Scholar
• Scheel H. 2001. Undesirable outputs in efficiency valuations. Eur J Oper Res. 132(2):400–410. doi: 10.1016/S0377-2217(00)00160-0.
   Web of Science ®Google Scholar
• Shafer G. 1976. A mathematical theory of evidence. Vol. 42. Princeton (NJ): Princeton University Press.
   Google Scholar
• Shafer G. 1981. Constructive probability. Synthese. 48(1):1–60. doi: 10.1007/BF01064627.
   Web of Science ®Google Scholar
• Shan Y, Guan D, Zheng H, Ou J, Li Y, Meng J, Mi Z, Liu Z, Zhang Q. 2018. China CO2 emission accounts 1997–2015. Sci Data. 5(1):170201. doi: 10.1038/sdata.2017.201.
   PubMedGoogle Scholar
• Shan Y, Huang Q, Guan D, Hubacek K. 2020. China CO2 emission accounts 2016–2017. Sci Data. 7(1):54. doi: 10.1038/s41597-020-0393-y.
   PubMed Web of Science ®Google Scholar
• Shan Y, Liu J, Liu Z, Xu X, Shao S, Wang P, Guan D. 2016. New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors. Appl Energy. 184:742–750. doi: 10.1016/j.apenergy.2016.03.073.
   Web of Science ®Google Scholar
• Shi GM, Bi J, Wang JN. 2010. Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy Policy. 38(10):6172–6179. doi: 10.1016/j.enpol.2010.06.003.
   Web of Science ®Google Scholar
• Sun Y, Wang D, Yang F, Ang S. 2023. Efficiency evaluation of higher education systems in China: a double frontier parallel DEA model. Comput. Ind. Eng. 176:108979. doi: 10.1016/j.cie.2023.108979.
   Web of Science ®Google Scholar
• Sun Y, Yang F, Wang D, Ang S. 2023. Efficiency evaluation for higher education institutions in China considering unbalanced regional development: a meta-frontier Super-SBM model. Socio-Econ Plann Sci. 88:101648. doi: 10.1016/j.seps.2023.101648.
   Web of Science ®Google Scholar
• Walheer B. 2018. Aggregation of metafrontier technology gap ratios: the case of European sectors in 1995–2015. Eur J Oper Res. 269(3):1013–1026. doi: 10.1016/j.ejor.2018.02.048.
   Web of Science ®Google Scholar
• Wang D, Wei F, Yang F. 2022. Efficiency evaluation of a two-stage production process with feedback: an improved DEA model. INFOR. 61(1):67–85. doi: 10.1080/03155986.2022.2104573.
   Web of Science ®Google Scholar
• Wang D, Zhao L, Yang F, Chen K. 2022. Performance evaluation of the Chinese high-tech industry: a two-stage DEA approach with feedback and shared resource. JIMO. 18(5):3315. doi: 10.3934/jimo.2021114.
   Google Scholar
• Wang Q, Hang Y, Hu JL, Chiu CR. 2018. An alternative metafrontier framework for measuring the heterogeneity of technology. Naval Res Logistic. 65(5):427–445. doi: 10.1002/nav.21815.
   Web of Science ®Google Scholar
• Wang YM, Elhag TM. 2007. A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks. Expert Syst Appl. 32(2):336–348. doi: 10.1016/j.eswa.2005.11.029.
   Web of Science ®Google Scholar
• Wang YM, Yang JB, Xu DL. 2006. Environmental impact assessment using the evidential reasoning approach. Eur J Oper Res. 174(3):1885–1913. doi: 10.1016/j.ejor.2004.09.059.
   Web of Science ®Google Scholar
• Wanke P, Barros CP. 2016. Efficiency drivers in Brazilian insurance: a two-stage DEA meta frontier-data mining approach. Econ Modell. 53:8–22. doi: 10.1016/j.econmod.2015.11.005.
   Web of Science ®Google Scholar
• Wei F, Zhang X, Chu J, Yang F, Yuan Z. 2021. Energy and environmental efficiency of China’s transportation sectors considering CO2 emission uncertainty. Transportation Research Part D: Transport and Environment. 97:102955 10.1016/j.trd.2021.102955.
   Web of Science ®Google Scholar
• Wu J, Zhu Q, Chu J, Liu H, Liang L. 2016. Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transport Res D Transport Environ. 48:460–472. doi: 10.1016/j.trd.2015.08.001.
   Web of Science ®Google Scholar
• Wu J, Zhu Q, Ji X, Chu J, Liang L. 2016. Two-stage network processes with shared resources and resources recovered from undesirable outputs. Eur J Oper Res. 251(1):182–197. doi: 10.1016/j.ejor.2015.10.049.
   Web of Science ®Google Scholar
• Xi X, Xi B, Miao C, Yu R, Xie J, Xiang R, Hu F. 2022. Factors influencing technological innovation efficiency in the Chinese video game industry: applying the meta-frontier approach. Technol Forecasting Social Change. 178:121574. doi: 10.1016/j.techfore.2022.121574.
   Web of Science ®Google Scholar
• Xie BC, Gao J, Chen YF, Deng NQ. 2018. Measuring the efficiency of grid companies in China: a bootstrapping non-parametric meta-frontier approach. J Cleaner Prod. 174:1381–1391. doi: 10.1016/j.jclepro.2017.11.016.
   Web of Science ®Google Scholar
• Yaisawarng S, Ng YC. 2014. The impact of higher education reform on research performance of Chinese universities. China Econ Rev. 31:94–105. doi: 10.1016/j.chieco.2014.08.006.
   Web of Science ®Google Scholar
• Yang F, Ang S, Xia Q, Yang C. 2012. Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis. Eur J Oper Res. 223(2):483–488. doi: 10.1016/j.ejor.2012.07.001.
   Web of Science ®Google Scholar
• Yang F, Sun Y, Wang D, Ang S. 2022. Network data envelopment analysis with two-level maximin strategy. RAIRO-Oper Res. 56(4):2543–2556. doi: 10.1051/ro/2022090.
   Web of Science ®Google Scholar
• Yang F, Wang D, Zhao L, Wei F. 2021. Efficiency evaluation for regional industrial water use and wastewater treatment systems in China: a dynamic interactive network slacks-based measure model. J Environ Manage. 279:111721. doi: 10.1016/j.jenvman.2020.111721.
   PubMed Web of Science ®Google Scholar
• Yang GL, Song YY, Xu DL, Yang JB. 2017. Overall efficiency and its decomposition in a two-stage network DEA model. J Manag Sci Eng. 2(3):161–192. doi: 10.3724/SP.J.1383.203008.
   Google Scholar
• Yang GL, Yang JB, Liu WB, Li XX. 2013. Cross-efficiency aggregation in DEA models using the evidential-reasoning approach. Eur J Oper Res. 231(2):393–404. doi: 10.1016/j.ejor.2013.05.017.
   Web of Science ®Google Scholar
• Yang JB, Sen P. 1994. A general multi-level evaluation process for hybrid MADM with uncertainty. IEEE Trans Syst Man Cybernet. 24(10):1458–1473.
   Web of Science ®Google Scholar
• Yang, J. B., & Singh, M. G. (1994). An evidential reasoning approach for multiple-attribute decision making with uncertainty. IEEE Trans Syst Man Cybern. 24(1), 1–18. doi: 10.1109/21.259681.
   Web of Science ®Google Scholar
• Yang JB, Xu DL. 2002. On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans Syst Man Cybernet A Syst Hum. 32(3):289–304.
   Web of Science ®Google Scholar
• Yang J, Cheng J, Huang S. 2020. CO2 emissions performance and reduction potential in China’s manufacturing industry: a multi-hierarchy meta-frontier approach. J Cleaner Prod. 255:120226. doi: 10.1016/j.jclepro.2020.120226.
   Web of Science ®Google Scholar
• Yang LH, Wang S, Ye FF, Liu J, Wang YM, Hu H. 2021. Environmental investment prediction using extended belief rule-based system and evidential reasoning rule. J Cleaner Prod. 289:125661. doi: 10.1016/j.jclepro.2020.125661.
   Web of Science ®Google Scholar
• Yu MM, Chen LH. 2020. Evaluation of efficiency and technological bias of tourist hotels by a meta-frontier DEA model. J Oper Res Soc. 71(5):718–732. doi: 10.1080/01605682.2019.1578625.
   Web of Science ®Google Scholar
• Yu MM, See KF, Hsiao B. 2022. Integrating group frontier and metafrontier directional distance functions to evaluate the efficiency of production units. Eur J Oper Res. 301(1):254–276. doi: 10.1016/j.ejor.2021.10.054.
   Web of Science ®Google Scholar
• Yu Y, Su Y, Qi C. 2022. Comparing potential cost savings of energy quota trading and carbon emissions trading for China’s industrial sectors. Resour Conserv Recycl. 186:106544. doi: 10.1016/j.resconrec.2022.106544.
   Web of Science ®Google Scholar
• Zadeh LA. 1984. Review of a mathematical theory of evidence. AI Magazine. 5(3):81–81.
   Google Scholar
• Zha Y, Liang N, Wu M, Bian Y. 2016. Efficiency evaluation of banks in China: A dynamic two-stage slacks-based measure approach. Omega. 60:60–72. doi: 10.1016/j.omega.2014.12.008.
   Web of Science ®Google Scholar
• Zha J, Tan T, Fan R, Xu H, Ma S. 2020. How to reduce energy intensity to achieve sustainable development of China’s transport sector? A cross-regional comparison analysis. Socio-Econ Plann Sci. 71:100772. doi: 10.1016/j.seps.2019.100772.
   Web of Science ®Google Scholar
• Zhang X, Xia Q, Wei F. 2023. Efficiency evaluation of two-stage parallel-series structures with fixed-sum outputs: An approach based on SMAA and DEA. Expert Systems with Applications. 227:120264 10.1016/j.eswa.2023.120264.
   Web of Science ®Google Scholar
• Zhang N, Wei X. 2015. Dynamic total factor carbon emissions performance changes in the Chinese transportation industry. Appl Energy. 146:409–420. doi: 10.1016/j.apenergy.2015.01.072.
   Web of Science ®Google Scholar
• Zhang N, Zhou P, Choi Y. 2013. Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: a meta-frontier non-radial directional distance function analysis. Energy Policy. 56:653–662. doi: 10.1016/j.enpol.2013.01.033.
   Web of Science ®Google Scholar
• Zhang X, Xia Q, Yang F, Song S, Ang S. 2021. Interval cross-efficiency for ranking decision making units using the stochastic multicriteria acceptability analysis-evidential reasoning approach. Comput Ind Eng. 156:107222. doi: 10.1016/j.cie.2021.107222.
   Web of Science ®Google Scholar
• Zhang YJ, Jiang L, Shi W. 2020. Exploring the growth-adjusted energy-emission efficiency of transportation industry in China. Energy Econ. 90:104873. doi: 10.1016/j.eneco.2020.104873.
   Web of Science ®Google Scholar
• Zhao L, Zha Y, Liang N, Liang L. 2016. Data envelopment analysis for unified efficiency evaluation: an assessment of regional industries in China. J Cleaner Prod. 113:695–704. doi: 10.1016/j.jclepro.2015.05.128.
   Web of Science ®Google Scholar
• Zhao L, Zha Y, Zhuang Y, Liang L. 2019. Data envelopment analysis for sustainability evaluation in China: tackling the economic, environmental, and social dimensions. Eur J Oper Res. 275(3):1083–1095. doi: 10.1016/j.ejor.2018.12.004.
   Web of Science ®Google Scholar
• Zhao L, Zhu Q, Zhang L. 2021. Regulation adaptive strategy and bank efficiency: a network slacks-based measure with shared resources. Eur J Oper Res. 295(1):348–362. doi: 10.1016/j.ejor.2021.02.050.
   Web of Science ®Google Scholar
• Zhao T, Xie J, Chen Y, Liang L. 2022. Coordination efficiency in two-stage network DEA: application to a supplier–manufacturer sustainable supply chain. Int J Logistic Res Appl. 25(4-5):656–677. doi: 10.1080/13675567.2021.1895976.
   Web of Science ®Google Scholar
• Zhu Y, Yang F, Wei F, Wang D. 2022. Measuring environmental efficiency of the EU based on a DEA approach with fixed cost allocation under different decision goals. Expert Syst Appl. 208:118183. doi: 10.1016/j.eswa.2022.118183.
   Web of Science ®Google Scholar