-Alirezaee, M.R., Afsharian, M., (2010). Improving the discrimination of data envelopment analysis models in multiple time periods. Int. Trans. Oper. Res. 17 (5),667-679.
-Amirteimoori, A., (2007). DEA efficiency analysis: efficient and anti-efficient frontier. Appl.Math. Comput. 186, 10-16.
-Badiezadeh, T., Farzipoor Saen, R., Samavati, T., (2018). Assessing sustainability of supplychains by double frontier network DEA: a big data approach. Comput. Oper. Res. 98, 284-290.
-Beltrán-Esteve, M., Picazo-Tadeo, A., (2015). Assessing environmental performance trendsin the transport industry: eco-innovation or catching-up? Energ. Econ. 51, 570-580.
-Cainelli, G., De Marchi, V., Grandinetti, R., (2015). Does the development of environmentalinnovation require different resources? Evidence from Spanish manufacturing firms. J. Clean. Prod. 94, 211-220.
-Carter, C.R., Jennings, M.M., (2002). Social responsibility and supply chain relationships.Transp. Res. E-Logist. 38 (1), 37-52.
-Caves, D.W., Christensen, L.R., Diewert, W.E., (1982). The economic theory of indexnumbers and the measurement of input, output, and productivity. Econometrica 50,1393-1414.
-Chang, Y.T., Zhang, N., Danao, D., Zhang, N., (2013). Environmental efficiency analysis oftransportation system in China: a non-radial DEA approach. Energy Policy, 58, 277-283.
-Chang, I., Leitner, H., Sheppard, E., (2016). A Green leap forward? Eco-State restructuringand the TianjineBinhai eco-city model. Reg. Stud. 50 (6),929-943.
-Charnes, A., Cooper, W.W., Rhodes, E., (1978). Measuring the efficiency of decision makingunits. Eur. J. Oper. Res. 2 (6), 429-444.
-Cohen, W.M., Levinthal, D.A., (1990). Absorptive capacity: a new perspective on innovationand learning. Admin. Sci Quart. 35, 128-152.
-Cook, W.D., Zhu, J., (2007). Within-group common weights in DEA: an analysis of powerplant efficiency. Eur. J. Oper. Res. 178,207-216.
-Costantini, V., Crespi, F., Marin, G., Paglialunga, E., (2016). Eco-innovation, sustainablesupply chains and environmental performance in European industries. J. Clean. Prod.155, 1-14.
-Cui, Q., Li, Y., (2014). The evaluation of transportation energy efficiency: an application ofthree-stage virtual frontier DEA. Transp. Res. D-Trans. Environ. 29, 1-11.
-den Hartog, H., Sengers, F., Xu, Y., Xie, L., Jiang, P., de Jong, M., 2018. Low-carbonpromises and realities: lessons from three socio-technical experiments in Shanghai. J. Clean. Prod. 181,692-702.
-Djekic, I., Smigic, N., Glavan, R., Miocinovic, J., Tomasevic, I., (2018). Transportation sustainability index in dairy industry-Fuzzy logic approach. J. Clean. Prod. 180,107-115.
-EIO, (2012). Paving the Way to a Green Economy Through Eco-innovation. Europe inTransition. European Commission, Paris, France.
-Environmental Protection Department, HKSAR, 2004a. Air Pollutant and Greenhouse GasEmission Inventory (1990–2003). Available at:. http://www.epd.gov.hk/epd/english/environmentinhk/air/data/emission_inve.html.
-Färe, R., Grosskopf, S., Lindgren, B., Roose, P., (1992). Productivity change in Swedishanalysis pharmacies 1980–1989: a nonparametric Malmquist approach. J. Prod. Anal.3 (1), 85-102.
-Farrell, M.J., (1957). The measurement of productive efficiency. J. R. Stat. Soc. 120 (3),253-281.
-Farzipoor Saen, R., 2009. A mathematical model for selecting third-party reverse logisticsproviders. Int. J. Procure Manage. 2, (2), 180-190.
-Fathi, A., Farzipoor Saen, R., (2018). A novel bidirectional network data envelopmentanalysis model for evaluating sustainability of distributive supply chains of transportcompanies. J. Clean. Prod. 184, 696-708.
-Florida, R., (1996). Lean and Green: the move to environmentally conscious manufacturing. Calif. Manage. Rev. 39 (1), 80-105.
-Fussler, C., James, P., (1996). Eco-innovation: a Breakthrough Discipline for Innovationand Sustainability. Pitman Publishing, London.
-Golany, B., Roll, Y., (1993). Some extensions of techniques to handle non-discretionaryfactors in data envelopment analysis. J. Psychoeduc. Assess. 4 (4), 419-–432.
-Gómez-Calvet, R., Conesa, D., Gómez-Calvet, A.R., Tortosa-Ausina, E., )2016(. On the dynamicsof eco-efficiency performance in the European Union. Comput. Oper. Res. 66, 336-350.
-Guimarães, V. de A., Leal Jr., I.C., Silva, M.A.V., )2018(. Evaluating the sustainability ofurban passenger transportation by Monte Carlo simulation. Renew. Sustain. EnergyRev. 93,732-752.
-Gupta, P., Mehlawat, M.K., Aggarwal, U., Charles, V., )2018(. An integrated AHPDEA multiobjectiveoptimization model for sustainable transportation in mining industry.Resour. Policy. 74, 101180.
-Hemmelskamp, J., )1999(. The Influence of Environmental Policy on Innovative Behavior:An Econometric Study. European Union—Institute for Prospective TechnologicalStudies (IPTS), Seville, Spain.
-Hojnik, J., Ruzzier, M., (2016a). The driving forces of process eco-innovation and its impacton performance: insights from Slovenia. J. Clean. Prod. 133, 812-825.
-Holstein, W., Tanenbaum, M., )2014(. Production system. Encyclopaedia Britannica. OnlineAcademic Edition. Encyclopaedia Britannica.
-Hosseinzadeh Lotfi, F., Hatami-Marbini, A., Agrell, P.J., Aghayi, N., Gholami, K., )2013(. Allocating fixed resources and setting targets using a common-weights DEA approach.Comput. Ind. Eng. 64 (2), 631-640.
Jaffe, A.B., Newell, R.G., Stavins, R.N., )2002(. Environmental policy and technologicalchange. Environ. Resour. Econ. 22, 41-69.
-Jaffe, A.B., Newell, R.G., Stavins, R.N., )2003(. Technological change and the environment.In: Màler, K.G., Vincent, J. (Eds.), Handbook of Environmental Economics. ElsevierScience, Amsterdam, 461-516.
-Jahanshahloo, G.R., Zohrehbandian, M., Alinezhad, A., Abbasian Naghneh, S., Abbasian,H., Kiani Mavi, R., )2011a(. Finding common weights based on the DM’s preferenceinformation. J. Oper. Res. Soc. 62, 1796-1800.
-Jahanshahloo, G.R., Lot., F.H., Rezaie, V., Khanmohammadi, M., )2011b(. Ranking DMUsby ideal points with interval data in DEA. Appl. Math. Model. 35, 218-229.
-Jang, E.K., Park, M.S., Roh, T.W., Han, K.J., (2015). Policy instruments for eco-innovationin Asian countries. Sustainability 7, 12586-12614.
-Jansson, J., Nordlund, A., Westin, K., )2017(. Examining drivers of sustainable consumption:the influence of norms and opinion leadership on electric vehicle adoption inSweden. J. Clean. Prod. 154, 176–187.
-Ji, Y., Lee, C., )2010(. Data envelopment analysis. Stata J. 10, 267-280.
-Kao, C., )2010(. Malmquist productivity index based on common weights DEA: the case ofTaiwan forests after reorganization. Omega 38, 484-491.
-Kemp, R., Arundel, A., )1998(. Survey Indicators for Environmental Innovation; Studies inTechnology. Innovation and Economic Policy. (STEP), Oslo, Norway.
-Kemp, R., Pearson, P., )2007(. Final Report MEI Project About Measuring Eco-innovation.UM-MERIT: Maastricht, The Netherlands.
-Kemp, R., Pearson, P., )2008(. Policy Brief About Measuring Eco-innovation and Magazine. Newsletter Articles. 17-18. Project deliverable. https://cordis.europa.eu/docs/publications/1245/124548931-6_en.pdf.
-Kiani Mavi, R., Standing, C., (2017). Eco-innovation analysis with DEA: an application to OECD countries. IADIS Int. J. Comput. Sci. Inform Syst. 12 (2), 133-147.
-Kiani Mavi, R., Kazemi, S., Jahangiri, J., (2013). Developing common set of weights with considering non-discretionary inputs and using ideal point method. J. Appl. Math. doi.org/10.1155/2013/906743.
-Kiani Mavi, R., Zarbakhshnia, N., Khazraei, A., (2018). Bus Rapid Transit (BRT): a simulationand multi criteria decision-making (MCDM) approach. Transp. Policy 72,187-197.
-Kiani Mavi, R., Farzipoor Saen, R., Goh, M., (2019). Joint analysis of eco-efficiency andeco-innovation with common weights in two-stage network DEA: a big data approach.Technol. Forecast. Soc. Change 144, 553-562.
-Klemmer, P., Lehr, U., Löbbe, K., (1999). Environmental Innovation: Incentives andBarriers. Analytica, Berlin, Germany.
-Läpple, D., Renwick, A., Thorne, F., (2015). Measuring and understanding the drivers ofagricultural innovation: evidence from Ireland. Food Policy 51, 1-8.
-Lu, H., de Jong, M., Chen, Y., (2017). Economic city branding in China: the multi-levelgovernance of municipal self-promotion in the greater Pearl River Delta. Sustainability 9 (4), 1-24.
-Makui, A., Alinezhad, A., Kiani Mavi, R., Zohrebandian, M., (2008). A goal programmingmethod for finding common weights in DEA with an improved discriminating powerfor efficiency. Int. J. Ind. Syst. Eng. 1 (4), 293-303.
-OECD, (2009). Sustainable Manufacturing and Eco-innovation: Framework, Practices andMeasurement. Organisation for Economic
Co-operation and Development. Publishing, Paris, France.
-Omrani, H., (2013). Common weights data envelopment analysis with uncertain data: arobust optimization approach. Comput. Ind. Eng. 66 (4), 1163-1170.
-Park, M., Bleischwitz, R., Han, K.J., Jang, E.K., Joo, J.H., (2017). Eco-innovation indices astools for measuring eco-innovation. Sustainability 9 (12),1-28.
-Picazo-Tadeo, A.J., Gómez-Limón, J.A., Reig-Martínez, E., (2011). Assessing farming coefficiency: a data envelopment analysis approach. J. Environ. Manage. 92 (4),1154-1164.
-Porter, M.E., van der Linde, C., (1995). Toward a new conception of the environmentcompetitiveness relationship. J. Econ. Perspect. 9, 97-118.
-Rashidi, K., Farzipoor Saen, R., (2015). Measuring eco-efficiency based on green indicatorsand potentials in energy saving and undesirable output abatement. Energy Econ. 50(C), 18-26.
-Rennings, K., (2000). Redefining innovation: eco-innovation research and the contributionfrom ecological economics. Ecol. Econ. 32, 319-332.
-Rodríguez, J., Wiengarten, F., (2017). The role of process innovativeness in the developmentof environmental innovativeness capability. J. Clean. Prod. 142, 2423-2434.
-Seiford, L.M., Zhu, J., (2002). Modeling undesirable factors in efficiency evaluation. Eur. J.Oper. Res. 142, 16-20.
Sueyoshi, T., Goto, M., (2011). Measurement of returns to scale and damages to scale forDEA-based operational and environmental assessment: how to manage desirable(good) and undesirable (bad) outputs? Eur. J. Oper. Res. 211 (1), 76-89.
-Sun, J., Wu, J., Guo, D., (2013). Performance ranking of units considering ideal and antiidealDMU with common weights. Appl. Math. Model. 37, 6301-6310.
-Takamura, Y., Tone, K., (2003). A comparative site evaluation study for relocatingJapanese government agencies out of Tokyo. Socioecon. Plann. Sci. 37, 85-102.
-Tavana, M., Kazemi, S., Kiani Mavi, R., (2015). A stochastic data envelopment analysismodel using a common set of weights and the ideal point concept. Int. J. Appl.Manag. Sci. Eng. 7 (2), 81-92.
-Thompson, R.G., Langemeier, L.N., Lee, C.T., Lee, E., Thrall, R.M., (1990). The role ofmultiplier bounds in efficiency analysis with application to Kansas farming. J. Econ.46, 93-108.
-Wang, Y.M., Chin, K.S., (2007). Discriminating DEA efficient candidates by consideringtheir least relative total scores. J. Comput. Appl. Math. 206, 209–215.
-Wang, Y., Chin, K., (2009). A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers. Int. J. Prod. Res. 47 (23), 6663-6679.
-Wang, Y.M., Lan, Y.X., (2011). Measuring Malmquist productivity index: a new approachbased on double frontiers data envelopment analysis. Math. Comput. Model. 54, 2760-2771.
-Wang, Y.M., Lan, Y.X., (2013). Estimating most productive scale size with double frontiersdata envelopment analysis. Econ. Model. 33, 182-186.
-Wang, Y.M., Luo, Y., Lan, Y.X., (2011). Common weights for fully ranking decision makingunits by regression analysis. Expert Syst. Appl. 38 (8),9122-9128.
-WCED, (1987). Our Common Future. World Commission on Environment andDevelopment. Oxford University Press, Oxford.
-Woo, C., Chung, Y., Chun, D., Seo, H., Hong, S., (2015). The static and dynamic environmental efficiency of renewable energy: a Malmquist index analysis of OECD countries.Renew. Sustain. Energy Rev. 47, 367-376.
-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.Transp. Res. D-Transport Environ. 48, 460-472.
_Yang, F., Yang, M., (2015). Analysis on China’s eco-innovations: regulation context, intertemporal change and regional differences. Eur. J. Oper. Res. 247 (3), 1003-1012.
-Ying-Ming, W., Yi-Xin, L., (2011). Measuring Malmquist productivity index: a new approachbased on double frontiers data envelopment analysis. Math. Comput. Model. 54, 2760-2771.
-Zheng, J., Garrick, N.W., Atkinson-Palombo, C., McCahill, C., Marshall, W., (2013). Guidelines on developing performance metrics for evaluating transportation sustainability. Res. Transport Bus Manage. 7, 4-13.
-Zhou, P., Ang, B.W., Poh, K.L., (2007). Mathematical programming approach to constructingcomposite indicators. Ecol. Econ. 62, 291-297.
-Zhou, G., Chung, W., Zhang, Y., (2014). Measuring energy efficiency performance ofChina’s transport sector: a data envelopment analysis approach. Expert Syst. Appl. 41(2),709-722.
-Ziolkowska, J.R., Ziolkowski, B., (2015). Energy efficiency in the transport sector in the EU-27: a dynamic dematerialization analysis. Energy Econ. 51, 21–30.