Eco-innovation in Transportation Industry a Double-Frontier Analysis Approach

Document Type : Original Article

Authors

1 Department of Applied Mathematics, Talesh Branch, Islamic Azad University, Talesh, Iran

2 Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran.

Abstract

Eco-innovation refers to the development of products and processes in the service of sustainable development, which uses scientific experiences, both directly and indirectly, to improve the environment. Eco-innovation is crucially important in the consumption of resources and pollution of the environment. Furthermore, it is one of the most fundamental factors in determining the success or failure of energy-consumption plans and protecting the environment. Thus, Eco-innovation is the cornerstone of sustainable development. The transportation industry is one of the main sources of environmental pollution. Analyzing efficiency of this industry helps people and societies to have a better understanding of its performance and develop better management strategies. The necessity of sustainable development in the transportation industry requires efficiency analysis over time. The Malmquist Productivity Index (MPI) is one of the common approaches to assess performance over the course of consecutive periods. This paper used a double-frontier analysis approach to propose a modern analysis to measure efficiency. In the proposed approach, the common set of weights for MPI is yielded by taking into account the undesirable outputs in the efficiency analysis. This paper demonstrated the application of the proposed models by investigating freight transport in Iran. The proposed models for the MPI analysis to decompose overall efficiency into efficiency change and technical change enable the decision-makers to meticulously trace the Eco-innovation and environmental efficiency in this industry.

Keywords

Main Subjects


-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.