DEA is a well-established methodology for assessing the efficiency of organizations with the same set of inputs and outputs. In recent years, conventional DEA methods have been extended to also measure eco-efficiency by including undesirable outputs. Such models are often called eco-DEA or environmental DEA models. As pointed out by Dyckhoff and Allen (2001), it is no longer appropriate to solely embrace the assumptions of the traditional DEA model: that is, maximizing the quantity of outputs, while minimizing the amount of inputs to achieve higher efficiency. Thus, new approaches have been proposed, incorporating undesirable outputs in the DEA framework.
For the purpose of this study, we collect data by conducting personal interviews with 16 dry port managers located in the North Central Region of India. Nine of the 16 dry ports are managed by private operators, whereas the remaining seven dry ports are under public administration. The container handling equipment deployed at each dry port, the number of employees and the terminal area are considered as inputs here, whereas the annual throughput and the total CO2 emissions are considered as outputs. All seven public dry ports enjoy a comparative advantage over their private counterparts, in that they possess their own rail heads located on their own premises; only three of the privately operated dry ports have this facility. The rest have to transport their containers by truck to the nearest rail head, resulting in additional generalized costs. Thus, the size of the dry port becomes an important input, as a dedicated rail head necessitates a larger area.
The next section provides the research background of this article, through a review of the major relevant literature. It is followed by the next section, which is a conceptual buildup and explanation of desirable and undesirable outputs, and the validation of the model for the quantification of the dry port production output. The subsequent section is an exposition of the DEA methodology employed in this article. The next section specifies input and output indices utilized in the model that are developed specifically for the achievement of the article's stated objective. This section also explains the nature and sources of the data collected for this study. The results of the data analysis are presented in the penultimate section. The results of the analytical exercise, along with conclusions and implications, are presented in the final section.
DEA is a well-established methodology for assessing the efficiency of organizations with the same set of inputs and outputs. In recent years, conventional DEA methods have been extended to also measure eco-efficiency by including undesirable outputs. Such models are often called eco-DEA or environmental DEA models. As pointed out by Dyckhoff and Allen (2001), it is no longer appropriate to solely embrace the assumptions of the traditional DEA model: that is, maximizing the quantity of outputs, while minimizing the amount of inputs to achieve higher efficiency. Thus, new approaches have been proposed, incorporating undesirable outputs in the DEA framework.
For the purpose of this study, we collect data by conducting personal interviews with 16 dry port managers located in the North Central Region of India. Nine of the 16 dry ports are managed by private operators, whereas the remaining seven dry ports are under public administration. The container handling equipment deployed at each dry port, the number of employees and the terminal area are considered as inputs here, whereas the annual throughput and the total CO2 emissions are considered as outputs. All seven public dry ports enjoy a comparative advantage over their private counterparts, in that they possess their own rail heads located on their own premises; only three of the privately operated dry ports have this facility. The rest have to transport their containers by truck to the nearest rail head, resulting in additional generalized costs. Thus, the size of the dry port becomes an important input, as a dedicated rail head necessitates a larger area.
The next section provides the research background of this article, through a review of the major relevant literature. It is followed by the next section, which is a conceptual buildup and explanation of desirable and undesirable outputs, and the validation of the model for the quantification of the dry port production output. The subsequent section is an exposition of the DEA methodology employed in this article. The next section specifies input and output indices utilized in the model that are developed specifically for the achievement of the article's stated objective. This section also explains the nature and sources of the data collected for this study. The results of the data analysis are presented in the penultimate section. The results of the analytical exercise, along with conclusions and implications, are presented in the final section.
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