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Investigation of Iranian Ports Performance in Oil Exchanges Using Data Envelopment Analysis

Article History: Received: 11 Oct. 2020 Accepted: 18 Nov. 2020 In this study, the performance and efficiency of 12 ports in the Persian Gulf, Oman and Caspian seas in Iran is evaluated using data envelopment analysis (DEA) method. DEA allows comparing the efficiency in revenue generation of ports with different economies. Two different modeling approaches were presented in this study; based on relating earnings to port operational capability and activity, and based on land indicators, infrastructure, and equipment. The results of this study showed that most of the ports in the Persian Gulf region had a relative efficiency in the field of petroleum products. On the other hand, a significant difference was observed between the nominal capacity of acceptance of petroleum products of ports and the volume of oil product exchanges.


Introduction
The near-and off-shore industrial facilities and their fleets threaten the marine ecosystem's health in the coastal areas. The pollutions caused by operations of these facilities gradually spread off to beaches and islands leading to fatal diseases and irreversible genetic disorders on humans. Iran's economy heavily relies on the oil industry, particularly through the naval fleets in the Persian Gulf and the Oman Sea in the South and the Caspian Sea in the North where major oil extraction facilities and export ports are located. For example, there are 34 oil fields with more than 800 wells (Fig. 1) and about 20,000 to 30,000 tankers per year (Fig. 2) in the Persian Gulf [18]. Therefore, there is a high risk of environmental hazards caused by these operations [8]. About 100 to 160 thousand tons of oil and petroleum products are shipped in the Persian Gulf annually, and the rate of oil contamination in the region is 47 times higher than the world average [17].
On the other hand, rich oil and gas resources in the Caspian Sea have attracted a flood of foreign investors since the mid-1990s, thereby strengthening the energy development platform in the region. These developments increased extraction of oil, gas and other industrial products in petrochemical plants and oil refineries significantly which lead to the spread of pollution in the Caspian Sea region [15]. Severe contamination of carbonic acid and petroleum products resulting from oil extraction and the construction of oil pipelines has caused the contamination of about 30,000 hectares of the region and environment hazards [3] (Fig. 3). According to the Department of Energy's Energy Information Agency (EIA), the Caspian Sea region has oil reserves of more than 17 to 33 billion barrels and oil production in the region was around 2.1 million barrels per day in 2005 and up to 3.8 million barrels per day in 2010 [2].  Oil spills and contaminations mainly occur in the main and secondary routes of vessels, tankers operation zones, as well as in ports or berthing regions (Fig. 4). Therefore, prevention and reduction of marine environment hazards are critical in the ports' operation. For addressing these issues, the Kuwait Regional

Data Envelopment Analysis Method
Data Envelopment Analysis (DEA) includes techniques and methods for assessing efficiency or measuring the productivity of decision-making units. The DEA is, in fact, the extension of Farrell's work in devising the first non-parametric method which measures productivity by considering a system of inputs and outputs. Using the inputs and outputs of the decision-making units and the principles governing them, Farrell [7] introduced a set called Production Possibility set and presented a part of its frontier as a production function. This frontier is also called the efficient frontier, and the decision-making units located on the frontier are considered efficient. But Farrell's method did not attract much attention until Charnes, Cooper, and Rhodes [5] established a method based on linear algebra. The DEA offers many opportunities for collaboration between analysts and decision-makers. These collaborations can be efficient in selecting inputs and outputs of the units under evaluation, and functioning and modeling relative to the frontier. Efficiency means that each unit performs well under the influence of inorganizational indicators, expressed as the ratio of output to input (Fig. 5). In the case of multiple inputs and outputs for the decision-making unit, the ratio of the total weighted output to the total weighted input for the efficiency of that unit is measured [16].
The used notations can be described as follows: x and y are the input and output vectors, respectively. output weight and input weight.

Output-Oriented CCR model
The name of this model is derived from the first letters of three scholars creating it, Charles, Copper and Rhodes. This model has a Constant Returns to Scale. Output-oriented models seek to increase or maximize outputs without increasing inputs or incremental resources. This model is shown in the following relation: The used notations can be described as follows: : Efficiency of the under investigated DMU in period p, m: Index of DMUs, n: Index of inputs, s: Index of outputs, p: Index of time periods, x: ith input of the jth DMU in period p, y: rth output of the jth DMU in period p, : Benchmark for the jth inefficient DMU in period p. Using this model of DEA to calculate efficiency and rank units, more than one unit may obtain the highest efficiency coefficient i.e. 1 and become efficient. In this case, it is not possible to compare and rank these units in relation to each other and the AP method can be used for ranking.

Results and Discussion
The Anderson-Peterson model or the super efficiency method was proposed by Anderson and Peterson [1] makes it possible to determine the most efficient unit to rank efficient units. In this method, the score of efficient units can be greater than 1 and thus the efficient units will be ranked just like inefficient units. The methodology is to remove the DMU from the production possibility set and apply the model to other DMUs. The larger the unit coefficient, the more efficient it will be. If the model is output-oriented, the goal is to drive an inefficient unit to the efficiency frontier by keeping the output constant and reducing input. The Min function type is used in this study.
The used notations can be described as follows: : DEA efficiency, , m: Index of DMUs, n: Index of inputs, s: Index of outputs, p: Index of time periods, x: ith input of the jth DMU in period p, y: rth output of the jth DMU in period p, : Benchmark for the jth inefficient DMU in period p.  Chang [4] examined the efficiency of South Korean ports in terms of the environment. Using the Slacks Based Measure (SBM) and analysis of carbon dioxide data, he investigated the effect of reducing CO2 emissions on port efficiency. Lee et al., [12], addressed the environmental efficiency of port cities affected by air pollution. By applying the SBM method and considering the Gross Regional Domestic Product (GRDP) as the model output, they investigated the role of air pollutants in the economic productivity of the port cities like Singapore, Busan, Rotterdam, Kaohsiung, Antwerp, and New York. Cheon et al., [6] analyzed the relationship between environmental and economic efficiency of 10 important ports of the United States using data envelopment analysis and found a close link between environmental efficiency and port economic productivity. Zahran et al., [19] analyzed the efficiency of port authorities in terms of monetization mechanism using two different modeling approaches, activity and infrastructure. Grigoroudis and Petridis [9] used DEA method to estimate countries' environmental efficiency using slack-based model under the consideration of constant returns to scale and variable returns to scale method. Mahmoudi and Emrouznejad [14] presented a literature review and classification of the applications of DEA in transportation systems by classifying 40 papers from 2007 to 2018. In this paper the origins of DEA in transportation problems have been reviewed and development of DEA applications have been presented.

Model Implementation
The Open Source DEA Project (© opensourcedea.org) is used to solve the data envelopment analysis model. As mentioned earlier, the Output-Oriented, Technical Efficiency and Constant Returns to Scale (RTS) method is used in this study (CCR_O). Technical Efficiency can be eliminated without changing the proportions between inputs and outputs. But Mix Efficiency can only be omitted by changing the proportion between inputs and outputs.
This DEA model attributes a virtual weight to each of input and output. Then the DMU performance is calculated using a linear optimization process which maximize each ratio by finding the best set of weight. The optimization process is imposed by existing data so that each dataset is compared against the best observed efficiency. The ports productivity assessment results for each year are shown in Table 8.   Table 8). Also, according to the super efficiency method, Shahid Rajaee and Imam Khomeini were identified as the most efficient ports, respectively. On the other hand, Abadan inefficient port has had the least efficiency in 2016. In addition, Khorramshahr's inefficient port has been improved over these 3 years. On the contrary, Lengeh's inefficient port had a declining trend over the study period and had become more inefficient (as shown by the red bars in Table 8).

Conclusion
In this paper, the port productivity as a unit has been analyzed by comparing the amount of output produced in comparison to the amount of input using Data Envelopment Analysis from two different points of view; based on relating earnings to port operational capability and activity and based on land indicators, infrastructures, and equipment. The proposed method to evaluate the performance can rank the ports with the same efficiency. The results of this study showed that most of the ports in the Persian Gulf region had a relative efficiency in the field of petroleum products. On the other hand, a significant difference could be seen between the nominal capacity of acceptance of petroleum products of ports and the volume of oil product exchanges over the study period which could be attributed to the lack of competitive environment due to oil sanctions imposed on Iran's petrochemical industry.
On the other hand, the efficiency of the ports reflected the proper use and adherence of the country's maritime organizations to guidelines and conventions under the supervision of the International Maritime Organization (IMO) and the conduct of practical programs and regional maneuvers, as well as planning for dealing with contingency and emergency cases. Subsequent studies can use more parameters as inputs and outputs to handle different combinations of evaluation factors, such as Proficiency of Operators and Personnel, Port Navigation Safety Information, Vessel Traffic Service (VTS), Hydrographic Surveys equipment and Nautical Charts, Port Accident Risk Assessment and so on.