Performance Evaluation and Investment Analysis for Container Port Sustainable Development in China: An Inverse DEA Approach

Container ports play an important role in international maritime trade. However, the rapid growth of the port and terminal industry has caused many environmental pollution problems. This paper intends to develop an inverse data envelopment analysis (IDEA) model for measuring container ports’ efficiency and analyzing their resource consumption by considering undesirable outputs. Statistical data from 16 main ports are empirically examined using the proposed method in accordance with the 13th Five-Year Plan in China. The results indicate that the proposed IDEA is a feasible approach for performance evaluation, and provides policymakers with insights into resource optimization of container ports. A comparative study with another DEA model is also discussed.

[1]  Cecilio Mar-Molinero,et al.  Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling , 2017 .

[2]  Jie Wu,et al.  Environmental efficiency evaluation based on data envelopment analysis: A review , 2012 .

[3]  Jie Wu,et al.  Performance evaluation of Chinese port enterprises under significant environmental concerns: An extended DEA-based analysis , 2017 .

[4]  Shabnam Razavyan,et al.  Input estimation and identification of extra inputs in inverse DEA models , 2004, Appl. Math. Comput..

[5]  Shu-Cherng Fang,et al.  Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale , 2011, Comput. Ind. Eng..

[6]  Gi-Tae Yeo,et al.  Environmental efficiency analysis of port cities: Slacks-based measure data envelopment analysis approach , 2014 .

[7]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[8]  Girish Gujar,et al.  On balancing supply chain efficiency and environmental impacts: An eco-DEA model applied to the dry port sector of India , 2012 .

[9]  Wen-Min Lu,et al.  Benchmarking the operating efficiency of Asia container ports , 2010, Eur. J. Oper. Res..

[10]  Mojtaba Ghiyasi,et al.  On inverse DEA model: The case of variable returns to scale , 2015, Comput. Ind. Eng..

[11]  Anyu Yu,et al.  China’s regional energy and carbon dioxide emissions efficiency evaluation with the presence of recovery energy: an interval slacks-based measure approach , 2017, Ann. Oper. Res..

[12]  J. Linton,et al.  Analysis, ranking and selection of R&D projects in a portfolio , 2002 .

[13]  Chuen Tse Kuah,et al.  Efficiency assessment of universities through data envelopment analysis , 2011, WCIT.

[14]  William W. Cooper,et al.  Introduction to Data Envelopment Analysis and Its Uses: With Dea-Solver Software and References , 2005 .

[15]  Dali Zhang,et al.  Environmental efficiency analysis of Chinese container ports with CO2 emissions: An inseparable input-output SBM model , 2017 .

[16]  W. B. Liu,et al.  DEA models with undesirable inputs and outputs , 2010, Ann. Oper. Res..

[17]  Jianzhong Zhang,et al.  An inverse DEA model for inputs/outputs estimate , 2000, Eur. J. Oper. Res..

[18]  F. Lai,et al.  An investment analysis for China's sustainable development based on inverse data envelopment analysis , 2017 .

[19]  Yuvraj Gajpal,et al.  Evaluating the Environmental Performance and Operational Efficiency of Container Ports: An Application to the Maritime Silk Road , 2019, International journal of environmental research and public health.

[20]  Khalid Bichou,et al.  A two-stage supply chain DEA model for measuring container-terminal efficiency , 2011 .

[21]  Yi-Ming Wei,et al.  China’s regional industrial energy efficiency and carbon emissions abatement costs , 2014 .

[22]  Hui Li,et al.  Impact of environment regulation on the efficiency of regional construction industry: A 3-stage Data Envelopment Analysis (DEA) , 2018, Journal of Cleaner Production.

[23]  GhiyasiMojtaba.  On inverse DEA model , 2015 .

[24]  Kaoru Tone,et al.  A slacks-based measure of efficiency in data envelopment analysis , 1997, Eur. J. Oper. Res..

[25]  Lawrence M. Seiford,et al.  Modeling undesirable factors in efficiency evaluation , 2002, Eur. J. Oper. Res..

[26]  Reza Kazemi Matin,et al.  Multi-period efficiency measurement of network production systems , 2019 .

[27]  Shabnam Razavyan,et al.  Data envelopment analysis for decision making unit with nonhomogeneous internal structures: An application to the banking industry , 2019, J. Oper. Res. Soc..

[28]  C. Ting,et al.  Assessing regional environmental efficiency in China with distinguishing weak and strong disposability of undesirable outputs , 2019, Journal of Cleaner Production.

[29]  Emmanuel Thanassoulis,et al.  Applied data envelopment analysis , 1991 .

[30]  Gholam R. Amin,et al.  Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas , 2019, Expert Syst. Appl..

[31]  Hidekazu Itoh.  Effeciency Changes at Major Container Ports in Japan: A Window Application of Data Envelopment Analysis , 2002 .

[32]  Qian Liu,et al.  A stochastic DEA model considering undesirable outputs with weak disposability , 2013, Math. Comput. Model..

[33]  Maja Škurić,et al.  Ship emissions and their externalities in cruise ports , 2015, Transportation Research Part D: Transport and Environment.

[34]  Jose L. Tongzon,et al.  Efficiency measurement of selected Australian and other international ports using data envelopment analysis , 2001 .

[35]  Ali Emrouznejad,et al.  A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries , 2018, J. Oper. Res. Soc..

[36]  Peng Zhou,et al.  A survey of data envelopment analysis in energy and environmental studies , 2008, Eur. J. Oper. Res..

[37]  Jingzheng Ren,et al.  Competitiveness prioritisation of container ports in Asia under the background of China’s Belt and Road initiative , 2018 .

[38]  Y Roll,et al.  Port performance comparison applying data envelopment analysis (DEA) , 1993 .

[39]  Jie Wu,et al.  Performances and benchmarks of container ports using data envelopment analysis , 2009 .

[40]  Joseph C. Paradi,et al.  Evaluation of bank branch growth potential using data envelopment analysis , 2015 .