Advertisement

Sustainability efficiency evaluation of seaports in China: an uncertain data envelopment analysis approach

  • Bao Jiang
  • Yu Li
  • Waichon Lio
  • Jian Li
Focus
  • 34 Downloads

Abstract

Sustainability is regarded as achieving economic, environmental, and social dimensions simultaneously that support an organization for long-term competitiveness. Port sustainability has attracted increasing attention because it is related to the issues of climate change and public health and safety. Therefore, it is urgent to measure the sustainability of ports. However, some variables (for instance, air pollutants and the neighboring relationship with surrounding communities) cannot be measured precisely by collecting quantitative data. This led us to select 23 seaports of China and use uncertain variables and uncertain data envelopment analysis model to measure their sustainability efficiency. Moreover, we captured the quantity to be improved of each output. The results show that 14 seaports such as Shanghai Port and Qingdao Port are deemed to be inefficient in terms of their sustainability. And our results can identify whether economic, environmental, or social dimensions contribute to the sustainability inefficiency of each seaport. On the basis of the results, we point out the managerial implications and put forward measures toward enhancing the efficiency of seaports with respect to these two dimensions.

Keywords

Seaport efficiency Environmental sustainability Social sustainability Sustainability efficiency Uncertain DEA model Sensitivity and stability analysis 

Notes

Acknowledgements

This study was funded by the Social Science Foundation of Shandong Province (Grant Nos. 17CCXJ19) and National Natural Science Foundation of China (Grant Nos. 61573210, 71722007).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Autry CW, Lirn T, Chen YJ, Wu YJ (2013) Green performance criteria for sustainable ports in Asia. Int J Phys Distrib Logist Manag 43:427–451CrossRefGoogle Scholar
  2. Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092CrossRefGoogle Scholar
  3. Beskovnik B, Bajec P (2015) Application of environmental and social sustainable measures by port of koper: the basis for the regional approach. Problemy Ekorozw 10:99–106Google Scholar
  4. Brundtland GH (1987) Report of the world commission on environment and development. Environ Policy Law 14:26–30Google Scholar
  5. Carter CR, Rogers DS (2008) A framework of sustainable supply chain management: moving toward new theory. Int J Phys Distrib Logist Manag 38:360–387CrossRefGoogle Scholar
  6. Chang YT (2013) Environmental efficiency of ports: a data envelopment analysis approach. Marit Policy Manag 40:467–478CrossRefGoogle Scholar
  7. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444MathSciNetCrossRefGoogle Scholar
  8. Chen X, Gao J (2013) Uncertain Term Structure Model of Interest Rate. Soft Comput 17:597–604CrossRefGoogle Scholar
  9. Chen H, Lam JS, Liu N (2018) Strategic investment in enhancing port–hinterland container transportation network resilience: a network game theory approach. Transp Res Part B Methodol 111:83–112CrossRefGoogle Scholar
  10. Cheng TC, Farahani RZ, Lai KH, Sarkis J (2015) Sustainability in maritime supply chains: challenges and opportunities for theory and practice. Transp Res Part E Logist Transp Rev 78:1–2CrossRefGoogle Scholar
  11. Chuang CC (2015) Social construction of port sustainability indicators: a case study of keelung port. Marit Policy Manag 42:26–42CrossRefGoogle Scholar
  12. Cook W, Seiford LM (2009) Data envelopment analysis (DEA)—thirty years on. Eur J Oper Res 192:1–17MathSciNetCrossRefGoogle Scholar
  13. Cooper WW, Park KS, Pastor JT (1999) Ram: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. J Product Anal 11:5–42CrossRefGoogle Scholar
  14. Cooper WW, Pastor JT, Borras F, Aparicio J, Pastor D (2011) Bam: a bounded adjusted measure of efficiency for use with bounded additive models. J Product Anal 35:85–94CrossRefGoogle Scholar
  15. Dooms M, Macharis C, Verbeke A (2004) Proactive stakeholder management in the port planning process: empirical evidence from the port of brussels. Eur Reg Sci Assoc 33:1–7Google Scholar
  16. Dorsey JW, Hardy LC (2018) Sustainability factors in dynamical systems modeling: simulating the non-linear aspects of multiple equilibria. Ecolog Model 368:69–77CrossRefGoogle Scholar
  17. Gao J, Yang X, Liu D (2017) Uncertain Shapley value of coalitional game with application to supply chain alliance. Appl Soft Comput 56:551–556CrossRefGoogle Scholar
  18. Ghareeb G (2009) The port of Los Angeles. Bill, SteamboatGoogle Scholar
  19. Glavi P, Lukman R (2007) Review of sustainability terms and their definitions. J Clean Prod 15:1875–1885CrossRefGoogle Scholar
  20. Hou D, Ding Z, Li G, Wu L, Hu P, Guo G (2017) A sustainability assessment framework for agricultural land remediation in China. Land Degrad Dev 4:1005–1018Google Scholar
  21. Kang D, Kim S (2017) Conceptual model development of sustainability practices: the case of port operations for collaboration and governance. Sustainability 9:2333CrossRefGoogle Scholar
  22. Lam JS, Lai KH (2015) Developing environmental sustainability by ANP-QFD approach: the case of shipping operations. J Clean Prod 105:275–284CrossRefGoogle Scholar
  23. Lam JS, Su S (2015) Disruption risks and mitigation strategies: an analysis of asian ports. Marit Policy Manag 42:415–435CrossRefGoogle Scholar
  24. Laxe FG, Bermdez FM, Palmero FM, Novo-Corti I (2016) Sustainability and the spanish port system: analysis of the relationship between economic and environmental indicators. Mar Pollut Bull 113:232CrossRefGoogle Scholar
  25. Lio W, Liu B (2018) Uncertain data envelopment analysis with imprecisely observed inputs and outputs. Fuzzy Optim Decis Mak 17:357–373MathSciNetCrossRefGoogle Scholar
  26. Liu B, Liu YK (2002) Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans Fuzzy Syst 10:445–450CrossRefGoogle Scholar
  27. Liu B (2007) Uncertainty theory, 2nd edn. Springer, BerlinzbMATHGoogle Scholar
  28. Liu B (2009a) Some research problems in uncertainty theory. J Uncertain Syst 3:3–10Google Scholar
  29. Liu B (2009b) Theory and practice of uncertain programming. Springer, BerlinCrossRefGoogle Scholar
  30. Liu B (2010) Uncertainty theory: a branch of mathematics for modeling human uncertainty. Springer, BerlinCrossRefGoogle Scholar
  31. Liu B (2012) Why is there a need for uncertainty theory. J Uncertain Syst 6:3–10Google Scholar
  32. Liu B, Chen XW (2015) Uncertain multiobjective programming and uncertain goal programming. J Uncertain Anal Appl 3:1–8CrossRefGoogle Scholar
  33. Liu YH, Ha MH (2010) Expected value of function of uncertain variables. J Uncertain Syst 4:181–186Google Scholar
  34. Liu B, Yao K (2015) Uncertain multilevel programming: algorithm and applications. Comput Ind Eng 89:235–240CrossRefGoogle Scholar
  35. Lu CS, Shang KC, Lin CC (2016) Examining sustainability performance at ports: port managers’ perspectives on developing sustainable supply chains. Marit Policy Manag 43:1–19CrossRefGoogle Scholar
  36. Mahdiloo M, Tavana M, Saen RF, Noorizadeh A (2014) A game theoretic approach to modeling undesirable outputs and efficiency decomposition in data envelopment analysis. Appl Math Comput 244:479–492MathSciNetzbMATHGoogle Scholar
  37. Nguyen HO, Nguyen HV, Chang YT, Chin AT, Tongzon J (2016) Measuring port efficiency using bootstrapped DEA: the case of vietnamese ports. Marit Policy Manag 43:644–659CrossRefGoogle Scholar
  38. Niavis S, Tsekeris T (2012) Ranking and causes of inefficiency of container seaports in south-eastern Europe. Eur Transp Res Rev 4:235–244CrossRefGoogle Scholar
  39. Quak HJ, Koster MD (2007) Exploring retailers’ sensitivity to local sustainability policies. J Oper Manag 25:1103–1122CrossRefGoogle Scholar
  40. Soltanzadeh E, Omrani H (2017) Dynamic network data envelopment analysis model with fuzzy inputs and outputs: an application for iranian airlines. Appl Soft Comput 63:268–288CrossRefGoogle Scholar
  41. Sun S, Wang Y, Liu J, Cai H, Wu P, Geng Q (2016) Sustainability assessment of regional water resources under the DPSIR framework. J Hydrol 532:140–148CrossRefGoogle Scholar
  42. Tone KA (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:498–509MathSciNetCrossRefGoogle Scholar
  43. Virginia LM, Spiegler Mohamed MN, Joakim W (2012) A control engineering approach to the assessment of supply chain resilience. Int J Prod Res 50:6162–6187CrossRefGoogle Scholar
  44. Wei YY, Lam JS (2013) 80 million-twenty-foot-equivalent-unit container port? Sustainability issues in port and coastal development. Ocean Coast Manag 71:13–25CrossRefGoogle Scholar
  45. Wen M (2014) Uncertain data envelopment analysis. Springer, BerlinzbMATHGoogle Scholar
  46. Wen M, Qin Z, Kang R (2015) Sensitivity and stability analysis of the additive model in uncertain data envelopment analysis (DEA). Soft Comput 19:1987–1996CrossRefGoogle Scholar
  47. Yang X, Gao J (2016) Linear quadratic uncertain differential game with application to resource extraction problem. IEEE Trans Fuzzy Syst 24:819–826CrossRefGoogle Scholar
  48. Yang X, Gao J (2017) Bayesian equilibria for uncertain bimatrix game with asymmetric information. J Intell Manuf 28:515–525CrossRefGoogle Scholar
  49. Yang X, Gao J, Ni Y (2018) Resolution principle in uncertain random environment. IEEE Trans Fuzzy Syst 26:1578–1588CrossRefGoogle Scholar
  50. Yao K (2017) Uncertain statistical inference models with imprecise observations. IEEE Trans Fuzzy Syst 26:409–415CrossRefGoogle Scholar
  51. Yao K, Zhou J (2018) Ruin time of uncertain insurance risk process. IEEE Trans Fuzzy Syst 26:19–28CrossRefGoogle Scholar
  52. Zhou P, Poh KL, Ang BW (2007) A non-radial DEA approach to measuring environmental performance. Eur J Oper Res 178:1–9CrossRefGoogle Scholar
  53. Zhou P, Poh KL, Ang BW (2008) A survey of data envelopment analysis in energy and environmental studies. Eur J Oper Res 189:1–18MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of EconomicsOcean University of ChinaQingdaoChina
  2. 2.Department of Mathematical SciencesTsinghua UniversityBeijingChina
  3. 3.Marine Development Studies InstituteOcean University of ChinaQingdaoChina

Personalised recommendations