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

  • Bao Jiang
  • Yu Li
  • Waichon Lio
  • Jian LiEmail author


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.


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



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.


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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

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