Container Port Efficiency and Productivity in 2019 and 2020: A Directional Distance Function Incorporating Vessel Waiting Time

This study examines change in operational efficiency and productivity across a balanced panel of 15 container ports for the years 2019 and 2020. Berth length, gantry cranes, and other cranes are inputs; high container throughput is desirable, and extended anchorage waiting time is undesirable. Waiting time is calculated as the unweighted mean of weekly port estimates published by the Asian Development Bank. A pooled convex variable-returns-to-scale directional distance function with weak disposability provides a common reference technology, and the VRS global Malmquist–Luenberger (GML) index compares the two years. The mean transformed efficiency score was 0.855 across 30 port-year observations, declining from 0.878 in 2019 to 0.833 in 2020. The arithmetic mean GML score was 0.951: one port improved, ten declined, and four remained unchanged. A throughput-only model and leave-one-port-out re-estimation show that waiting time treatment and influential reference ports affect individual comparisons. In particular, the omission of Shanghai or Tanjung Pelepas substantially changed some rankings and scores.

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Publication Details

Journal
Journal of Marine Science and Engineering
Published
2026-10-09
DOI
https://doi.org/10.3390/jmse14201866
Primary Topic
Maritime Ports and Logistics
Type
article
Field-Weighted Citation Impact
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article

Container Port Efficiency and Productivity in 2019 and 2020: A Directional Distance Function Incorporating Vessel Waiting Time

Yong Shin Park, Yuan Xu, Seunghyun An, Ju Dong Park
Journal of Marine Science and Engineering
Maritime Ports and Logistics
article

Container Port Efficiency and Productivity in 2019 and 2020: A Directional Distance Function Incorporating Vessel Waiting Time

Yong Shin Park, Yuan Xu, Seunghyun An, Ju Dong Park
article en

Abstract

This study examines change in operational efficiency and productivity across a balanced panel of 15 container ports for the years 2019 and 2020. Berth length, gantry cranes, and other cranes are inputs; high container throughput is desirable, and extended anchorage waiting time is undesirable. Waiting time is calculated as the unweighted mean of weekly port estimates published by the Asian Development Bank. A pooled convex variable-returns-to-scale directional distance function with weak disposability provides a common reference technology, and the VRS global Malmquist–Luenberger (GML) index compares the two years. The mean transformed efficiency score was 0.855 across 30 port-year observations, declining from 0.878 in 2019 to 0.833 in 2020. The arithmetic mean GML score was 0.951: one port improved, ten declined, and four remained unchanged. A throughput-only model and leave-one-port-out re-estimation show that waiting time treatment and influential reference ports affect individual comparisons. In particular, the omission of Shanghai or Tanjung Pelepas substantially changed some rankings and scores.

Journal of Marine Science and EngineeringVol. 14(20)
Gyeongsang National University (KR), Korea Maritime Institute (KR), Dalian Maritime University (CN), St. Edward's University (US)
Openalex Percentile: Top 12%
Maritime Ports and Logistics
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