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Sustainable Development of Seaports
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Sustainable Development of Seaports

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 15761

Special Issue Editors

Prof. Dr. Robert Nicholls

grade E-Mail Website
Guest Editor
Tyndall Centre for Climate Change Research, University of East Anglia, Norwich NR4 7TJ, UK
Interests: sustainability; coastal engineering
Dr. Yu Gong

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Guest Editor
University of Southampton, Southampton, UK
Interests: sustainable supply chain management; supply chain learning
Prof. Changping Zhao

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Guest Editor
Dalian Maritime University, Dalian, China
Interests: sustainable seaports; complex network
Prof. Houming Fan

E-Mail Website
Guest Editor
Dalian Maritime University, Dalian, China
Interests: green seaports; reverse logistics

Special Issue Information

Dear Colleagues,

Ports are important nodes in the global supply chain and transportation network. They stand between sea and land transportation and involve large workforces and significant operational activities. Ports contribute to a particular country or region from the economic perspective; however, they can also cause severe environmental and social issues. For instance, ports generate air pollution, particularly greenhouse gas emissions, which contributes to global warming; these emissions can also have a major impact on the health of the local communities. Thus, ports are facing mounting pressure from the public in terms of their sustainability performance. Although ports in Asia are competitive and efficient in their operations, developed countries are more advanced in supporting the sustainable development of seaports. There is an urgent need to explore the sustainability of seaports worldwide from environmental, social, and economic perspectives.

We are delighted to launch this Special Issue on the sustainable development of seaports. The topics include but are not limited to the following:

1) Current status and future perspectives on sustainable seaports;
2) The policy on sustainable seaports;
3) Performance indicators of sustainable seaports;
4) Best practices of sustainable seaports;
5) Sustainable supply chain management for seaports;
6) Green seaports;
7) Free-trade ports and sustainable seaports;
8) Sustainable seaports and port cities;
9) Port industry development and sustainable seaports;
10) Alliances and mergers on sustainable seaports;
11) Technology application/management of sustainable seaports;
12) Sustainable seaports and climate change.

Prof. Robert Nicholls
Dr. Yu Gong
Prof. Changping Zhao
Prof. Houming Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable seaports
  • green seaports

Published Papers (4 papers)

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Research

18 pages, 5409 KiB  
Article
Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia
by T. Olivia Muslim, Ali Najah Ahmed, M. A. Malek, Haitham Abdulmohsin Afan, Rusul Khaleel Ibrahim, Amr El-Shafie, Michelle Sapitang, Mohsen Sherif, Ahmed Sefelnasr and Ahmed El-Shafie
Sustainability 2020, 12(3), 1193; https://doi.org/10.3390/su12031193 - 7 Feb 2020
Cited by 18 | Viewed by 3199
Abstract
This study aims to investigate the impact of meteorological parameters such as wind direction, wind speed, rainfall, and mean cloud cover on sea-level rise projections for different time horizons—2019, 2023, 2028, 2048, and 2068—at three stations located in Kudat, Sandakan, and Kota Kinabalu, [...] Read more.
This study aims to investigate the impact of meteorological parameters such as wind direction, wind speed, rainfall, and mean cloud cover on sea-level rise projections for different time horizons—2019, 2023, 2028, 2048, and 2068—at three stations located in Kudat, Sandakan, and Kota Kinabalu, which are districts in the state of Sabah, Malaysia. Herein, two different scenarios, scenario1 (SC1) and scenario2 (SC2), were investigated, with each scenario comprising a different combination of input parameters. This study proposes two artificial intelligence techniques: a multilayer perceptron neural network (MLP-ANN) and an adaptive neuro-fuzzy inference system (ANFIS). Furthermore, three evaluation indexes were adopted to assess the performance of the proposed models. These indexes are the correlation coefficient, root mean square error, and scatter index. The trial and error method were used to tune the hyperparameters: the number of neurons in the hidden layer, training algorithms, transfer and activation functions, and number and shape of the membership function for the proposed models. Results show that for the above mentioned three stations, the ANFIS model outperformed MLP-ANN by 0.740%, 6.23%, and 9.39%, respectively. To assess the uncertainties of the best model, ANFIS, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPUs) and the band width of 95 percent confidence intervals (d-factors) are selected. The obtained values bracketed by 95PPUs are show about 75.2%, 77.4%, 76.8% and the d-factor has a value of 0.27, 0.21 and 0.23 at Kudat, Sandakan and Kota Kinabalu stations, respectively. A comparison between the two scenarios shows that SC1 achieved a high level of accuracy on Kudat and Sandakan data, whereas SC2 outperformed SC1 on Kota Kinabalu data. Full article
(This article belongs to the Special Issue Sustainable Development of Seaports)
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26 pages, 3980 KiB  
Article
Truck Arrivals Scheduling with Vessel Dependent Time Windows to Reduce Carbon Emissions
by Mengzhi Ma, Houming Fan, Xiaodan Jiang and Zhenfeng Guo
Sustainability 2019, 11(22), 6410; https://doi.org/10.3390/su11226410 - 14 Nov 2019
Cited by 14 | Viewed by 3285
Abstract
Irregular external truck arrivals at a marine container terminal often leads to long queues at gates and substantial greenhouse gas emissions. To relieve gate congestion and reduce carbon emissions, a new truck arrival pattern called “vessel dependent time windows (VDTWs)” is proposed. A [...] Read more.
Irregular external truck arrivals at a marine container terminal often leads to long queues at gates and substantial greenhouse gas emissions. To relieve gate congestion and reduce carbon emissions, a new truck arrival pattern called “vessel dependent time windows (VDTWs)” is proposed. A two-phase queuing model is established to describe the queuing process of trucks at gate and yard. An optimization model is established to assign time window and appointment quota for each vessel in a marine container terminal running a terminal appointment system (TAS) with VDTWs. The objective is to minimize the total carbon dioxide emissions of trucks and rubber-tired gantry cranes (RTGCs) during idling. The storage capacity constraints of each block and maximum queue length are also taken into consideration. A hybrid genetic algorithm based on simulated annealing is developed to solve the problem. Results based on numerical experiments demonstrate that this model can substantially reduce the waiting time of trucks at gate and yard and carbon dioxide emissions of trucks and RTGCs during idling. Full article
(This article belongs to the Special Issue Sustainable Development of Seaports)
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20 pages, 3096 KiB  
Article
The Analysis of a Simulation of a Port–City Green Cooperative Development, Based on System Dynamics: A Case Study of Shanghai Port, China
by Yan Li, Xiaohan Zhang, Kaiyue Lin and Qingbo Huang
Sustainability 2019, 11(21), 5948; https://doi.org/10.3390/su11215948 - 25 Oct 2019
Cited by 15 | Viewed by 4847
Abstract
Ports are important network nodes for cargo transportation between cities and even countries, and they play vital roles in stimulating urban economies. However, at the same time, port production activities also consume various resources, such as water, electricity, coal, and land. In addition, [...] Read more.
Ports are important network nodes for cargo transportation between cities and even countries, and they play vital roles in stimulating urban economies. However, at the same time, port production activities also consume various resources, such as water, electricity, coal, and land. In addition, ports inevitably produce waste—solid, water, gas, and other pollutants—which damages the environment of their hinterland cities, hindering the growth of urban GDP. Therefore, this study sought to build a reasonable system dynamics model to measure the positive and negative effects of ports on cities, and to put forward countermeasures and suggestions for promoting port–city green cooperative development. We selected Shanghai Port as a case study, estimated its parameters with 2010–2017 data, and tested the historical fitness of the model. We then carried out a scheme simulation by changing relevant parameters and comparing coordinated port–city development under different schemes. The simulation results show that increases in sea transportation activity and economic pull coefficients help to propel the growth of port–city GDP to a certain extent, but also cause environmental pollution and resource wastage. Therefore, effective energy-saving and emission-reduction measures are needed to achieve the coordinated development of economic growth and green environmental protection in ports and cities. Full article
(This article belongs to the Special Issue Sustainable Development of Seaports)
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26 pages, 6383 KiB  
Article
Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms
by Vivien Lai, Ali Najah Ahmed, M.A. Malek, Haitham Abdulmohsin Afan, Rusul Khaleel Ibrahim, Ahmed El-Shafie and Amr El-Shafie
Sustainability 2019, 11(17), 4643; https://doi.org/10.3390/su11174643 - 26 Aug 2019
Cited by 24 | Viewed by 3759
Abstract
The estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and [...] Read more.
The estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and genetic programming (GP) models for predicting the monthly mean sea level variations and comparing their prediction accuracies in terms of the model performances. The input dataset was obtained from Kerteh, Tioman Island, and Tanjung Sedili in Malaysia from January 2007 to December 2017 to predict the sea levels for five different time periods (1, 5, 10, 20, and 40 years). Further, the SVM and GP models are subjected to preprocessing to obtain optimal performance. The tuning parameters are generalized for the optimal input designs (SVM2 and GP2), and the results denote that SVM2 outperforms GP with R of 0.81 and 0.86 during the training and testing periods, respectively, at the study locations. However, GP can provide values of 0.71 and 0.79 for training and testing, respectively, at the study locations. The results show precise predictions of the monthly mean sea level, denoting the promising potential of the used models for performing sea level data analysis. Full article
(This article belongs to the Special Issue Sustainable Development of Seaports)
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