Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports

Park, Yong Shin; Ghani, N. Muhammad Aslaam Mohamed Abdul; Gebremikael, Fesseha; Eğilmez, Gökhan

doi:10.1080/13675567.2018.1504903

Cited by 20 publications

(6 citation statements)

References 44 publications

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“…In the same way, the authors of reference [28] benchmarked the top 10 ports in the US and considered water pollution and emissions within port areas. Furthermore, the authors of reference [29] benchmarked operating practices of 20 ports in the US for enhancing environmental efciency in terms of greenhouse gas emissions, oil spill prevention, and energy efciency of port operations. Similarly, the authors of reference [30] benchmarked 24 container ports in Europe regarding emissions and energy-saving problems.…”

Section: Overall Port Areasmentioning

confidence: 99%

Machine Learning for Promoting Environmental Sustainability in Ports

Mansoursamaei

Moradi

González-Ramírez

et al. 2023

Journal of Advanced Transportation

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Maritime transportation is one of the essential drivers of the global economy as it enables both lower transportation costs and intermodal operations across multiple forms of transportation. Maritime ports are essential interfaces that support cargo handling between sea and hinterland transportation. Besides, in this area, environmental protection is becoming extremely important. Global warming, air pollution, and greenhouse gas emissions are all having a detrimental influence on the environment and will most likely continue to do so for future generations. Hence, there is a growing need to promote environmental sustainability in maritime-based transportation. The application of machine learning (ML), as one of the main subdomains of artificial intelligence (AI), can be considered a component within the process of digital transformation to advance green activities in maritime port logistics. Thus, this article presents the results of a systematic literature review of the recent literature on machine learning for promoting environmentally sustainable maritime ports. It collects and analyses the articles whose contributions lie in the interplay between three main dimensions, i.e., machine learning, port-related operations, and environmental sustainability. Throughout a review protocol, this research is constituted on the major focuses of impact, problems, and techniques to discern the current state of the art as well as research directions. The research findings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. Moreover, in terms of environmental sustainability, emissions and energy consumption are the most studied problems. mAccording to the research gaps observed in the review, two broad directions for future research are identified: (i) altering attention on a greater diversity of machine learning approaches for promoting environmental sustainability in ports and (ii) leveraging new outlooks to perform more green practical works on port-related operations.

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Section: Overall Port Areasmentioning

confidence: 99%

Machine Learning for Promoting Environmental Sustainability in Ports

Mansoursamaei

Moradi

González-Ramírez

et al. 2023

Journal of Advanced Transportation

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“…en, targeted improvement measures were taken to improve the operating efficiency of the air conditioning system, achieve effective energy saving in building air conditioning, and promote the pace of building energy construction. e building energy consumption benchmark can be determined by comparing it with other buildings of the same type or with the energy consumption of its own history [18].…”

Section: Related Workmentioning

confidence: 99%

Application of Data Mining in Traditional Benchmark Evaluation Model for Buildings Energy Consumption

Guo

2021

Scientific Programming

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Since the beginning of data mining technologies, buildings have become not just energy-intensive but also information-centric. Data mining technologies have been widely used to utilize the huge quantities of buildings’ operational data to improve their energy systems. Conventional benchmarking of buildings’ energy performance reflects a variety of parameters, such as the number of inhabitants, the environment, the energy efficiency of equipment utilized, and the adjustment of internal temperature. These various elements are then assigned weights to generate a single general indicator. This study presents a reasonable benchmark assessment methodology of conventional buildings’ energy usage based on a data-mining algorithm for acquiring more specific information, like the energy management efﬁcacy of a building, and aiming at the problem of ineffective use of large amounts of energy consumption in public buildings. A mathematical-statistical approach and a data-mining tool are used to analyse the data. The degree of connection between numerous influencing variables (i.e., characteristic parameters) and building’s energy usage is determined using grey correlation analysis. In this work, we have used an enhanced Apriori algorithm to identify the link between the different forms of systems in the same area. In short, the fundamental idea and process of the Apriori algorithm are presented, and preliminary designs of the preprocessing of experimental data as well as the analysis methods are studied to analyse the outcome of the proposed work.

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“…Attempts to set up a benchmarking on greening ports have been made in existing literature (Arof et al, 2021;Elzarka & Elgazzar, 2014;Molavi et al, 2020a;Park et. al, 2019;Teerawattana & Yang, 2019), but did not take into consideration SMSPs as specific research target group.…”

Section: Introductionmentioning

confidence: 99%

A Digital or Sustainable Small and Medium-Sized Port? Sustainable Port Blueprint in the Baltic Sea Region Based on Port Benchmarking

Meyer

Gerlitz

Philipp

et al. 2021

Transport and Telecommunication Journal

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Small and medium-sized ports (SMSPs) in the Baltic Sea Region (BSR) are caught in a dilemma of less financial support by the European funding programmes due to their peripheral position in the TEN-T Core Network. Most of the BSR ports belong to the TEN-T Comprehensive Network, which is rationally less important in the topical financial, infrastructural and policy discourses. Despite this, BSR ports are of a colossal importance for the regional economic development serving as gateways for the individual regions and drivers of socio-economic and environmental transition. In addition, ports are essential in pursuing new innovation avenues. The environmental targets published by the European Commission for maritime sector in 2030 and 2050 are applicable for SMSPs as well, creating further obstacles and future challenges on the one hand, but opening new horizons to grow and innovate on the other one. A successful environmental and digital transformation going hand in hand in SMSPs enable sustainable boost of sustainable development. Thus, as a first step, the assessment of sustainable readiness in SMSPs is incumbent in order to implement tailor-made solutions on individual basis, by ensuring efficient usage of available resources and capabilities. In line with the Connect2SmallPorts project, part-financed by the INTERREG South Baltic Programme, 38 SMSPs in the BSR were approached to mapping their digital readiness according to the methodology of the Digital Readiness Index for Ports (DRIP), published within the project in 2020. Building upon this, this paper introduces the idea of digital and environmental twinning to conclude on sustainable development potentials in SMSPs with an adaptation of the DRIP score. Hence, the research contributes to the sustainable port concept and illustrates the positioning of SMSPs in the progress of sustainable development.

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Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports

Cited by 20 publications

References 44 publications

Machine Learning for Promoting Environmental Sustainability in Ports

Machine Learning for Promoting Environmental Sustainability in Ports

Application of Data Mining in Traditional Benchmark Evaluation Model for Buildings Energy Consumption

A Digital or Sustainable Small and Medium-Sized Port? Sustainable Port Blueprint in the Baltic Sea Region Based on Port Benchmarking

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