Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Logistics and Maritime Studies | en_US |
| dc.contributor.advisor | Yang, Dong (LMS) | en_US |
| dc.creator | Li, Lu | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13536 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Advancing maritime governance and management through empirical methodologies : practical and policy insights | en_US |
| dcterms.abstract | The international maritime industry encompasses a diverse array of participants, such as shipping lines, port operators, shipbuilding companies, and other supportive enterprises and institutes. Given the global distribution of participants across countries, maritime governance’s spatial scope extends beyond national and regional boundaries. It is challenging for the traditional form of maritime governance, which is centered on a single nation or region, to effectively manage cross-border and cross-regional maritime activities. Furthermore, the exponential advancement of information technology has generated a significant amount of heterogeneous data across various domains. The proliferation of data sources, formats, and granularity has brought new difficulties for efficient maritime governance. Accurate grasp of the complicated and changing dynamics of maritime activity has been challenging using conventional manual monitoring techniques. Therefore, it is imperative to develop novel approaches for data collection and analysis and enhance the integration of multi-source data in order to bolster the efficacy of sustainable and innovative maritime regulations. | en_US |
| dcterms.abstract | In recent years, technological advancement has opened up new possibilities for the development of maritime governance methodologies. For example, the Automatic Information System (AIS) generates high-frequency ship trajectory data, which helps to characterize the spatial and temporal distribution of ship movements between ports and allows for the monitoring of ship behavior in the international shipping market. Visual surveillance networks in ports can generate a large amount of optical video and image data in real time, which can capture dynamic information about ships entering and leaving ports and berthing, providing rich training samples for automatic ship identification algorithms, and helping to improve the efficiency and accuracy of ship supervision in ports. The National Enterprise Credit Information Publicity System provides enterprise information across various industries, such as the enterprise’s address, registered capital, and industry category. Such enterprise data can depict the scale, structure, and spatial distribution characteristics of maritime-related industries. This thesis aims to leverage multi-source data to advance the efficiency of maritime governance in different maritime markets. | en_US |
| dcterms.abstract | The first study examines the price impact of shipping alliances in the international shipping market. It innovatively utilizes data from the Shanghai Shipping Exchange Institute, AIS, and Lloyd’s List Shipping Intelligence databases to measure the level of competition among alliances in major shipping markets. The Herfindahl-Hirschman Index (HHI) and Concentration Ratios (CR) are employed to quantify this competition. Then an instrumental approach was used to address the endogeneity issue between market concentration and freight rate. The primary conclusion is that an increase in the alliance-level competition resulted in a decrease in prices, and the results hold consistent when using different instrumental variables and their alternatives. This finding provides policymakers with confidence that competition in the global shipping sector is robust enough to counteract the market dominance caused by significant consolidations. | en_US |
| dcterms.abstract | The second study estimates the berth capacity for different types of ships in ports. Image data from an unmanned aerial vehicle (UAV) survey is collected to reflect the spatial information of all types of ships. A deep learning-based computer vision algorithm is used to automatically detect, identify, and classify ships in sample images. This study introduces the occupancy factor to empirically quantify the spatial proportional correlation between the ship size and the requisite berthing capacity. A validation test of the proposed method is conducted across all sheltered spaces in Hong Kong. The findings have the potential to contribute to the governance of ships and planning for sheltered space facilities in coastal waters. | en_US |
| dcterms.abstract | The third study utilizes data from firms to quantitatively assess the extent of industry-relatedness among different maritime clusters, drawing upon the principles of industrial agglomeration theory. A comparative analysis of maritime industry-relatedness is conducted among three typical maritime clusters: the Yangtze River Delta, the Guangdong-Hong Kong-Macao Greater Bay Area, and Western Europe. Findings will provide a reference for policymakers to manage local and regional maritime industries. | en_US |
| dcterms.abstract | Three studies will demonstrate the potential applications of various types of data in different maritime fields, thus helping to improve the efficiency and quality of maritime governance and management. | en_US |
| dcterms.extent | xiv, 134 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Shipping -- Management | en_US |
| dcterms.LCSH | Harbors -- Management | en_US |
| dcterms.LCSH | Shipping -- Data processing | en_US |
| dcterms.LCSH | Harbors -- Data processing | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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