Agricultural lands are an indispensable resource for ensuring food security in Ukraine. Ukraine is on the path of reforming land relations and forming a market for agricultural land. Since Poland is ...close to Ukraine in terms of climatic conditions and land use, its experience had been taken into account. The purpose of the article is to form the process of the agricultural land market in Ukraine based on the experience of Poland. To study the research problems, comparative methods of research and observation have been used, as well as methods of synthesis, analysis and abstract logical approaches to the search, justification and determination of periods of development of land relations in Ukraine and Poland. The article discusses the approaches of scientists to determining the essence of the “land market”, outlines the conditions for the development of the land market in European countries (Holland, Switzerland, Austria, Germany, Great Britain, Scotland, Spain, Poland, Bulgaria, Romania, Croatia, and Estonia). The periods of development of the land market in Poland and Ukraine have been highlighted. An analytical assessment of the market for the purchase and sale of agricultural land in the regions of Ukraine has been carried out (indicating the number of transactions completed and the total area of land plots). Comparative information on the cost of land in the European Union countries has been provided. The authors’ vision of the process of forming the agricultural land market in Ukraine is summarized, highlighting: I - Legal regulation; II - The step-by-step process of implementing the reform in accordance with the legislative framework, features of the implementation of land reform under martial law; III - Implementation of the land market using a transparent system through the Application (Unified portal of public services) Action; VI – State control over available agricultural lands and their legal structure; V – Conducting an expert assessment of land plots and/or determining their regulatory assessment; VI – State control over available agricultural lands and their legal structure; VII - Constant monitoring of the functional use of agricultural land.
•Develop highly accurate machine learning models and provide their explanations to facilitate decision making in maritime transport policy.•Use factors directly from or derived from current ship ...selection method in Asia Pacific region to enhance model explainability and applicability.•Provide consistent explanation and comprehensive analysis of the prediction for individual ships based on SHAP method.•Extent the local SHAP method to a near linear-form global surrogate model in a white-box manner to disclose the decision process of the black-box prediction model.
Port state control is the safeguard of maritime transport achieved by inspecting foreign visiting ships and supervising them to rectify the non-compliances detected. One key issue faced by port authorities is to identify ships of higher risk accurately. This study aims to address the ship selection issue by first developing two data-driven ship risk prediction frameworks using features the same as or derived from the current ship selection scheme. Both frameworks are empirically shown to be more efficient than the current ship selection method. Like existing ship risk prediction models, the proposed frameworks are of black-box nature whose working mechanism is opaque. To improve model explainability, local explanation of the prediction of individual ships by the Shapley additive explanations (SHAP) is provided. Furthermore, we innovatively extend the local SHAP model to a near linear-form global surrogate model which is fully-explainable. This demonstrates that the behavior of black-box data-driven models can be as interpretable as white-box models while retaining their prediction accuracy. Numerical experiments demonstrate that the white-box global surrogate models can accurately show the behavior of the original black-box models, shedding light on model validation, fairness verification, and prediction explanation. This study makes the very first attempt in the maritime transport area to quantitatively explain the rationale of black-box prediction models from both local and global perspectives, which facilitates the application of data-driven models and promotes the digital transformation of the traditional shipping industry.
As an administrative measure to ensure maritime safety, Port State Control inspections are implemented and regarded as an important line of defence in coping with potential maritime accidents. To ...reinforce its role, a New Inspection Regime (NIR) was developed and put into practice by Paris MoU in 2011. It is widely recognized that the implementation of NIR has transformed and modernized the PSC inspection system in the Paris MoU region, stimulated the vessel qualit, and further improved vessel quality. In this paper, the influence of the implementation of NIR on the PSC inspection system and vessel quality is revealed for the first time. Based on inspection data and records collected from the Paris MoU online database, a comparative analysis between the ‘Pre-NIR’ (time before the implementation of NIR) and ‘Post-NIR’ (time after the implementation of NIR) periods is conducted from two perspectives. A macroscopic approach is first adopted to characterise the overall changes in inspection results and inspected vessels' quality through a statistical analysis of extrinsic Key Performance Indicators (KPIs) such as detention rate and deficiency rate. Then, a microscopic analysis is conducted to assess the influence of the NIR on intrinsic attributes of the vessels and of the inspection results based on Bayesian Network models derived from Pre-NIR and Post-NIR periods. The findings of this research systematically reveal the aspects from which the NIR improves the PSC inspection system, vessel quality and maritime safety. It will generate significant impact on and contribution to the promotion and stimulation of the adoption of NIR in more ports and regions to improve safety at sea in the whole world.
•Conduct a comparative analysis of the impact of New Inspection Regime on port state control inspection.•Characterise the changes in PSC inspections and inspected vessels' quality through a statistical analysis of extrinsic KPIs.•Assess the influence of the NIR on intrinsic attributes of the vessels and the inspections based on Bayesian Network models.•Reveal the aspects from which the NIR improves the PSC inspection system, vessel quality and maritime safety.
As an accident prevention mechanism, Port State Control (PSC) is essential for the development of maritime transportation and security. To evaluate the effectiveness of PSC and provide ...recommendations for its enhancement, we develop Dynamic Bayesian Network (DBN) models using the latest five inspection records from the Tokyo and Indian Memorandum of Understandings (MoUs). These models incorporate a hidden variable called “ship condition” to represent the overall influence of four ship characteristic factors. Additionally, a “risk level” hidden variable is determined by two inspection results during each inspection: detention and the number of deficiencies. Two analyses are conducted in this paper. One aims to identify changes in risk levels, thereby investigating the effectiveness of dynamic inspections. The other involves comparing inspected and uninspected ships, with the goal of assessing the impact of PSC and ship condition on accident type, consequences, and severity. The results indicate that the inspection effects of the two MoUs demonstrate the same changing pattern, with the ship risk levels initially rising and then falling. However, the higher detention rate of the Indian MoU justifies its lower level of risk. Ships with inspection records are less likely to encounter capsizing accidents and have a lower probability of being taken out of service, while collision accidents and casualties are more likely to occur. The findings provide valuable insights and practical suggestions on how to enhance the effectiveness of PSC.
•It investigates the dynamic changes of ships' risk levels and their impacts on ship accidents.•Dynamic BNs are developed considering “ship condition” and “risk level” measuring by five consecutive PSC inspections.•It analyzes and discusses the effectiveness of dynamic inspections on ship risk and accident consequences.•It discloses the risk level pattern of rising first and then decreasing with the increase of inspection times.
•Data-driven Bayesian network classifier as a real-time predictor of the coming ships for PSC inspection.•Dynamic programming for optimal discretization the input data in the TAN classifier.•Prove ...random selection of root attribute variable in the TAN classifier will not influence the classification process.•Identify the sub-standard ships with more deficiencies more accurately compared with the currently used Ship Risk Profile ship selection scheme.
Maritime transportation plays a pivotal role in the economy and globalization, while it poses threats and risks to the maritime environment. In order to maintain maritime safety, one of the most important mitigation solutions is the Port State Control (PSC) inspection. In this paper, a data-driven Bayesian network classifier named Tree Augmented Naive Bayes (TAN) classifier is developed to identify high-risk foreign vessels coming to the PSC inspection authorities. By using data on 250 PSC inspection records from Hong Kong port in 2017, we construct the structure and quantitative parts of the TAN classifier. Then the proposed classifier is validated by another 50 PSC inspection records from the same port. The results show that, compared with the Ship Risk Profile selection scheme that is currently implemented in practice, the TAN classifier can discover 130% more deficiencies on average. The proposed classifier can help the PSC authorities to better identify substandard ships as well as to allocate inspection resources.
•Probabilistic framework to assess the risk of ships.•Hybrid approach and multiple data sources.•Assessment of ship dynamic risk and static risk.
The paper proposes a probabilistic framework for ...assessing the risk of ships based on a hybrid approach and multiple data sources. A Bayes-based network learning approach uses data from the New Inspection Regime of the Paris MoU on Port State Control to characterise the relationships among risk parameters and uses these parameters to evaluate the ship static risk. Other data sources are used to develop a Bayesian Network model to assess the dynamic risk of the ship. The data is aggregated by Bayesian Network and Evidential Reasoning approaches to evaluate the overall risk of ships in coastal waters. The objective of the study is to develop a model to assess the risk of an individual ship by considering its static risk profile and the geographical-dependant risk factors related to the characteristics of the maritime traffic flow and other local characteristics that influence the navigational risk of the ship. The results show that the integrated approach is able to assess the overall risk of a ship based on multiple data sources, providing empirical evidence of using multiple data sources in risk analysis applications. Moreover, the developed model identifies the most critical circumstances and the key impact factors in the study waters, which can support decisions on risk prevention and mitigation measures and local maritime traffic management.
Port State Control (PSC) is the inspection mechanism that ensures commercial vessels comply with internationally binding rules and regulations. During the PSC inspections, non-compliances with ...international maritime conventions may result in deficiencies or even the detention of the vessel. On the other side, deficiencies identified during PSC inspections are significant for the safe, secure, clean seas and sustainable marine environment. In this line, International Maritime Organization (IMO) aims to reduce ship-related pollution within the scope of the International Convention for the Prevention of Pollution from Ships (MARPOL). Specifically, Annex-VI is the most recently ratified regulation of MARPOL covering air pollution. Although Annex-VI deficiencies pose significant risks to maritime stakeholders and the natural environment, this topic has been broadly neglected by researchers. For this reason, a detailed risk analysis for the MARPOL Annex-VI deficiencies was carried out to demonstrate a prioritization concerning the marine environment and maritime stakeholders in this study. An improved Failure Mode and Effects Analysis (FMEA) approach is employed in the computation of risk priority numbers (RPN) by taking O, S, and D inputs from two different expert groups and the Paris MoU database. According to the results, the riskiest MARPOL Annex-VI failure modes are identified as 14615-Fuel change-over procedure (RPN: 235.71), 14608-Incinerator operations and operating manual (RPN: 232.65), and 14601-Technical files and if applicable, monitoring manual (RPN: 219.86). Consequently, the presented risk analysis and findings are intended to be a reference for maritime stakeholders to prevent harmful scenarios involving ship-related air pollution.
The article examines the peculiarities of the functioning of state control (supervisory) bodies in the Russian Federation at the present time. The subtleties of the mechanism for evaluating the ...effectiveness of the functioning of the system of control (supervisory) bodies are analyzed using the example of evaluating the work of these bodies in the field of social services in St. Petersburg. The current problems of the functioning of the system of state control (supervisory) bodies in the Russian Federation are reflected. Based on the identified problems, the authors propose a set of measures based on the principles of legality and transparency, a balance of interests aimed at achieving the most useful result for all participants in control (supervisory) activities. The implementation of the proposed measures should follow the path of using modern approaches to managing the system of control (supervisory) bodies based on the results of scientific and technological progress.
The EU Regulation 2015/757 established a monitoring, reporting and verification (EU MRV) mechanism for CO2 emissions from shipping. Certainly, given the international nature of shipping, the optimum ...solution would be to introduce appropriate mechanisms within the framework of the International Maritime Organisation (IMO). However, the IMO Data Collection System for ship fuel oil consumption data (IMO DCS), established in 2016 and similar to the EU MRV mechanism, has not been perceived as sufficient given the EU's ambitions to achieve swift decarbonisation of maritime transport. The fragmentation of greenhouse gas emission regulation has also consequences for the Port State Control inspections. The article explores the EU MRV and IMO DCS co-existence in terms of financial penalties and other types of sanctions in case of non-compliance with the EU MRV or IMO DCS obligations.
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