A multi-criteria decision analysis model for ship biofouling management in the Baltic Sea.

Biofouling of ship hulls form a vector for the introduction of non-indigenous organisms worldwide. Through increasing friction, the organisms attached to ships' hulls increase the fuel consumption, leading to both higher fuel costs and air emissions. At the same time, ship biofouling management causes both ecological risks and monetary costs. All these aspects should be considered case-specifically in the search of sustainable management strategies. Applying Bayesian networks, we developed a multi-criteria decision analysis model to compare biofouling management strategies in the Baltic Sea, given the characteristics of a ship, its operating profile and operational environment, considering the comprehensive environmental impact and the monetary costs. The model is demonstrated for three scenarios (SC1-3) and sub-scenarios (A-C), comparing the alternative biofouling management strategies in relation to NIS (non-indigenous species) introduction risk, eco-toxicological risk due to biocidal coating, carbon dioxide emissions and costs related to fuel consumption, in-water cleaning and hull coating. The scenarios demonstrate that by the careful consideration of the hull fouling management strategy, both money and environment can be saved. We suggest biocidal-free coating with a regular in-water cleaning using a capture system is generally the lowest-risk option. The best biocidal-free coating type and the optimal in-water cleaning interval should be evaluated case-specifically, though. In some cases, however, biocidal coating remains a justifiable option.

Sustainability manifesting as a multi-material and -sited network effect: How boat-sourced sewage management facilities serve as governance artefacts advancing sustainability in nautical tourism.

Marinas are a part of coastal areas' touristic appeal, but also hotspots for boat-sourced pollution. Considering the manifestation of sustainability in marina operation, we utilize actor-network theory (ANT) in demonstrating a conceptual systems analysis on boat-sourced sewage management (BSSM) as one important socio-eco-technical sub-system of sustainable nautical tourism. We describe a multi-material collective of dynamically interacting human and non-human entities to understand how and under what conditions BSSM facilities advance the sustainability of marina operation. Our analysis insightfully uncovers BSSM facilities as both core marina services and governance artefacts and reveals that managing boat-sourced sewage successfully is an outcome of a multi-sited network of heterogeneous elements that together enable both sustainable boating practices and marina operation. We suggest the presented ANT-based systemic thinking has potential for providing novel perspectives to sustainability analyses in diverse tourism-related contexts.

Developing a conceptual influence diagram for socio-eco-technical systems analysis of biofouling management in shipping - A Baltic Sea case study.

Ship hulls create a vector for the transportation of harmful non-indigenous species (NIS) all over the world. To sustainably prevent NIS introductions, the joint consideration of environmental, economic and social aspects in the search of optimal biofouling management strategies is needed. This article presents a multi-perspective soft systems analysis of the biofouling management problem, based on an extensive literature review and expert knowledge collected in the Baltic Sea area during 2018-2020. The resulting conceptual influence diagram (CID) reveals the multidimensionality of the problem by visualizing the causal relations between the key elements and demonstrating the entanglement of social, ecological and technical aspects. Seen as a boundary object, we suggest the CID can support open dialogue and better risk communication among stakeholders by providing an illustrative and directly applicable starting point for the discussions. It also provides a basis for quantitative management optimization in the future.

A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: A Bayesian network model for the Gulf of Finland.

Large-scale oil accidents can inflict substantial costs to the society, as they typically result in expensive oil combating and waste treatment operations and have negative impacts on recreational and environmental values. Cost-benefit analysis (CBA) offers a way to assess the economic efficiency of management measures capable of mitigating the adverse effects. However, the irregular occurrence of spills combined with uncertainties related to the possible effects makes the analysis a challenging task. We develop a probabilistic modeling approach for a CBA of oil spill management and apply it in the Gulf of Finland, the Baltic Sea. The model has a causal structure, and it covers a large number of factors relevant to the realistic description of oil spills, as well as the costs of oil combating operations at open sea, shoreline clean-up, and waste treatment activities. Further, to describe the effects on environmental benefits, we use data from a contingent valuation survey. The results encourage seeking for cost-effective preventive measures, and emphasize the importance of the inclusion of the costs related to waste treatment and environmental values in the analysis. Although the model is developed for a specific area, the methodology is applicable also to other areas facing the risk of oil spills as well as to other fields that need to cope with the challenging combination of low probabilities, high losses and major uncertainties.

A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland.

The growth of maritime oil transportation in the Gulf of Finland (GoF), North-Eastern Baltic Sea, increases environmental risks by increasing the probability of oil accidents. By integrating the work of a multidisciplinary research team and information from several sources, we have developed a probabilistic risk assessment application that considers the likely future development of maritime traffic and oil transportation in the area and the resulting risk of environmental pollution. This metamodel is used to compare the effects of two preventative management actions on the tanker collision probabilities and the consequent risk. The resulting risk is evaluated from four different perspectives. Bayesian networks enable large amounts of information about causalities to be integrated and utilized in probabilistic inference. Compared with the baseline period of 2007-2008, the worst-case scenario is that the risk level increases 4-fold by the year 2015. The management measures are evaluated and found to decrease the risk by 4-13%, but the utility gained by their joint implementation would be less than the sum of their independent effects. In addition to the results concerning the varying risk levels, the application provides interesting information about the relationships between the different elements of the system.

Optimizing the recovery efficiency of Finnish oil combating vessels in the Gulf of Finland using Bayesian Networks.

Oil transport has greatly increased in the Gulf of Finland over the years, and risks of an oil accident occurring have risen. Thus, an effective oil combating strategy is needed. We developed a Bayesian Network (BN) to examine the recovery efficiency and optimal disposition of the Finnish oil combating vessels in the Gulf of Finland (GoF), Eastern Baltic Sea. Four alternative home harbors, five accident points, and ten oil combating vessels were included in the model to find the optimal disposition policy that would maximize the recovery efficiency. With this composition, the placement of the oil combating vessels seems not to have a significant effect on the recovery efficiency. The process seems to be strongly controlled by certain random factors independent of human action, e.g. wave height and stranding time of the oil. Therefore, the success of oil combating is rather uncertain, so it is also important to develop activities that aim for preventing accidents. We found that the model developed is suitable for this type of multidecision optimization. The methodology, results, and practices are further discussed.