To reduce air pollutants, the International Maritime Organisation and port authorities use ship emissions regulations, such as MARPOL Annex VI and green port policies. To measure the effectiveness of these air emissions regulatory policies, accurate calculations of pollutant emissions and estimations of the social environmental costs of emissions are important. However, Busan Port still suffers from a lack of research on continuous monitoring and easy access to data-based emission calculation methods and estimation of the social environmental costs. Therefore, the purpose of this study is to present quantitative emission calculations based on an open source and social environmental cost estimation method. To this end, the discharge of pollutants (NOx, SO₂, CO₂, VOC, PM2.5, and PM10) from ships in Busan Port was calculated using Port-MIS open data from 2015–2019. Subsequently, when the original study on estimating the social and environmental impact of air pollution from ships in Busan Port was difficult, the international benefit transfer method (an economic valuation method) was applied to estimate the social environmental costs. Our results can provide a basis for verifying the effectiveness of Busan Port’s air quality improvement policy in the future.

Ship collision accidents account for the majority of marine accidents. The collision risk can be even greater in ports where the traffic density is high and terrain conditions are difficult. The proximity assessment model of the Korea Maritime Safety Audit (KMSA), which is a tool for improving maritime traffic safety, employs a normal distribution of ship traffic to calculate the ship collision risk. However, ship traffic characteristics can differ according to the characteristics of the sea area and shipping route. Therefore, this study simulates collision probabilities by estimating the best-fit distribution function of ship traffic flow in Ulsan Port, which is the largest hazardous cargo vessel handling port in Korea. A comparison of collision probability simulation results using the best-fit function and the normal distribution function reveals a difference of approximately 1.5–2.4 times for each route. Moreover, the collision probability estimates are not accurate when the normal distribution function is uniformly applied without considering the characteristics of each route. These findings can be used to improve the KMSA evaluation method for ship collision risks, particularly in hazardous port areas.

Lee, S. and Kim, T.-G., 2023. Economic value of ocean education in Korea using the Contingent Valuation Method (CVM). In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 304-308. Charlotte (North Carolina), ISSN 0749-0208. This study aims to measure the value of experience of ocean education and estimate its economic value in monetary terms. Through this process, this study intends to conduct an empirical analysis on the impact of ocean education that the Korean government has implemented over the last 18 years on society as well as the economy. For analyzing the economic value of ocean education, this study estimated Willingness-to-Pay (WTP) based on a Contingent Valuation Method (CVM). While using the question format of double-bounded dichotomous choice, this study randomly distributed a total of 10 suggested prices to 1,065 respondents who have participated in ocean education and have not participated. According to the result of analysis, the social economic value of the government's ocean education programs is estimated to be 331.1 billion KRW to the minimum to 873.3 billion KRW, having 1.2 times to 3 times the effect of the MOF's estimated budget of 286 billion KRW. Next gender, age, education level, number of children, characteristics of residence area, ocean education experience, and household monthly income were analyzed as socioeconomic factors of respondents that affect WTP. As a result of the analysis, it was found that the higher age, education level, and monthly income in the probit model, had a statistically significant effect on the willingness to pay. And the more they lived in the coastal area, the more they had the experience of participating in the ocean education program at least once, the higher the willingness to pay. These results signify that ocean education policies that have been implemented thus far have been able to successfully achieve policy goals initially intended. At the same time, this study verifies that ocean education policies have brought a positive impact on promoting the ocean knowledge of the people.