Oyster reefs are currently at risk of severe decline due to dangerous human interference and its aftermath; hence, artificial oyster reefs (AORs) have been utilized for their restoration. AORs with high vertical reliefs interact with the surrounding flow, constitute a reverse flow, and create a wake region in which concentrated nutrients and food organisms exist. However, the correlations of the structural characteristics of an AOR with its wake regions have not been studied. Thus, we established 96 AOR models, carried out flow analyses, and obtained their wake volumes, considering shell orientation, composition, penetration depth, and growth stage. We found that the growth stage is the most critical parameter for establishing a normalized wake volume. This implies that the number of oyster shells (N) is the most critical factor in securing a normalized wake volume, in which their correlation was linear and significant (R2=0.89). We also found that the correlations of the normalized wake volume with blocking and surface complexity indices were linearly significant, respectively. Additionally, wake volume efficiency increased with the number of oyster shells; specifically, the criterion for wake volume efficiency of EI (efficiency index) ≥ 2.0 was satisfied when N≥50 per 900 cm2.

Several studies have identified stability issues related to artificial reefs (ARs) installed on the seabed. However, initial AR settlement during free-fall installation has not been well examined. We performed a laboratory-scale experiment, regression analysis, and numerical simulation to evaluate the initial settlements of two cube-type AR models during free-fall installation into soft soil. Settlement tended to increase by impact velocity and AR mass but to decrease as soil density increased. The most significant parameter controlling settlement was soil density, followed by the impact velocity and the AR mass. Of the 56 cases examined, 76.79, 71.43, and 98.21% exhibited relative differences of ±20% as revealed experimentally, and by regression analysis and simulation. Given the terminal velocities of the two AR models, the corresponding settlement ranges were 2.12–5.36 mm and 4.56–11.56 mm, respectively. Thus, the effective useable volumes of the AR models fell by approximately 4–12% (depending on clay composition) if the models attained their terminal velocities during free-fall installation. These initial settlement data indicate that free-fall installation negatively affects both AR stability and functionality.

Marine environmental education projects have been operated by O-RCC (ocean keeping regional collaboration center) of Pukyong National University, for regional community innovation. These projects can be classified into either of two models i.e. stepping stone–living lab or community innovation project. Of 79 undergraduate and graduate students who carried out the 17 projects, 31 students participated in a three-step survey; hence, the pros and cons of the projects have been revealed. Although, in overall, the students satisfied the project process and outcome, we need to improve the projects and their outcome as follows. First, we need to consider students’ majors in the RCC projects, improve students’ knowledge, and help the students develop or choose their career path. The second one is extending students’ roles such as encouraging the students create new ideas to solve the problems, creating students’ deep involvement into the projects, and making the project outcomes contain a social ripple effect. Third one is taking on-time administrative actions such as promoting students for the participation, giving detail relevant notice, and providing experts for the students to solve the problem. Besides, we found that university image, systematic pre-education and performance inspection, and realizing the six-step spiral model are important to successfully carry out the regional innovation.

Consider social value (or contribution) of community-based education of the Busan Green Environment Center (BGEC), we analyzed the outcomes of environmental education programs in the last three years (2015~2017). Accordingly, we developed ten indices (E1~E10) and proposed equations to calculate the environmental education social value (EESV) of each program. The EESVs in the last three years of the BGEC have positive correlations with the number of trainees (E1), rather than program hours (E2). This is because the higher the number of trainees, the more likely it is to create 8 indices (E3~E10). When analyzing the results of the past three years, the average of the EESV fluctuated slightly from year to year, but the total EESV increased by year. This indicates that the social responsibility of the BGEC and the social value of environmental education are increasing. To utilize the social return on investment (SROI), it is necessary to accumulate detailed information on stakeholder inputs, outputs and outcomes. In addition, it is necessary to find substitute criteria for estimating monetary value, and detailed activity information of stakeholders is required. Therefore, in order to apply the SORI in the future, it is necessary to accumulate data more closely, and it is necessary to measure the extent of administrative and financial efforts for this purpose and determine whether to utilize the SROI.