This study presents a system dynamics methodology to evaluate quantitatively the effect of the Korean government’s development policy, such as tax reductions, on the industrial economy. System dynamics is often perceived as an optimized means to identify the dynamic inter-relationships among various factors of development policies, and in particular the industrial characteristics and uncertainties of the coastal shipping industry. The results of simulations used in this study shows that the impact of development policies such as tax reductions would increase shipping demand for about 4 years, and that tax incentives could raise the demand volume for cabotage cargo from 5.26 to 11.11%, through the available freight- down by 90∼95% points. The system dynamics approach used in this paper represents an initial attempt to use this methodology in studies of the coastal shipping industry. On the basis of our simulations, the industrial effects of other development policies, such as ship financing support, investment of social overhead, or crew supply, could also be analyzed effectively. Additionally, it should be possible to extend these results by developing a comprehensive model encompassing these various analyses.

Monitoring sunspots consistently is the most basic step required to study various aspects of solar activity. To achieve this goal, the observers must regularly calculate their own correction factor k and keep it stable. Relatively recently, two observing teams in South Korea have presented interesting papers which claim that revisions that take the yearly-basis k into account lead to a better agreement with the international relative sunspot number Ri, and that yearly k apparently varies with the solar cycle. In this paper, using artificial data sets we have modeled the sunspot numbers as a superposition of random noise and a slowly varying background function, and attempted to investigate whether the variation in the correction factor is coupled with the solar cycle. Regardless of the statistical distributions of the random noise, we have found the correction factor increases as sunspot numbers increase, as claimed in the reports mentioned above. The degree of dependence of correction factor k on the sunspot number is subject to the signal-to-noise ratio. Therefore, we conclude that apparent dependence of the value of the correction factor k on the phase of the solar cycle is not due to a physical property, but a statistical property of the data.