• Journal of Korea Port Economic Association
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• 제31권2호
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• pp.23-40
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• 2015

This study measures how Korean shipping policies influence the expansion of the country's merchant fleet using system dynamics. It uses various indexes as factors influencing the gross tonnage of the Korean merchant fleet, such as the Baltic Dry Index, Howe Robinson Container Index, China Containerized Freight Index, and Worldscale Index, as well as the US dollar-Korean won exchange rate, world merchant fleet statistics, and the debt ratio of Korean shipping companies. After establishing the simulation model, the mean absolute percentage error is found to be less than 10%, confirming the accuracy of the model. Therefore, a sensitivity analysis is conducted to measure the influence of the selected shipping policies, including the gross tonnage of vessels registered under the Korean second registry system, loans of publicly owned financial institutions to shipping companies, ship investment fund, and the number of shipping companies participating in the tonnage tax scheme. The sensitivity analysis reveals that the influence of vessel tonnage and loans to shipping companies is the most significant, while that of the number of companies participating in the tonnage tax scheme is minimal.

• STI Policy Review
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• 제6권1호
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• pp.1-23
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• 2015

Based on the work of Anand et al. (2013) we measure inclusive income growth, which combines growth in gross domestic product (GDP) per capita and growth in the equity of the income distribution. Extending the work of Causa et al. (2014), we estimate a dynamic simultaneous structural equations model of GDP per capita and inclusive income on panel data for 63 countries over the 1990-2013 period. We estimate both equations in error correction form by difference GMM (generalized method of moments). Among the explanatory variables of the level and the distribution of GDP per capita we include R&D (research and development) expenditure per capita. In OECD countries we obtain a large positive effect of R&D on GDP. R&D is found to have a positive effect on the social mobility index but its impact on the income equity index at first decreases, then switches around to become slightly positive in the long run. In non- OECD countries, R&D is found to decrease inclusive income, mostly through a negative growth effect but also because of a slightly increasing income inequity effect.

• Korean Journal of Agricultural and Forest Meteorology
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• 제21권1호
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제20권12호
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• pp.574-580
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• 2019

In this paper, the economic performance of the Jeju Free International City and the Free Economic Zone is investigated using statistical testing and the difference in differences (DID) model with data on foreign direct investment (FDI), gross regional domestic product (GRDP), and employment-to-population ratio (EPR). The relationships among FDI, GRDP, and EPR are also investigated using the panel vector error-correction model on the regional data. The compound average growth rate of actual investment, and the ratio of FDI received to FDI declared in the capital region were higher than in the non-capital region. For the growth and relative volume of FDI received, seven regions out of 16 were found to be low in growth and small in relative volume. The results of statistical testing showed statistically significant differences in some variables, except for two regions, but DID estimates that determine the pure policy effect of zone designation showed statistical insignificance. On the other hand, the explanatory power among the three variables was found to be quite limited, but it was greater in the cities, provinces, and non-capital region. In summary, it is necessary to establish the FDI inducement mechanism so the inflow of FDI can increase GRDP and EPR.

• International Commerce and Information Review
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• 제18권1호
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• pp.107-127
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• 2016

This paper attempts to investigate determinants of foreign direct investment in transition countries of Mongolia and Central Asia five countries of Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan and Turkmenistan. FDI inflows in this transition economies have been far increasing due to their rapid growth, GDP, gross capital formation, wage, labor force, open trading, infrastructure and natural resources as well as the factors demonstrating the economic variables and political variables of these countries by Vector Error Correction Model. The results of empirical analysis based on data from 1993 to 2013 confirmed that FDI and open trade and gross capital formation and political than GDP, wage, labor force, infrastructure and natural resources had a significant impact on Central Asia and Mongolia. In addition, if Mongolia and Central Asian five countries can maintain the country's economic growth, reduce unemployment level, achieve certain improvements in domesticating new technologies and improving skills and knowledge sphere as well as promoting stable domestic price increase, attracting and improving the FDI by paying more attention to the indicators focusing on country's GDP, wage, labor force, infrastructure and natural resource.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제25권6_2호
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• pp.613-623
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• 2007

The main purpose of this study is to investigate the coordinate transformation based on two different systems between local geodetic datum(tokyo datum) and international geocentric datum(new Korea geodetic datum). For this purpose, three methods were used to determine seven parameters as follows: Bursa-Wolf model, Molodensky-Badekas model, and Veis model. Also, we adopted multiple regression equation method to convert from Tokyo datum to KTRF. We used 935 control points as a common points and applied gross error analysis for detecting the outlier among those control points. The coordinate transformation was carried out using similarity transformation applied the obtained seven parameters and the precision of transformed coordinate was evaluated about 9,917 third or forth order control points. From these results, it was found that Bursa-Wolf model and Molodensky-Badekas model are more suitable than other for the determination of transformation parameters in Korea. And, transforming accuracy using MRE is lower than other similarity transformation model.

• Journal of the Korea Society of Computer and Information
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• 제13권4호
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• pp.231-240
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• 2008

In respect complication, group and period, the foreign direct investment of korea is composed of various factors. This paper studies focus on estimating the determinants of foreign direct investment of korea. The region of analysis consist of 7 groups, that is, Asia, Europe, Central and South America, Oceania, Africa, Middle East. Analyzing period be formed over a 67 point(2002. 6${\sim}$2007. 12). In this paper dependent variable setting up an amount of foreign direct investment, explanatory(independent) variables composed of gross domestic product, a balance of current accounts, the foreign exchange rate, employment to population ratio, an average of the rate of operation(the manufacturing industry), consumer price index, the amount of export, wages(a service industry). For an actual proof analysis, LIMDEP 8.0 software, analysis model is random effect in TWECR The result of estimating the determinants of foreign direct investment of korea provides empirical evidences of significance positive relationships between employment to population ratio and wages(a service industry). However this study provides empirical evidences of significance negative relationships between the foreign exchange rate, censurer price index and the amount of export. The explanatory variables, that is, an average of the rate of operation(the manufacturing industry), gross domestic product and a balance of current accounts, are non-significance variables.

• Land and Housing Review
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• 제12권4호
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• pp.93-101
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• 2021

As the number of small households (1 to 2 persons per household) in Korea gradually increases, so does the importance of housing supply policies for small households. In response to the increase in small households, the government has been continuously supplying urban housing for these households. Since housing for small households is a sales and rental business similar to apartments and general business facilities, it is important for the building owner to calculate the project's estimated construction duration during the planning stage. Review of literature found a model for estimating the duration of construction of large-scale buildings but not for small-scale buildings such as urban housing for small households. Therefore this study aimed to develop and verify a model for estimating construction duration for urban housing at the planning stage based on multiple regression analysis. Independent variables inputted into the estimation model were building site area, building gross floor area, number of below ground floors, number of above ground floors, number of buildings, and location. The modified coefficient of determination (R_a²) of the model was 0.547. The developed model resulted in a Root Mean Square Error (RMSE) of 171.26 days and a Mean Absolute Percentage Error (MAPE) of 26.53%. The developed estimation model is expected to provide reliable construction duration calculations for small-scale urban residential buildings during the planning stage of a project.

• Korean Journal of Remote Sensing
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• 제35권6_2호
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Journal of Korean Society for Atmospheric Environment
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• 제23권4호
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• pp.449-456
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• 2007

Dispersion coefficient preprocessing schemes have been examined to improve plume dispersion model performance in complex coastal areas. The performances of various schemes for constructing the sigma correction order were evaluated through estimations of statistical measures, such as bias, gross error, R, FB, NMSE, within FAC2, MG, VG, IOA, UAPC and MRE. This was undertaken for the results of dispersion modeling, which applied each scheme. Environmental factors such as sampling time, surface roughness, plume rising, plume height and terrain rolling were considered in this study. Gaussian plume dispersion model was used to calculate 1 hr $SO_2$ concentration 4 km downwind from a power plant in Boryeung coastal area. Here, measured data for January to December of 2002 were obtained so that modelling results could be compared. To compare the performances between various schemes, integrated scores of statistical measures were obtained by giving weights for each measure and then summing each score. This was done because each statistical measure has its own function and criteria; as a result, no measure can be taken as a sole index indicative of the performance level for each modeling scheme. The best preprocessing scheme was discerned using the step-wise method. The most significant factor influencing the magnitude of real dispersion coefficients appeared to be sampling time. A second significant factor appeared to be surface roughness, with the rolling terrain being the least significant for elevated sources in a gently rolling terrain. The best sequence of correcting the sigma from P-G scheme was found to be the combination of (1) sampling time, (2) surface roughness, (3) plume rising, (4) plume height, and (5) terrain rolling.