• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.161-173
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• 2021

The shipping industry plummeted and was depressed due to the global economic crisis caused by the bankruptcy of Lehman Brothers in the US in 2008. In 2020, the shipping market also suffered from a collapse in the unstable global economic situation due to the COVID-19 pandemic, but unexpectedly, it changed to an upward trend from the end of 2020, and in 2021, it exceeded the market of the boom period of 2008. According to the Clarksons report published in May 2021, the decrease in cargo volume due to the COVID-19 pandemic in 2020 has returned to the pre-corona level by the end of 2020, and the tramper bulk carrier capacity of 103~104% of the Panamax has been in the ports due to congestion. Earnings across the bulker segments have risen to ten-year highs in recent months. In this study, as factors affecting BDI, the capacity and congestion ratio of Cape and Panamax ships on the supply side, iron ore and coal seaborne tonnge on the demand side and Granger causality test, IRF(Impulse Response Function) and FEVD(Forecast Error Variance Decomposition) were performed using VAR model to analyze the impact on BDI by congestion caused by strengthen quarantine at the port due to the COVID-19 pandemic and the loading and discharging operation delay due to the infection of the stevedore, etc and to predict the shipping market after the pandemic. As a result of the Granger causality test of variables and BDI using time series data from January 2016 to July 2021, causality was found in the Fleet and Congestion variables, and as a result of the Impulse Response Function, Congestion variable was found to have significant at both upper and lower limit of the confidence interval. As a result of the Forecast Error Variance Decomposition, Congestion variable showed an explanatory power upto 25% for the change in BDI. If the congestion in ports decreases after With Corona, it is expected that there is down-risk in the shipping market. The COVID-19 pandemic occurred not from economic factors but from an ecological factor by the pandemic is different from the past economic crisis. It is necessary to analyze from a different point of view than the past economic crisis. This study has meaningful to analyze the causality and explanatory power of Congestion factor by pandemic.

• Korean journal of applied entomology
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• v.52 no.4
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• pp.341-348
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• 2013

We desinged and evaluated a remote-sensing sex pheromone trap for real-time monitoring of Mythimna separata (Lepidoptera: Noctuidae), a migratory insect in Korea. The system consisted of a modified cone-trap with a sex pheromone lure, a sensing module based on light interruption, a signal transmission module based on code division multiple access, a main electronic board for system control, a power supply based on a solar collector, a stainless steel-pole supporting the system, and a signal collection and display system based on an internet web page. The ratio (>92%) of the actual number of insects to the signal number in the remote-sensing trap was improved by sensing only within a limited period at night on the basis of the insect's circadian rhythm, control of signal sensitivity on the basis of sensing software programming, 1-h interval for signal transmission, and adjustment of the signal transmission program. The signal occurrence pattern in the remote-sensing trap was conclusively similar (correlation coefficient, >0.98) to the actual pattern of adult occurrence in the trap. The result indicated that the remote-sensing trap based on the attraction of the sex pheromone lure for M. separata has a promising potential for practical use. Occurrence of M. separata adults was observed several times in 2011 and 2012, and the peaks were sharp.

• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.1109-1133
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• 2013

In an effort to discover new technologies and to forecast social changes of technologies, a number of technology life-cycle models have been developed and employed. The hype cycle, a graphical tool developed by a consulting firm, Gartner, is one of the most widely used models for the purpose and it is recognised as a practical one. However, more research is needed on theoretical frames, relations and empirical practices of the model. In this study, hype cycle comparisons in Korean and global search websites were performed by means of web-search traffic which is proposed as an empirical measurement of public expectation, analysed in a specific product or country in previous researches. First, search traffic and market share for new cars were compared in Korea and the U.S. with a view to identifying differences between the hype cycles in the two countries about the same product. The results show the similarity between the two countries with the statistical significance. Next, comparative analysis between search traffic and supply rate for several products in Korea was conducted to check out their patterns. According to the analysis, all the products seem to be at the "Peak of inflated expectations" in the hype cycles and they are similar to one another in the hype cycle. This study is of significance in aspects of expanding the scope of hype cycle analysis with web-search traffic because it introduced domestic web-search traffic analysis from Naver to analyse consumers' expectations in Korea by comparison with that from Google in other countries. In addition, this research can help to explain social phenomina more persuasively with search traffic and to give scientific objectivity to the hype cycle model. Furthermore, it can contribute to developing strategies of companies, such as marketing strategy.

• The Journal of Fisheries Business Administration
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• v.51 no.2
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• pp.51-69
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• 2020

The purpose of this study is to construct an outlook model that is consistent with the "Fisheries Outlook" monthly published by the Fisheries Outlook Center of the Korea Maritime Institute(KMI). In particular, it was designed as a partial equilibrium model limited to abalone items, but a model was constructed with a dynamic ecological equation model(DEEM) system taking into account biological breeding and shipping time. The results of this study are significant in that they can be used as basic data for model development of various items in the future. In this study, due to the limitation of monthly data, the market equilibrium price was calculated by using the recursive model construction method to be calculated directly as an inverse demand. A model was built in the form of a structural equation model that can explain economic causality rather than a conventional time series analysis model. The research results and implications are as follows. As a result of the estimation of the amount of young seashells planting, it was estimated that the coefficient of the amount of young seashells planting from the previous year was estimated to be 0.82 so that there was no significant difference in the amount of young seashells planting this year and last year. It is also meant to be nurtured for a long time after aquaculture license and limited aquaculture area(edge style) and implantation. The economic factor, the coefficient of price from last year was estimated at 0.47. In the case of breeding quantity, it was estimated that the longer the breeding period, the larger the coefficient of breeding quantity in the previous period. It was analyzed that the impact of shipments on the breeding volume increased. In the case of shipments, the coefficient of production price was estimated unelastically. As the period of rearing increased, the estimation coefficient decreased. Such result indicates that the expected price, which is an economic factor variable and that had less influence on the intention to shipments. In addition, the elasticity of the breeding quantity was estimated more unelastically as the breeding period increased. This is also correlated with the relative coefficient size of the expected price. The abalone supply and demand forecast model developed in this study is significant in that it reduces the prediction error than the existing model using the ecological equation modeling system and the economic causal model. However, there are limitations in establishing a system of simultaneous equations that can be linked to production and consumption between industries and items. This is left as a future research project.

• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.3-20
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• 1990

The multisector model is designed to analyze and forecast structural change in industrial output, employment, capital and relative price as well as macroeconomic change in aggregate income, interest rate, etc. This model has 25 industrial sectors, containing about 1,300 equations. Therefore, this model is characterized by detailed structural disaggregation at the sectoral level. Individual industries are based on many of the economic relationships in the model. This is what distinguishes a multisector model from a macroeconomic model. Each industry is a behavioral agent in the model for industrial investment, employment, prices, wages, and intermediate demand. The strength of the model lies in the simulating the interactions between different industries. The result of its simulation will be introduced in the next paper. In this paper, we only introduce the structure of the multisector model and the coefficients of the equations. The multisector model is a dynamic model-that is, it solves year by year into the future using its own solutions for earlier years. The development of a dynamic, year-by-year solution allows us to combine the change in structure with a consideration of the dynamic adjustment required. These dynamics have obvious advantages in the use of the multisector model for industrial planning. The multisector model is a medium-term and long-term model. Whereas a short-term model can taken the labor supply and capital stock as given, a long-term model must acknowledge that these are determined endogenously. Changes in the medium-term can be analyzed in the context of long-term structural changes. The structure of this model can be summarized as follow. The difference in domestic and world prices affects industrial structure and the pattern of international trade; domestic output and factor price affect factor demand; factor demand and factor price affect industrial income; industrial income and relative price affect industrial consumption. Technical progress, as measured in terms of total factor productivity and relative price affect input-output coefficients; input-output coefficients and relative price determine the industrial input cost; input cost and import price determine domestic price. The differences in productivity and wage growth among different industries affect the relative price.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.113-123
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• 2011

Recently, the global warming problem has arised around world, many nations has set up a various regulations for decreasing $CO_2$. In particular, $CO_2$ emissions reduction effect is very powerful in transport part, so there is a rising interest about development of green car, or electric vehicle in auto industry. For this reason, it is important to make a strategy for charging infra and forcast electric power demand, but it hasn't introduced about demand forecasting electric vehicle. Thus, this paper presents a demand forecasting for electric vehicles using choice based multigeneration diffusion model. In this paper, it estimates innovation coefficient, immitation coefficient in Bass model by using hybrid car market data and forecast electric vehicle market by year using potential demand market through SP(Stated Preference) experiment. Also, It facilitates more accurate demand forecasting electric vehicle market refelcting multigeneration diffusion model in accordance with attribute progress in development of electric vehicle. Through demand forecasting methodology in this paper, it can be utilized power supply and building a charging infra in the future.

• Land and Housing Review
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• v.7 no.4
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• pp.165-175
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• 2016

The Sejong Metropolitan Autonomous City is launched on July 1, 2012, and Phase 1 of the Multifunctional Administrative City Construction Project was completed in late 2015. Therefore, it is necessary through the results of the first phase of the project to check whether Sejong city can achieve the target population and number of households by 2030 and to use to determine the number and type of housing to be supplied next. Based on the presented results of the Phase 1 project period, this study estimate the population and number of households in 2030. For forecasting future population the population growth rate seen in the future of Sejong City's population forecast published by the National Statistical Office and the performance against plans Step 1 were used for forecasting future population. The results of analysis showed that the Multi-functional Administrative City is difficult to attract five hundred thousand people and two hundred thousand houses. In the analysis of households by type The Multi-functional Administrative City is The large proportion of 3-4 person households and high-income earners and Homeowners. But it increased the proportion of households with 1-2 people and rent house of the city grows in size and it is likely to change the level of income. Therefore, it is determined that there is a need to reflect these elements in next housing.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.7
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• pp.819-826
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• 2019

To accurately forecast the supply and demand of harbor pilots, it is necessary to derive the determinants of demand because they are directly linked to securing the safety of ships and ports. The securing of an appropriate numbers of harbor pilots can create conflicts of interest among the pilots, the Ministry of Oceans and Fisheries, and users of pilotage services as it is also a matter directly related to harbor pilots' income. Therefore, a measure is needed to ensure a suitable number of pilots can be maintained, through which high quality pilotage services can be provided. This can be achieved by deriving reasonable determinants for estimating and forecasting demand, which satisfy all stakeholders involved in pilotage service. To reveal the challenges posed by the current determinants regarding the demand for harbor pilots used by the Central Pilotage Operation Council, and arrive at solutions, this study derived three determining factors, namely the total annual average piloting time, the average working hours of pilots, and the current number of pilots. These were used to determine the demand for harbor pilots. This study used a survey and analysis of current determining factors, a questionnaire survey administered to the interested parties, a case study of selected countries, and so on, as the research methodology.

• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.187-194
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• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• Korea journal of population studies
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• v.31 no.1
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• pp.27-54
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• 2008

The purpose of this study is to analyze the effect of policy boosting fertility and labor participation rate on potential GDP growth rate. To do this, we employ a growth accounting approach, which decomposes per capita GDP into two parts. The first one is the change of dependency ratio and the other is the change of labor input. The labor input is again decomposed into the qualitative and quantitative parts. The quantitative part considers the change of labor participation rate and working time. The qualitative aspects is based on the trend of productivity of labor. From the scenarios of NSO(National Statistics Office), the effect of the fertility-raising policy on per capita potential GDP growth rate is calculated and projected to the year of 2050. We also forecast the policy effect inducing high labor participating rate of female labor and beyond 55-year old labor. The baseline results show that the per capita GDP growth rate will show mid 4% to the year of 2010, gradually declining to 3.94% by 2020, 3.03% by 2030, 2.41% by 2040. The high fertility rate scenario will not have effects on the potential growth by 2030, but show 0.10%p higher per capita GDP growth rate than that of baseline scenario result. By the high female labor participation policy, the per capita GDP growth rate will reach 0.04%p higher per capita GDP growth rate than that of baseline scenario. Based on the results of this paper, we conclude that the quantitative labor input cannot solely account for the trend decline of potential GDP, and the qualitative aspect, like labor productivity, is much more important element to sustain and boots the economic growth.