• Journal of Wetlands Research
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• v.19 no.3
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• pp.345-352
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• 2017

The average global temperature on Earth has increased by about $0.85^{\circ}C$ since 1880 due to the global warming. The temperature increase affects hydrologic phenomenon and so the world has been suffered from natural disasters such as floods and droughts. Therefore, especially, in the aspect of water deficit, we may require the accurate prediction of water demand considering the uncertainty of climate in order to establish water resources planning and to ensure safe water supply for the future. To do this, the study evaluated future water balance and water deficit under the climate change for Anseong river basin in Korea. The future rainfall was simulated using RCP 8.5 climate change scenario and the runoff was estimated through the SLURP model which is a semi-distributed rainfall-runoff model for the basin. Scenario and network for the water balance analysis in sub-basins of Anseong river basin were established through K-WEAP model. And the water demand for the future was estimated by the linear regression equation using amounts of water uses(domestic water use, industrial water use, and agricultural water use) calculated by historical data (1965 to 2011). As the result of water balance analysis, we confirmed that the domestic and industrial water uses will be increased in the future because of population growth, rapid urbanization, and climate change due to global warming. However, the agricultural water use will be gradually decreased. Totally, we had shown that the water deficit problem will be critical in the future in Anseong river basin. Therefore, as the case study, we suggested two alternatives of pumping station construction and restriction of water use for solving the water deficit problem in the basin.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.

• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.788-818
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• 2009

Renewable energy refers to solar energy, biomass energy, hydrogen energy, wind power, fuel cell, coal liquefaction and vaporization, marine energy, waste energy, and liquidity fuel made out of byproduct of geothermal heat, hydrogen and coal; it excludes energy based on coal, oil, nuclear energy and natural gas. Developed countries have recognized the importance of these energies and thus have set the mid to long term plans to develop and commercialize the technology and supported them with drastic political and financial measures. Considering the growing recognition to the field, it is necessary to analysis up-to-now achievement of the government's related projects, in the standards of type of renewable energy, management of sectional goals, and its commercialization. Korean government is chiefly following suit the USA and British policies of developing and distributing renewable energy. However, unlike Japan which is in the lead role in solar rays industry, it still lacks in state-directed support, participation of enterprises and social recognition. The research regarding renewable energy has mainly examinedthe state of supply of each technology and suitability of specific region for applying the technology. The evaluation shows that the research has been focused on supply and demand of renewable as well as general energy and solution for the enhancement of supply capacity in certain area. However, in-depth study for commercialization and the increase of capacity in industry followed by development of the technology is still inadequate. 'Cost-benefit model for each energy source' is used in analysis of technology development of renewable energy and quantitative and macro economical effects of its commercialization in order to foresee following expand in related industries and increase in added value. First, Investment on the renewable energy technology development is in direct proportion both to the product and growth, but product shows slightly higher index under the same amount of R&D investment than growth. It indicates that advance in technology greatly influences the final product, the energy growth. Moreover, while R&D investment on renewable energy product as well as the government funds included in the investment have proportionate influence on the renewable energy growth, private investment in the total amount invested has reciprocal influence. This statistic shows that research and development is mainly driven by government funds rather than private investment. Finally, while R&D investment on renewable energy growth affects proportionately, government funds and private investment shows no direct relations, which indicates that the effects of research and development on renewable energy do not affect government funds or private investment. All of the results signify that although it is important to have government policy in technology development and commercialization, private investment and active participation of enterprises are the key to the success in the industry.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Korean Journal of Construction Engineering and Management
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• v.9 no.2
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• pp.146-158
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• 2008

This paper examines the relationship between the real estate policies of Korean government and the stock market of Korea. It is the purpose of this paper whether the government policies are effective or not when the Korean government release new real estate policies outlining higher taxes and more housing supply as part of its plan to suppress speculation. This paper studies the properties of daily stock returns of the construction sector in Korea securities market when the government announcements of the real estate policies are released. On the demand side, multiple home owners and those purchasing property for speculative purposes are expected to be hit the hardest If the government policies are effective. The empirical results of this paper show that most of the cumulative abnormal returns(CARs) are statistically significant from the year 2002 to the year 2006 except the year 2004.

• Korean Business Review
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• v.21 no.1
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• pp.91-117
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• 2008

Since the late 20th century, due to the globalization of markets and the rapid developments of information and communication technologies, the world has entered into the age of fierce competition. With their abundant low-cost labor, former communist countries such as China, Russia and East Europe and former low-developed countries such as India and Brazil have received foreign investments from major industrial companies. Thus, there are no other alternatives remained for Korea and OECD countries other than moving the industrial economies to the knowledge based innovation systems. In the knowledge based innovation systems, economic developments and employments of regions and nations depend solely on companies situated in their territories. In this article, after reviewing the current state of technological based innovation small firms in korea, we provide a theoretical framework to promote the level of technology based innovation and entrepreneurship. The theoretical framework for analysis and policy suggestions is based on the Schumpeter's value system model that consist of supply factors, demand factors, and supporting institutions. In order to create more innovative small businesses, we insist on the followings. First, more R&D and innovation activities related with promising new technologies should be performed. Second, more R&D funds for technological innovation small companies should be provided from governments. Third, more M&A transactions and technology transactions should be marketed freely and competitively. Fourth, more business incubating services and venture capital services should be provided. Fifth, entrepreneurship and innovation culture should be popular and institutionized among people.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.631-640
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• 2017

The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.

• KDI Journal of Economic Policy
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• v.36 no.4
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• pp.135-169
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• 2014

This study empirically analyzes changes in production patterns of farmers by agricultural disaster insurance. The aim of this project is to achieve stability of farm management by paying insurance in case of a natural disaster. However, it causes farmers to change production patterns in the direction of increasing production, and leads the crop price to drop. This can be explained by producers' risk reduction through the disaster insurance. The empirical analysis is based on IV approach with using two stage least squares method. The first stage estimates by difference-in-differences methodology indicate that the production of insurable crops increases more about 80,000ton on average than that of non-insurable crops. In addition, to solve the endogeneity problem caused by general supply and demand model, I use the first stage estimates and find that the price index of the crops drops about 2.3% according to the production increase by 10,000ton. The credibility of these results is also attained by various robustness checks. These findings suggest that it is necessary for government to analyze the whole economy which consists of producer and consumer welfare when it determines the policy. Besides, it implies that it is essential to develop a new market to cope with the unintended effect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.440-447
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• 2020

The purpose of this study is to identify the causation and variation among the various types of onion prices in the major production sites to predict these prices. The Granger causal relationship was tested on the basis of VECM by setting the onion price of the early, middle, and late species as individual variables. The analysis shows that the amount of onions produced in the prior term affects the price of onions for the later period, while garlic in the substitution relationship with onions also affects the prices of onions for the early and middle-variety. On the other hand, the price of the late-variety is affected by the price of the early-variety, and the price of the middle-variety is also affected by the price of the early-variety. If the price of onions on Jeju changes due to other factors, the prices of onions in Jeollanam-do and Gyeongsangnam-do provinces will be affected. Accordingly, when the production of late-variety increases or decreases in production under any factor and to promote stability of the prices of middle and late-variety through preemptive supply and demand measures when the prices of ultra-breed onions rise or fall due to any factor (Ed- I cannot understand this last sentence and cannot guess at the correct meaning. Please try to rewrite very simply).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.303-311
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• 2020

The acreage of tangerines in the U.S. sharply rose from 19,000 ha in 2009 to 27,000 ha in 2016, an increase of 42% in 7 years. Considering the recent surge in tangerine exports to Japan, the export volume of 6-7 thousand tons is highly likely to increase in the future. Therefore, the purpose of this study was to analyze the economic impact of U.S. tangerine imports on the Korean citrus industry under various scenarios. In order to examine the possibility of imports of U.S. tangerines, the unit price of U.S. exports to Japan was used since U.S. tangerines are not imported to South Korea. Citrus fruits are divided into field citrus, house citrus, and late-maturing citrus (including winter season citrus) based on the cultivation method and variety used to analyze. Considering both the field and house seasons, the import volume of U.S. tangerines can be expected to rise from roughly 4,700 tons in 2021 to 10,000 tons in 2027. Imports of U.S. tangerines may be pushed up or delayed depending not only on the harvest method and quality of domestic field and house citrus but also on the harvest of U.S. tangerines. However, it is necessary to note that tangerines could be imported after 2021, when the tariff rate on U.S. tangerines will fall below 50%.