• KDI Journal of Economic Policy
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• v.34 no.2
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• pp.55-93
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• 2012

In Korea, energy policies are actualized through various energy-related plans. Recently, however, as high-ranking plans, which are very vision-oriented, continually set higher sector-by-sector goals, subordinate action plans, which require consistency, encounter distortions in their establishment process. Also, each subordinate action plan reveals limitations in terms of securing flexibility of the plan in responding to uncertainties of the future. These problems pose potential risks such as causing huge social costs. In this regard, with an aim to provide empirical evidence for discussions on improving the procedure for developing and executing Korea's energy plans, this study mainly analyzes the Basic Plan on Electricity Demand and Supply-one of the most important subordinate action plans-in order to explain the problems of the Basic Plan in a logical manner, and potential problems that could occur in the process of sustaining consistency between the Basic Plan and its higher-ranking plans. Further, this paper estimates the scale of social costs caused by those problems assuming realistic conditions. According to the result, in the case of where maximum electric power is estimated to be 7% (15%) less than the actual amount in the Basic Plan on Electricity Demand and Supply, the annual generation cost will rise by 286 billion won and (1.2 trillion won) in 2020. Such social costs are found to occur even when establishing and executing the Basic plan according to the target goal set by its higher-ranking plan, the National Energy Master Plan. In addition, when another higher-ranking GHG reduction master plan requires the electricity sector to reduce emissions by additional 5% in the GHG emissions from the right mix in electricity generation with 'zero' cost of carbon emission, the annual generation cost will rise by approximately 915 billion won in 2020. On the other hand, the analysis finds that since economic feasibility of electric powers in Korea varies significantly depending on their type, Korea is expected to face very small potential social costs caused by uncertainties over the future price of carbon dioxide in the process of establishing the Basic Plan.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.