• Microbiology and Biotechnology Letters
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• v.37 no.4
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• pp.293-305
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• 2009

Methane, as a greenhouse gas, is some 21~25 times more detrimental to the environmental than carbon dioxide. Landfills generally constitute the most important anthropogenic source, and methane emission from landfill was estimated as 35~73 Tg per year. Biological approaches using biocover (open system) and biofilter (closed system) can be a promising solution for older and/or smaller landfills where the methane production is too low for energy recovery or flaring and installation of a gas extraction system is inefficient. Methanotrophic bacteria, utilizing methane as a sole carbon and energy source, are responsible for the aerobic degradation (oxidation) of methane in the biological systems. Many bench-scale studies have demonstrated a high oxidation capacity in diverse filter bed materials such as soil, compost, earthworm cast and etc. Compost had been most often employed in the biological systems, and the methane oxidation rates in compost biocovers/boifilters ranged from 50 to $700\;g-CH_4\;m^{-2}\;d^{-1}$. Some preliminary field trials have showed the suitability of biocovers/biofilters for practical application and their satisfactory performance in mitigation methane emissions. Since the reduction of landfill methane emissions has been linked to carbon credits and trading schemes, the verified quantification of mitigated emissions through biocovers/biofilters is very important. Therefore, the assessment of in situ biocovers/biofilters performance should be standardized, and the reliable quantification methods of methane reduction is necessary.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.409-416
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• 2013

Recently, the property damage has been increasing due to climate change in South Korea. While the general public has become more aware of the environmental issues, but the environmental education system has not been able to meet up with the demands of the public. The purpose of this study is to suggest preliminary data which is needed for developing a environmental textbook. A survey was conducted to meet the following requirements. Respondent's level of interest in problems or situations concerning the following eight themes: fundamental science, health and medicine, aerospace engineering, life science, electrical electronics, telecommunication, mineral and energy resources, environment. The data was collected from 139 students in Seoul and Gyeonggi province. The results showed that health and medicine issues interest students the most (49.6%), followed by environment (46.8%). We asked the respondents who were very interested in each question for their reasons, and they answered that environmental issue is related to the improvement of their life quality (53.8%) than their curiosity (38.5%). Students were very interested in the other issues because of just curiosity. Most students (90.6%) said seasonal change was not same each year. 18.0% of respondents replied that they and their friends had experienced climate change. The majority of students (94.2%) thought that they will experience natural disaster blamed on climate change during their life. In other words, climate change is already the day-to-day events of their lives. The majority of their opinions, more then three than ten students(30.9%) said the South Korean government should conduct an energy saving campaign to climate change problems followed by expanding new renewable energy (24.5%), conducting adaptation policies of climate change(22.3 %), introducing of a system as like $CO_2$ emissions trading(20.9%) and so on. There are more Stu- dents (69.1%) who know of new renewable energy than students who don't know it; however, respondents who know the meaning very well were just 18.7% showing that most students dimly know the meaning of new renewable energy.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Convergence Security Journal
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• v.17 no.3
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• pp.73-81
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• 2017

In April 2009, in the wake of President Obama's Prague speech, the international community held four nuclear sec urity summits from 2010 to 2016 to promote nuclear security and prevent nuclear terrorism. The Nuclear Security S ummit has made significant progress in preventing terrorists from attempting to acquire nuclear weapons or fissile materials, but it still has limitations and problems. To solve this problem, the international community should resume the joint efforts for strengthening bilateral cooperation and multilateral nuclear security regime, and the participating countries should strive to protect their own nuclear materials and fulfill their commitments to secure nuclear facilitie s. Second, the United Nations(UN), the IAEA(International Atomic Energy Agency), International Criminal Police Or ganization(INTERPOL), the Global Initiative to Combat Nuclear Terrorism(GICNT), and the Global Partnership(G P) must continue their missions to promote nuclear security in accordance with the five action plans adopted at the Fourth Nuclear Security Summit. Third, the participating countries should begin discussions on the management and protection of military nuclear materials that could not be covered by the Nuclear Security Summit. Fourth, the intern ational community must strive to strengthen the implementation of the Convention on the Physical Protection of Nuc lear Material(CPPNM) Amendment and International Convention for the Suppression of Acts of Nuclear Terrori sm(ICSANT), prepare for cyber attacks against nuclear facilities, and prevent theft, illegal trading and sabotage invo lving nuclear materials.

• Environmental and Resource Economics Review
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• v.24 no.3
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• pp.463-488
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• 2015

A more detailed design of offset system is needed according to the emission trading system started in 2015. This study aims to estimate the supply of potential offset that can be secured by expanding high-efficiency household electric appliances. The target commodities for analysis are three different householding electric appliances: TV, washing machine, electric fan, refrigerator and air conditioner. By using the ARDL model, we estimated the coefficients of diffusion of these high-efficiency appliances from 2016 to 2022. Then, the potential supply of offset was drawn by calculating the amount of electricity saving by efficiency improvement and by applying the rates of carbon exchange. Supposing that the electricity savings rates of high-efficiency appliances are each 10% and 20%, the accumulated carbon decrement in 2022 was respectively $361,899CO_2t$ and $723,797CO_2t$. The appliance that showed the biggest carbon decrement was air conditioner, and the second biggest was refrigerator and the next was TV, followed by washing machine, electric fan.

• Environmental and Resource Economics Review
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• v.16 no.2
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• pp.213-237
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• 2007

Discussions on regionalization of the world crude oil markets have provided important implications for the establishment of national energy policies. In particular, due to arbitrage trading, if these markets are regionalized, Korea who imports approximately 80% of the annual oil consumption from a single region may be faced with a crucial problem. Therefore, in this study, we analyzed regionalization of the world crude oil markets using causality analysis as well as cointegration method to consider temporal relationship and time lags. To analyze regionalization, we chose Dubai price for the Middle East market, Brent for the European, WTI for the U.S., and Tapis for the East Asian. For the case that long-run equilibrium existed between market prices, we used vector error correction model to analyze causal relationship, and for the case that equilibrium did not exist, we used Hsiao (1981)'s framework that can consider asymmetric time lags in the model for causality analysis. By the results of cointegration analysis, there did not exist long-run equilibrium among Dubai price and the other prices. However, we found the causal relationship among Dubai price and the other prices with one to four weeks time lags. Therefore, in effect, we could conclude that the world crude oil markets are unified supporting Adelman (1984)'s hypothesis.

• Journal of Climate Change Research
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• v.2 no.2
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• pp.115-131
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• 2011

Out of all the possible actions that can be taken to respond to greenhouse gas reduction, including development of greenhouse gas reduction technology, infrastructure, actions to improve energy saving and efficiency, and offset with carbon emission reductions (CERs), this study shall focus on the investment on CERs. This study will take a look at risks involved with investing in CERs such as UN registration refusal risk and CERs price fluctuation, and will design risk management model which shall be verified. The goal of this paper is to provide optimized CERs investment strategies for different types of investors, such as general trading companies seeking for investment opportunities and financial companies with plans for green products development and investment by preparation for carbon market. It is expected that the global competitiveness of domestic financial companies shall be improved by taking actions on carbon market instead of previous passive response to climate change and that Korea, the number two Carbon Emissions supplier and number one derivatives market in terms of volume, shall be able to lead the worldwide carbon market.

• Economic and Environmental Geology
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• v.54 no.6
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• pp.733-741
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• 2021

Globally, in accordance with the goals set forth in the 2015 Paris Climate Agreement, each country has established and declared a reduction target for carbon neutrality by 2050. The roadmaps for establishing long-term greenhouse gas emissions development strategies and setting reduction targets have been announced. As the international community accelerates the transition to the net-zero society, 128 countries have declared net-zero by the end of 2020, and the net-zero declaration continues to expand around G20 member states. In December 2020, Korea announced the "2050 Net-zero Strategy" to establish a foundation for simultaneously achieving carbon reduction, economic growth, and improved quality of life for the people through active response to the net-zero, and pursuing policy tasks in stages to do this. Comprehensive carbon management is insufficient due to the lack of comprehensive carbon management due to the departure from the areas of mandatory reduction, such as the GHG energy target management system and the GHG emissions trading offset system implemented to reduce greenhouse gases in Korea. Currently, there is no cases for estimation or calculation of carbon dioxide emissions for the Mine Reclamation projects. It is reviewed the standard methods proposed by domestic and foreign carbon emission calculation methods and proposed appropriate carbon emission estimation methods for the Mine Reclamation projects in this study.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.582-583
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• 2021

As the production of new and renewable energy such as solar and wind power has diversified, microgrid systems that can simultaneously produce and consume have been introduced. . In general, a decrease in electricity prices through solar power is expected in summer, so producer protection is required. In this paper, we propose a transparent and safe gift power transaction system between users using blockchain in a microgrid environment. A futures is simply a contract in which the buyer is obligated to buy electricity or the seller is obliged to sell electricity at a fixed price and a predetermined futures price. This system proposes a futures trading algorithm that searches for futures prices and concludes power transactions with automated operations without user intervention by using a smart contract, a reliable executable code within the blockchain network. If a power producer thinks that the price during the peak production period (Hajj) is likely to decrease during production planning, it sells futures first in the futures market and buys back futures during the peak production period (Haj) to make a profit in the spot market. losses can be compensated. In addition, if there is a risk that the price of electricity will rise when a sales contract is concluded, a broker can compensate for a loss in the spot market by first buying futures in the futures market and liquidating futures when the sales contract is fulfilled.