• Environmental and Resource Economics Review
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• v.17 no.3
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• pp.1-29
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• 2008

The goal of this study is to analyze the effects of the second energy tax reform of the transportation sector in Korea. For this purpose, we estimated the elasticities of energy demand(for gasoline, diesel and LPG) by using the ARDL(Auto-Regressive Distributed Lag) Model during the period of 1997 and 2005. We have the empirical results that the demand for diesel would decrease as much as of 382 million barrel per year and the demand for LPG would increase as much as of 20 million barrel per year since 2007. The second energy tax reform would also result in the decrease of 27,346 ton of air pollutants and 0.96 million ton of carbon dioxide per year. This shows that the second energy tax reform would have achieved its own policy goals by reducing energy demand and improving the quality of environment.

• Environmental and Resource Economics Review
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• v.14 no.2
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• pp.475-491
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• 2005

This study reviews various problems associated with the method of estimating the demand for industrial water that was employed in the Water Vision 2020 and it suggests an alternative econometric method. Comparing with the data cited in the Report on Industrial Census, estimates obtained by employing the concept of demand function are more exact compared to those offered by the Water Vision 2020. The amount of industrial water in 1998 was estimated at 2.8 billion tons decreasing by 2003. By employing the concept of demand function, this study shows that the amount of industrial water was 2.1 billion tons in 2003 while according to the Water Vision 2020 it amounted to 3.3 billion tons in 2001. Thus, it appears that the amount of industrial water in the Water Vision 2020 has been overestimated. This study also shows that the industrial water demand can be controlled by means of certain pricing policies. Finally, we argue that the demand for industrial water should be estimated by taking account of economic variables such as water price and output.

• Environmental and Resource Economics Review
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• v.27 no.1
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• pp.195-214
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• 2018

This study estimates the price elasticity of residential water demand in Korea. For that, annual panel data from the year of 2010 to 2013 for 161 local water services is estimated by using panel quantile model. As a result, the price elasticities of residental water demand in Korea are estimated to be between -0.156 and -0.189 depending on its quantile. In addition, the study finds that the estimated elasticity of residential water demand by traditional conditional mean regression is relatively more influenced by high demand areas because the distribution of residental water demand in Korea is left-skewed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1103-1111
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• 2011

This paper is developed to Energy Balance Flow show the flow of total energy resource be used nationally. The Energy Balance Flow is applicable of demand management factor through the analysis of foreign energy model of supply and demand and energy statistic data in the country. This study is based on and developed to Energy system management model is able to appraisal efficient of energy cost cutting, CO2 emission reduction and Energy saving at the national level calculated effect reached amount of primary energy to change of energy flow followed application of demand side management factor is able to appraisal quantitatively at the total energy to model of demand and supply.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1111-1115
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• 2014

With the increasing interest of a demand side management using a Smart Grid infrastructure, the demand resources and energy usage data management becomes an important factor in energy industry. In addition, with the help of Advanced Measuring Infrastructure(AMI), energy usage data becomes a Big Data System. Therefore, it becomes difficult to store and manage the demand resources big data using a traditional relational database management system. Furthermore, not many researches have been done to analyze the big energy data collected using AMI. In this paper, we are proposing a Hadoop based Big Data system to manage the demand resources energy data and we will also show how the demand side management systems can be used to improve energy efficiency.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.219-230
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• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.

• Management & Information Systems Review
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• v.38 no.2
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• pp.47-66
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• 2019

The purpose of this study is to investigate the mediating role of proactive behaviors in the mechanism which job crafting affects organizational effectiveness. Through this study, we tried to contribute to the following theoretical and practical aspects. First, we tried to extend the theoretical discussion by identifying the relationship between job crafting, proactive behavior, and organizational effectiveness. Second, we provide practical implications for Korean organizational members based on proven job crafting, proactive behavior, and organizational effectiveness, and contribute to creating a work environment that enhances job satisfaction and reduces turnover intention. The results of this study are as follows. First, increasing structural job resource, increasing social job resource and increasing challenging job demand, which are sub-factors of job crafting, had a positive effect on job satisfaction. Second, increasing structural job resource, increasing social job resource and increasing challenging job demand, which are sub-factors of job crafting, had a negative effect on turnover intention. Third, job crafting had a positive effect on proactive behavior. Fourth, proactive behavior mediated the relationship of increasing structural job resource, increasing social job resource, increasing challenging job demand, and job satisfaction. Fifth, proactive behavior mediated the relationship of increasing structural job resource, increasing social job resource and turnover intention. This study provides an understanding of the impact of job crafting on organizational effectiveness and is expected to contribute to future research on job crafting.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.589-597
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• 2014

The increasing demand for various mobile telecommunication services calls for more spectrum resource. Considering the scarcity of spectrum resource and the difficulties of development of higher frequency band, the unique way to meet the increasing demand is to maximize the utilization efficiency of the current spectrum resource. Under the circumstances, various spectrum sharing plans have been in recent studies to dissolve the excess spectrum demand. In addition, spectrum efficiency measurement methods become more important to verify the effectiveness of spectrum sharing and estimate the performance of various wireless systems. Our research addresses measurement methods to estimate spectrum utilization efficiency in a rational manner. Based on ITU-R recommendations, various measuring methods for spectrum utilization efficiency are reviewed. Then a queueing based approach is newly presented as alternatives to the current models. By using the proposed model, the spectrum efficiency increment with the adoption of spectrum sharing plans can be estimated more properly.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.381-398
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• 2011

Recently, electricity peaks have frequently occurred in winter, and the concerns about failure in power supply are soaring. One of reasons is due to the increase in industrial demand with economic recovery, and the increases in household and commercial demand with unusual cold waves are spotted as a primary cause. Especially, the diffusion of electricity heat pumps (EHP) has led to the rapid increase in commercial electricity demand. The EHP diffusion is mainly associated with low electricity tariff and cheaper heating and cooling costs compared to those of gas-engine heat pumps (GHP). The problem is that distorted electricity tariff and herd behaviour toward heating and cooling devices could bring about nation-wide inefficiencies in resource allocation. The key countermeasures are as follows. The electricity tariff should be readjusted to a realistic level. The tariff scheme should be so modified that consumers rationally respond to the tariff. In addition, one should find ways to effectively manage electricity demand.

• Environmental and Resource Economics Review
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• v.32 no.1
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• pp.27-46
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• 2023

This paper investigates how interfuel substitution affects carbon dioxide (CO₂) emissions with a focus on the use of biodiesel blended fuels. The results show that the Divisia elasticity of diesel demand is the greatest because the transportation sector relies heavily on diesel. Also, while the own-price elasticity of each fuel demand is negative, the results reveal that diesel demand is more inelastic than the demand for gasoline and LPG. Moreover, gasoline is a substitute for diesel and electricity, and diesel is a substitute for LPG and a complement for electricity. Regarding the effects on carbon dioxide emissions, this paper computes the potential CO₂ emissions associated with interfuel substitution using the coefficients of CO₂ emissions. The results show that using biodiesel blended fuels contributes to reducing CO₂ emissions, but it appears that the price-induced interfuel substitution is a main factor affecting CO₂ emissions.