• Environmental and Resource Economics Review
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• v.33 no.1
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• pp.1-32
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• 2024

The emissions trading system stands as a pivotal climate policy in Korea, incentivizing abatement equivalent to 87% of total emissions (as of 2021). As the system likely has a far-reaching impact, it is crucial to understand how the real economic activity, energy sector, as well as environment would be influenced by its implementation. Employing a macroeconometric model, this paper is the first study analyzing the effects of the Korean emissions trading policy. It interconnects the Korean Standard Industrial Classification (Economy), Energy Balance (Energy), and National Inventory Report (Environment), enhancing its real-world explanatory power. We find that a 50% increase in emission permit price over four years results in a decrease in greenhouse gas emissions (-0.043%) and downward shifts in key macroeconomic variables, including real GDP (-0.058%), private consumption (-0.003%), and investment (-0.301%). The price increase in emission permit is deemed crucial for achieving greenhouse gas reduction targets. To mitigate transition risk associated with price shocks, revenue recycling using auction could ensure the sustainability of the economy. This study confirms the comparative advantage of expanded current transfers expenditure over corporate tax reduction, particularly from an economic growth perspective.

• Information Systems Review
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• v.23 no.1
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• pp.69-91
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• 2021

Credit Bureaus in Korea commonly use financial transaction information of the past and present time for calculating an individual's credit scores. Compared to other rating factors, the repayment history information accounts for a larger weights on credit scores. Accordingly, despite full redemption of overdue payments, late payment history is reflected negatively for the assessment of credit scores for certain period of the time. An individual with debt delinquency can be classified into two groups; (1) the individuals who have faithfully paid off theirs overdue debts(Normal Repayment), and (2) those who have not and as differences of creditworthiness between these two groups do exist, it needs to grant relatively higher credit scores to the former individuals with normal repayment. This study is designed to analyze the factors of normal repayment of Korean financial debt delinquents based on credit information of personal loan, overdue payments, redemption from Korea Credit Information Services. As a result of the analysis, the number of overdue and the type of personal loan and delinquency were identified as significant variables affecting normal repayment and among applied methodologies, neural network models suggested the highest classification accuracy. The findings of this study are expected to improve the performance of individual credit scoring model by identifying the factors affecting normal repayment of a financial debt delinquent.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.119-132
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• 2014

This study analyzed the failure prediction model of the firms listed on the KOSDAQ by considering whether there are embezzlement, malpractice and the largest shareholder changes or not. This study composed a total of 166 firms by using two-paired sampling method. For sample of failed firm, 83 manufacturing firms which delisted on KOSDAQ market for 4 years from 2009 to 2012 are selected. For sample of normal firm, 83 firms (with same item or same business as failed firm) that are listed on KOSDAQ market and perform normal business activities during the same period (from 2009 to 2012) are selected. This study selected 80 financial ratios for 5 years immediately preceding from delisting of sample firm above and conducted T-test to derive 19 of them which emerged for five consecutive years among significant variables and used forward selection to estimate logistic regression model. While the precedent studies only analyzed the data of three years immediately preceding the delisting, this study analyzes data of five years immediately preceding the delisting. This study is distinct from existing previous studies that it researches which significant financial characteristic influences the insolvency from the initial phase of insolvent firm with time lag and it also empirically analyzes the usefulness of data by building a firm's fail prediction model which considered embezzlement/malpractice and the largest shareholder changes as dummy variable(non-financial characteristics). The accuracy of classification of the prediction model with dummy variable appeared 95.2% in year T-1, 88.0% in year T-2, 81.3% in year T-3, 79.5% in year T-4, and 74.7% in year T-5. It increased as year of delisting approaches and showed generally higher the accuracy of classification than the results of existing previous studies. This study expects to reduce the damage of not only the firm but also investors, financial institutions and other stakeholders by finding the firm with high potential to fail in advance.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.465-482
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• 2016

Previous researches have introduced various ways to analyze the impact of standardization on innovation while the works are not only small in number but based on interview or case study. This paper addresses the impact of standardization activities within South Korean industries on technological innovation applying an empirical analysis of standardization activities and technological innovation. Drawing on Korean Industrial Standards Classification from panel data of 2003 to 2012, we employed corresponding data of each industrial classification: Number of standards, Accumulated number of standards, Number of patents applied in Korea, Sales, Operational profit, Intangible asset, and R&D invest. In the first model, we run panel data models employing the number of patents applied in Korea as an independent variable, and the number of standards, accumulated number of standards, sales, and operational profit as dependent variables to observe industrial impacts upon the relationship between standards and patents, along with time lagged consideration. The result shows that number of standards are revealed to have a negative influence on patent applications in the year of research, and no significant effect appears for the next two years while positive effect shows up on the third year. Meanwhie, accumulated number of standards turned out to have positive effects on patent applications in Korea. This implies it takes time for innovation subjects to embrace newly established standards while having a significant amount of positive effect on technological innovation in the long term. In the second model, we use canonical correlation analysis to find industrial-wide characteristics. The result of this model is equivalent to the result of panel data analysis except in a few industries, where some industry specific characteristics appear. The implications of our results present that Korean policy makers have to take account of industrial effects on standardization to promote technological innovation.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Journal of Chest Surgery
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• v.29 no.10
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• pp.1095-1101
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• 1996

From July 1983 to June 1995, 95 consecutive patients with mitral stenosis were treated surgically in the Department of Thoracic and Cardiovascular Surgery, Chonbuk national University Hospital, mitral valve replacement(MVR) in 62 patients and open mitral commissurotomy(OMC) in 33 patients. Mitral stenosis combined with coronary artery disease, with aortic valve disease, or wish mitral valvular Insufficiency, were excluded from this study. Surgical procedures for mitral stenosis were evaluated, according to complication, reoperation, mor- tality, nd functional change at mid- and long-term postoperative period. Cardiothoracic ratio in the MVR group was greater than the OMC group(0.59 $\pm$0.07 in MVR, 0.53 $\pm$0. 07 in OMC, p<0.05), but other variables(age, sex, MYHA functional classification, EKG finding, echocardiographic finding) did not show significant difference between two groups in the preoperative periods. Even though pathologic valvular lesion(Sellor's pathologic type m: 35 in MVR, 13 in OMC) and valvular calcification(35 in MVR, 11 in OMC) were severe in the MVR group(p=0.001) at intraoperative observation, OMC was possible in 11 patients(23.9%) among 46 patients with valvular calcification and in 13 patients(27.1 %) among 61 patients with Sellor's pathologic type IH . There was no significant difference in early and late mortality, actuarial survival(75% in MVR, 87.6% in OMC at 12 year), but early and late hemorrhagic, thromboembolic complications in the MVR group were greater than in the OMC. Functional changes in NYHA functional classification, EKG lEnding, cardiothoraclc ratio, and echocardiographic finding(EF, LVIDS, LWDd, LAD) did not differ between two groups in mid- and long-term postoperative periods. We conclude that our efforts for preservation of the native valve would be continued, because hemorrhagic and thromboembolic complications in the MVR were greater than in the OMC, and OMC was possible even in patients with severely stenotic and calcified mitral valve, although there was no sis-nificant difference in the functional change, mortality, and survival between the M VR and OMC.

• Korean Chemical Engineering Research
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• v.55 no.3
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• pp.322-331
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• 2017

District heating was first introduced in Korea in 1985. As the service life of the underground thermal piping network has increased for more than 30 years, the maintenance of the underground thermal pipe has become an important issue. A variety of complex technologies are required for periodic inspection and operation management for the maintenance of the aged thermal piping network. Especially, it is required to develop a model that can be used for decision making in order to derive optimal maintenance and replacement point from the economic viewpoint in the field. In this study, the analysis was carried out based on the repair history and accident data at the operation of the thermal pipe network of five districts in the Korea District Heating Corporation. A failure probability model was developed by introducing statistical techniques of qualitative analysis and binomial logistic regression analysis. As a result of qualitative analysis of maintenance history and accident data, the most important cause of pipeline damage was construction erosion, corrosion of pipe and bad material accounted for about 82%. In the statistical model analysis, by setting the separation point of the classification to 0.25, the accuracy of the thermal pipe breakage and non-breakage classification improved to 73.5%. In order to establish the failure probability model, the fitness of the model was verified through the Hosmer and Lemeshow test, the independent test of the independent variables, and the Chi-Square test of the model. According to the results of analysis of the risk of thermal pipe network damage, the highest probability of failure was analyzed as the thermal pipeline constructed by the F construction company in the reducer pipe of less than 250mm, which is more than 10 years on the Seoul area motorway in winter. The results of this study can be used to prioritize maintenance, preventive inspection, and replacement of thermal piping systems. In addition, it will be possible to reduce the frequency of thermal pipeline damage and to use it more aggressively to manage thermal piping network by establishing and coping with accident prevention plan in advance such as inspection and maintenance.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• The Journal of Bigdata
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• v.6 no.1
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• pp.83-89
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• 2021

Lifestyle is changing rapidly, and food consumption patterns vary widely among households as dietary and food processing technologies evolve. This paper reclassified the food group of consumer panel data established by the Rural Development Administration, which contains information on purchasing agricultural products by household unit, and compared the consumption characteristics of agricultural products by age group. The criteria for age classification were divided into groups in their 60s and older with a prevalence of 20% or more metabolic diseases and groups in their 30s and 40s with less than 10%. Using the LightGBM algorithm, we classified the differences in food consumption patterns in their 30s and 50s and 60s and found that the precision was 0.85, the reproducibility was 0.71, and F1_score was 0.77. The results of variable importance were confectionery, folio, seasoned vegetables, fruit vegetables, and marine products, followed by the top five values of the SHAP indicator: confectionery, marine products, seasoned vegetables, fruit vegetables, and folio vegetables. As a result of binary classification of consumption patterns as a median instead of the average sensitive to outliers, confectionery showed that those in their 30s and 40s were more than twice as high as those in their 60s. Other variables also showed significant differences between those in their 30s and 40s and those in their 60s and older. According to the study, people in their 30s and 40s consumed more than twice as much confectionery as those in their 60s, while those in their 60s consumed more than twice as much marine products, seasoned vegetables, fruit vegetables, and folioce or logistics as much as those in their 30s and 40s. In addition to the top five items, consumption of 30s and 40s in wheat-processed snacks, breads and noodles was high, which differed from food consumption patterns in their 60s.

• Korean Journal of Culture and Social Issue
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• v.27 no.4
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• pp.561-584
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• 2021

This study was conducted to classify 601 Korean adults into latent classes according to their love types and identify the differences in depression and find variables that affect the latent classes classification. As a result of the latent class analysis, the latent group for love types of Korean adults were classified into the L-H (7.7%) group, which showed the highest level of all three factors of intimacy, passion, and commitment, and the L-MH (33.6%) group, which all three factors were higher than the average, the L-M (39.8%) group with the mean of all three factors, the L-ML (14.6%) group with all three factors lower than the mean, and the L-L (4.3%) group with the lowest all three factors. Also, as a result of ANOVA, the L-MH group was psychologically healthier and more adaptive than the L-ML group. As a result of multinomial logistic analysis, females were more likely to belong to L-M, L-ML and L-L groups than males. In addition, singles were more likely to belong to the L-M and L-ML groups than those who were married. Also, the higher the anxiety attachment level, the higher the likelihood of belonging to the L-M, L-ML, and L-L groups than the L-H and L-MH groups, the L-ML and L-L groups than the L-M groups, and the L-L group rather than the L-ML groups. However, age, neuroticism, and emotional regulation did not affect the classification of latent classes. This study is meaningful in that it identified the various latent classes for the love types of Korean adults more three-dimensionally and suggested the possibility of differential interventions according to the characteristics of each group.