42 research outputs found

    ํšŒ์ƒ๊ธฐ์—…์˜ ์ด์ต์กฐ์ • ์œ ์ธ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2023. 2. ์ด์šฐ์ข….This study examined the earnings management method and earnings management patterns at each point in time of rehabilitation firms subject to external audit. Due to the conflict of interest between creditors and shareholders, the pursuit of maximization of shareholder value, the risk of deprivation of management rights, and the possibility of liability for damages for insolvent management, there is a possibility that insolvent firms may not choose the rehabilitation procedure in a timely manner. Accordingly, controlling shareholders and managers, who are insiders, have an opportunistic incentive to report sound financial conditions through earnings management to outsiders such as creditors in order to delay debt repayment requests and maximize their profits. As a result of this study, the rehabilitation firms are managing earnings, and report negative accrual earnings management since two years before the start of the rehabilitation procedure. This is presumed to be due to negative earnings management in the process of reflecting the actual poor performance of the firms because of no room to inflate earnings anymore, and consideration of management rights and management's liability for damages, and reflecting the existing insolvency revealed in the investigator's investigation in the start year. In addition, in this study, I found that there is a possibility that the measurement of earnings management through discretionary accruals may be diluted because rehabilitation firms tend to secure insufficient operating cash flows by adjusting working capital. The rehabilitation process is based on the sacrifice of stakeholders. Therefore, the balance between the creditors and the debtors is important, and in order to minimize the social loss, it is necessary to improve the information balance of the outsiders.๋ณธ ๋…ผ๋ฌธ์€ ์™ธ๋ถ€๊ฐ์‚ฌ๋Œ€์ƒ ํšŒ์ƒ๊ธฐ์—…์˜ ์ด์ต์กฐ์ • ๋ฐฉ๋ฒ•๊ณผ ์‹œ์  ๋ณ„ ์ด์ต์กฐ์ •์–‘์ƒ์— ๋Œ€ํ•˜์—ฌ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ฑ„๊ถŒ์ž-์ฃผ์ฃผ๊ฐ„์˜ ์ดํ•ด์ƒ์ถฉ๊ด€๊ณ„, ์ฃผ์ฃผ๊ฐ€์น˜ ๊ทน๋Œ€ํ™” ์ถ”๊ตฌ, ๊ฒฝ์˜๊ถŒ ์ƒ์‹ค์˜ ์œ„ํ—˜๊ณผ ๋ถ€์‹ค๊ฒฝ์˜์— ๋Œ€ํ•œ ์†ํ•ด๋ฐฐ์ƒ์ฑ…์ž„ ๊ฐ€๋Šฅ์„ฑ ๋“ฑ์œผ๋กœ ์ธํ•˜์—ฌ ๋ถ€์‹คํšŒ์‚ฌ๋Š” ์ ์‹œ์— ํšŒ์ƒ์ ˆ์ฐจ๋ฅผ ์„ ํƒํ•˜์ง€ ์•Š์„ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ๋‚ด๋ถ€์ž์ธ ์ง€๋ฐฐ์ฃผ์ฃผ์™€ ๊ฒฝ์˜์ง„์€ ์ฑ„๋ฌด์ƒํ™˜์š”์ฒญ์„ ์ง€์—ฐ์‹œํ‚ค๊ณ  ์ž์‹ ์˜ ์ด์ต์„ ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ฑ„๊ถŒ์ž ๋“ฑ ์™ธ๋ถ€์ž์—๊ฒŒ ์ด์ต์กฐ์ •์„ ํ†ตํ•˜์—ฌ ๊ฑด์ „ํ•ด ๋ณด์ด๋Š” ์žฌ๋ฌด์ƒํ™ฉ์„ ๋ณด๊ณ ํ•  ๊ธฐํšŒ์ฃผ์˜์ ์ธ ์œ ์ธ์ด ์žˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ํšŒ์ƒํšŒ์‚ฌ๋Š” ์ด์ต์กฐ์ •์„ ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ํšŒ์ƒ์ ˆ์ฐจ ๊ฐœ์‹œ 2๋…„์ „๋ถ€ํ„ฐ ์Œ์˜ ๋ฐœ์ƒ์•ก ์ด์ต์กฐ์ •์„ ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ์ด๋Š” ํšŒ์ƒ์ ˆ์ฐจ๊ฐ€ ๊ฐ€๊นŒ์›Œ์งˆ์ˆ˜๋ก ๋” ์ด์ƒ ์ด์ต์„ ๋ถ€ํ’€๋ฆด ์—ฌ์ง€๊ฐ€ ์—†๊ณ , ๊ฒฝ์˜๊ถŒ ์ƒ์‹ค๊ณผ ๊ฒฝ์˜์ง„์˜ ์†ํ•ด๋ฐฐ์ƒ์ฑ…์ž„ ๋“ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์‹ค์ œ ๋ถ€์‹คํ•œ ๊ธฐ์—…์˜ ๊ฒฝ์˜์„ฑ๊ณผ๋ฅผ ๋ฐ˜์˜ํ•˜๋ฉฐ, ํšŒ์ƒ์ ˆ์ฐจ ๊ฐœ์‹œ์—ฐ๋„์—๋Š” ์กฐ์‚ฌ์œ„์›์˜ ์กฐ์‚ฌ๊ณผ์ •์—์„œ ๋“œ๋Ÿฌ๋‚œ ๊ธฐ์กด์˜ ๋ถ€์‹ค์„ ์žฅ๋ถ€์— ๋ฐ˜์˜ํ•˜๋Š” ๊ณผ์ •์—์„œ ์Œ์˜ ๋ฐœ์ƒ์•ก ์ด์ต์กฐ์ •์ด ๋‚˜ํƒ€๋‚˜๊ธฐ ๋•Œ๋ฌธ์ธ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋œ๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํšŒ์ƒํšŒ์‚ฌ์˜ ๊ฒฝ์šฐ ์šด์ „์ž๋ณธ์„ ์กฐ์ •ํ•˜์—ฌ ์‹ค์ œ ๋ถ€์กฑํ•œ ์˜์—…ํ˜„๊ธˆํ๋ฆ„์„ ํ™•๋ณดํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ด๊ณ  ์žˆ์œผ๋ฏ€๋กœ ์žฌ๋Ÿ‰์  ๋ฐœ์ƒ์•ก์„ ํ†ตํ•œ ์ด์ต์กฐ์ •์˜ ์ธก์ •์ด ํฌ์„๋  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ํšŒ์ƒ์ ˆ์ฐจ๋Š” ์—ฌ๋Ÿฌ ์ดํ•ด๊ด€๊ณ„์ž์˜ ํฌ์ƒ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ๋”ฐ๋ผ์„œ ์ฑ„๊ถŒ์ž์™€ ์ฑ„๋ฌด์ž๊ฐ„ ๊ท ํ˜•์ด ์ค‘์š”ํ•˜๋ฉฐ, ์‚ฌํšŒ์  ์†์‹ค์„ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์™ธ๋ถ€์ž์˜ ์ •๋ณด๋ถˆ๊ท ํ˜•์„ ๋ณด์™„ํ•˜์—ฌ์•ผ ํ•  ๊ฒƒ์ด๋‹ค.Chapter 1. Introduction 1 Chapter 2. Prior research and hypothesis development 9 Chapter 3. Data and Sample selection 12 Chapter 4. Empirical results 19 Chapter 5. Conclusion 26 References 29 Appendix 31 Abstract in Korean(๊ตญ๋ฌธ์ดˆ๋ก) 42์„

    Serum 25-hydroxyvitamin D and DHEA-S Level Are Associated with Depression in Community-dwelling Older Women: Surveyed in One Season

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    Background: Even some previous studies suggested that vitamin D deficiency may contribute to depressive disorder, the association between vitamin D deficiency and depression has been controversial. Furthermore, only few studies have done in general population. Dehydroepiandrosterone-sulfate (DHEA-S) is most abundant steroid hormone secreted by adrenal cortex, known to have diverse role in human includingstabilizing mood. Aim of this study was to investigate the association between serum 25-hydroxyvitmin D and DHEA-S and depression in community dwelling apparently healthy women. Methods: This study conducted as a part of the Yonsei Aging Study (YAS). YAS was designed as a survey to investigate the factors related to depression, cognitive function and physical performance in community dwelling old people in Korea. A total of 136 women aged older than 60 years. Blood sample is obtained in October and November in 2008. Depressive symptoms are assessed by self-reported by Geriatric depression scale-15 (GDS-15). Depressive disorder is defined as a GDS-15 score of 7 or higher. Cognitive abilities were assessed using the Mini-Mental State Examination (MMSE). Additionally, cardiometabolic risk factors, DHEA-S and 25-(OH) vitamin D level and physical performance index (by gait speed, chair stand test, and tandem standing test) were measured. Results: The prevalence of depression was vitamin D deficiency is 46.3% in this study population. Mean values of 25(OH) vitamin D and DHEA-S (18.47ยฑ7.10 ng/mL, 52.98ยฑ31.96 ฮผg/dL respectively) in women with depression were lower than those in normal women (22.01ยฑ8.09 ng/mL, 44.58ยฑ32.54 ฮผg/dL respectively; P๏ผœ0.02, P=0.12 respectively). The prevalence of vitamin deficiency (25-hydroxy vitamin D ๏ผœ10 ng/mL) and vitamin insufficiency (25-hydroxy vitamin D ๏ผœ20 ng/mL) were 6.62% and 46.32% respectively. Correlation analysis showed GDS scores associated with 25-(OH) vitamin D level (r=โˆ’0.26, P๏ผœ0.005), physical performance index (r=โˆ’0.22, P๏ผœ0.01), and DHEA-S level (โˆ’0.20, P ๏ผœ0.05). Women with history of diabetes mellitus and current smoking habit had a higher prevalence of depression in statistical significance. A Multiple linear regression analysis showed that 25(OH) vitamin D level was an independent risk factor for depression after adjustment for confounding variables. Conclusion: This study suggested that 25-hydroxy vitamin D and DHEA-S levels are the independently associated with depression in apparently healthy community-dwelling older women.ope

    Hand Grip Power Is Independently Associated with Physical Function in Community Dwelling Elderly

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    Background: There are some studies that declined muscle power emerged as an early markers for age-related physical disability. However those studies had a limitation that they did not use quantitative and objective tools in assessing physical function. Furthermore there has been no study conducted in the Korean elderly. This study aimed to investigate the association between hand grip power and declined physical function in community dwelling elderly. Methods: A total of 77 community dwelling apparently healthy old people who can carry out daily life independently were recruited in this study. History taking, blood sampling and physical examination were obtained. Depression was assessed by the 15-item geriatric depression scale (GDS), and cognitive function was examined by the Mini-Mental State Examination (MMSE). We also measured hand grip power and physical performances (gait speed, chair-stand times, tandem standing times). Results: Hand grip power was positively correlated with physical performance score (r=0.49, P๏ผœ0.0001). After adjustment of confounding factors using step-wise multiple regression analysis, hand grip power, age and serum cortisol levels were significantly associated with physical performance score (ฮฒ=0.14, P๏ผœ0.0001; ฮฒ=โˆ’0.11, P=0.0006; ฮฒ=โˆ’0.20, P=0.03, respectively). Conclusion: Hand grip power was independently associated with physical performance score. The result suggests that the declined physical function in the elderly may be assessed easily by measuring hand grip power in clinical setting.ope

    Characteristic Features Observed in the East-Asian Cold Anomalies in January 2011

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    East Asia experienced extremely cold weather in January 2011, while the previousDecember and the following February had normal winter temperature. In this study NationalCenters for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data are used to investigate the characteristic features observed in the meteorologicalfields such as temperature, sea-level pressure, geopotential height, and wind duringthis winter period. In January the planetary-wave pattern is dominated by stationary-wave formin the mid-to-high latitude region, while transient waves are significant in the previous month.To understand the planetary-wave features quantitatively, harmonic analyses have been donefor the 500-hPa geopotential height field. In the climatological-mean geopotential heights thewave numbers 1, 2, and 3 are dominant during the whole winter. In January 2011 the waves ofnumber 1, 2, and 3 are dominant and stationary as in the climatological-mean field. In December2010 and February 2011, however, the waves of number 4, 5, and 6 play a major role andshow a transient pattern. In addition to the distinctive features in each month the planetarywavepatterns dependent on the latitude are also discussed.OAIID:oai:osos.snu.ac.kr:snu2013-01/102/0000002478/2SEQ:2PERF_CD:SNU2013-01EVAL_ITEM_CD:102USER_ID:0000002478ADJUST_YN:YEMP_ID:A004842DEPT_CD:3345CITE_RATE:0DEPT_NM:์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€SCOPUS_YN:NCONFIRM:

    ์ค‘์‚ฐ์ธต ๊ฒฝ๋ ฅ๋‹จ์ ˆ ์—ฌ์„ฑ์˜ ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ต์œกํ•™๊ณผ ํ‰์ƒ๊ต์œก ์ „๊ณต, 2016. 2. ํ•œ์ˆญํฌ.์—„๋งˆ ๋˜๊ธฐ๋Š” ์—ฌ์„ฑ ์•ˆ์— ๋‚ด์žฌ๋˜์–ด ์žˆ๋Š” ๋ชจ์„ฑ์ด ์ถœ์‚ฐ์„ ํ†ตํ•ด ๋ฐœํ˜„๋˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์‚ฌํšŒ์  ์—ญํ• ๋กœ์„œ ์ผ์ข…์˜ ํ•™์Šต์„ ํ†ตํ•ด ํš๋“๋˜๋Š” ์ •์ฒด์„ฑ ํ˜•์„ฑ์˜ ๊ณผ์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์—„๋งˆ๋“ค์ด ๋ชจ์„ฑ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•  ๋•Œ ๊ฒฝํ—˜ํ•˜๋Š” ๋‹ค์–‘ํ•œ ์ธต์œ„์˜ ๊ฐˆ๋“ฑ์„ ํ•ด๊ฒฐํ•˜๋ฉด์„œ ์—„๋งˆ๋ผ๋Š” ์ •์ฒด์„ฑ์„ ํ˜•์„ฑํ•˜๊ณ , ์—„๋งˆ๋กœ์„œ ์ž์‹ ์˜ ์กด์žฌ์˜๋ฏธ๋ฅผ ์žฌ๊ทœ์ •ํ•˜๋ฉฐ, ์ข‹์€ ์—„๋งˆ ์ƒ์„ ์ˆ˜๋ฆฝํ•˜๋Š” ๊ณผ์ •์„ ์—ฌ์„ฑ์ฃผ์˜์  ํ‰์ƒํ•™์Šต์˜ ๊ด€์ ์—์„œ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์•„๋ž˜์™€ ๊ฐ™์€ ์—ฐ๊ตฌ๋ฌธ์ œ๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ์ฒซ์งธ, ์ถœ์‚ฐ ํ›„ ์—ฌ์„ฑ๋“ค์€ ์—„๋งˆ๋กœ์„œ ์–ด๋–ค ๊ฒฝํ—˜์„ ํ•˜๋Š”๊ฐ€? ๊ทธ๋Ÿฐ ๊ฒฝํ—˜๋“ค ์ค‘์—์„œ ๋ชจ์„ฑ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์—„๋งˆ๋“ค์€ ์–ด๋–ค ๊ฒฝํ—˜์„ ์ƒˆ๋กญ๊ฒŒ ์ฒดํ™”ํ•˜๋Š”๊ฐ€? ๋‘˜์งธ, ๋ชจ์„ฑ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์—„๋งˆ๊ฐ€ ์ง๋ฉดํ•˜๋Š” ๊ฐˆ๋“ฑ์€ ๋ฌด์—‡์ด๋ฉฐ, ๊ฐˆ๋“ฑ์˜ ์–‘์ƒ์€ ์–ด๋– ํ•œ๊ฐ€? ์ด ๊ฐˆ๋“ฑ์€ ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต๊ณผ์ •์—์„œ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”๋˜๋Š”๊ฐ€? ์…‹์งธ, ์—ฌ์„ฑ์ฃผ์˜์  ๊ด€์ ์—์„œ ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋ฅผ ์–ด๋–ป๊ฒŒ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ์ถœ์‚ฐ์„ ํ•˜๊ณ  ์—„๋งˆ๋“ค์€ ๊ธ‰๊ฒฉํ•œ ์ผ์ƒ์˜ ๋ณ€ํ™”๋ฅผ ๊ฒฝํ—˜ํ•œ๋‹ค. ํ•˜๋ฃจ ์ข…์ผ ๋ชจ์œ ์ˆ˜์œ ๋ฅผ ํ•˜๊ณ , ๋ฐค์ค‘์ˆ˜์œ ๋กœ ์ˆ˜๋ฉด๋ถ€์กฑ์„ ๊ฒฝํ—˜ํ•˜๋ฉฐ, ์•„๊ธฐ๋ฅผ ๋‹ฌ๋ž˜๊ณ  ์žฌ์›Œ์•ผ ํ•˜๋Š” ๋Œ๋ด„์˜ ๋…ธ๋™์€ ์œก์ฒด์  ํ”ผ๋กœ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค. ์ถœ์‚ฐ ํ›„ ํ˜ธ๋ฅด๋ชฌ์˜ ๋ณ€ํ™”์™€ ๋น„์ผ์ƒ์ ์ธ ํ™œ๋™์œผ๋กœ ์ฑ„์›Œ์ง€๋Š” ์ผ์ƒ ๋•Œ๋ฌธ์— ์—„๋งˆ๋“ค์€ ์šฐ์šธ๊ฐ์„ ๋Š๋ผ๊ธฐ๋„ ํ•œ๋‹ค. ์—„๋งˆ๋“ค์€ ์ „ํ†ต์ ์ธ ์„ฑ ์—ญํ• ์„ ์•”๋ฌต์ ์œผ๋กœ ์š”๊ตฌ๋ฐ›์œผ๋ฉด์„œ ๋‚จํŽธ, ์‹œ์–ด๋จธ๋‹ˆ, ์นœ์ •์–ด๋จธ๋‹ˆ์™€ ๊ฐˆ๋“ฑ์„ ๊ฒฝํ—˜ํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ์—„๋งˆ๋ผ๋Š” ์—ญํ• ์ด ๋‚˜๋ณด๋‹ค ์šฐ์„  ๋˜๋Š” ์ƒํ™ฉ๊ณผ ์œก์•„์— ๋Œ€ํ•œ ๋ชจ๋“  ์ฑ…์ž„์ด ์—„๋งˆ์—๊ฒŒ๋งŒ ๋ถ€์—ฌ๋˜๋Š” ์ƒํ™ฉ์— ๋ถ€๋‹นํ•จ์„ ๋Š๋ผ๋ฉฐ, ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ๋ฐ›๊ธฐ๋„ ํ•œ๋‹ค. ์—„๋งˆ๋“ค์€ ์œก์•„ ์ŠคํŠธ๋ ˆ์Šค์— ๋ถ„๋…ธ์™€ ํ™”๋กœ ๋ฐ˜์‘ํ•˜๋Š” ์ž์‹ ์˜ ๋ชจ์Šต์„ ๋ฐœ๊ฒฌํ•˜๋ฉด์„œ ์ฃ„์ฑ…๊ฐ์„ ๊ฐ–๊ธฐ๋„ ํ•œ๋‹ค. ์ด๋Ÿฐ ๋น„์ผ์ƒ์ ์ธ ์ƒํ™œ์ด ์—„๋งˆ๋“ค์˜ ์ผ์ƒ์ด ๋˜๋ฉด์„œ ๊ฐœ๋ณ„์  ์ฃผ์ฒด์ธ ๋‚ด๊ฐ€ ๋ถ€์ •๋˜๋Š” ๊ฒฝํ—˜์„ ํ•œ๋‹ค. ์ด๋Ÿฐ ์กด์žฌ๋ถ€์ •์˜ ๊ฒฝํ—˜์€ ํ•™์Šต ๋™๊ธฐ๋กœ ์ž‘์šฉํ•˜๋ฉฐ, ์—„๋งˆ๋“ค์€ ์ด๋Ÿฐ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ผ์ข…์˜ ํ•™์Šต์„ ํ•˜๊ฒŒ ๋œ๋‹ค. ์—„๋งˆ๋“ค์€ ๊ธ‰๊ฒฉํ•œ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”๋กœ ๋Š๋ผ๋Š” ํ˜ผ๋ž€์Šค๋Ÿฌ์›€์„ ํ•ด์†Œํ•˜๊ธฐ ์œ„ํ•ด ์ž์•„๋ฅผ ์„ฑ์ฐฐํ•˜๊ฒŒ ๋œ๋‹ค. ์„ฑ์ฐฐ์„ ํ†ตํ•ด์„œ ์„ฑ๊ฒฉ์ด๋‚˜ ์Šต๊ด€์˜ ๋ณ€ํ™”๊ฐ€ ํ•„์š”ํ•จ์„ ์ธ์ง€ํ•œ๋‹ค. ์‹œ๊ฐ„์ด ํ๋ฆ„์— ๋”ฐ๋ผ ์ž๋…€์™€ ์œ ๋Œ€๊ฐ์„ ํ˜•์„ฑํ•˜๊ณ  ๋ชจ์„ฑ์• ๊ฐ€ ๋ฐœํ˜„๋˜๋ฉด์„œ ์—„๋งˆ์˜ ์˜๋ฏธ๋ฅผ ์ƒˆ๋กญ๊ฒŒ ๊ทœ์ •ํ•œ๋‹ค. ์ต์ˆ™ํ•˜์ง€ ์•Š์€ ๋ชจ์„ฑ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋ฉด์„œ ์—„๋งˆ๋“ค์€ ๋งŒ์กฑ๊ฐ๊ณผ ๋ฟŒ๋“ฏํ•จ์„ ๋Š๋ผ๊ธฐ๋„ ํ•˜๊ณ , ๋‚ด ์ƒˆ๋ผ, ๋‚ด ํ”ผ๋ถ™์ด๋กœ ์ž๋…€๋ฅผ ๋ถ„์‹ ํ™”ํ•œ๋‹ค. ์ž๋…€์™€ ์˜์‚ฌ์†Œํ†ต์ด ๊ฐ€๋Šฅํ•ด์ง€๋ฉด์„œ ์ž๋…€๊ฐ€ ํ‘œํ˜„ํ•˜๋Š” ์‚ฌ๋ž‘๊ณผ ๊ด€์‹ฌ์œผ๋กœ ์—„๋งˆ๋“ค์€ ํ–‰๋ณต๊ณผ ๊ธฐ์จ์„ ๋Š๋‚€๋‹ค. ์ด๋ ‡๊ฒŒ ์ž๋…€์™€ ์†Œํ†ตํ•˜๋ฉด์„œ ์—„๋งˆ๋“ค์€ ์ž๋…€์˜ ์„ฑ์žฅ์„ ๋„๋ชจํ•˜๊ธฐ ์œ„ํ•œ ๋ณด์กฐ์ ์ด๊ณ  ์ฃผ๋ณ€์ ์ธ ์กด์žฌ๋กœ ์ž์‹ ์„ ์ˆ˜์šฉํ•˜๋ฉด์„œ ์ „์—… ์–ด๋จธ๋‹ˆ๋กœ์„œ์˜ ์ •์ฒด์„ฑ์„ ํ˜•์„ฑํ•œ๋‹ค. ์—„๋งˆ๋“ค์€ ๋ชจ์„ฑ์ด๋ฐ์˜ฌ๋กœ๊ธฐ์™€ ํ•œ๊ตญ ์‚ฌํšŒ์˜ ๊ฐ€์กฑ์ฃผ์˜์— ์˜ํ•ด ํ˜•์„ฑ๋œ ํŠน์ •ํ•œ ๋ชจ์„ฑ์—ญํ• ์„ ์ฃผ๋ณ€ ์‚ฌ๋žŒ๋“ค์— ์˜ํ•ด ๊ฐ•์š”๋ฐ›์œผ๋ฉด์„œ ๊ฐˆ๋“ฑ์„ ๊ฒฝํ—˜ํ•œ๋‹ค. ์ถœ์‚ฐ ์ „๊ณผ ๋‹ค๋ฅด๊ฒŒ ์ „ํ†ต์ ์ธ ์•„๋‚ด์˜ ์—ญํ• ๊ณผ ์—„๋งˆ์˜ ์—ญํ• ์„ ๋‚จํŽธ์ด๋‚˜ ์นœ์ •์–ด๋จธ๋‹ˆ ๋˜๋Š” ์‹œ์–ด๋จธ๋‹ˆ์—๊ฒŒ ์š”๊ตฌ๋ฐ›๊ธฐ๋„ ํ•œ๋‹ค. ๋ฐ˜๋Œ€๋กœ, ์ „์—…์ฃผ๋ถ€๋กœ ์‚ด๊ณ  ์‹ถ์ง€๋งŒ ์ฃผ๋ณ€ ์‚ฌ๋žŒ๋“ค์ด ์ž์‹ ์—๊ฒŒ ๊ธฐ๋Œ€ํ•˜๋Š” ์‚ฌํšŒ์ ์ธ ์„ฑ๊ณต์„ ์œ„ํ•ด์„œ ์ผ๊ณผ ๊ฐ€์ •์„ ์–‘๋ฆฝํ•ด์•ผ ํ•˜๋Š” ์ƒํ™ฉ์— ์ฒ˜ํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ฐ€์กฑ์ฃผ์˜, ์‹ ์ž์œ ์ฃผ์˜, ๋Šฅ๋ ฅ์ฃผ์˜๊ฐ€ ์–ฝํ˜€์žˆ๋Š” ํ•œ๊ตญ์‚ฌํšŒ์˜ ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ์ž๋…€ ๊ต์œก์˜ ์ „์ ์ธ ์ฑ…์ž„์ด ์—„๋งˆ์—๊ฒŒ ๋ถ€์—ฌ๋˜๋ฉด์„œ ์—„๋งˆ๋“ค์€ ๋น„์ •์ƒ์ ์œผ๋กœ ํ™•๋Œ€๋œ ์Šˆํผ๋ง˜ ์ด๋ฐ์˜ฌ๋กœ๊ธฐ์™€ ๊ฐˆ๋“ฑ์„ ๊ฒฝํ—˜ํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ์ด๋Ÿฐ ๊ฐˆ๋“ฑ์˜ ๋ฐœ์ƒ ์›์ธ์„ ์ฐพ์œผ๋ ค๊ณ  ์—„๋งˆ๋“ค์€ ์ž์‹ ์˜ ์„ฑ์žฅ๊ณผ์ •์„ ๋˜๋Œ์•„๋ณธ๋‹ค. ์„ฑ์žฅ๊ณผ์ •์˜ ๊ฒฝํ—˜๊ณผ ์–ด๋จธ๋‹ˆ์˜ ์–‘์œกํƒœ๋„๋ฅผ ๊ฒฐํ•ฉํ•˜์—ฌ ์žฌ๊ตฌ์„ฑํ•˜๋ฉด์„œ ์–ด๋จธ๋‹ˆ์˜ ์–‘์œกํƒœ๋„๋ฅผ ๋ฐ˜์„ฑํ•˜๊ณ  ํ‰๊ฐ€ํ•œ๋‹ค. ์ด๋Ÿฐ ์ผ๋ จ์˜ ๊ณผ์ •์„ ํ†ตํ•ด์„œ ์—„๋งˆ์ธ ์ž์‹ ์„ ๊นŠ์ด ์ดํ•ดํ•˜๊ฒŒ ๋˜๊ณ , ์ด๋Ÿฐ ์ดํ•ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜์—ฌ ์ข‹์€ ์—„๋งˆ๊ฐ€ ๋˜๊ธฐ ์œ„ํ•ด์„œ ํ•„์š”ํ•œ ์ž์งˆ์ด ๋ฌด์—‡์ธ๊ฐ€๋ฅผ ๋ฐœ๊ฒฌํ•œ๋‹ค. ์—„๋งˆ๋“ค์€ ๋ชจ์„ฑ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋ฉด์„œ ์ง๋ฉดํ•˜๋Š” ๊ฐˆ๋“ฑ์„ ๊ฒฝํ—˜ํ•˜๋ฉด์„œ ๋ชจ์ข…์˜ ํ•™์Šต์„ ํ•˜๊ณ , ๊ทธ ํ•™์Šต์˜ ๊ฒฐ๊ณผ๋กœ ์ „์—… ์–ด๋จธ๋‹ˆ์ •์ฒด์„ฑ์˜ ํ•˜์œ„๋ฒ”์ฃผ์— ํ•ด๋‹นํ•˜๋Š” ํ›ˆ์œก์ž ์ •์ฒด์„ฑ, ๋Œ๋ด„์ด ์ •์ฒด์„ฑ, ๊ต์œก ๊ธฐํš์ž ์ •์ฒด์„ฑ, ์•ˆ๋‚ด์ž ์ •์ฒด์„ฑ์„ ๋ชจ์„ฑ์—ญํ• ์˜ ๋ณ€ํ™”์™€ ๊ฐˆ๋“ฑ์˜ ๋ณ€ํ™”๋ฅผ ๊ฒฝํ—˜ํ•˜๋ฉด์„œ ํ˜•์„ฑํ•œ๋‹ค. ์ƒˆ๋กœ์šด ์ •์ฒด์„ฑ์„ ํ˜•์„ฑํ•˜๋ฉด์„œ ์ž์‹ ์˜ ์„ฑ์žฅ๊ณผ์ •์˜ ๊ฒฝํ—˜๊ณผ ํ˜„์žฌ ์‚ฌํšŒ์˜ ๋ชจ์Šต์„ ํ†ต์ฐฐํ•˜๋ฉด์„œ ์ข‹์€ ์—„๋งˆ ์ƒ์„ ์ˆ˜๋ฆฝํ•œ๋‹ค. ์—„๋งˆ๋“ค์ด ์ˆ˜๋ฆฝํ•˜๋Š” ์ข‹์€ ์—„๋งˆ์˜ ๋ชจ์Šต์€ ๋ชจ์„ฑ ์ด๋ฐ์˜ฌ๋กœ๊ธฐ๋ฅผ ์—ญ์ˆœํ™˜์ ์œผ๋กœ ๋‚ด๋ฉดํ™”ํ•œ ์–‘์ƒ์„ ๋ค๋‹ค. ์ด๋“ค์ด ์ƒ๊ฐํ•˜๋Š” ์ข‹์€ ์—„๋งˆ๋Š” ์ž๋…€์™€ ์นœ๋ฐ€ํ•œ ๊ด€๊ณ„๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ ์ž๋…€์˜ ์ƒํ™œ์„ ์ถฉ๋ถ„ํžˆ ํŒŒ์•…ํ•˜๊ณ  ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ž๋…€์—๊ฒŒ ์ ์ •ํ•œ ๊ฑฐ๋ฆฌ๋ฅผ ๋‘๋ฉด์„œ๋„ ์ง„๋กœ ์„ ํƒ์„ ์œ„ํ•œ ์กฐ์–ธ๊ณผ ๋‹ค์–‘ํ•œ ๊ฒฝํ—˜์„ ์ œ๊ณตํ•ด์•ผ ํ•˜๊ณ , ์„ฑ์ ์„ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด์„œ ์‚ฌ๊ต์œก์„ ๊ธฐํšํ•˜๊ณ  ์‹ค์ฒœํ•˜๋Š” ๋ชจ์Šต์„ ๋ณด์ธ๋‹ค. ์ž๋…€๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ž์‹ ์˜ ์ƒํ™œ์ด ๊ตฌ์„ฑ๋˜๋Š” ๊ฒƒ์„ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋ฐ›์•„๋“ค์ด๋ฉฐ, ์ž๋…€์˜ ์„ฑ์žฅ๊ณผ ๊ต์œก์„ ์œ„ํ•ด์„œ ์ž์‹ ์˜ ์ปค๋ฆฌ์–ด๋ฅผ ํฌ๊ธฐํ•˜๊ณ  ์ž์•„์„ฑ์ทจ ์š•๊ตฌ๋ฅผ ๋ฏธ๋ž˜๋กœ ์œ ๋ณดํ•˜๋Š” ์„ ํƒ์„ ํ•จ์œผ๋กœ ๊ฐˆ๋“ฑ์„ ํ†ตํ•ด ๋ชจ์„ฑ์ด๋ฐ์˜ฌ๋กœ๊ธฐ๋ฅผ ๋ชจ์ˆœ์ ์œผ๋กœ ์ˆ˜์šฉํ•˜๊ณ  ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต์˜ ํŠน์ง•์€ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ํ•™์Šต์ด ์ค‘์ธต์ ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค๋Š” ์ ์ด๋‹ค. ์—„๋งˆ๋“ค์€ ์ž๋…€๋ฅผ ํ‚ค์šฐ๋ฉด์„œ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ž์‹ ์˜ ์„ฑ์žฅ๊ณผ์ •์˜ ๊ฒฝํ—˜์„ ์—ฐ์ƒํ•˜๊ฒŒ ๋œ๋‹ค. ์—ฐ์ƒ๋œ ๊ธฐ์–ต๋“ค์„ ์žฌ๊ตฌ์„ฑํ•˜๋ฉด์„œ ์—„๋งˆ๋“ค์€ ์ž์‹ ์˜ ์‚ถ์„ ์„ฑ์ฐฐํ•˜๊ณ  ๊ทธ๊ฒƒ์„ ๊ธฐ์ค€์œผ๋กœ ํ•˜์—ฌ ์–ด๋จธ๋‹ˆ์˜ ์–‘์œก๋ฐฉ์‹์„ ํ‰๊ฐ€ํ•œ๋‹ค. ํ‰๊ฐ€ํ•œ ์–ด๋จธ๋‹ˆ์˜ ์–‘์œก๋ฐฉ์‹์„ ํ๊ธฐํ•˜๊ธฐ๋„ ํ•˜๊ณ , ์ž์‹ ์˜ ์–‘์œก๋ฐฉ์‹์— ์ ‘๋ชฉํ•˜์—ฌ ๋ชจ๋ฐฉํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ์ฆ‰, ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต์€ ๊ฒฝํ—˜ํ•™์Šต๊ณผ ์„ฑ์ฐฐํ•™์Šต์˜ ์ด์ค‘๊ตฌ์กฐ์ ์ธ ํ•™์Šต์–‘์‹์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ๋ชจ๋ฐฉํ•™์Šต๊ณผ ํ๊ธฐํ•™์Šต์˜ ๋ชจ์Šต๋„ ๋‹ด๊ณ  ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต์€ ๋ณ€์ฆ๋ฒ•์ ์ธ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ž๋…€๊ฐ€ ์„ฑ์žฅํ•จ์— ๋”ฐ๋ผ ์—„๋งˆ๋“ค์ด ์ˆ˜ํ–‰ํ•ด์•ผ ํ•˜๋Š” ๋ชจ์„ฑ์—ญํ• ์ด ๋‹ฌ๋ผ์ง€๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์˜ยท์œ ์•„์˜ ์ž๋…€๋ฅผ ์œ„ํ•œ ๋ชจ์„ฑ์—ญํ• ์€ ์ฃผ๋กœ ์‹ ์ฒด์ ์ธ ๋Œ๋ด„์ด์ง€๋งŒ ์ฒญ์†Œ๋…„๊ธฐ์˜ ์ž๋…€๋ฅผ ์œ„ํ•œ ๋ชจ์„ฑ์—ญํ• ์€ ์ง„๋กœ์— ๋Œ€ํ•œ ์กฐ์–ธ์ž์˜ ์—ญํ• , ์ž๋…€์˜ ์ •์„œ์  ๋ฐœ๋‹ฌ์„ ๋„์™€์ฃผ๋Š” ์—ญํ•  ๋“ฑ ์–ด๋ฆฐ์ด๋ฅผ ํ‚ค์šธ ๋•Œ์™€๋Š” ๋‹ค๋ฅธ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•ด์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์—„๋งˆ๋“ค์€ ์ž๋…€์˜ ์„ฑ์žฅ์— ๋งž์ถ”์–ด ์ƒˆ๋กœ์šด ๋ชจ์„ฑ์—ญํ• ์„ ๊ณ„์†์ ์œผ๋กœ ํ•™์Šตํ•ด์•ผ ํ•˜๊ณ , ๊ทธ๊ฒƒ์„ ์ž๋…€ ์–‘์œก์— ์‹ค์ฒœํ•œ๋‹ค. ์—„๋งˆ๋“ค์˜ ์ด์ „๊นŒ์ง€์˜ ๋ชจ์„ฑ์—ญํ• ์˜ ๊ฒฝํ—˜์€ ์ƒˆ๋กœ์šด ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต์˜ ๋ฐฐ๊ฒฝ์ด ๋˜๋ฉด์„œ, ํ•™์Šต์ž์›์ด ๋œ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์—„๋งˆ๋˜๊ธฐ ๊ณผ์ •์€ ํ•™์Šต์˜ ์ˆœํ™˜์ ์ธ ํŠน์ง•๊ณผ ๋ชจ์„ฑ์—ญํ• ์˜ ๊ฒฝํ—˜์ด ์ถ•์ ๋˜๋ฉด์„œ ์ƒˆ๋กœ์šด ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต์ด ์ถ”๋™๋˜๋Š” ๋ณ€์ฆ๋ฒ•์ ์ธ ํŠน์ง•์ด ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฒฝ๋ ฅ๋‹จ์ ˆ์„ ๊ฒฝํ—˜ํ•œ ์ค‘์‚ฐ์ธต ์—„๋งˆ๋“ค์ด ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต์„ ํ†ตํ•ด ์ •์ฒด์„ฑ์„ ํ˜•์„ฑํ•˜๊ณ , ์ž๋…€์˜ ์„ฑ์žฅ๋ฐœ๋‹ฌ์— ๋”ฐ๋ผ ์ •์ฒด์„ฑ์ด ๋ณ€์ฆ๋ฒ•์ ์œผ๋กœ ๋ณ€ํ•˜๋ฉฐ, ์—ญ์ˆœํ™˜์ ์œผ๋กœ ๋ชจ์„ฑ ์ด๋ฐ์˜ฌ๋กœ๊ธฐ๋ฅผ ๋‚ด๋ฉดํ™”ํ•˜๋ฉด์„œ ์ •์ฒด์„ฑ์„ ๋ฐœ๋‹ฌ์‹œํ‚ค๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ–ˆ๋‹ค๋Š”๋ฐ ๊ทธ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์ด๋Ÿฐ ๋ฐœ๊ฒฌ์ ์„ ์ ์šฉํ•˜์—ฌ ์—ฌ์„ฑ์ฃผ์˜ ํ•™์Šต์˜ ๊ด€์ ์—์„œ ์ค‘์‚ฐ์ธต ๊ฒฝ๋ ฅ๋‹จ์ ˆ ์—ฌ์„ฑ๋“ค์„ ์œ„ํ•ด ํ‰์ƒ๊ต์œก์ด ๋‚˜์•„๊ฐ€์•ผ ํ•  ๋ฐฉํ–ฅ์„ ๋…ผ์˜ํ•˜์˜€๋‹ค.I. ์„œ๋ก  1 1. ๋ฌธ์ œ์ œ๊ธฐ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ๋ฌธ์ œ 4 3. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 5 II. ์„ ํ–‰์—ฐ๊ตฌ ๋ฐ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 13 1. ์‹œ๋Œ€์— ๋”ฐ๋ผ ๋ณ€ํ•˜๋Š” ๋ชจ์„ฑ์—ญํ•  13 2. ํ•œ๊ตญ์˜ ๊ฐ€์กฑ์ฃผ์˜์™€ ๋ชจ์„ฑ ์ด๋ฐ์˜ฌ๋กœ๊ธฐ 15 3. ์—ฌ์„ฑ์ฃผ์˜์™€ ํ‰์ƒ๊ต์œก 17 1) ์—ฌ์„ฑ์˜ ๊ฐˆ๋“ฑ๊ฒฝํ—˜๊ณผ ํ‰์ƒํ•™์Šต 18 2) ํŽ˜๋ฏธ๋‹ˆ์ŠคํŠธ ํŽ˜๋‹ค๊ณ ์ง€์™€ ํ‰์ƒํ•™์Šต 20 III. ์—„๋งˆ๋กœ ์„ฑ์žฅํ•˜๊ธฐ 23 1. ์‹ ์ฒด์  ๋ณ€ํ™”๋ฅผ ๊ฒช์œผ๋ฉฐ ๋‚˜์—์„œ ์—„๋งˆ ๋˜๊ธฐ 23 1) ์ž„์‚ฐ๋ถ€ ์ƒํ™œ์€ ํ–‰๋ณตํ–ˆ์—ˆ์–ด์š” 23 2) ์ •๋ณด๋งŒ ์ˆ˜์ง‘ํ–ˆ์–ด์š” 25 3) ์„ ๋‹ฌ์€ ๊ณ ์ƒํ–ˆ๋˜ ๊ฑฐ ๊ฐ™์•„์š” 30 4) ์‹ ๊ธฐํ•˜๊ณ  ๋‚ฏ์„ค๊ณ  31 2. ํ•™์Šต ์ƒํ™ฉ, ์ผ์ƒ์ด ์•„๋‹Œ ์ผ์ƒ 33 1) ๋ชจ์œ ์ˆ˜์œ ๊ฐ€ ๊ทธ๋ ‡๊ฒŒ ํž˜๋“  ๊ฑด์ง€ ๋ชฐ๋ž์–ด์š” 34 2) ์ž  ๋ชป ์ž๋Š” ๊ฒŒ ํž˜๋“ค์—ˆ์–ด์š” 40 3) ์šฐ์šธ์ฆ์„ ๊ฒช์€ ๊ฑฐ ๊ฐ™์•„์š” 41 4) ์™œ ๋‚˜๋งŒ ๊ณ ์ƒ์„ ํ•ด์•ผ ํ•˜์ง€ 42 5) ํ™”๋ฅผ ๋ง‰ ํญ๋ฐœ์ ์œผ๋กœ ๋ƒˆ์–ด์š” 45 3. ๋ชจ์„ฑ์—ญํ• ๊ณผ ๋‚˜์˜ ์š•๊ตฌ 47 1) ์ง์žฅ ๋ง˜, ์ด์ค‘์œผ๋กœ ๋” ํž˜๋“ค ๊ฒƒ ๊ฐ™์•„์š” 47 2) ์• ๊ธฐ๋ฅผ ๋‚ณ๊ณ  ๋‚˜์„œ๋Š” ์•ˆ๋˜๋Š”๊ฒŒ ์žˆ์–ด์š” 54 3) ์•„์คŒ๋งˆ๊ฐ€ ๋˜๋Š” ๊ฑด๊ฐ€ 55 4. ์—„๋งˆ๋กœ์„œ์˜ ์‚ถ์— ์˜๋ฏธ ์ฐพ๊ธฐ 57 1) ๋งŒ์กฑ๊ฐ, ๋ฟŒ๋“ฏํ•จ, ์„ฑ์ทจ๊ฐ 57 2) ๋‚ด ์ƒˆ๋ผ, ๋‚ด ํ”ผ ๋ถ™์ด 59 3) ๋‚  ์ฐพ๋Š” ๊ฑฐ, ์ข‹์€ ๋Š๋‚Œ 61 4) ์ œ์ผ ์—„๋งˆ๋‹ค์šด ๋ชจ์Šต์ด ์ œ์ผ ์—ฌ์ž๋‹ค์šด ๋ชจ์Šต 62 5) ์ œ 2์˜ ์ธ์ƒ์„ ์‚ด๊ฑฐ์•ผ 64 5. ์†Œ๊ฒฐ 66 IV. ์—„๋งˆ์—์„œ ์ข‹์€ ์—„๋งˆ ๋˜๊ธฐ 68 1. ์—„๋งˆํ•œํ…Œ ์—†๋˜ ๋ชจ์Šต์„ ๋” ์ฃผ๊ณ  ์‹ถ์–ด์š” 68 1) ์–ด๋ ค์šด ์ผ์„ ํ—ค์ณ ๋‚˜๊ฐˆ ์ˆ˜ ์žˆ๋Š” ์‹ฌ๋ ฅ(ๅฟƒๅŠ›)๊ณผ ์‚ฌํšŒ์„ฑ 68 2) ์• ๋“ค ํ•™๊ต์— ์—ด์‹ฌํžˆ ๋‹ค๋…€์š” 73 3) ๋‹ค์–‘ํ•œ ๊ฒฝํ—˜์„ ์‹œ์ผœ์ฃผ๊ณ  ์‹ถ์–ด์š” 76 4) ๋งˆ์Œ์ด ํ†ตํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ‚ค์šฐ๊ณ  ์‹ถ์–ด์š” 80 2. ์šฐ๋ฆฌ ์—„๋งˆ์ฒ˜๋Ÿผ๋งŒ ์• ๋ฅผ ํ‚ค์šธ ์ˆ˜ ์žˆ์œผ๋ฉด ์ข‹๊ฒ ์–ด์š” 84 1) ๋„ˆ๋ฌด ์ข‹์•˜๋˜ ๊ธฐ์–ต์ด ๋‚˜์š” 84 2) ์—„๋งˆ์˜ ๊ต์œก์  ์ง€์›์€ ๋ณธ๋ฐ›์„ ๊ฑฐ์˜ˆ์š” 87 3) ์ œ์ผ ์นœํ•œ ์นœ๊ตฌ๋Š” ์—„๋งˆ์˜ˆ์š” 88 4) ์‹ ์•™ ๊ต์œก์„ ๋จผ์ € ํ•  ๊ฑฐ์˜ˆ์š” 89 3. ๋‚ด ์•ˆ์— ์žˆ๋Š” ํ‹€์„ ๊นฌ ๊ฑฐ์ง€ 91 1) ๋‚ด ์„ฑ๊ฒฉ๊ณผ ์Šต๊ด€ ๋ฐ”๊พธ๋Š” ๊ฒƒ 91 2) ์—„๋งˆ ๊ฐ์ •์— ๋”ฐ๋ผ ํ˜ผ๋‚ด์š” 96 3) ๋ฐ”์ดํ”„๋กœ๋•ํŠธ(by-product)๊ฐ™์€ ์œก์•„ 97 4) ์—„๋งˆ๊ฐ€ ๋œ๋‹ค๋Š” ๊ฒƒ, ์ธ๊ฐ„์— ๋Œ€ํ•ด ๋‹ค์‹œ ์ƒ๊ฐํ•˜๋Š” ๊ฒƒ 98 4. ์†Œ๊ฒฐ 100 V. ์—„๋งˆ ๋˜๊ธฐ์˜ ์‚ฌํšŒ๋ฌธํ™”์  ๋งฅ๋ฝ 102 1. ์—ญํ•  ๊ธฐ๋Œ€๊ฐ์œผ๋กœ ์ธํ•œ ๊ฐ€์กฑ๊ณผ์˜ ๊ฐˆ๋“ฑ 102 1) ๋‚จํŽธ์— ๋Œ€ํ•œ ์ •์‹ ์ ์ธ ์ŠคํŠธ๋ ˆ์Šค 102 2) ์นœ๊ตฌ ๊ฐ™์€ ์•„๋น  106 3) ์• ๊ธฐ ์—„๋งˆ๊ฐ€ ํ•˜๊ณ  ์‹ถ์€ ๊ฑฐ๋ฅผ ๋‹คํ•˜๋ ค๊ณ  ํ•˜๋‹ˆ 107 2. ๋ชจ์„ฑ ์ด๋ฐ์˜ฌ๋กœ๊ธฐ์™€์˜ ๊ฐˆ๋“ฑ 110 1) ์ž๋…€ ๊ต์œก์€ ์—„๋งˆ๊ฐ€ ๊ด€๋ฆฌํ•ด์•ผ ํ•˜๋Š” ์ผ 110 2) ์Šˆํผ ๋ง˜์„ ์›ํ•˜๋Š”๊ฒŒ ์•„๋‹Œ๊ฐ€ 113 3. ์†Œ๊ฒฐ 116 VI. ๋…ผ์˜ 117 1. ๋ชจ์„ฑ์—ญํ•  ํ•™์Šต์œผ๋กœ ํ˜•์„ฑ๋œ ์ •์ฒด์„ฑ 118 2. ๋ชจ์„ฑ์ด๋ฐ์˜ฌ๋กœ๊ธฐ๋ฅผ ๊ฐ•ํ™”ํ•œ ์ข‹์€ ์—„๋งˆ ์ƒ 122 3. ์ค‘์‚ฐ์ธต ๊ฒฝ๋ ฅ๋‹จ์ ˆ ์—ฌ์„ฑ์„ ์œ„ํ•œ ํ‰์ƒ๊ต์œก 126 VII. ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  132 1. ์š”์•ฝ 132 2. ๊ฒฐ๋ก  134 ์ฐธ๊ณ ๋ฌธํ—Œ 141 Abstract 148Maste

    ์ผ๋ฐ˜์—ฐ์‚ฐ๊ท ํ˜•๋ชจํ˜• ๋ชจ๋ธ๋ง์„ ํ†ตํ•œ ๋กœ๋ด‡์ž๋ณธ ๋“ฑ์žฅ์ด ์‚ฌํšŒ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2022. 8. ๊ตฌ์œค๋ชจ.๋กœ๋ด‡์˜ ๋“ฑ์žฅ ๋ฐ ํ™•์‚ฐ์€ ์‚ฐ์—…, ๋…ธ๋™, ๊ฒฝ์ œ์„ฑ์žฅ ๋“ฑ ๊ฒฝ์ œ์— ๋งŽ์€ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ ์‚ฌํšŒ๊ฒฝ์ œ์ ์œผ๋กœ ๋งŽ์€ ๊ธยท๋ถ€์ •์  ๋ณ€ํ™”๋ฅผ ์˜ˆ๊ณ ํ•˜๊ณ  ์žˆ๋‹ค. ๋กœ๋ด‡์˜ ๋“ฑ์žฅ์œผ๋กœ ์ธํ•ด ์ƒ์‚ฐํ™˜๊ฒฝ์ด ๋ฐ”๋€Œ๊ณ  ์žˆ์œผ๋ฉฐ, ๋กœ๋ด‡์ด ๋…ธ๋™์ž์—๊ฒŒ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์€ ๋…ธ๋™์ง๊ตฐ์— ๋”ฐ๋ผ ๋‹ค๋ฅผ ์ˆ˜ ์žˆ๋‹ค. ์ด ๊ฐ™์€ ๋ฐฐ๊ฒฝ ํ•˜์—, ๋ณธ ์—ฐ๊ตฌ๋Š” ๋…ธ๋™๋Œ€์ฒด ํ˜„์ƒ์ด ์‚ฐ์—…, ์ง๊ตฐ, ๊ทธ๋ฆฌ๊ณ  ๊ฐ€๊ณ„ ๋ณ„๋กœ ์–ด๋–ป๊ฒŒ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚˜๋ฉฐ ๊ฒฝ์ œ์„ฑ์žฅ์„ ํฌํ•จํ•œ ๊ฐ€๊ฒฉ, ์ˆ˜์š”, ๊ณต๊ธ‰, ํšจ์šฉ ๋“ฑ ๊ฒฝ์ œ ์š”์ธ ๊ฐ„์— ์–ด๋–ค ์ƒํ˜ธ ์ž‘์šฉ์„ ๋ถˆ๋Ÿฌ ์ผ์œผํ‚ค๋Š”์ง€ ์‚ดํŽด๋ณด๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ์‚ฐ์—… ๋ฐ ์ง๊ตฐ ๊ฐ„ ๋‹ค๋ฅธ ๋…ธ๋™๋Œ€์ฒด์œจ์„ ๋ฐ˜์˜ํ•˜์—ฌ ์—ฐ์‚ฐ๊ฐ€๋Šฅ ์ผ๋ฐ˜๊ท ํ˜• (Computable General Equilibrium; CGE) ๋ชจํ˜•์„ ์„ค๊ณ„ํ•˜๊ณ  ์ œ์•ˆํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ์—ฐ์‚ฐ๊ฐ€๋Šฅ ์ผ๋ฐ˜๊ท ํ˜• ๋ชจํ˜•์€ ๋‹ค์–‘ํ•œ ์ •์ฑ…์˜ ํŒŒ๊ธ‰ํšจ๊ณผ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ๋ถ„์„ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์ด ์žˆ์–ด, ๊ฒฝ์ œ์„ฑ์žฅ๊ณผ ํ˜์‹ ์ •์ฑ… ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” CGE๋ชจํ˜•์„ ์ด์šฉํ•˜์—ฌ ์ตœ๊ทผ์— ํฌ๊ฒŒ ์‚ฌํšŒ์ ์œผ๋กœ ์šฐ๋ ค๊ฐ€ ๋˜๊ณ  ์žˆ๋Š” ๋กœ๋ด‡์ž๋ณธ์— ์˜ํ•œ ๋…ธ๋™๋Œ€์ฒด ๋ฌธ์ œ์™€ ์‚ฌํšŒยท๊ฒฝ์ œ์  ์˜ํ–ฅ์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ํŠนํžˆ, ๋…ธ๋™๋Œ€์ฒด ์ด์Šˆ๋Š” ์‚ฌํšŒ์  ์˜ํ–ฅ์ด ํด ๊ฒƒ์œผ๋กœ ์šฐ๋ ค๋˜์–ด ๊ฒฝ์ œ์„ฑ์žฅ์— ๋ฏธ์น˜๋Š” ํŒŒ๊ธ‰ํšจ๊ณผ ๋ฐ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ๋ฉด๋ฐ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์š”๊ตฌ๋œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์ด ์—ฐ๊ตฌ๋Š” ๋‹ค๋ฅธ ์‚ฐ์—…๊ฐ„ ๋…ธ๋™์˜ ์ด์งˆ์ ์ธ ํŠน์„ฑ์— ๋”ฐ๋ผ ๋…ธ๋™๋Œ€์ฒด์— ์˜ํ•ด ๋‹ค๋ฅด๊ฒŒ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‚ฌํšŒ๊ณ„์ •ํ–‰๋ ฌ (Social Accounting Matrix; SAM) ์ž๋ฃŒ์ฒด๊ณ„ ๋‚ด ๋…ธ๋™ ๋ฐ ๊ฐ€๊ณ„ ๊ณ„์ •์„ ์„ธ๋ถ„ํ™”ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋กœ๋ด‡์ž๋ณธ์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ์œ ํ˜•์˜ ์ž๋ณธ ๊ฐœ๋…์„ ์ƒˆ๋กญ๊ฒŒ ์ •์˜ํ•˜๊ณ , ์ด์— ๋”ฐ๋ผ ํˆฌ์ž์™€ ์ž๋ณธ์„ ์ผ๋ฐ˜๊ณผ ๋กœ๋ด‡์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด, ๊ฒฝ์ œ ํ˜น์€ ์ •์ฑ… ์ถฉ๊ฒฉ์— ๋”ฐ๋ผ ๊ฒฝ์ œ์ฃผ์ฒด์— ๋ฏธ์น˜๋Š” ์ƒ์ดํ•œ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจํ˜• ๋ฐ ์ž๋ฃŒ์ฒด๊ณ„๋ฅผ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด์ฒ˜๋Ÿผ ์„ค๊ณ„๋œ ์—ฐ์‚ฐ์ผ๋ฐ˜๊ท ํ˜• ๋ชจํ˜•์„ ๋ฐ”ํƒ•์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์‹ค์ฆ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ƒ์ดํ•œ ๋…ธ๋™๋Œ€์ฒด๊ฐ€ ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์ƒ์ดํ•œ ํšจ๊ณผ์™€ ๊ฐ ์ฃผ์ฒด์˜ ์ƒํ˜ธ์ž‘์šฉ์ด ํŒŒ๊ธ‰๋˜๋Š” ๊ฒฝ์ œ์ฒด์ œ ๋‚ด ๊ฒฝ๋กœ๋ฅผ ์‹๋ณ„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” recursive dynamic CGE๋ชจํ˜•์„ ์ด์šฉํ•˜์—ฌ, 2015-2050๋…„ ๋™์•ˆ ๊ธฐ์ˆ ๋ฐœ์ „์— ์˜ํ•œ ๋…ธ๋™ ๋Œ€์ฒด๊ฐ€ ์‚ฌํšŒ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ๋‹ค. ๊ฐ€๊ณ„, ๋…ธ๋™, ํˆฌ์ž, ์ž๋ณธ์„ ๊ตฌ๋ถ„ํ•œ SAM์„ ๊ตฌ์ถ•ํ•˜๊ณ  ์‚ฐ์—… ๋ฐ ๋…ธ๋™์ง๊ตฐ์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ๋Œ€์ฒดํƒ„๋ ฅ์„ฑ์„ ์‚ฐ์ •ํ•˜์˜€๋‹ค. ๋กœ๋ด‡์ž๋ณธ์˜ ๋…ธ๋™ ๋Œ€์ฒด๊ฐ€ ๊ฐ ์‚ฐ์—…, ๊ฐ€๊ณ„, ๋…ธ๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ณ , ๋กœ๋ด‡์ž๋ณธ ๋“ฑ์žฅ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์‚ฌํšŒ์ ์œผ๋กœ ์šฐ๋ ค๋˜๋Š” ์ƒํ™ฉ์— ๋Œ€ํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์„ค์ •ํ•˜์—ฌ ๋กœ๋ด‡์ž๋ณธ์˜ ๋…ธ๋™๋Œ€์ฒด๋กœ ์ธํ•œ ์‚ฌํšŒ์  ์˜ํ–ฅ์„ ๋‹ค์–‘ํ•œ ์ธก๋ฉด์—์„œ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด, ๋กœ๋ด‡์ž๋ณธ์˜ ์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ๊ณผ ์–‘์˜ ์ฆ๊ฐ€๋กœ ์ธํ•ด ๋กœ๋ด‡์ž๋ณธ์˜ ๊ฐ€๊ฒฉ์ด ํ•˜๋ฝํ•˜๊ณ  ๋Œ€์ฒด๊ฐ€๋Šฅํ™•๋ฅ ์— ๋”ฐ๋ผ ๋…ธ๋™๋Œ€์ฒด์œจ์ด ๋†’์€ ๋…ธ๋™์ผ์ˆ˜๋ก ๋กœ๋ด‡์— ์˜ํ•ด ๋งŽ์ด ๋Œ€์ฒด๋˜๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ด๋•Œ, ๋…ธ๋™๋Œ€์ฒด์œจ์ด ๋†’์€ ์ง๊ตฐ์˜ ๋…ธ๋™๊ฐ€๊ฒฉ ํ•˜๋ฝํญ์ด ๊ฐ€์žฅ ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์ด ๋•Œ๋ฌธ์— ๋Œ€์ฒด์œจ์ด ๋†’์€ ๋…ธ๋™์ง๊ตฐ์— ํ•ด๋‹นํ•˜๋Š” ๊ฐ€๊ณ„์˜ ๋…ธ๋™์†Œ๋“์˜ ์ฆ๊ฐ€์œจ์ด ๊ฐ€์žฅ ์ ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ƒ์‚ฐ ์ธก๋ฉด์—์„œ์˜ ๋น„๊ต๋ฅผ ์œ„ํ•ด 35๊ฐœ์˜ ์‚ฐ์—…์„ ๋Œ€์ฒดํ™•๋ฅ (probability of replacement)๊ณผ ์ž๋ณธ์ง‘์•ฝ ์ •๋„(capital intensity)์— ๋”ฐ๋ผ ๋„ค ๊ฐ€์ง€ ์‚ฐ์—…์ข…๋ฅ˜๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ๋Œ€์ฒดํ™•๋ฅ ์ด ๋†’๊ณ , ์ž๋ณธ์ง‘์•ฝ์ ์ธ ์‚ฐ์—…์˜ ๊ฒฝ์šฐ ๋…ธ๋™๊ฐ€๊ฒฉ์˜ ์ƒ๋Œ€์  ํ•˜๋ฝ์œผ๋กœ ์ธํ•ด, 2015-2050๋…„๋„์— ๋Œ€ํ•ด ์ƒ์‚ฐ์ž ๊ฐ€๊ฒฉ๊ณผ ์†Œ๋น„์ž ๊ฐ€๊ฒฉ์˜ ์ฆ๊ฐ€์œจ์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ์‚ฐ์—…๊ตฐ์€ ์ €์†Œ๋“์ธต์˜ ์†Œ๋น„๋น„์ค‘์ด ๋†’์€ ์‚ฐ์—…์œผ๋กœ ๊ฐ€๊ฒฉ์ฆ๊ฐ€์— ๋”ฐ๋ผ ํšจ์šฉ๊ฐ์†Œํญ์ด ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ฐ˜๋Œ€๋กœ ๋Œ€์ฒดํ™•๋ฅ ์ด ๋‚ฎ๊ณ , ๋…ธ๋™์ง‘์•ฝ์ ์ธ ์‚ฐ์—…์˜ ๊ฒฝ์šฐ ์ƒ์‚ฐ์ž ๊ฐ€๊ฒฉ๊ณผ ์†Œ๋น„์ž ๊ฐ€๊ฒฉ์ด ํ•˜๋ฝํ•˜์˜€๋‹ค. ํ•ด๋‹น ์‚ฐ์—… ๊ตฐ์€ ๊ณ ์†Œ๋“์ธต์ด ๋น„๊ต์  ๋” ๋งŽ์ด ์†Œ๋น„ํ•˜๋Š” ์‚ฐ์—…์— ํ•ด๋‹นํ•˜์—ฌ, ํ•ด๋‹น ์‚ฐ์—… ๊ตฐ์˜ ๋ฌผ๊ฑด ๊ฐ€๊ฒฉ์ด ์ €๋ ดํ•ด์ง„ ํšจ๊ณผ, ์ฆ‰ ๋ฌผ๊ฑด๊ตฌ๋งค๋ ฅ์ด ๋” ์ข‹์•„์กŒ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ๊ฐ€๊ณ„ ํšจ์šฉ๋ณ€ํ™”๋กœ ์ด์–ด์„œ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ, ๊ณ ์†Œ๋“์ธต์˜ ํšจ์šฉ์ฆ๊ฐ€์œจ์ด ๋” ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ์ด์œ ๊ฐ€ ๋ฐ”๋กœ ์ด ๋•Œ๋ฌธ์ด๋‹ค. ๋” ๋‚˜์•„๊ฐ€ ๋กœ๋ด‡์ž๋ณธ์˜ ํŠน์„ฑ๊ณผ ๊ด€๋ จํ•œ ์‚ฌํšŒ์  ์šฐ๋ ค ์ƒํ™ฉ์— ๋Œ€ํ•ด ์‹œ๋‚˜๋ฆฌ์˜ค ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋กœ๋ด‡์ž๋ณธ์˜ ํŽธ์ค‘๋œ ๋ถ„ํฌ์™€ ๋…ธ๋™์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ์œผ๋กœ ์ธํ•œ ๋…ธ๋™๋Œ€์ฒด์˜ ์‚ฌํšŒ์  ์˜ํ–ฅ ์‹ฌํ™” ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ํ†ตํ•ด ๋กœ๋ด‡์ž๋ณธ์ด ์ €์†Œ๋“์ธต๊ณผ ๊ณ ์†Œ๋“์ธต๊ฐ„์— ์†Œ๋“ ์–‘๊ทนํ™”๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋กœ๋ด‡์„ธ ๋ถ€๊ณผ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ํ†ตํ•ด ๋กœ๋ด‡์ž๋ณธ์— ๋Œ€ํ•ด ์„ธ๊ธˆ์„ ๋ถ€๊ณผํ•˜๊ฒŒ ๋˜๋ฉด, ๊ณ ์†Œ๋“์ธต์˜ ๋กœ๋ด‡์ž๋ณธ์œผ๋กœ ์ธํ•œ ์†Œ๋“์„ ๊ฐ์†Œ์‹œํ‚ค๋Š”๋ฐ ์ผ์กฐํ•˜์—ฌ ์–‘๊ทนํ™” ์ •๋„๋Š” ์™„ํ™”๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์ง€๋งŒ, ์ด๋Š” ์ƒ์‚ฐ ๊ฐ์†Œ๋กœ ์ด์–ด์ ธ ์„ฑ์žฅ๋‘”ํ™”๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์‹œ์‚ฌ์ ์„ ๊ฐ€์ง„๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฐฉ๋ฒ•๋ก ๊ณผ ์‹ค์šฉ์ ์ธ ์ธก๋ฉด์—์„œ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ธฐ์—ฌ์ ์„ ๊ฐ€์ง„๋‹ค. ๋จผ์ €, ์ผ๋ฐ˜์ž๋ณธ๊ณผ ๋Œ€์ฒด ๋ฐ ์ถ•์ ์˜ ์†๋„ ์ธก๋ฉด์—์„œ ์ฐจ๋ณ„ํ™”๋œ ํŠน์„ฑ์„ ๊ฐ€์ง„ ๋กœ๋ด‡์ž๋ณธ์„ ์ •์˜ํ•˜๊ณ  ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‚ฐ์—…๋ณ„ ๋…ธ๋™์ง๊ตฐ์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ๋Œ€์ฒด์œจ์„ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋„๋ก CGE๋ชจํ˜•์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ๋ฐฉ๋ฒ•๋ก ์  ์ธก๋ฉด์˜ ๊ธฐ์—ฌ๊ฐ€ ์žˆ๋‹ค. ๋˜ํ•œ, ๋กœ๋ด‡์ž๋ณธ์˜ ๋…ธ๋™๋Œ€์ฒด๊ฐ€ ์ค‘์š”ํ•œ ์‚ฌํšŒ์  ๋ฌธ์ œ๋กœ ๋– ์˜ค๋ฅด๊ณ  ์žˆ๋Š” ํ˜„ ์ƒํ™ฉ์—์„œ ๋…ธ๋™๋Œ€์ฒด๊ฐ€ ๊ฐ€๊ณ„์™€ ์‚ฐ์—…์— ๋Œ€ํ•ด ์ฐจ๋ณ„์ ์œผ๋กœ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ์  ์ธก๋ฉด์—์„œ ๊ฐ ์ฃผ์ฒด๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ดํ•ดํ•˜๋Š”๋ฐ ์‹ค์งˆ์ ์ธ ๊ธฐ์—ฌ๋ฅผ ํ•œ๋‹ค. ํŠนํžˆ, ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋…ธ๋™๋Œ€์ฒด๋กœ ์ธํ•œ ์ฐจ๋ณ„์ ์ธ ์˜ํ–ฅ์„ ๊ณ ๋ คํ•˜์—ฌ ์ •์ฑ…์ž…์•ˆ์ž๊ฐ€ ๋…ธ๋™๋Œ€์ฒด๋ผ๋Š” ์‚ฌํšŒ์  ์ด์Šˆ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ํ˜์‹  ์ •์ฑ…์„ ์„ค๊ณ„ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋‹ค์–‘ํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค ๊ด€์ ์—์„œ ์‹ค์งˆ์ ์ธ ๋ฐฉํ–ฅ์„ฑ์„ ์ œ๊ณตํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ๊ทธ ๊ฐ€์น˜๊ฐ€ ์žˆ๋‹ค.The emergence and diffusion of robot capital impact the economy and its aspects, such as industry, labor, and economic growth, signaling many positive and negative socioeconomic changes. Robotization is altering production environments, and its impact on labor varies by labor type and industries, as robots replace labor to a different degree. Therefore, this study aims to examine how the labor replacement phenomenon differs by industries, labor types, and households, as well as to observe inter-relationships among economic variables, including price, demand, supply, utility level, and economic growth. Thus, based on these findings, a computable general equilibrium (CGE) model that considers different labor replacement rates between industries and labors can be constructed. The CGE model is able to systematically analyze the ripple effects of various policies, and it is used in various research fields such as economic growth and innovation policies. Using this model, the study aimed to elucidate the labor replacement problem and social and economic impacts of robot capital, which has recently become a major social concern. In particular, labor replacement issues are feared to have a large social impact, thereby requiring research on its mechanisms and ripple effects on economic growth. This study subdivided labor and household accounts within the social accounting matrix (SAM) data to reflect the different effects of labor replacement depending on the heterogeneous characteristics of labor types and industries. In addition, the model in this study defines a new type of capital concept termed robot capital. Accordingly, investment and capital are divided into general and robot capitals. Furthermore, a model and data system that can analyze different effects on economic subjects according to economic or policy shocks were established. Based on the designed and proposed CGE model, this study attempted to empirically identify different paths and effects of labor replacement on the economy. Furthermore, the effect of labor replacement due to the technological development in society during 2015โ€“2050 was analyzed using the recursive dynamic CGE model. SAM classifies households, labor, investment, and capital, and different elasticities of substitution are estimated according to industries and labor occupations. This study analyzed the impact of robot capitalโ€™s labor replacement on each industry, household, and labor and examined the social impact of technologyโ€™s labor replacement in various aspects through the scenario analysis. The results show that the price of robot capital decreases due to the productivity improvement and increase in the amount of robot capital. The higher the labor replacement rate, the more labors are replaced with robots. The decrease in labor prices was the largest in the occupational group with a high replacement rate, and for this reason, the increase in the labor income of the households belonging to the labor types with the high replacement rate was the smallest. To compare results from the production perspective, 35 industries were classified into four industrial types according to the probability of replacement and capital intensity. In case of capital-intensive industries with high replacement probability, the growth rate of producer prices and consumer prices was high for the period 2015โ€“2050 due to the relative decline in labor prices. The results indicate that this industrial type with high replacement probability and being capital intensive has a high consumption ratio of low-income class, and the decrease in utility is large as the price increases. Conversely, in the case of labor-intensive industries with low replacement probability, producer prices and consumer prices fell. This industrial type corresponds to an industry in which the high-income group exhibits a relatively higher consumption. This can be used to explain the effect of lowering the product price of the products of corresponding industrial types, that is, the purchasing power of the product has improved. This further can be interpreted as a change in household utility, which is why the utility growth rate of the high-income class is higher. Furthermore, scenario analysis of socially concerned situations related to the characteristics of robot capital was conducted. In the scenario with unbalanced distribution of robot capital, there is income polarization between low- and high-income households. For the robot tax scenario, the degree of polarization would be alleviated as the income from robot capital of the high-income households is reduced, but this scenario leads to a decrease in the production and lowered economic growth rate. This study made the following contributions in terms of methodology and practicality. First, from the methodological perspective, robot capital with different characteristics in terms of the speed of replacement and accumulation from general capital was defined, and the CGE model was designed to reflect different replacement rates considering various industries. In addition, in the current situation where the labor replacement of robot capital is emerging as an important social problem, it makes a practical contribution to the understanding of the interaction mechanism between each subject, in terms of influence, that can discriminate against households and industries. In particular, the results of this study are valuable as they provide policymakers with practical directions, considering various perspectives to design innovative policies reflecting the social issue of labor replacement.Chapter 1. Introduction 1 1.1 Research background 1 1.2 Research motivation 5 1.3 Research outline 6 Chapter 2. Literature Review on Theoretical and Methodological Approaches 8 2.1 Computable General Equilibrium model 8 2.2 Labor replacement 11 2.2.1 Technology and economic growth from the historical perspectives 11 2.2.2 Mechanization, computerization, and robotization 15 2.2.3 Robot capitals and robot tax 27 2.2.4 Economic impact of technology changes and social issues 30 2.3 Contribution of this study 34 Chapter 3. Social Accounting Matrix Data 36 3.1 Construction of social accounting matrix (SAM) 36 3.1.1 Concept of SAM 36 3.1.2 Main characteristics of SAM in this study 40 3.2 Construction of micro-SAM for household, labor, investment, and capital accounts 41 3.2.1 Inter-industry transaction composition 41 3.2.2 Household division 43 3.2.3 Labor division 48 3.2.4 Capital division 53 3.2.5 Investment division 58 3.2.6 Scaling the Social Accounting Matrix 59 Chapter 4. The Computable General Equilibrium Model 61 4.1 Overall structure of CGE model 61 4.1.1 The CGE model 61 4.1.2 Main features of CGE model equations in this study 62 4.2 Production 66 4.3 Households 78 4.4 Government 81 4.5 Investments and savings 83 4.6 Exports and imports (International trade) 85 4.7 Market and aggregation equilibrium conditions 88 4.7.1 Consumer price index 88 4.7.2 Market clearing 88 4.8 Recursive equation 89 Chapter 5. Economic impact analysis of labor replacement by robots 91 5.1 Business as usual (BAU) scenario: labor replacement by robot capitals 92 5.2 Robot capital scenario 114 5.2.1 Biased distribution of robot capital 114 5.2.2 Labor productivity biased scenario 122 5.3 Imposing tax on robot capital scenario 128 5.4 Sensitivity analysis 135 5.4.1 Sensitivity analysis on elasticity of substitution 135 5.4.2 Sensitivity test results 137 Chapter 6. Conclusion 139 Bibliography 145 Abstract (Korean) 155๋ฐ•

    A comparative study on feedback strategy for patient safety incidents: Focused on the distinctive patient safety incidents

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    ์˜๋ฃŒ๋ฒ•์œค๋ฆฌํ•™ํ˜‘๋™๊ณผ์ •/์„์‚ฌ์ด๋ฏธ ๋ฐœ์ƒํ•œ ์‚ฌ๊ณ ๋กœ๋ถ€ํ„ฐ์˜ ํ•™์Šต์„ ํ†ตํ•ด ์‚ฌ๊ณ ์˜ ์žฌ๋ฐœ์„ ์˜ˆ๋ฐฉํ•˜๊ณ ์ž ์™ธ๊ตญ์—์„œ๋Š” ๊ตญ๊ฐ€ ์ฐจ์›์˜ ๋ณด๊ณ ํ•™์Šต์‹œ์Šคํ…œ์„ ์šด์˜ํ•˜๊ณ  ์žˆ๋‹ค. ๊ตญ๋‚ด์—์„œ๋Š” ใ€Œํ™˜์ž์•ˆ์ „๋ฒ•ใ€์˜ ์ œ์ •์— ๋”ฐ๋ผ 2016๋…„ 7์›”, ๊ตญ๊ฐ€ ์ฐจ์›์˜ ๋ณด๊ณ ํ•™์Šต์‹œ์Šคํ…œ์ด ๊ตฌ์ถ•๋˜์–ด ์šด์˜๋  ์˜ˆ์ •์ด๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ตญ๋‚ด์—์„œ ๋ฐœ์ƒํ•˜์˜€๋˜ ๋นˆํฌ๋ฆฌ์Šคํ‹ด ํˆฌ์•ฝ์‚ฌ๊ณ , ์ผํšŒ์šฉ ์˜๋ฃŒ๊ธฐ๊ธฐ ์žฌ์‚ฌ์šฉ ๊ด€๋ จ ์‚ฌ๊ณ , ์š”์–‘๋ณ‘์› ํ™”์žฌ์‚ฌ๊ณ  ๋ฐ ์ˆ˜ํ˜ˆ ์‚ฌ๊ณ ์˜ ์‚ฌ๋ก€๋ฅผ ํ†ตํ•ด ํ•ด๋‹น ์‚ฌ๊ณ ์— ๋Œ€ํ•œ ๊ตญ๋‚ด์˜ ์กฐ์น˜์‚ฌํ•ญ๊ณผ ์™ธ๊ตญ ์ •์ฑ… ๋ฐ ์ œ๋„, ๋ณด๊ณ ํ•™์Šต์‹œ์Šคํ…œ์—์„œ์˜ ํ™˜๋ฅ˜๋ฅผ ๋น„๊ตยท๊ณ ์ฐฐํ•˜์—ฌ ๊ตญ๋‚ด ํ™˜์ž์•ˆ์ „์‚ฌ๊ณ  ๋ณด๊ณ ํ•™์Šต์‹œ์Šคํ…œ์—์„œ ์กฐ์น˜ํ•˜์—ฌ์•ผ ํ•  ํ™˜๋ฅ˜๋ฐฉ์•ˆ์— ๋Œ€ํ•˜์—ฌ ์•Œ์•„๋ณด์•˜๋‹ค. ๊ฐ ์‚ฌ๋ก€๋ฅผ ํ†ตํ•˜์—ฌ ์•Œ์•„๋ณธ ํ™˜๋ฅ˜๋ฐฉ์•ˆ์„ ์ข…ํ•ฉํ•œ ๊ฒฐ๊ณผ, ์™ธ๊ตญ์˜ ํ™˜๋ฅ˜ ๋ฐ ํ™˜์ž์•ˆ์ „ ๊ด€๋ จ ์ œ๋„์™€ ๋น„๊ตํ•˜์—ฌ ๋ณด์•˜์„ ๋•Œ ์šฐ๋ฆฌ๋‚˜๋ผ์—๋Š” ์‚ฌ๊ณ  ๋ฐœ์ƒ์„ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•๋“ค์— ๋Œ€ํ•ด ์ฒด๊ณ„์ ์œผ๋กœ ์•ˆ๋‚ด๋ฅผ ํ•˜๋Š” ๊ธฐ์ „์ด ์—†์–ด ์˜ˆ๋ฐฉ ๋Œ€์ฑ…์„ ์•Œ์•„๋ณด๋Š” ๋ฐ์— ์–ด๋ ค์›€์ด ์žˆ์—ˆ์œผ๋ฉฐ, ์˜๋ฃŒ์ œ๊ณต์ž์— ๋Œ€ํ•œ ์ง€์› ๋ณด๋‹ค๋Š” ๊ทœ์ œ๋ฅผ ๊ฐ•ํ™”ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋˜ํ•œ ํŠน์ • ๊ทœ์ œ์‚ฌํ•ญ์„ ์ผ๊ด„์ ์œผ๋กœ ์ค€์ˆ˜ํ•  ๊ฒƒ์„ ์š”๊ตฌํ•˜๊ธฐ๋„ ํ•˜์˜€์œผ๋ฉฐ ํŠน์ • ์‚ฌ์•ˆ์— ๋Œ€ํ•˜์—ฌ ๊ทœ์ •์ด ํ†ต์ผ๋˜์–ด์žˆ์ง€ ์•Š์•„ ํ˜ผ๋ž€์„ ์•ผ๊ธฐํ•˜๊ธฐ๋„ ํ•˜์˜€๋‹ค. ์ด์— ๊ตญ๊ฐ€ ์ฐจ์›์˜ ํ™˜์ž์•ˆ์ „ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋…ธ๋ ฅ์„ ๊ธฐ์šธ์ด๋Š” ๋ฐ์— ์žˆ์–ด์„œ๋Š” ์˜๋ฃŒ๊ธฐ๊ด€์˜ ๊ทœ๋ชจ ๋ฐ ์ˆ˜์ค€์— ๋”ฐ๋ผ ๋‹จ๊ณ„๋ณ„ ๊ถŒ๊ณ ์‚ฌํ•ญ์„ ์ œ์•ˆํ•˜์—ฌ ์ผ๊ด„์ ์œผ๋กœ ์ค€์ˆ˜์‚ฌํ•ญ์„ ์ง€ํ‚ฌ ๊ฒƒ์„ ์š”๊ตฌํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹Œ ๊ธฐ๊ด€์˜ ์ž…์žฅ์—์„œ ์‹ค์งˆ์ ์œผ๋กœ ์ค€์ˆ˜ํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌํ•ญ์„ ์ œ์‹œํ•˜์—ฌ์•ผ ํ•˜๋ฉฐ, ์˜ˆ๋ฐฉ๋Œ€์ฑ…์— ๋Œ€ํ•œ ํšจ๊ณผ์„ฑ๊ณผ ๋น„์šฉ์„ ํ‰๊ฐ€ํ•˜์—ฌ ์šฐ์„ ์ˆœ์œ„๋ฅผ ์„ ์ •ํ•˜๊ณ  ๋‹จยท์ค‘ยท์žฅ๊ธฐ์ ์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ์ ‘๊ทผํ•˜๋Š” ๊ฒƒ๋„ ํ•„์š”ํ•˜๋‹ค. ๋˜ํ•œ ๊ด€๋ จ ํ•™ํšŒ ๋ฐ ๊ธฐ๊ด€, ์ „๋ฌธ๊ฐ€๋“ค๊ณผ ํ˜‘๋ ฅ์„ ํ•˜์—ฌ ํŠน์ • ์‚ฌ์•ˆ์— ๋Œ€ํ•œ ๊ทœ์ •์„ ํ†ต์ผํ•˜๊ณ  ํšจ๊ณผ์ ์ด๋ฉด์„œ ๊ณตํ†ต๋œ ์žฌ๋ฐœ๋ฐฉ์ง€๋Œ€์ฑ…์„ ๋„์ถœํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ตญ๋‚ด์™ธ ์œ ๊ด€๊ธฐ๊ด€์—์„œ ์ œ์•ˆํ•œ ํ™˜์ž์•ˆ์ „์— ๋Œ€ํ•œ ๊ถŒ๊ณ ์•ˆ ๋ฐ ์ด์Šˆ ๋“ฑ์„ ํ•˜๋‚˜์˜ ๊ธฐ์ „์—์„œ ํ™•์ธ ๊ฐ€๋Šฅํ•˜๋„๋ก ๋…ธ๋ ฅํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ํ™˜์ž์•ˆ์ „์˜ ํ–ฅ์ƒ์„ ์œ„ํ•ด์„œ๋Š” ๊ตญ๊ฐ€ ์ฐจ์›์—์„œ์˜ ๋…ธ๋ ฅ๋งŒ์ด ํ•„์š”ํ•œ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ํ™˜์ž ๋ฐ ๋ณดํ˜ธ์ž์™€ ์˜๋ฃŒ์ œ๊ณต์ž์˜ ๋…ธ๋ ฅ๋„ ํ•„์š”ํ•˜๋‹ค. ํ™˜์ž ๋ฐ ๋ณดํ˜ธ์ž๋Š” ํ™˜์ž์•ˆ์ „ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋Šฅ๋™์ ์ธ ์ž์„ธ๋ฅผ ๊ฐ€์ ธ์•ผ ํ•˜๋ฉฐ, ์˜๋ฃŒ์ธ ๋ฐ ์˜๋ฃŒ๊ธฐ๊ด€์€ ๊ตญ๊ฐ€ ์ฐจ์›์—์„œ ์ œ์•ˆํ•œ ํ™˜๋ฅ˜ ๋‚ด์šฉ์„ ์ค€์ˆ˜ํ•˜๊ณ ์ž ํ•˜๋Š” ๋…ธ๋ ฅ์„ ํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ํ™˜์ž์™€ ์˜๋ฃŒ์ œ๊ณต์ž๋“ค์€ ๊ตญ๋‚ด์˜ ์•ˆ์ „ํ•œ ๋ณด๊ฑด์˜๋ฃŒ ํ™˜๊ฒฝ์„ ์กฐ์„ฑํ•˜๊ณ ์ž ํ•˜๋Š” ๊ตญ๊ฐ€ ์ฐจ์›์˜ ๋…ธ๋ ฅ์„ ์ธ์ง€ํ•˜์—ฌ์•ผ ํ•  ๊ฒƒ์ด๊ณ , ์ •๋ถ€๋Š” ์‹ค์งˆ์ ์œผ๋กœ ์•ˆ์ „ํ•œ ์˜๋ฃŒ ํ™˜๊ฒฝ ์กฐ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉํ–ฅ์œผ๋กœ ๋ณด๊ฑด์˜๋ฃŒ์ฒด๊ณ„๋ฅผ ์ด๋Œ์–ด๋‚˜๊ฐ€ ๊ถ๊ทน์ ์œผ๋กœ ์„œ๋กœ ์‹ ๋ขฐ๋ฅผ ํ˜•์„ฑํ•˜์—ฌ ํ™˜์ž์•ˆ์ „ ๋ฌธํ™”๋ฅผ ํ˜•์„ฑํ•˜์—ฌ์•ผ ํ•  ๊ฒƒ์ด๋‹คope

    Biophysical Characterization of the Interaction between the Receptor for Advanced Glycation End Product and High Mobility Group Box 1

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์ƒ๋ช…๊ณตํ•™๋ถ€(๋ฐ”์ด์˜ค๋ชจ๋“ˆ๋ ˆ์ด์…˜์ „๊ณต), 2014. 2. ์œค์ฒ ํฌ.Receptor for glycation end product (RAGE) ๋Š” ๋ฉด์—ญ๊ธ€๋กœ๋ถˆ๋ฆฐ ๊ณผ์— ์†ํ•˜๋Š” ์ˆ˜์šฉ์ฒด ๋‹จ๋ฐฑ์งˆ๋กœ์„œ ๋‚ดํ”ผ์„ธํฌ, ํ˜ˆ๊ด€ ํ‰ํ™œ๊ทผ ์„ธํฌ, ์‹ ๊ฒฝ ์„ธํฌ, ๋Œ€์‹์„ธํฌ/๋‹จํ•ต๊ตฌ ๋“ฑ์—์„œ ๋ฐœํ˜„๋œ๋‹ค. RAGE ๋Š” 3๊ฐœ์˜ ์„ธํฌ ๋ฐ– ๋„๋ฉ”์ธ(V, C1, C2), ์„ธํฌ ๋ง‰ ๋„๋ฉ”์ธ, ๊ทธ๋ฆฌ๊ณ  ์„ธํฌ ๋‚ด์˜ ์‹ ํ˜ธ์ „๋‹ฌ ๋„๋ฉ”์ธ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๋‹ค. RAGE์˜ ligand ์ค‘ ํ•˜๋‚˜์ธ HMGB1์€ ํ”ํ•˜๊ณ  ํ’๋ถ€ํ•œ ํ•ต ๋‹จ๋ฐฑ์งˆ๋กœ์„œ nuclear factor B ๋ฐ MAP ์ธ์‚ฐํ™” ํšจ์†Œ์˜ ํ™œ์„ฑํ™”๋ฅผ ์œ ๋ฐœํ•จ์œผ๋กœ์จ ์„ธํฌ ๋‚ด ์—ผ์ฆ์„ฑ ์งˆํ™˜๊ณผ ์•”์˜ ๋ฐœ๋‹ฌ๋กœ ์ด์–ด์ง€๊ฒŒ ํ•œ๋‹ค. HMGB1์€ ๋‚˜์„ ํ˜•์˜ A์™€ B๋„๋ฉ”์ธ ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๋’ค์˜ ์‚ฐ์„ฑ์˜ ๋ถ€๋ถ„์ธ C-terminal ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ์ด์ „์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์—์„œ HMGB1์ด ์—ผ์ฆ์„ฑ ์‹ ํ˜ธ์ „๋‹ฌ์— ๊ด€์—ฌํ•˜๋Š” RAGE์˜ ๋ฆฌ๊ฐ„๋“œ ๋‹จ๋ฐฑ์งˆ ์ค‘ ํ•˜๋‚˜๋ผ๊ณ  ๋ณด๊ณ ํ•˜์˜€์œผ๋‚˜ ๊ทธ ๊ฒฐํ•ฉ๋ถ€์œ„๋ฅผ ๊ทœ๋ช…ํ•˜๋Š” ์‹คํ—˜์ ์ธ ์ฆ๊ฑฐ๋Š” ๋ฏธ๋ฏธํ•œ ์ƒํƒœ์ด๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๊ทธ ๊ฒฐํ•ฉ๋ถ€์œ„๋ฅผ ๊ทœ๋ช… ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋‹ค์–‘ํ•œ ๋„๋ฉ”์ธ์˜ HMGB1๊ณผ RAGE๋ฅผ ํด๋กœ๋‹, ๋ฐœํ˜„, ์ •์ œ ํ•˜์˜€๋‹ค.I. Introduction ะ†ะ†. Materials and Methods 1. Bacterial strains and plasmid 2. Overexpression 3. Purification 4. CD spectroscopy 5. NMR spectroscopy III. Results and Discussion 1. Cloning, expression and purification of HMGB1 2. Cloning, expression and purification of RAGE 3. Biophysical characterization ะ†V.Conclusion V. Summary VI. References Abstract in KoreanMaste
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