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    Transition to Green Mobility

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2022. 8. ๊ตฌ์œค๋ชจ.์‹ ๊ณ ์ „ํŒŒ์˜ ์œ ์ธ๋œ ํ˜์‹  ์ ‘๊ทผ๋ฒ•์€ ํ˜์‹ ์ด ์ˆ˜์š”์™€ ์ƒ๋Œ€์š”์†Œ๊ฐ€๊ฒฉ ๋ณ€ํ™”์— ๋”ฐ๋ผ ๊ทธ ์†๋„์™€ ๋ฐฉํ–ฅ์ด ๊ฒฐ์ •๋œ๋‹ค๊ณ  ๋ณด์•˜์œผ๋ฉฐ, ๊ธฐ์ˆ  ํ˜์‹ ์— ์žˆ์–ด์„œ ์ˆ˜์š”์˜ ์—ญํ• ์„ ๊ฐ•์กฐํ•˜์˜€๋‹ค. ์ฆ‰, ์‹ ๊ธฐ์ˆ ์ด ๋„์ž…๋˜๋ฉด ์†Œ๋น„์ž์˜ ์ˆ˜์š”๋กœ ํ˜์‹ ์ด ํ™•์‚ฐ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹œ์žฅ์—์„œ์˜ ๊ธฐ์กด ๊ธฐ์ˆ ์˜ ์ƒ๋Œ€์  ์šฐ์œ„, ๋†’์€ ์ง„์ž… ๋น„์šฉ ๋ฐ ๋ถˆํ™•์‹ค์„ฑ ๋“ฑ์œผ๋กœ ์ธํ•ด ์†Œ๋น„์ž์˜ ์˜์‚ฌ๊ฒฐ์ • ๋งŒ์œผ๋กœ๋Š” ์‚ฌํšŒ์ ์œผ๋กœ ์ตœ์ ์˜ ์ˆ˜์ค€๊นŒ์ง€ ํ™•์‚ฐ์ด ์ผ์–ด๋‚˜์ง€ ์•Š์„ ์ˆ˜ ์žˆ๋‹ค. ์ด๋กœ ์ธํ•ด ์ •๋ถ€๋Š” ์‹œ์žฅ์˜ ์ค‘์žฌ์ž๋กœ์„œ ํ˜์‹ ์˜ ํ™•์‚ฐ์„ ์œ„ํ•ด ๊ฐœ์ž…์„ ํ•˜๊ฒŒ ๋˜๋ฉฐ ๊ตฌ์ฒด์ ์ธ ์ •์ฑ… ์ˆ˜๋‹จ์„ ์„ค๊ณ„ํ•œ๋‹ค. ๊ทธ๋ ‡๋‹ค๋ฉด ์ด๋Ÿฌํ•œ ์ •๋ถ€ ๊ฐœ์ž…์ด ์†Œ๋น„์ž ์„ ํƒ๊ณผ ์‹œ์žฅ์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ ์–ด๋–ค ๊ฒฐ๊ณผ๋ฅผ ์ดˆ๋ž˜ํ•˜๋Š”๊ฐ€? ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ทธ๋ฆฐ ๋ชจ๋นŒ๋ฆฌํ‹ฐ๋ฅผ ์—ฐ๊ตฌ ๋Œ€์ƒ์œผ๋กœ ํ•˜์—ฌ ์‹œ์žฅ์œ ์ธ์  (๊ทœ์ œ) ์ˆ˜๋‹จ์— ์ง‘์ค‘ํ•˜์˜€๋‹ค. ์ž๋™์ฐจ ์‚ฐ์—…์€ ๋Œ€ํ‘œ์ ์ธ B2C ์‹œ์žฅ์œผ๋กœ ์†Œ๋น„์ž์˜ ์„ ํ˜ธ๋ฅผ ํŒŒ์•…ํ•˜์—ฌ ์‹ ๊ธฐ์ˆ ์˜ ํ™•์‚ฐ์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์—ฐ์‡„ ํšจ๊ณผ๊ฐ€ ํฌ๊ธฐ ๋•Œ๋ฌธ์— ์‚ฐ์—… ๋ฐ ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ํฌ๋‹ค. ์ •๋ถ€๋Š” ๊ทธ๋ฆฐ ๋ชจ๋นŒ๋ฆฌํ‹ฐ๋กœ ์ธํ•ด ์•ผ๊ธฐ๋˜๋Š” ๊ธ์ •์  ์™ธ๋ถ€ํšจ๊ณผ (ํ™˜๊ฒฝ ๊ฐœ์„  ๋ฐ ์‹  ์‚ฐ์—… ์ฐฝ์ถœ์„ ํ†ตํ•œ ๊ฒฝ์ œ ์„ฑ์žฅ ๋“ฑ)๋ฅผ ๊ธฐ๋Œ€ํ•˜๋ฉฐ ๋‹ค์–‘ํ•œ ์ •์ฑ…์ˆ˜๋‹จ์œผ๋กœ ์‹ ๊ธฐ์ˆ ์˜ ํ™•์‚ฐ์„ ์ง€์›ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์นœํ™˜๊ฒฝ์ฐจ ๋ณด๊ธ‰ ์ •์ฑ…์ˆ˜๋‹จ ์ค‘ ๋Œ€ํ‘œ์ ์œผ๋กœ ์กฐ์„ธ ๋ฐ ๋ณด์กฐ๊ธˆ, ์ถฉ์ „ ์ธํ”„๋ผ ์„ค์น˜ ํˆฌ์ž์— ๋Œ€ํ•˜์—ฌ ๊ทœ์ œ์™€ ์„ฑ์žฅ, ์ •์ฑ… ํšจ๊ณผ์„ฑ ๊ทธ๋ฆฌ๊ณ  ํ˜•ํ‰์„ฑ ์ธก๋ฉด์—์„œ ํŒŒ๊ธ‰ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด์‚ฐ์„ ํƒ๋ชจํ˜•์€ ๊ฐœ์ธ์˜ ์„ ํ˜ธ์— ๋”ฐ๋ผ ์ œํ’ˆ ๋ฐ ๊ธฐ์ˆ ์˜ ์ˆ˜์š”๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ‘œ์ ์ธ ๋ฐฉ๋ฒ•๋ก ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ œํ’ˆ ๋ฐ ๊ธฐ์ˆ  ๊ฐ„ ๋Œ€์ฒดํšจ๊ณผ์— ์น˜์ค‘ํ•˜์—ฌ ๋‹ค๋ฅธ ์‚ฐ์—…๊ณผ ๊ฒฝ์ œ ๊ฐ„์˜ ์—ฐ์‡„ํšจ๊ณผ๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ํ•œํŽธ ๊ณ„์‚ฐ๊ฐ€๋Šฅํ•œ ์ผ๋ฐ˜๊ท ํ˜•๋ชจํ˜•์€ ๊ฒฝ์ œ ์ฃผ์ฒด ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๊ฒฝ์ œ ๋ณ€์ˆ˜(๊ฐ€๊ฒฉ ๋ฐ ์ˆ˜์š” ๋“ฑ)์˜ ๋ณ€ํ™”๋ฅผ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ๋ถ„์„ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ผ๋ฐ˜๊ท ํ˜•๋ชจํ˜•์€ ๊ธฐ์ˆ ์— ๋Œ€ํ•œ ์„ค๋ช…์ด ์ œํ•œ์ ์ด๋ฉฐ, ์‹œ์žฅ ๋ณ€ํ™”๊ฐ€ ์žฌํ™”์˜ ๊ฐ€๊ฒฉ ๋ฐ ์ˆ˜๋Ÿ‰์—๋งŒ ์˜์กดํ•œ๋‹ค. ๋‘ ๋ชจํ˜•์„ ํ†ตํ•ฉํ•จ์œผ๋กœ์จ ์ด์‚ฐ์„ ํƒ๋ชจํ˜•์€ ์ผ๋ฐ˜๊ท ํ˜•๋ชจํ˜•์˜ ๊ฒฐ๊ณผ๋ฅผ ๋‚ด์ƒ์ ์œผ๋กœ ๋ฐ˜์˜ํ•˜์—ฌ ์†์„ฑ ์ˆ˜์ค€์˜ ๋ณด๋‹ค ํƒ„๋ ฅ์ ์ธ ๋ณ€ํ™”๋ฅผ ํฌ์ฐฉํ•˜๊ณ , ์ผ๋ฐ˜๊ท ํ˜•๋ชจํ˜•์€ ์ด์‚ฐ์„ ํƒ๋ชจํ˜•์˜ ๊ตฌ์ฒด์ ์ธ ๊ธฐ์ˆ  ์‚ฌ์–‘์„ ๋ฐ˜์˜ํ•œ ๋Œ€์ฒด ๊ด€๊ณ„๋ฅผ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ตฌ์ถ•๋œ ํ†ตํ•ฉ๋ชจํ˜•์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐœ์ธ ๋‹จ์œ„์˜ ์†Œ๋น„์ž ์„ ํ˜ธ์— ๋”ฐ๋ฅธ ์ˆ˜์š” ๋ณ€๋™์ด ์‹ ๊ธฐ์ˆ ์˜ ํ™•์‚ฐ๊ณผ ๊ตญ๊ฐ€ ์ „์ฒด์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ „๊ธฐ์ฐจ์™€ ์ˆ˜์†Œ์ฐจ์˜ ํ™•์‚ฐ์€ ๊ฒฝ์ œ ์„ฑ์žฅ์œผ๋กœ ์ด์–ด์กŒ๋‹ค. ํ™˜๊ฒฝ์ ์ธ ์ธก๋ฉด์—์„œ ์ „๊ธฐ์ฐจ ๋ฐ ์ˆ˜์†Œ์ฐจ๋กœ์˜ ์ˆ˜์š” ์ „ํ™˜์— ๋”ฐ๋ผ ์ˆ˜์†ก ๋ถ€๋ฌธ์˜ ๋ฐฐ์ถœ๋Ÿ‰์ด ํฌ๊ฒŒ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ „ ์‚ฐ์—…์˜ ๋ฐฐ์ถœ๋Ÿ‰์€ ์ด ์ƒ์‚ฐ ์ฆ๊ฐ€๋กœ ์ธํ•ด ์˜คํžˆ๋ ค ์ฆ๊ฐ€ํ•˜์—ฌ, ์ˆ˜์†ก ๋ถ€๋ฌธ์˜ ๋ฐฐ์ถœ ์ €๊ฐ ํšจ๊ณผ๋ฅผ ์ƒ์‡„ํ•˜๋Š” ๋ฐ˜๋“ฑ ํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ ๊ทธ๋ฆฐ ๋ชจ๋นŒ๋ฆฌํ‹ฐ๊ฐ€ ์ดˆ๊ธฐ์— ๊ธ‰์ฆํ•˜๋Š” ๊ฒฝ์šฐ ์„ํƒ„ ํ™”๋ ฅ ๋ฐœ์ „ ๋ฐ LNG ๊ฐœ์งˆ ์œ„์ฃผ์˜ ์ˆ˜์†Œ ์ƒ์‚ฐ์œผ๋กœ ์ธํ•ด ์˜คํžˆ๋ ค ๋ฐฐ์ถœ๋Ÿ‰์ด ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ทธ๋ฆฐ ๋ชจ๋นŒ๋ฆฌํ‹ฐ์˜ ๊ธ‰์ง„์ ์ธ ์ˆ˜์š” ํ™•์‚ฐ ์ด์ „์— ์นœํ™˜๊ฒฝ ๋ฐœ์ „์ด ์ „์ œ ๋˜์–ด์•ผ ๋ฐ”๋žŒ์งํ•œ ํ™˜๊ฒฝ ๊ฐœ์„  ํšจ๊ณผ๋ฅผ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋ฆฐ ๋ชจ๋นŒ๋ฆฌํ‹ฐ ๋ณด๊ธ‰์„ ์œ„ํ•œ ์ •์ฑ… ์ˆ˜๋‹จ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์กฐ์„ธ๊ฐ€ ๋ถ€๊ณผ๋จ์— ๋”ฐ๋ผ ๊ธฐ์—…์˜ ์ƒ์‚ฐ ๋น„์šฉ์€ ์ฆ๊ฐ€ํ•  ์ˆ˜ ์žˆ์œผ๋‚˜, ํ•™์Šต๋ฅ ์— ๋”ฐ๋ผ ํ˜์‹ ์ด ์ด ๋น„์šฉ์„ ์ƒ์‡„ํ•˜๋Š” ๊ฐ€๋Šฅ์„ฑ์€ ํ›จ์”ฌ ๋” ๋น ๋ฅด๊ฒŒ ์ฆ๊ฐ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฆ‰, ์กฐ์„ธ์™€ ๊ฐ™์€ ํ™˜๊ฒฝ์ •์ฑ…๊ณผ ๊ธฐ์—…์˜ ์ƒ์‚ฐ์„ฑ์„ ๋†’์ด๋Š” ๊ธฐ์ˆ ์ •์ฑ…์„ ๋™์‹œ์— ์‹œํ–‰ํ•  ๋•Œ ๋ณด๋‹ค ํšจ๊ณผ์ ์ธ ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ, ์†Œ๋น„์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์ง์ ‘์ ์ธ ๊ฒฝ์ œ์  ์ธ์„ผํ‹ฐ๋ธŒ์ธ ๋ณด์กฐ๊ธˆ ์ •์ฑ… ๋ณด๋‹ค ๋ณด์™„์žฌ ์‹œ์žฅ์œผ๋กœ์„œ ์ธํ”„๋ผ์— ๋Œ€ํ•œ ํˆฌ์ž๊ฐ€ ์‹ ๊ธฐ์ˆ  ํ™•์‚ฐ ๋ฐ ๊ฒฝ์ œ ์„ฑ์žฅ์— ๋” ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ์ค€๋‹ค. ์ฆ‰, ๋ณด์กฐ๊ธˆ์„ ๋” ๋งŽ์ด ์ฃผ์–ด ํ˜„์žฌ ์‹œ์žฅ์„ ํ™•๋Œ€ํ•˜๊ธฐ ๋ณด๋‹ค๋Š” ์ถฉ์ „ ์ธํ”„๋ผ์— ํˆฌ์žํ•˜์—ฌ ๋ฏธ๋ž˜ ์‹œ์žฅ ํ™˜๊ฒฝ์„ ๊ฐœ์„ ํ•˜๋Š” ๊ฒƒ์ด ์žฅ๊ธฐ์ ์œผ๋กœ ๊ตญ๊ฐ€ ๊ฒฝ์ œ์— ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณด์กฐ๊ธˆ์˜ ์ฐจ๋“ฑ ์ง€๊ธ‰์€ ๋‹จ๊ธฐ์ ์œผ๋กœ ์ €์†Œ๋“์ธต์˜ ์†Œ๋“ ํ–ฅ์ƒ์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ์ง€๋งŒ, ์žฅ๊ธฐ์ ์œผ๋กœ๋Š” ๊ตญ๊ฐ€ ๊ฒฝ์ œ ์„ฑ์žฅ์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ๋ณด์กฐ๊ธˆ์˜ ์ฐจ๋“ฑ ์ง€๊ธ‰์€ ๊ถ๊ทน์ ์œผ๋กœ ์‹ ๊ธฐ์ˆ ์˜ ๋ณด๊ธ‰์„ ๋Šฆ์ถ”๊ธฐ ๋•Œ๋ฌธ์— ์žฅ๊ธฐ์ ์œผ๋กœ ๊ฐ€๊ณ„ ์†Œ๋“ ์ฆ๊ฐ€์— ๋œ ๋„์›€์ด ๋˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ๋ชจํ˜•์„ ๊ฒฐํ•ฉํ•จ์œผ๋กœ์จ ๊ฐœ์ธ์˜ ๊ธฐ์ˆ  ์ฑ„ํƒ(technology adoption)์—์„œ๋ถ€ํ„ฐ ์‚ฌํšŒ ์ „์ฒด๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๊ธฐ์ˆ  ํ™•์‚ฐ(technology diffusion)์˜ ํ˜์‹  ๊ณผ์ •์„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ ์ด์‚ฐ์„ ํƒ๋ชจํ˜• ํ˜น์€ ์ผ๋ฐ˜๊ท ํ˜•๋ชจํ˜•๋งŒ ์‚ฌ์šฉํ•˜์—ฌ ์ •์ฑ…์„ ํ…Œ์ŠคํŠธํ•˜๋Š” ๊ฒฝ์šฐ ๋ณด๋‹ค ๋‘ ๋ชจํ˜•์„ ํ†ตํ•ฉํ•œ ํ˜„์žฌ์˜ ํ”„๋ ˆ์ž„์›Œํฌ๊ฐ€ ์ •๋ถ€ ์ •์ฑ…์˜ ์˜ํ–ฅ์„ ๋” ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ •๋ถ€์˜ ์˜์‚ฌ๊ฒฐ์ •์—์„œ ๋ช…ํ™•ํ•œ ๊ทผ๊ฑฐ๋ฅผ ์ œ์‹œํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค.The neoclassical-induced innovation approach views the speed and direction of innovation as being determined by changes in demand and relative factor prices and emphasizes the role of demand in technological innovation. In other words, innovation spreads from consumer demand with the introduction of new technologies into the market. However, the diffusion on a socially optimal level may not fully occur solely based on the decision-making of consumers due to the relative superiority of existing technologies, high entry costs and uncertainty. Consequently, the government intervenes in the diffusion of innovation and acts as a mediator in the market by designing specific policies to address the shortfalls. This study explored how the governmentโ€™s intervention affects consumer choices and markets, as well as the consequences thereof. This study examined green mobility and focused on market-inducing (regulatory) measures. The automobile industry is a representative business-to-consumer market, and therefore, it is possible to predict the spread of new technologies by understanding consumer preferences. In anticipation of positive externalities (environmental improvement and economic growth through new industry creation), the government supports the diffusion of green mobility through various policy instruments. This study analyzed the ripple effects of regulation and growth, policy effectiveness and equity on tax and subsidy as well as investment in infrastructure as representative of green mobility dissemination policy measures. The discrete choice (DC) model is a representative methodology that can predict demand for products and technologies according to individual preferences However, it is difficult to grasp the cascading effect between other industries and the economy because it focuses on the substitution effect between products and technologies. On the contrary, the computable general equilibrium (CGE) model broadly analyzes changes in economic variables such as price and demand through considering the relationship between economic agents; however, the CGE model has a limited explanation of technology and market changes, depending on the price and quantity of goods. Through an integration of both models, it can be noted that the DC model captures more elastic changes in the attribute level by endogenously reflecting the results of the CGE model, whilst the CGE model implements a substitution relationship reflecting the specific technical specifications of the DC model. Therefore, using the integrated model, this study investigated the effect of demand fluctuations according to individual consumer preferences on the diffusion of new technologies within the whole country. Consequently, the proliferation of electric vehicles and hydrogen cars has led to economic growth. From an environmental point of view, the transport sector's CO2 emissions decreased significantly because of the shift in demand for electric and hydrogen vehicles. However, emissions from other industries increased owing to the increase in production output, resulting in a rebound effect that offset the emission reduction effect in the transport sector. In addition, if green mobility surges in the early stages, emissions will increase because of coal-fired power generation and hydrogen production centered on liquefied natural gas reforming. Therefore, an environmental benefit will only be observed when a clean power mix is a prerequisite before the demand for green mobility spreads. The impact of policy measures on green mobility dissemination is as follows. Firstly, the imposition of a tax may cause the cost of production for many companies to increase; however, depending on a learning rate, innovation may offset this cost rapidly. In other words, more effective results can be obtained when environmental policies such as taxation and technological policies that increase corporate productivity are implemented simultaneously. Secondly, investment in the complementary goods (infrastructure) market to improve the future market environment has proven to have a longer-term beneficial effect on the national economy than direct economic incentives (subsidies) for consumers. Finally, the differential payment of subsidies has a positive effect on the income improvement of the low-income class in the short-term; however, it is less beneficial to household income growth and national economic growth in the long-term as it slows the adoption of new technologies. By combining the two models in this study, it was possible to observe the innovation process from individual technology adoption to technology diffusion, targeting the entire economy. In addition to the above, the current framework that integrates the two models can more accurately predict the impact of government policies and provide a clear rationale for government decision-making than when testing policies using only an independent model.Abstract iii Contents vii List of Tables x List of Figures xi Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Objectives 9 1.3 Research Outline 15 Chapter 2. Literature Review and Theoretical and Methodological Background 18 2.1 Theoretical Background 18 2.1.1 Debates on Environmental Regulation and Innovation 18 2.1.2 Transport Policy for the Diffusion of Green Mobility 21 2.2 Methodological Background 25 2.2.1 Demand Forecasting on Individual Level 25 2.2.2 General Equilibrium Theory 30 2.3 Assessment of the Effects of Technology Diffusion: Green Mobility 31 2.3.1 Environmental Effects 31 2.3.2 Economic Effects 34 2.4 Integrated Studies of Consumption Behavior in the Transport Sector 36 2.5 Limitations of Previous Studies and Contribution of the Dissertation 40 Chapter 3. Methodology 43 3.1 Discrete Choice Model 43 3.1.1 Conceptual Background 43 3.1.2 Method 45 3.2 CGE Model 52 3.2.1 Social Accounting Matrix 52 3.2.2 Model Structure 60 3.3 Model Linkage 79 3.3.1 Choice Probability 81 3.3.2 Household Sector 83 3.3.3 Industry (Private Car Service) Sector 88 Chapter 4. Empirical Analysis 92 4.1 DC and Integrated Model Results 92 4.1.1 DC Estimation Results 92 4.1.2 Comparison of DC Model and Integrated Model 95 4.2 Baseline Scenario Analysis 99 4.2.1 Scenario Description 99 4.2.2 Validation 106 4.2.3 Scenario Results 110 4.3 Scenario Analysis 1: Fuel Tax and Learning Effects 124 4.3.1 Scenario Description 124 4.3.2 Scenario Results 126 4.4 Scenario Analysis 2: Subsidy and Charging Infrastructure Investment 138 4.4.1 Scenario Description 138 4.4.2 Scenario Results 141 4.5 Scenario Analysis 3: Differential Subsidy Payment 148 4.5.1 Scenario Description 148 4.5.2 Scenario Results 150 Chapter 5. Conclusion 160 5.1 Concluding Remarks and Contributions of This Study 160 5.2 Limitations and Suggestions for Future Research 165 Bibliography 169 Appendix 1: Respondentโ€™s Demographics in Conjoint Survey 190 Appendix 2: Classification of Industry in the CGE Model 191 Abstract (Korean) 192๋ฐ•

    ์ง‘์ค‘์†Œ์œ ์ง€๋ฐฐ๊ตฌ์กฐ ์•„๋ž˜ CEO-Board Dynamics

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2021.8. ๊น€์šฐ์ง„.This paper examines CEO-board dynamics in a country with concentrated ownership, using a Korean panel dataset for the period of 1998 to 2017, and a firm-year of 23,135 observations. This is the first paper to test both within firm and within CEO effects under Korean CEO-board dynamics and examines the differences of CEO-board dynamics when considering the family dimension. First, the paper finds evidence that the bargaining model and dynamic agency models are overall consistent in a market with highly concentrated ownership. CEO tenure is negatively correlated with board independence. Likewise, firm performance is negatively correlated with board independence. As CEO tenure increases by one year, chair duality and CEO pay respectively increase. Second, the same results are found in non-family firms. Third, family firms show CEO board dynamic test results to be inconsistent with the bargaining models expect for relationships between CEO tenure and Chair duality, and CEO tenure CEO pay.๋ณธ ์—ฐ๊ตฌ๋Š” bargaining model๊ณผ dynamic agency model ํ•˜์— ์„ฑ๋ฆฝ๋˜๋Š” CEO์™€ ์ด์‚ฌํšŒ์˜ ์ด๋ก ์ ์ธ ๊ด€๊ณ„๋ฅผ ์ง‘์ค‘์†Œ์œ ์ง€๋ฐฐ๊ตฌ์กฐ์ธ ํ•œ๊ตญ ์‹œ์žฅ์—์„œ๋„ ์‹ค์ฆ์ ์œผ๋กœ ์กด์žฌํ•˜๋Š”์ง€ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค๊ณผ๋Š” ๋‹ฌ๋ฆฌ ์ตœ์ดˆ๋กœ ํ•œ๊ตญ์‹œ์žฅ์—์„œ CEO-board dynamics ์ด๋ก ์„ ๊ธฐ์—… ๊ณ ์ •ํšจ๊ณผ์™€ CEO ๊ณ ์ •ํšจ๊ณผ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ํ…Œ์ŠคํŠธ๋ฅผ ํ–ˆ๊ณ , ์ „๋ฌธ๊ฒฝ์˜์ธํšŒ์‚ฌ์™€ ๊ฐ€์กฑ๊ฒฝ์˜ํšŒ์‚ฌ๋กœ ๋‚˜๋‰˜์–ด ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ–ˆ๋‹ค๋Š” ์ ์— ์˜์˜๋ฅผ ๋‘”๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ์ฃผ์š”ํ•œ ๋ฐœ๊ฒฌ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ง‘์ค‘์†Œ์œ ์ง€๋ฐฐ๊ตฌ์กฐ ์‹œ์žฅ์—์„œ๋„ ์ „๋ฐ˜์ ์œผ๋กœ bargaining model๊ณผ dynamic agency model์€ ์„ฑ๋ฆฝํ•œ๋‹ค. CEO ์ž„๊ธฐ๋Š” ์ด์‚ฌํšŒ ๋…๋ฆฝ์„ฑ๊ณผ ์Œ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. ๊ธฐ์—… ์„ฑ๊ณผ๋Š” ์ด์‚ฌ ๋…๋ฆฝ์„ฑ๊ณผ ์Œ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. CEO ์ž„๊ธฐ๊ฐ€ 1๋…„์”ฉ ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ์˜์žฅ ๊ฒธ์ง ์—ฌ๋ถ€์™€ CEO ๋ด‰๊ธ‰์€ ์ฆ๊ฐ€ํ•œ๋‹ค. ๋‘˜์งธ, ์ด์™€ ๊ฐ™์€ ๊ฒฐ๊ณผ๋Š” ์ „๋ฌธ๊ฒฝ์˜์ธํšŒ์‚ฌ์—์„œ๋„ ์ผ์น˜ํ•œ๋‹ค. ์…‹์งธ, ๋ฐ˜๋ฉด์— ๊ฐ€์กฑ๊ฒฝ์˜ํšŒ์‚ฌ์—์„œ๋Š” ๊ฒฐ๊ณผ๊ฐ’์ด bargaining model๊ณผ ๋ถˆ์ผ์น˜ํ•œ๋‹ค. ๋‹จ, CEO ์ž„๊ธฐ์™€ ์˜์žฅ ๊ฒธ์ง ์—ฌ๋ถ€์˜ ๊ด€๊ณ„, ๊ทธ๋ฆฌ๊ณ  CEO ์ž„๊ธฐ์™€ CEO ๋ด‰๊ธ‰ ๊ฐ„์˜ ๊ด€๊ณ„๋Š” ์˜ˆ์™ธ์ด๋‹ค.1. Introduction 1 2. Literature Review and Hypothesis 1 3. Data and Descriptive Statistics 5 3.1 Data 5 3.2 Descriptive Statistics 5 4. Results 8 4.1 CEO Tenure and Board Independence 8 4.2 Firm Performance and Board Independence 11 4.3 CEO Tenure, Chair Duality, and CEO Pay 13 4.4 Family Dimension of Korean CEO-Board Dynamics 14 4.5 Change in Dynamic Board After CEO Turnover 18 5. Conclusion 20 References 21 Abstract in Korean 22์„

    Preference for Different Types of Food in Women with Bulimia Nervosa and Binge Eating Disorder

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    Objective ๏ผš Recurrent episodes of binge eating are a cardinal symptom of both bulimia nervosa (BN) and binge eating disorder (BED). However, the type of food consumed during binge eating varies among individuals. We investigated what types of foods were preferred by women with recurrent binge eating episodes, and compared the differences between BN and BED. Methods ๏ผš We selected 30 photographs, which included four different types of food to stimulate appetite : 1) desserts/snacks, 2) meat, 3) fruits/vegetables, 4) rice/pasta, and 5) stationary objects as a control condition. Each type was composed of six different items. After six hours of fasting, 39 participants (15 BN, 12 BED, and12 healthy controls) were instructed to score their appetite using Likert scales from 1 to 7 for each photograph. Results ๏ผš The BN group reported stronger appetite response to photographs of desserts/snacks, while the BED group and healthy controls reported stronger appetite response to photographs of rice/pasta and meat. Statistical analysis showed that the mean appetite rank to desserts/snacks were significantly higher in BN group compared to BED group and healthy controls (Kruskal- Wallis test ; BN=26.4 ; BED=19.2 ; Control=15.5 ; p=0.047). The differences between groups for other types of food were not significant. Conclusion : Women with BN showed a stronger preference for desserts/snacks, while women with BED demonstrated no difference with the healthy control group. They ranked photographs of meat (beef, pork and poultry) highest. This suggests that there are different trigger foods contributing to binge episodes between bulimia nervosa and binge eating disorder

    UV/Chlorination ๊ณต์ • ์ค‘ 1H-benzotriazole์˜ ๋ถ„ํ•ด ํŠน์„ฑ๊ณผ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ณด๊ฑด๋Œ€ํ•™์› ํ™˜๊ฒฝ๋ณด๊ฑดํ•™๊ณผ, 2018. 2. ์กฐ๊ฒฝ๋•.Benzotriazole (BTA)์€ ๊ธˆ์†๊ณผ ๋งŒ๋‚˜ ์ฐฉ๋ฌผ์„ ํ˜•์„ฑํ•˜์—ฌ ๋ถ€์‹์„ ๋ฐฉ์ง€ํ•˜๋Š” ํŠน์„ฑ์„ ๊ฐ€์ง€๋Š” ํ™”ํ•ฉ๋ฌผ๋กœ, ์—ฌ๋Ÿฌ ์‚ฐ์—…์—์„œ ๊ธˆ์†์˜ ๋ถ€์‹๋ฐฉ์ง€์ œ, ํ•ญ๊ณต๊ธฐ์˜ ์ œ๋น™์žฅ์น˜, ์‹๊ธฐ์„ธ์ฒ™๊ธฐ ์„ธ์ œ ์†์˜ ๋ณดํ˜ธ์ œ ๋“ฑ์œผ๋กœ ๋„๋ฆฌ ์ด์šฉ๋œ๋‹ค (Pillard et al., 2001). ๋†’์€ ๊ทน์„ฑ๊ณผ ๋‚ฎ์€ ์ƒ๋ถ„ํ•ด์„ฑ์„ ๊ฐ€์ง„ ์ด ๋ฌผ์งˆ์€ ๊ธฐ์กด์˜ CAS(Conventional Activated Sludge) ๊ณต์ •์˜ ํ•˜์ˆ˜์ฒ˜๋ฆฌ์žฅ์—์„œ ๋ถ€๋ถ„์ ์œผ๋กœ ์ œ๊ฑฐ๋˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค (์•ฝ 37%์˜ ์ œ๊ฑฐ์œจ) (Weiss et al., 2006). BTA๋Š” ๋…์ผ๊ณผ ์ค‘๊ตญ์˜ ํ•˜์ˆ˜์ฒ˜๋ฆฌ์žฅ ์œ ์ถœ์ˆ˜์—์„œ ๊ฐ๊ฐ 7.7๊ณผ 2.7 ยตg/L์˜ ๋†๋„๋กœ ๊ฒ€์ถœ๋œ๋‹ค๊ณ  ๋ณด๊ณ ๋˜๊ณ  ์žˆ๋‹ค (Weiss et al, 2006Liu et al, 2013). ์ด์— ๋”ฐ๋ผ, ์Šค์œ„์Šค์—์„œ๋Š” Benzotriazole์„ ํ•˜์ˆ˜์ฒ˜๋ฆฌ์žฅ์˜ Well eliminated indicator compounds๋กœ ์ง€์ •ํ•˜์˜€์œผ๋ฉฐ, ์ตœ๊ทผ์—๋Š” AOP ๊ณต์ •์— ์˜ํ•œ BTA์˜ ์ œ๊ฑฐ ํŠน์„ฑ ์—ฐ๊ตฌ๋“ค์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค (Sichel et al., 2011Borowska et al., 2016). ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” UV ๊ด‘๋ถ„ํ•ด์™€ ์—ผ์†Œ์ฒ˜๋ฆฌ๋ฅผ ๊ฒฐํ•ฉํ•œ UV/Chlorination ๊ณต์ •์„ ์ด์šฉํ•˜์—ฌ BTA์˜ ๋ถ„ํ•ด ํŠน์„ฑ์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•˜์—ฌ ํŠน๋ณ„ํžˆ ์ œ์ž‘๋œ 2L ์šฉ๋Ÿ‰์˜ batch type reactor๋ฅผ UV/์‚ฐํ™”๋ฐ˜์‘ ์‹คํ—˜์— ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ์‹คํ—˜์— ์ด์šฉ๋œ UV ๋žจํ”„ (UV-A, UV-B, UV-C)์˜ ์„ธ๊ธฐ๋Š” 3.3-4.3 mW/cm2์ด์—ˆ๋‹ค. ๋ณธ ์‹คํ—˜์—์„œ BTA๋Š” ์ดˆ๊ธฐ ๋†๋„ 1 ยตM (119 ยตg/L)๋กœ, ์ •ํ•ด์ง„ ๋ฐ˜์‘ ์‹œ๊ฐ„ ๋งˆ๋‹ค 50 mL์”ฉ ์ฑ„์ทจ ๋˜์—ˆ์œผ๋ฉฐ ์ด 3์‹œ๊ฐ„ ๋™์•ˆ ๋ฐ˜์‘ํ•˜์˜€๋‹ค. Chlorination ๋ฐ UV/chlorination์„ ์œ„ํ•œ ์ดˆ๊ธฐ ์ž”๋ฅ˜ ์—ผ์†Œ ๋†๋„๋Š” 25, 50, 75, 100 ยตM (1.8, 3.6, 5.3, 7.1 mg/L)์ด์—ˆ์œผ๋ฉฐ ์‹œ๋ฃŒ ์ฑ„์ทจ ํ›„ Sodium Thiosulfate ์šฉ์•ก์„ Chlorination quencher๋กœ ์ด์šฉํ•˜์˜€๋‹ค. quencher ์ฃผ์ž…์„ ๋งˆ์นœ ์‹œ๋ฃŒ๋Š” 1 mL๋ฅผ ๋ฐ”์ด์•Œ์— ์˜ฎ๊ฒจ LC-ESI-MS/MS (Shimazu, Japan)๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” UV ํŒŒ์žฅ, ์ž”๋ฅ˜ ์—ผ์†Œ ๋†๋„, pH์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ, BTA์˜ ๋ถ„ํ•ด ํŠน์„ฑ์„ ์•Œ์•„๋ณด์•˜๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์— ์˜ํ•˜๋ฉด, BTA๋Š” UV-C ์˜์—ญ์—์„œ์˜ ํก๊ด‘๋„๊ฐ€ ๊ฐ€์žฅ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค (Wu et al., 2016). ์ด์— ๋”ฐ๋ผ, UV ํŒŒ์žฅ ๋ณ„ ์ œ๊ฑฐ ํšจ์œจ ๋˜ํ•œ UV-C ์˜์—ญ์—์„œ ๊ฐ€์žฅ ๋†’์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. UV-A์™€ ์—ผ์†Œ์ฒ˜๋ฆฌ์˜ ๋‹จ์ผ ๊ณต์ •, ์กฐํ•ฉ ๊ณต์ •์˜ ๊ฒฝ์šฐ, UV-A์™€ ์—ผ์†Œ์˜ ๋‹จ์ผ ๊ณต์ •์—์„œ๋Š” ๊ฑฐ์˜ ์ œ๊ฑฐ๊ฐ€ ๋˜์ง€ ์•Š์•˜์œผ๋‚˜, ๋‘ ๊ฐ€์ง€์˜ ์กฐํ•ฉ ๊ณต์ •์—์„œ๋Š” ์—ผ์†Œ์™€ UV์˜ ๋ฐ˜์‘์— ์˜ํ•œ OH radical ์ƒ์„ฑ์œผ๋กœ ์ธํ•˜์—ฌ, BTA ์ œ๊ฑฐ๊ฐ€ ํšจ์œจ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. UV-A/chlorination์— ๋Œ€ํ•œ ์ž”๋ฅ˜์—ผ์†Œ ๋†๋„ ๋ณ„ ์ œ๊ฑฐ ์†๋„ ์‹คํ—˜์—์„œ๋Š”, ์ฃผ์ž…ํ•˜๋Š” ์œ ๋ฆฌ ์—ผ์†Œ์˜ ์–‘์ด ๋งŽ์„์ˆ˜๋ก ์ƒ์„ฑ๋˜๋Š” ๋ผ๋””์นผ์˜ ์–‘์ด ์ฆ๊ฐ€ํ•˜์—ฌ BTA์˜ ์ œ๊ฑฐ ๋ฐ˜์‘ ์†๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. pH์˜ ๊ฒฝ์šฐ์—๋Š”, ์—ผ๊ธฐ์„ฑ > ์‚ฐ์„ฑ > ์ค‘์„ฑ์˜ ์ˆœ์„œ๋กœ ๋ฐ˜์‘ ์†๋„๊ฐ€ ๋นจ๋ž์œผ๋ฉฐ, UV-A ์˜์—ญ์˜ ๊ฒฝ์šฐ HOCl ๋ณด๋‹ค๋Š” OCl-์˜ ํก๊ด‘๋„๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์•„ OCl-์ด ์ฃผ ์ข…์ธ ์—ผ๊ธฐ์„ฑ์—์„œ ๊ฐ€์žฅ ๋น ๋ฅธ ๋ฐ˜์‘ ์†๋„๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. ๋˜ํ•œ, ๋ณธ ๊ณต์ • ์ค‘ BTA์˜ ๋ฌด๊ธฐํ™” ์ •๋„์™€ ๋ฐ˜์‘๋ถ€์‚ฐ๋ฌผ, ์ƒํƒœ๋…์„ฑ์˜ ๋ณ€ํ™”๋ฅผ ์•Œ์•„๋ณด์•˜๋‹ค. ๋ถ„์„ ๊ธฐ๊ธฐ์˜ ๊ฒ€์ถœ ๋†๋„ ๋ฒ”์œ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ, ๊ธฐ์กด ์‹คํ—˜๋ณด๋‹ค BTA ๋ฐ ์œ ๋ฆฌ์ž”๋ฅ˜์—ผ์†Œ์˜ ๋†๋„๋ฅผ 100๋ฐฐ ๋†’์—ฌ ์‹คํ—˜ํ•˜์˜€๋‹ค. BTA๋Š” 5์‹œ๊ฐ„ ์•ˆ์— ๋ชจ๋‘ ์ œ๊ฑฐ๋˜์—ˆ์œผ๋ฉฐ, TOC๋Š” 5์‹œ๊ฐ„ ๋™์•ˆ ์•ฝ 40 %, 12์‹œ๊ฐ„ ๋™์•ˆ ์•ฝ 50 % ์ œ๊ฑฐ๋˜์–ด, 50%์˜ ๋ฌด๊ธฐํ™”๊ฐ€ ์ผ์–ด๋‚ฌ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ, 50%์˜ ์ž”๋ฅ˜ ์œ ๊ธฐ๋ฐ˜์‘ ๋ถ€์‚ฐ๋ฌผ์ด ์žˆ์„ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋˜์–ด, ๋ฐ˜์‘๋ถ€์‚ฐ๋ฌผ์„ IC ๋ฐ UPLC-qTOF-MS๋กœ screening ๋ฐ identification ํ•˜์˜€๋‹ค. ์ด ๋„ค ๊ฐ€์ง€์˜ ์ด์˜จ๋ถ€์‚ฐ๋ฌผ (HCOO-, NO3-, ClO2-, ClO3-)์ด ๊ฒ€์ถœ๋˜์—ˆ์œผ๋ฉฐ, ๋‹ค์„ฏ ๊ฐ€์ง€์˜ ์œ ๊ธฐ ๋ฐ˜์‘ ๋ถ€์‚ฐ๋ฌผ (m/z 150.0307, 166.0252, 124.0147, 140.0071, 154.0245)์ด ESI(positive, negative) ์ด์˜จํ™” ๋ชจ๋“œ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋ณธ ๊ณต์ • ์ค‘ ์ƒํƒœ๋…์„ฑ์˜ ๋ณ€ํ™”๋Š” Microtox test๋ฅผ ํ†ตํ•ด ์ธก์ •๋˜์—ˆ์œผ๋ฉฐ, BTA์˜ UV-A/chlorination ๋ฐ˜์‘ ๋™์•ˆ ์ƒํƒœ๋…์„ฑ์€ ์ดˆ๋ฐ˜์— ์ฆ๊ฐ€ํ•˜์ง€๋งŒ, ๋ฐ˜์‘ ์ข…๋ฃŒ ์‹œ์ ์—๋Š” ๋…์„ฑ ์˜ํ–ฅ์ด ํ˜„์ €ํ•˜๊ฒŒ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ, ๊ธฐ์กด ์ฒ˜๋ฆฌ๊ณต์ •์—์„œ ์ž˜ ์ œ๊ฑฐ ๋˜์ง€ ์•Š์•„ indicator compounds๋กœ ์ง€์ •๋˜๋Š” ๋ฌผ์งˆ์ธ BTA์— ๋Œ€ํ•˜์—ฌ, Chlorination๊ณผ UV ์ฒ˜๋ฆฌ๋ฅผ ์กฐํ•ฉํ•˜์—ฌ ํšจ๊ณผ์ ์œผ๋กœ ์ œ๊ฑฐ๋˜๋ฉฐ, ๋…์„ฑ ๋˜ํ•œ ๊ฐ์†Œ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ, ๊ธฐ์กด ์—ผ์†Œ์ฒ˜๋ฆฌ๋ฅผ ํฌํ•จํ•œ ๊ณต์ •์— UV/chlorination์„ ์ ์šฉํ•œ๋‹ค๋ฉด ๋‚œ๋ถ„ํ•ด์„ฑ ๋ฌผ์งˆ์˜ ์ œ๊ฑฐ์— ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์Œ์„ ๊ธฐ๋Œ€ํ•  ์ˆ˜ ์žˆ๋‹ค.Benzotriazole (BTA) is widely used as a corrosion inhibitor of yellow metals, antifreezes, cutting fluids, and coating materials in various industries or even in domestic products. Because of its high polarity and low biodegradability, BTA is expected to be mobile in the aquatic environment. The reported removal of BTA ranges between 29% and 58% in wastewater treatment plant. In this study, the removal kinetics and degradation mechanisms of BTA during UV/chlorination process were investigated, especially focusing on UV-A/chlorination process. The experiment was performed with batch type photo reactor. The light intensity of UV lamps (UV-A, B, C) used were 3.3 โ€“ 4.3 mW/cm2. UHPLC- MS/MS was used for BTA analysis and UPLC-qTOF-MS was used for byproducts identification. The result showed that the removal rate of BTA was fast in the order of UV-C, UV-B, UV-A. UV-A/chlorination process showed a synergetic effect compared to UV-A photolysis and chlorination only processes. The synergetic effect is due to the OH radical generated in the UV/chlorination process. The kinetics followed the pseudo-first order kinetics. More chlorine dosage, faster removal rate was achieved. Alkaline pH increased the removal of BTA UV-A/chlorination process. Additionally, reaction byproducts during UV/chlorination process were identified (m/z 150.0307, 166.0252, 124.0147, 140.0071, 154.0245). Using the identified byproducts we proposed the degradation pathway of BTA during UV-A/chlorination process. Also, toxic profiles during UV-A/chlorination of BTA was investigated with Microtox bioassay. The ecotoxicity during UV-A/chlorination of BTA was decreased. As a result, it was confirmed that UV-A/Chlorination process was quite effective not only for BTA elimination, but also for ecotoxicity decrease.I.Introduction 1 1.1.Background 1 1.2.Benzotriazole in water environment 1 1.3.Toxicity of 1H-benzotriazole 5 1.4.Advanced Oxidation Processes (AOPs) 5 1.5.Preliminary studies on 1H-benztriazole degradation by AOPs 6 1.6.Objectives 8 II.Materials and Methods 10 2.1.Chemicals 10 2.2.Experimental procedures 11 2.3.Analytical methods 14 III.Results and Discussion 18 3.1.Removal efficiencies of 1H-benzotriazole by different UV wavelength 18 3.2.Degradation kinetics of 1H-benzotriazole during UV-photolysis, chlorination, and UV/chlorination process 21 3.3.Degradation kinetics of 1H-benzotriazole during UV-A/chlorination depends on chlorine dosage and pH 24 3.4.Mineralization and Identification of byproducts of 1H-benzotriazole during UV/chlorination 29 3.5.Toxic profiles during UV/chlorination of 1H-benzotriazole 38 IV.Conclusion 40 References 41 Supplementary material 46 ๊ตญ๋ฌธ์ดˆ๋ก 53Maste

    Results of major upper extremity replantation or revascularization: Thirteen years of experiences in a single center, and an indirect comparison of outcomes with reported arm transplantations

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2014. 2. ์ด์˜ํ˜ธ.์„œ๋ก : ์ƒ์ง€์—์„œ์˜ ์†๋ชฉ๋ณด๋‹ค ๊ทผ์œ„๋ถ€ ์ ˆ๋‹จ์— ๋Œ€ํ•œ ์žฌ์ ‘ํ•ฉ์ˆ ์€ ์ฃผ์š” ์‚ฌ์ง€ ์ ‘ํ•ฉ์ˆ (major replantation) ํ˜น์€ ๊ฑฐ๋Œ€ ์ ‘ํ•ฉ(macro-replantation)์œผ๋กœ ๋ถ„๋ฅ˜๋œ๋‹ค. ์ตœ๊ทผ ๋ฏธ์„ธ ์ˆ˜์ˆ  ๊ธฐ๋ฒ•์˜ ๋ฐœ๋‹ฌ๋กœ, ์žฌ์ ‘ํ•ฉ์ˆ ์˜ ๊ถ๊ทน์ ์ธ ๋ชฉ์ ์€ ์‚ฌ์ง€๋ฅผ ๋ณด์กดํ•˜๋Š” ๊ฒƒ์— ๊ทธ์น˜์ง€ ์•Š๊ณ , ์ •์ƒ ๊ธฐ๋Šฅ์˜ ํšŒ๋ณต๊ณผ ๋งŒ์กฑ์Šค๋Ÿฌ์šด ์™ธ๊ด€์— ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ 2000๋…„ 1์›”๋ถ€ํ„ฐ 2013๋…„ 10์›”๊นŒ์ง€ ๋‹จ์ผ ๊ธฐ๊ด€์—์„œ ์ƒ์ง€ ์†๋ชฉ ๊ทผ์œ„๋ถ€ ์™„์ „ ํ˜น์€ ๋ถˆ์™„์ „ ์ ˆ๋‹จ์œผ๋กœ ์žฌ์ ‘ํ•ฉ์ˆ  ํ˜น์€ ์žฌํ˜ˆ๊ด€ํ™”์ˆ ์„ ์‹œํ–‰ ๋ฐ›์€ ํ™˜์ž์™€ ๊ทธ ์น˜๋ฃŒ ๊ฒฐ๊ณผ์— ๋Œ€ํ•˜์—ฌ ๋ถ„์„ํ•ด ๋ณด๊ณ , ๋ฌธํ—Œ์— ๋ณด๊ณ ๋œ ์ƒ์ง€์—์„œ์˜ ํŒ”์ด์‹์ˆ (arm transplantation)์˜ ๊ฒฐ๊ณผ์™€ ๊ฐ„์ ‘ ๋น„๊ตํ•˜์—ฌ ๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋ฐฉ๋ฒ•: ์ƒ์ง€์—์„œ ์†๋ชฉ ๊ทผ์œ„๋ถ€ ์™„์ „, ํ˜น์€ ๋ถˆ์™„์ „ ์ ˆ๋‹จ์œผ๋กœ ์น˜๋ฃŒ ๋ฐ›์€ ํ™˜์ž, 25๋ก€๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ํ‰๊ท  ์ถ”์‹œ ๊ด€์ฐฐ ๊ธฐ๊ฐ„์€ 34.4(๋ฒ”์œ„: 7~85)๊ฐœ์›” ์ด์—ˆ์œผ๋ฉฐ, ํ™˜์ž์˜ ํ‰๊ท  ๋‚˜์ด๋Š” 35.6(๋ฒ”์œ„: 14-70)์„ธ์˜€๋‹ค. ์ƒ์ง€ ์–‘์ธก ์ ˆ๋‹จ์˜ ๊ฒฝ์šฐ๋Š” ์—†์—ˆ๋‹ค. ์™„์ „ ์ ˆ๋‹จ์€ 4๋ก€, ๋ถˆ์™„์ „ ์ ˆ๋‹จ์€ 21๋ก€์˜€๋‹ค. ์†์ƒ์˜ ๊ธฐ์ „์€ ์••๊ถค-๊ฒฌ์—ด ์†์ƒ์ด 5๋ก€, ์ ˆ๋‹จ ์†์ƒ์ด 16๋ก€, ์••๊ถค ์†์ƒ์ด 2๋ก€, ๊ฒฌ์—ด ์†์ƒ์ด 2๋ก€์˜€๋‹ค. ์ƒ์ง€์—์„œ์˜ ์ ˆ๋‹จ ์œ„์น˜๋Š” ๊ฒฌ๊ด€์ ˆ์—์„œ ์›์œ„ ์ƒ์™„๋ถ€ ์‚ฌ์ด๊ฐ€ 8๋ก€, ์ฃผ๊ด€์ ˆ๊ณผ ์ „์™„๋ถ€ ์ค‘๊ฐ„๋ถ€๊นŒ์ง€๋Š” 7๋ก€, ์›์œ„ ์ „์™„๋ถ€ ์—์„œ ์†๋ชฉ ๊ด€์ ˆ ์ƒ๋ถ€๊นŒ์ง€ 10๋ก€์˜€๋‹ค. ํ™˜์ž์˜ ์น˜๋ฃŒ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ํ‰๊ฐ€๋Š” DASH (Disabilities of the Arms, Shoulder and Hand) ์ ์ˆ˜ ๋“ฑ์„ ์‚ฌ์šฉํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: ํ‰๊ท  ํ—ˆํ˜ˆ ์‹œ๊ฐ„์€ 380.1(๋ฒ”์œ„: 120-600)๋ถ„์ด์—ˆ๋‹ค. ์ด 25๋ก€์ค‘ 23๋ก€์ธ 95.6%์—์„œ ์ƒ์ง€๋ฅผ ๋ณด์กดํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰ ์ถ”์‹œ ๊ด€์ฐฐ์—์„œ ํ‰๊ท  ์ด์  ์‹๋ณ„๋ ฅ์€ ํ‰๊ท  7.7(๋ฒ”์œ„: 4~15)mm์˜€์œผ๋ฉฐ, ํ‰๊ท  VAS ์ ์ˆ˜๋Š” 2.91(๋ฒ”์œ„: 1~7), ํ‰๊ท  DASH ์ ์ˆ˜๋Š” 13.2(๋ฒ”์œ„: 2~85)์ด์˜€๋‹ค. ๊ฒฐ๋ก : ์ƒ์ง€์—์„œ ์žฌ์ ‘ํ•ฉ์ˆ , ํ˜น์€ ์žฌํ˜ˆ๊ด€ํ™”์ˆ ์€ ๊ธฐ๋Šฅ์„ ํšŒ๋ณตํ•˜๋Š” ์ข‹์€ ๋ฐฉ๋ฒ•์ด๋ฉฐ, ์ ˆ๋‹จ ํ™˜์ž์˜ ์น˜๋ฃŒ ๋ฐฉ์นจ์„ ๊ฒฐ์ •ํ•  ๋•Œ ์žฌ์ ‘ํ•ฉ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ตœ์šฐ์„ ์œผ๋กœ ์—ผ๋‘์— ๋‘๊ณ  ์‹ ์ค‘ํ•˜๊ฒŒ ๊ฒฐ์ •ํ•ด์•ผ ํ•œ๋‹ค.์ดˆ๋ก i ๋ชฉ์ฐจ iii ํ‘œ ๋ฐ ๊ทธ๋ฆผ ๋ชฉ๋ก โ…ณ ์„œ๋ก  1 ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 4 ๊ฒฐ๊ณผ 8 ๊ณ ์ฐฐ 22 ๊ฒฐ๋ก  29 ์ฐธ๊ณ ๋ฌธํ—Œ 30 ์ดˆ๋ก (์˜๋ฌธ) 34Maste

    ํ‘œ๋ฉด์ฒ˜๋ฆฌ๋œ ํ‹ฐํƒ€๋Š„์—์„œ ํƒ€์•ก ์ฝ”ํŒ…์ด ์ž์™ธ์„ ์— ์˜ํ•œ ๊ด‘์ด‰๋งค ์‚ด๊ท  ํšจ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์น˜์˜๊ณผํ•™๊ณผ ์น˜๊ณผ๊ต์ •ํ•™ ์ „๊ณต, 2012. 8. ์•ˆ์„์ค€.๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํƒ€์•ก ์ฝ”ํŒ…์ด ์ž์™ธ์„  ์กฐ์‚ฌ์— ์˜ํ•œ ํ‹ฐํƒ€๋Š„ ํ‘œ๋ฉด์˜ Streptococcus sanguinis์— ๋Œ€ํ•œ ๊ด‘์ด‰๋งค ์‚ด๊ท ํšจ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ํ‹ฐํƒ€๋Š„ ๋””์Šคํฌ๋ฅผ ํ‘œ๋ฉด ์ฒ˜๋ฆฌํ•˜์—ฌ ์ ˆ์‚ญ์ฒ˜๋ฆฌ๊ตฐ(Machined titanium surface, MA), ์—ด์ฒ˜๋ฆฌ๊ตฐ(Heat-treated titanium surface, HT), ์–‘๊ทน ์‚ฐํ™”์ฒ˜๋ฆฌ๊ตฐ(Anodized titanium surface, AO)์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ๊ฐ๊ฐ์˜ ๋””์Šคํฌ๋ฅผ ๋น„์ž๊ทน์„ฑ ์ „ํƒ€์•ก(unstimulated whole saliva)๊ณผ PBS(phosphate-buffered saline)์— 2์‹œ๊ฐ„ ๋™์•ˆ ๋ฐฐ์–‘ํ•œ ํ›„, ๊ด‘์ด‰๋งค ์‚ด๊ท ํšจ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ํ‹ฐํƒ€๋Š„ ๋””์Šคํฌ๋ฅผ Streptococcus sanguinis ๋ฐฐ์–‘์•ก์— ๋„ฃ๊ณ  ์ž์™ธ์„ ์„ 90๋ถ„ ํ˜น์€ 180๋ถ„ ๋™์•ˆ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์‚ด์•„์žˆ๋Š” ์„ธ๊ท  ์ˆ˜๋Š” ์„ธํฌ ๋ฐฐ์–‘์•ก์—์„œ ๋‹จ๊ณ„ํฌ์„๋ฒ•์„ ํ†ตํ•˜์—ฌ ๊ณ„์‚ฐํ•˜์˜€๊ณ , ์„ธ๊ท ์˜ ์ƒ์กด์œจ์„ ์ด์šฉํ•˜์—ฌ ์ž์™ธ์„ ์— ์˜ํ•œ ๊ด‘์ด‰๋งค ์‚ด๊ท ํšจ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ํƒ€์•ก ์ฝ”ํŒ…์ด ์—†์„ ๋•Œ๋Š” AO ๊ตฐ์ด MA ๊ตฐ์— ๋น„ํ•ด ์„ธ๊ท ์˜ ์ƒ์กด์œจ์ด ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•˜์œผ๋ฉฐ, HT ๊ตฐ์˜ ์„ธ๊ท  ์ƒ์กด์œจ์€ MA๊ตฐ๊ณผ AO๊ตฐ์˜ ์ค‘๊ฐ„์ด์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํƒ€์•ก ์ฝ”ํŒ… ๊ตฐ์—์„œ๋Š” ๋ชจ๋“  ํ‘œ๋ฉด์ฒ˜๋ฆฌ ๊ตฐ์—์„œ ์„ธ๊ท  ์ƒ์กด์œจ์ด ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜์—ฌ ํ‘œ๋ฉด์ฒ˜๋ฆฌ ๊ตฐ ๊ฐ„์˜ ์„ธ๊ท  ์ƒ์กด์œจ์—์„œ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํƒ€์•ก์ด ์กด์žฌํ•˜๋Š” ๊ตฌ๊ฐ• ๋‚ด ์ƒํ™ฉ์—์„œ ์ž์™ธ์„ ์„ ์ด์šฉํ•œ ํ‹ฐํƒ€๋Š„ ํ‘œ๋ฉด์—์„œ์˜ ๊ด‘์ด‰๋งค ์‚ด๊ท ํšจ๊ณผ๋ฅผ ๊ธฐ๋Œ€ํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค.The purpose of this study was to investigate the ultraviolet-light-induced photocatalytic bactericidal effects of titanium surfaces on Streptococcus sanguinis in the presence of saliva-coating. Three different titanium disks were prepared: machined (MA), heat-treated (HT), and anodized surfaces (AO). Each disk was incubated with whole saliva or phosphate-buffered saline for 2 hours. Antibacterial tests were performed by incubating a S. Sanguinis suspension with each disk for 90 or 180 minutes under ultraviolet (UV) illumination. The viable counts of bacteria were enumerated from the cell suspension and the UV-light-induced photocatalytic bactericidal effects were determined by the bacterial survival rate. Without saliva-coating, AO disks exhibited significantly decreased bacterial survival rates compared to MA disks. The bacterial survival rates of the HT disks were intermediate between MA and AO in the absence of saliva-coating. However, saliva-coating significantly increased bacterial survival rates in all surface types. There was no significant difference in bacterial survival rates among the three surface types after saliva-coating. This study suggests that Ti-based antibacterial implant materials using TiO2 photocatalyst may have a limitation for intraoral use.โ… . INTRODUCTION โ…ก. REVIEW OF LITERATURE โ…ข. MATERIAL AND METHODS โ…ฃ. RESULTS โ…ค. DISCUSSION โ…ฅ. CONCLUSION REFERENCESDocto

    A Case of Continuous Ambulatory Peritonitis Dialysis Peritonitis Due to Stenotrophomonas maltophilia Using Antibiotic Combination

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    Continuous ambulatory peritoneal dialysis (CAPD) peritonitis is a major complication of peritoneal dialysis (PD) and leads to the discontinuation of PD. Despite its limited pathogenicity, CAPD peritonitis caused by Stenotrophomonas maltophilia (S. maltophilia), an important nosocomial pathogen that is present in nature and is usually associated with plastic indwelling devices. Infection of S. maltophilia is associated with a poor prognosis, including inability to maintain the CAPD catheter, because of its resistance to multiple antibiotics. We report a case of CAPD peritonitis due to S. maltophilia that was treated successfully using oral Trimethoprim-sulfame-thoxazole and intraperitoneal Ticarcillin/clavulanate without removing the dialysis catheter.ope

    ์šฐ๋ฃจ๊ณผ์ด ๋Œ€ํ†ต๋ น ํŽ˜ํŽ˜ ๋ฌดํžˆ์นด๊ฐ€ ์šฐ๋ฆฌ์—๊ฒŒ ๋‚จ๊ธด ๋ฉ”์„ธ์ง€

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    ์ง€๋‚œ 10์›” 26์ผ ์น˜๋Ÿฌ์ง„ ๋Œ€ํ†ต๋ น ์„ ๊ฑฐ์ผ, ๋ฌดํžˆ์นด ๋Œ€ํ†ต๋ น์ด ํ—ˆ๋ฆ„ํ•œ ์ž๊ฐ€์šฉ์„ ๋ชฐ๊ณ  ํˆฌํ‘œ์žฅ์— ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์„ธ๊ณ„์—์„œ ๊ฐ€์žฅ ๊ฒ€์†Œํ•œ ๋Œ€ํ†ต๋ น ๋ถ€๋ถ€๋กœ ์šฐ๋ฆฌ์—๊ฒŒ๋„ ์ž˜ ์•Œ๋ ค์ง„ ๋ฌดํžˆ์นด ๋ถ€๋ถ€๋Š” ์˜ค๋žœ ๋™๊ฑฐ์ƒํ™œ ๋์— 2005๋…„์— ์ •์‹์œผ๋กœ ๊ฒฐํ˜ผํ•˜์˜€๋‹ค. ๋ฌดํžˆ์นด ๋Œ€ํ†ต๋ น์€ ๋‚ด๋…„ 2015๋…„ 2์›” 28์ผ๋ถ€๋กœ ์ž„๊ธฐ๋ฅผ ๋‹คํ•˜๊ณ  ๋Œ€ํ†ต๋ น ์ž๋ฆฌ์—์„œ ๋‚ด๋ ค์˜ค๊ฒŒ ๋œ๋‹ค. ์šฐ๋ฃจ๊ณผ์ด ์—ญ์‚ฌ์ƒ ์–ด๋–ค ๋Œ€ํ†ต๋ น๋ณด๋‹ค ๊ตญ์ œ์ ์œผ๋กœ ์œ ๋ช…ํ•ด์ง„ ๋ฌดํžˆ์นด ๋Œ€ํ†ต๋ น, ๊ทธ์—๊ฒŒ๋Š” ์–ด๋–ค ํŠน๋ณ„ํ•œ ์ ์ด ์žˆ์„๊นŒ? ์ด ๊ธฐํšŒ๋ฅผ ํ†ตํ•ด ๋Œ€ํ†ต๋ น ๋ฌดํžˆ์นด๋Š” ๋ฌผ๋ก  ์ธ๊ฐ„ ๋ฌดํžˆ์นด์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋ฌดํžˆ์นด ๋Œ€ํ†ต๋ น ๋ถ€๋ถ€๋Š” ์šฐ๋ฃจ๊ณผ์ด์˜ ์ˆ˜๋„ ๋ชฌํ…Œ๋น„๋ฐ์˜ค์— ์œ„์น˜ํ•œ ๋Œ€ํ†ต๋ น๊ถ์„ ๋งˆ๋‹คํ•˜๊ณ  ์‹œ ์™ธ๊ณฝ์˜ ๋ถ€์ธ ์‚ฌํƒ์—์„œ ์ทจ์ž„ ์ „์ฒ˜๋Ÿผ ์‚ด๊ณ  ์žˆ๋‹ค. ๊ทธ์˜ ์ผ์ƒ์€ ๋Œ€ํ†ต๋ น์œผ๋กœ ์„ ์ถœ๋˜๊ณ  ์ทจ์ž„ํ•œ ์ดํ›„ ์•ˆ์ „๊ณผ ๊ฒฝํ˜ธ๋ฅผ ์œ„ํ•œ ๋ช‡ ๊ฐ€์ง€ ์‚ฌํ•ญ์ด ์ถ”๊ฐ€๋œ ๊ฒƒ ๋ง๊ณ ๋Š” ํฐ ๋ณ€ํ™”๊ฐ€ ์—†๋‹ค. ๋‹ค๋ฅธ ์šฐ๋ฃจ๊ณผ์ด ๊ตญ๋ฏผ๋“ค๊ณผ ํ•จ๊ป˜ ์ผ๋ฐ˜์Œ์‹์ ์—์„œ ์‹์‚ฌ๋ฅผ ํ•˜๊ณ , ์ง์ ‘ ์ž๊ฐ€์šฉ๊ณผ ํŠธ๋ž™ํ„ฐ๋ฅผ ๋ชฐ๊ณ  ๋‹ค๋‹ˆ๋Š” ๋“ฑ ์šฐ๋ฆฌ๋กœ ์„œ๋Š” ์ƒ์ƒํ•˜๊ธฐ ํž˜๋“  ๋Œ€ํ†ต๋ น์˜ ์ผ์ƒ์„ ๋ณด๊ณ  ์žˆ์ž๋ฉด, ๋ฌดํžˆ์นด ๋Œ€ํ†ต๋ น์˜ ์‹ค์งˆ์ ์œผ๋กœ ์†Œ์œ ํ•œ ์œ ์ผํ•œ ์žฌ์‚ฐ์ด 1987๋…„ ์ถœ์‹œ๋œ ํญ์Šค๋ฐ”๊ฒ ๋น„ํ‹€์ด๋ผ๋Š” ์‚ฌ์‹ค์ด ๊ทธ๋ฆฌ ๋†€๋ž์ง€ ์•Š๋‹ค

    ์กฐ์„ธํšŒํ”ผ์™€ ์‹ค์ œ์˜์—…ํ™œ๋™์„ ํ†ตํ•œ ์ด์ต์กฐ์ •

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜ํ•™๊ณผ, 2017. 2. ์ •์šด์˜ค.In line with the literature on several determinants of tax avoidance, this paper considers the impact of real earnings management on tax avoidance. Under the circumstances of divergent reporting incentives in tax and financial accounting purposes, firms may properly exploit discretionary accruals and tax subsidy for dual goals. On the other hand, firms that engage in real earnings management have relatively little chance to achieve it, and thus would have stronger motivation to seek for aggressive tax planning. In addition, firms that engage more in real earnings management are safer from regulator scrutiny relative to accrual management, which leads firm to be less sensitive to the risks of tax avoidance. Given greater motivation and favorable circumstances for tax avoidance, firms that engage more in real earnings management are expected to seek for more aggressive tax planning. Results from OLS regression support the prediction, and the impact of real earnings management on tax avoidance remains unchanged after controlling for the discretionary accruals.โ… . Introduction 1 โ…ก. Literature Review and Hypothesis Development 4 2.1 Comparison between REM and AM 4 2.2 Hypotheses Development 6 โ…ข. Research Design 8 3.1. Sample 8 3.2. Research Design 8 โ…ฃ. Results 10 4.1. Summary Statistics 10 4.2. Empirical Results 11 โ…ค. Conclusion 13 References 15 Appendix 18 Tables 19 Abstract in Korean(๊ตญ๋ฌธ์ดˆ๋ก) 25Maste

    GH3, FRTL-5 ์„ธํฌ์ฃผ์™€ ์ œ๋ธŒ๋ผํ”ผ์‰ฌ๋ฅผ ํ™œ์šฉํ•œ ๋ฒค์กฐํŽ˜๋…ผ๋ฅ˜์˜ ๊ฐ‘์ƒ์„  ๊ต๋ž€ ์˜ํ–ฅ ์Šคํฌ๋ฆฌ๋‹

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› ํ™˜๊ฒฝ๋ณด๊ฑดํ•™๊ณผ, 2017. 8. ์ตœ๊ฒฝํ˜ธ.Benzophenones (BPs) are UV protection agents frequently used in various personal care products (PCPs). BPs have been widely detected in the environment and biota. Endocrine disrupting effects of some BPs have been documented. However, significant knowledge gaps are present for thyroid disrupting effects of these compounds. Thyroid disruption of various BPs was investigated using rat pituitary and thyroid follicle cell lines, and zebrafish. First, in vitro assays employing a rat pituitary cell line (GH3) and a rat follicular cell line were conducted on six BPs, i.e., benzophenone (BP), benzophenone-1 (BP-1), benzophenone-2 (BP-2), benzophenone-3 (BP-3), benzophenone-4 (BP-4), and benzophenone-8 (BP-8). Then, BP-3, mainly used BP-type UV filter, and its major metabolites BP-1 and โ€“8 were employed for subsequent in vivo tests with zebrafish (Danio rerio) embryo. Following in vitro GH3 exposure, all six BPs except BP-4 down-regulated Tshฮฒ, Trhr, and Trฮฒ genes and up-regulated Dio2 gene in the rat pituitary cells. In addition, some BPs significantly up-regulated Nisand Tg genes while down-regulating Tpo gene on various level in FRTL-5 cells. In zebrafish embryo, significantly decreases of whole-body T4 and T3 level were observed following exposure to each BP until 144 hour post fertilization (hpf). BP-3 and โ€“8 decreased T3 in zebrafish at lower exposure concentrations compared to that for BP-1, implying greater thyroid hormone disrupting potencies of both BPs. Transcriptional changes in several thyroid hormone regulating genes in hypothalamic-pituitary-thyroid axis were observed as well. The results of this study showed that all tested BPs caused thyroid disrupting responses in a rat pituitary gland and a thyroid gland, crucial organs regulating homeostasis of thyroid system. Embryo-larval exposure of zebrafish also demonstrated that BP-1, -3, and โ€“8 could alter thyroid hormone levels. Since thyroid hormone regulation plays key role in early development and normal physiology, consequences of this thyroid hormone disruption in later life stages of the fish warrant further investigation.1. Introduction 1 2. Materials and Methods 4 2.1 Chemicals 4 2.2 GH3 cell culture and exposure 6 2.3 FRTL-5 cell culture and exposure 7 2.4 Zebrafish culture and embryo/larval exposure 7 2.5 Thyroid hormone extractions and measurement 8 2.6 RNA isolation and quantitative real-time polymerase chainsreaction (qRT-PCR) 9 2.7 Statistical analysis 10 3. Results 11 3.1 Transcriptional changes related to thyroid system in GH3 cell 11 3.2 Transcriptional changes related to thyroid system in FRTL-5 cell 15 3.3 Effects on thyroid hormone level in zebrafish 19 3.4 Transcriptional changes related to thyroid system in zebrafish 21 3.5 Effects on survival, hatchabilityand body weight of zebrafish 23 4. Discussion 25 5. Conclusion 29 6. References 30 Supplementary Information 36 Abstract in Korean 40Maste
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