152 research outputs found

    ๋ถ„์œ„ํšŒ๊ท€๋ชจํ˜•์„ ํ™œ์šฉํ•˜์—ฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2022.2. ์œค์ˆœ์ง„.Cities with an area of 3% worldwide consume 67% of the world's energy. The urbanization pace gradually accelerated, and at the end of 2019, for the first time ever, more than half of the country's total population lived in the metropolitan area in Korea. As the urban population increases, negative environmental impacts such as soaring energy consumption, greenhouse gas emissions, and air pollution affect cities. Therefore, the need for appropriate urban energy demand management to maintain a sustainable city is gradually emerging. Compact urban development is to concentrate social and economic activities in specific areas such as the station area and the existing downtown for sustainable city and energy utilization, and to develop complex and high-density residential, commercial, and business functions. These compact cities can function as mediator or moderator variables in mitigating the negative impact on climate change by shortening vehicle miles traveled(VMT), and reducing urban heat island effects. Cities are spaces where the population lives, and have various regional characteristics such as the number of household members, proportion of population age, and economic activities, and have a unique urban form. This urban form is affected by temperature and is also a factor influencing per capita energy consumption. In other words, in order to respond to the mid-to-long-term goals of the Post Kyoto Protocol at the urban level, identifying urban form and fundamentally rebuilding the city for establishing countermeasures on climate change is necessary. To understand the effect of factors influencing the urban energy consumption, exploration of physical, demographic, sociological, economic, and temperature factors should be preceded. In addition, analyzing the differences in urban form and characteristics is essential, since urban development in the metropolitan area and other regions is different and the stages of urbanization are progressing at different stages. The factors influencing energy consumption analyzed by reflecting differences between cities can be suggested as policy countermeasures for regional energy plans. Per capita energy consumption was used as a dependent variable. To control the impact of different industrial characteristics by city, energy in transport, household, commercial, and public sectors except for the industrial sector were summed up and divided by the number of registered resident population. In addition, this paper accepts urban form variables such as population density, mixed land use, housing supply type, public transportation/walk access to educational facilities, job-housing balance and green area per capita to examine the effects on per capita energy consumption. Regional characteristics of single-person households, aging rates, per capita local tax payments, cooling and heating degree days were used as control variables. Empirical analysis was conducted after classifying into national, metropolitan, and other regional models using 2019 municipal data. Per capita energy consumption was subdivided into 10, 30, 50, 70, and 90 quartiles to confirm the effect of each variable. In the case of the national model, in most quantities, per capita energy consumption decreases as the population density increases, and as the public transportation/walk access to educational facilities increased. This means that the accumulated population density and the establishment of high accessibility to local services will help reduce per capita energy consumption. Unlike previous studies, the mixed land use has increased per capita energy consumption. It can be seen as inefficient energy consumption has been incurred so far due to indiscriminate mixed land use rather than rational convergence. In the case of regional characteristics, in most quartiles, per capita energy consumption increases as the number of heating degree days increase. In the future, improving energy efficiency of old buildings and strengthening energy standards for new buildings are needed. To compare the analysis results of the metropolitan area model and other regional models, the metropolitan area's per capita energy consumption and urban form factors related to compact city were mainly significant. On the other hand, in other regions, urban form factors excluding the population density did not show significant significance, and regional characteristic factors such as per capita local tax payment, cooling and heating degree days were significant. Through these results, it reveal that the urban form factor differed in the effect of per capita energy consumption according to the degree of urbanization, and the metropolitan area and other regions derived the justification for different approach directions when establishing regional energy plans. The metropolitan areas have already crossed the threshold of optimal population density required to reduce per capita energy consumption, and the impact is minimal. Inspections should be made to ensure that citizens have easy access to local services using public transport facilities. In other regions, before establishing a regional energy plan to reduce energy consumption due to the urban form, it is matter to reduce energy consumption by improving the population density, prioritizing the formation of sufficient neighborhood living areas. Since it is more affected by temperature factors among regional characteristics, heating energy can be reduced by considering the introduction of district heating, which is currently mainly installed in the Seoul metropolitan area. Providing incentives to high-energy efficiency home appliances could be another policy. Therefore, when establishing a regional energy plan as climate change countermeasures in the future, to derive an appropriate regional energy plan suitable for each urban form by considering the different factors is essential. This study will be able to contribute to the regional energy plan to respond to climate change at the urban level by illuminating the urban form and regional characteristics according to each quintile of per capita energy consumption.์ „ ์„ธ๊ณ„์ ์œผ๋กœ 3%์˜ ๋ฉด์ ์— ์œ„์น˜ํ•œ ๋„์‹œ๋Š” ์„ธ๊ณ„ ์—๋„ˆ์ง€์˜ 67%๋ฅผ ์†Œ๋น„ํ•˜๊ณ  ์žˆ๋‹ค. ๋„์‹œํ™” ์†๋„๋Š” ์ ์ฐจ ๊ฐ€์†๋˜๊ณ  ํ•œ๊ตญ์—์„œ๋Š” 2019๋…„ ๋ง ์‚ฌ์ƒ ์ฒ˜์Œ์œผ๋กœ ๊ตญ๋‚ด ์ „์ฒด ์ธ๊ตฌ์˜ ์ ˆ๋ฐ˜ ์ด์ƒ์ด ์ˆ˜๋„๊ถŒ์— ๊ฑฐ์ฃผํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๋„์‹œ ๊ฑฐ์ฃผ ์ธ๊ตฌ๊ฐ€ ์ฆ๊ฐ€ํ•˜๋ฉด์„œ ๋„์‹œ๋Š” ์—๋„ˆ์ง€ ์†Œ๋น„ ๊ธ‰์ฆ, ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ, ๋Œ€๊ธฐ์˜ค์—ผ๊ณผ ๊ฐ™์€ ๋ถ€์ •์ ์ธ ํ™˜๊ฒฝ ์˜ํ–ฅ์„ ๋ฐ›๊ณ  ์žˆ๊ณ  ์ง€์†๊ฐ€๋Šฅํ•œ ๋„์‹œ์˜ ์œ ์ง€๋ฅผ ์œ„ํ•ด ์ ์ ˆํ•œ ์—๋„ˆ์ง€ ๊ณต๊ธ‰๊ณผ ๋”๋ถˆ์–ด ๋„์‹œ ์—๋„ˆ์ง€์ˆ˜์š” ๊ด€๋ฆฌ์˜ ํ•„์š”์„ฑ์ด ์ ์ฐจ ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ๋„์‹œ์˜ ์••์ถ•์ ์ธ ๊ฐœ๋ฐœ์€ ์ง€์†๊ฐ€๋Šฅํ•œ ๋„์‹œ ์œ ์ง€์™€ ์—๋„ˆ์ง€ ํ™œ์šฉ์„ ์œ„ํ•ด ์—ญ์„ธ๊ถŒ๊ณผ ๊ธฐ์กด ๋„์‹ฌ ๋“ฑ ํŠน์ • ์ง€์—ญ์— ์‚ฌํšŒ-๊ฒฝ์ œ์  ํ™œ๋™์„ ์ง‘์ค‘์‹œํ‚ค๊ณ  ์ฃผ๊ฑฐ-์ƒ์—…-์—…๋ฌด ๊ธฐ๋Šฅ์„ ๋ณตํ•ฉ์ ์ด๊ณ  ๊ณ ๋ฐ€๋„๋กœ ๋ฐœ์ „์‹œํ‚ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์••์ถ•์ ์ธ ๋„์‹œ๋Š” ์ž๋™์ฐจ ์ด๋™๊ฑฐ๋ฆฌ ๋‹จ์ถ•, ๋„์‹œ ์—ด์„ฌํšจ๊ณผ ๊ฐ์†Œ์™€ ๊ณต๊ณต์„œ๋น„์Šค ์ „๋‹ฌ ํšจ์œจ์„ฑ ์ฆ์ง„ ๋“ฑ์˜ ํšจ๊ณผ๋ฅผ ํ†ตํ•ด ๊ธฐํ›„๋ณ€ํ™”์˜ ํ•œ ๋Œ€์‘์ „๋žต์œผ๋กœ์„œ ๋„์‹œ ๋‚ด ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ ๊ฐ์†Œ์— ๋งค๊ฐœ๋ณ€์ˆ˜๋‚˜ ์กฐ์ ˆ๋ณ€์ˆ˜๋กœ ๊ธฐ๋Šฅํ•  ์ˆ˜ ์žˆ๋‹ค. ๋„์‹œ๋Š” ์ธ๊ตฌ๊ฐ€ ์‚ถ์„ ๊พธ๋ ค๋‚˜๊ฐ€๋Š” ๊ณต๊ฐ„์œผ๋กœ ๊ฐ€๊ตฌ์› ์ˆ˜, ์ธ๊ตฌ ์—ฐ๋ น ๋น„์ค‘, ๊ฒฝ์ œํ™œ๋™์ฒ˜๋Ÿผ ์ง€์—ญ์˜ ๋‹ค์–‘ํ•œ ํŠน์„ฑ์„ ์ง€๋‹ˆ๊ณ , ์ด๋Ÿฌํ•œ ํŠน์„ฑ์œผ๋กœ ํ˜•์„ฑ๋˜๋Š” ๊ณ ์œ ํ•œ ์ง€์—ญ์˜ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๋„์‹œ๊ตฌ์กฐ๋Š” ๋„์‹œ ๊ธฐ์˜จ์— ์˜ํ–ฅ์„ ์ฃผ๊ธฐ๋„ ๋ฐ›๊ธฐ๋„ ํ•˜๋ฉฐ 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์ด ๋˜๊ธฐ๋„ ํ•œ๋‹ค. ์ฆ‰ ํŒŒ๋ฆฌํ˜‘์ •์— ๊ธฐ์ดˆํ•œ ์‹ ๊ธฐํ›„์ฒด์ œ์˜ ์ค‘์žฅ๊ธฐ ๋ชฉํ‘œ์— ๋„์‹œ ์ฐจ์›์—์„œ ๋Œ€์‘ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋„์‹œ ์—๋„ˆ์ง€ ์†Œ๋น„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋„์‹œ๊ตฌ์กฐ์™€ ๊ตํ†ต ์ฒด๊ณ„์™€ ๊ฐ™์€ ๋„์‹œ์˜ ํŠน์„ฑ์„ ํŒŒ์•…ํ•˜๊ณ  ๋„์‹œ์˜ ๊ทผ๋ณธ์ ์ธ ์žฌ๊ตฌ์ถ•, ํŠนํžˆ ๋„์‹œ๊ฐœ๋ฐœ์‚ฌ์—… ๋ถ€๋ฌธ์—์„œ์˜ ์˜จ์‹ค๊ฐ€์Šค ๊ฐ์ถ• ๋“ฑ ๊ธฐํ›„๋ณ€ํ™” ๋Œ€์ฑ…์„ ์ˆ˜๋ฆฝํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„์‹œ์˜ ์—๋„ˆ์ง€์†Œ๋น„์— ๋Œ€ํ•œ ์˜ํ–ฅ ์š”์ธ์˜ ํšจ๊ณผ ํŒŒ์•…์„ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ๋„์‹œ์˜ ๋ฌผ๋ฆฌ์ , ์ธ๊ตฌ-์‚ฌํšŒํ•™์ , ๊ฒฝ์ œ์ , ๊ทธ๋ฆฌ๊ณ  ๊ธฐ์˜จ ์š”์ธ์— ๊ด€ํ•œ ํƒ์ƒ‰์ด ์„ ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ๋˜ํ•œ, ์ˆ˜๋„๊ถŒ-๊ด‘์—ญ์‹œ์™€ ๊ทธ ์™ธ ์ง€์—ญ์€ ๋„์‹œ์˜ ๊ฐœ๋ฐœ ์ •๋„๊ฐ€ ๋‹ค๋ฅด๊ณ  ๋„์‹œํ™”์˜ ๋‹จ๊ณ„๊ฐ€ ๋‹ฌ๋ฆฌ ์ง„ํ–‰๋˜๊ณ  ์žˆ๊ธฐ์— ์ง€์—ญ์— ๋”ฐ๋ฅธ ๋„์‹œ๊ตฌ์กฐ์™€ ํŠน์„ฑ ์ฐจ์ด๋ฅผ ๋ฐ˜์˜ํ•˜์—ฌ ๋ถ„์„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„์‹œ ๊ฐ„ ์ฐจ์ด๋ฅผ ๋ฐ˜์˜ํ•˜์—ฌ ๋ถ„์„ํ•œ ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์— ๋Œ€ํ•œ ์˜ํ–ฅ ์š”์ธ์€ ์ง€์—ญ์—๋„ˆ์ง€๊ณ„ํš์„ ์œ„ํ•œ ์ •์ฑ…์  ๋Œ€์‘ ๋ฐฉ์•ˆ์œผ๋กœ ์ œ์–ธํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ, ์„ ํ–‰์—ฐ๊ตฌ์—์„œ๋Š” ๊พธ์ค€ํžˆ ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ตœ์  ๋ฐ€๋„์™€ ์ž„๊ณ„์  ๋“ฑ ๊ด€๊ณ„์˜ ๋น„์„ ํ˜•์„ฑ์„ ์˜์‹ฌํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋ถ„์œ„ํšŒ๊ท€๋ถ„์„์„ ์ง„ํ–‰ํ•˜์—ฌ 1์ธ๋‹น ์—๋„ˆ์ง€์†Œ๋น„๋Ÿ‰ ๋ถ„์œ„์— ๋”ฐ๋ฅธ ๋„์‹œ๊ณต๊ฐ„๊ตฌ์กฐ ํšจ๊ณผ ๋ณ€ํ™” ๊ฐ€๋Šฅ์„ฑ์„ ์‚ดํ”ผ๊ณ  ํ•ด๋‹น ๊ด€๊ณ„์˜ ๋น„์„ ํ˜•์„ฑ ์ž ์žฌ๋ ฅ์„ ์‹ค์ฆ์ ์œผ๋กœ ํ™•์ธํ•˜์˜€๋‹ค. ๋„์‹œ๋ณ„๋กœ ๋‹ค๋ฅธ ์‚ฐ์—… ํŠน์„ฑ์˜ ์˜ํ–ฅ์„ ํ†ต์ œํ•˜๊ณ ์ž ์‚ฐ์—…๋ถ€๋ฌธ์„ ์ œ์™ธํ•œ ์ˆ˜์†ก-๊ฐ€์ •-์ƒ์—…-๊ณต๊ณต ๋ถ€๋ฌธ์˜ ์—๋„ˆ์ง€๋ฅผ ํ•ฉ์‚ฐํ•˜์—ฌ ์ฃผ๋ฏผ๋“ฑ๋ก์ธ๊ตฌ์ˆ˜๋กœ ๋‚˜๋ˆ„์–ด์ค€ 1์ธ๋‹น ์—๋„ˆ์ง€์†Œ๋น„๋Ÿ‰์„ ์ข…์† ๋ณ€์ˆ˜๋กœ ํ™œ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋„์‹œ์ง€์—ญ ์ธ๊ตฌ๋ฐ€๋„, ํ˜ผํ•ฉํ† ์ง€์ด์šฉ, ์ฃผํƒ๊ณต๊ธ‰ํ˜•ํƒœ, ๊ต์œก์‹œ์„ค ๋Œ€์ค‘๊ตํ†ต/๋„๋ณด ์ ‘๊ทผ์„ฑ, ์ง์ฃผ๊ทผ์ ‘๋น„์œจ๊ณผ 1์ธ๋‹น ๋…น์ง€๋ฉด์ ์œผ๋กœ ๋Œ€ํ‘œ๋˜๋Š” ๋„์‹œ๊ตฌ์กฐ์™€ 1์ธ๊ฐ€๊ตฌ, ๊ณ ๋ นํ™”์œจ, 1์ธ๋‹น ์ง€๋ฐฉ์„ธ๋‚ฉ๋ถ€์•ก, ๋ƒ‰๋ฐฉ๋„์ผ, ๋‚œ๋ฐฉ๋„์ผ์˜ ์ง€์—ญ ํŠน์„ฑ ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํ•ด๋‹น ๋ณ€์ˆ˜๋“ค์ด 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ํšจ๊ณผ๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. 2019๋…„ ์ „๊ตญ ์‹œ๊ตฐ๊ตฌ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ „๊ตญ, ์ˆ˜๋„๊ถŒ-๊ด‘์—ญ์‹œ, ๊ทธ ์™ธ ์ง€์—ญ ๋ชจํ˜•์œผ๋กœ ๊ตฌ๋ถ„ ํ›„ ์‹ค์ฆ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์— ๋ฏธ์น˜๋Š” ๋ณ€์ˆ˜๋ณ„ ํšจ๊ณผ๋ฅผ ๋ถ„์œ„์— ๋”ฐ๋ผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•˜์—ฌ 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์„ 10, 30, 50, 70, 90๋ถ„์œ„๋กœ ์„ธ๋ถ„ํ™”ํ•˜์—ฌ ํŒŒ์•…ํ•˜์˜€๋‹ค. ์ „๊ตญ ๋ชจํ˜•์˜ ๊ฒฝ์šฐ ๋Œ€๋ถ€๋ถ„์˜ ๋ถ„์œ„์—์„œ ๋„์‹œ์ง€์—ญ์˜ ์ธ๊ตฌ๋ฐ€๋„๊ฐ€ ์ฆ๊ฐ€ํ• ์ˆ˜๋ก 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์ด ๊ฐ์†Œํ•˜๊ณ , ๊ต์œก์‹œ์„ค์— ๋Œ€ํ•œ ๋Œ€์ค‘๊ตํ†ต๊ณผ ๋„๋ณด ์ ‘๊ทผ ๊ฐ€๋Šฅ ์ธ๊ตฌ๋น„์œจ์ด ์ฆ๊ฐ€ํ• ์ˆ˜๋ก 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์ด ๊ฐ์†Œํ•˜๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋„์ถœ๋˜์–ด ์„ ํ–‰์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋’ท๋ฐ›์นจํ•˜์˜€๋‹ค. ์ด๋Š” ๋„์‹œ ํŠน์ • ์ง€์—ญ์— ์ง‘์ ๋œ ์ธ๊ตฌ๋ฐ€๋„, ๊ต์œก์‹œ์„ค๋กœ ๋Œ€ํ‘œ๋˜๋Š” ์ง€์—ญ ์„œ๋น„์Šค์— ๋Œ€ํ•œ ๊ณต๊ณต๊ตํ†ต์ฒด๊ณ„์˜ ๋†’์€ ์ ‘๊ทผ์„ฑ ๊ตฌ์ถ•์ด 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰ ๊ฐ์ถ•์— ๋„์›€์ด ๋จ์„ ์˜๋ฏธํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์„ ํ–‰์—ฐ๊ตฌ์™€ ๋‹ฌ๋ฆฌ ๋™์ผ ์ง€์—ญ ๋‚ด ์ฃผ๊ฑฐ-์ƒ์—…-๊ณต๊ณต-๋…น์ง€ ์šฉ๋„์˜ ํ˜ผํ•ฉ์  ์ด์šฉ์€ 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ํ˜„์žฌ๊นŒ์ง€ ํ† ์ง€ ์ด์šฉ์ด ํ•ฉ๋ฆฌ์ ์ธ ์œตํ•ฉ์ด ์•„๋‹Œ ๋ฌด๋ถ„๋ณ„ํ•œ ์šฉ๋„ ํ˜ผ์žฌ๋กœ ์˜คํžˆ๋ ค ๋น„ํšจ์œจ์ ์ธ ์—๋„ˆ์ง€ ์†Œ๋น„๊ฐ€ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์—ˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ์ง€์—ญ ํŠน์„ฑ ์š”์ธ์˜ ๊ฒฝ์šฐ์—๋Š” ๋Œ€๋ถ€๋ถ„์˜ ๋ถ„์œ„์—์„œ ๋‚œ๋ฐฉ๋„์ผ์ด ์ฆ๊ฐ€ํ• ์ˆ˜๋ก 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์ด ์ฆ๊ฐ€ํ•˜๋Š”๋ฐ, ์ด๋Š” ํ–ฅํ›„ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•ด ๋„์‹œ์— ๋ฏธ์น˜๋Š” ๊ธฐ์˜จ ํŠน์„ฑ ์˜ํ–ฅ๋ ฅ์ด ๋†’์•„์งˆ ์ƒํ™ฉ์—์„œ ๋…ธํ›„ํ™”๋œ ๊ฑด๋ฌผ์˜ ์—๋„ˆ์ง€ ํšจ์œจ ์ฆ์ง„๊ณผ ์‹ ๊ทœ ๊ฑด์ถ•๋ฌผ์˜ ์—๋„ˆ์ง€ ๊ธฐ์ค€ ๊ฐ•ํ™” ๋“ฑ์˜ ์ •์ฑ…์ด ํ•„์š”ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ˆ˜๋„๊ถŒ-๊ด‘์—ญ์‹œ ๋ชจํ˜•๊ณผ ๊ทธ ์™ธ ์ง€์—ญ ๋ชจํ˜•์˜ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ต ๋ถ„์„ํ•˜์ž๋ฉด, ์ˆ˜๋„๊ถŒ-๊ด‘์—ญ์‹œ๋Š” 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์— ๋„์‹œ์˜ ์••์ถ•์„ฑ๊ณผ ๊ด€๋ จํ•œ ๋„์‹œ๊ตฌ์กฐ ์š”์ธ์ด ์ฃผ๋กœ ์œ ์˜์„ฑ์„ ๋ณด์˜€๋‹ค. ๋ฐ˜๋ฉด, ๊ทธ ์™ธ ์ง€์—ญ์€ ๋„์‹œ์ง€์—ญ ์ธ๊ตฌ๋ฐ€๋„๋ฅผ ์ œ์™ธํ•œ ๋„์‹œ๊ตฌ์กฐ ์š”์ธ์€ ํฐ ์œ ์˜์„ฑ์„ ๋ณด์ด์ง€ ์•Š์•˜๊ณ  1์ธ๋‹น ์ง€๋ฐฉ์„ธ๋‚ฉ๋ถ€์•ก, ๋ƒ‰๋ฐฉ๋„์ผ, ๋‚œ๋ฐฉ๋„์ผ๊ณผ ๊ฐ™์€ ์ง€์—ญ ํŠน์„ฑ ์š”์ธ์ด ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋„์‹œํ™” ์ •๋„์— ๋”ฐ๋ผ ๋„์‹œ๊ตฌ์กฐ์˜ ์••์ถ•์„ฑ ์š”์ธ์ด 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ์ฐจ์ด๊ฐ€ ์žˆ์œผ๋ฉฐ ์ˆ˜๋„๊ถŒ-๊ด‘์—ญ์‹œ์™€ ๊ทธ ์™ธ ์ง€์—ญ์€ ์ง€์—ญ์—๋„ˆ์ง€๊ณ„ํš ์ˆ˜๋ฆฝ ์‹œ ์ ‘๊ทผ ๋ฐฉํ–ฅ์„ ๋‹ฌ๋ฆฌํ•ด์•ผ ํ•œ๋‹ค๋Š” ํ•จ์˜๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ˆ˜๋„๊ถŒ-๊ด‘์—ญ์‹œ๋Š” 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์„ ์ ˆ๊ฐํ•˜๊ธฐ ์œ„ํ•œ ๊ทผ๋ณธ์ ์ธ ๋„์‹œ๊ตฌ์กฐ ์žฌ๊ตฌ์ถ• ์ •์ฑ… ์ˆ˜๋ฆฝ ์‹œ ์ธ๊ตฌ๋ฐ€๋„ ํ–ฅ์ƒ๋ณด๋‹ค๋Š” ํ† ์ง€ ์ด์šฉ ๊ฐœ์„ ์ด๋‚˜ ๊ณต๊ณต๊ตํ†ต์ฒด๊ณ„ ์ ๊ฒ€์„ ํ†ตํ•ด ์‹œ๋ฏผ๋“ค์ด ๋Œ€์ค‘๊ตํ†ต์‹œ์„ค์„ ์‚ฌ์šฉํ•˜์—ฌ ์ง€์—ญ ์„œ๋น„์Šค์— ์‰ฝ๊ฒŒ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๊ทธ ์™ธ ์ง€์—ญ์€ ๋„์‹œ๊ตฌ์กฐ์˜ ์••์ถ•์„ฑ์œผ๋กœ ์ธํ•œ ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰ ๊ฐ์†Œ ๊ณ„ํš ์ˆ˜๋ฆฝ ์ „ ์ถฉ๋ถ„ํ•œ ๊ทผ๋ฆฐ์ƒํ™œ๊ถŒ ํ˜•์„ฑ์„ ์šฐ์„ ์œผ๋กœ ๋„์‹œ์ง€์—ญ ์ธ๊ตฌ๋ฐ€๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผœ 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์„ ๊ฐ์ถ•ํ•  ํ•„์š”๊ฐ€ ์žˆ๊ณ  ์ง€์—ญ ํŠน์„ฑ ์ค‘ ๊ธฐ์˜จ ์š”์ธ์— ๋” ์˜ํ–ฅ์„ ๋ฐ›๊ณ  ์žˆ๊ธฐ์— ์—๋„ˆ์ง€ ํšจ์œจ์ด ๋†’์€ ๊ฐ€์ „์ œํ’ˆ ๊ตฌ์ž…์— ์ธ์„ผํ‹ฐ๋ธŒ ์ œ๊ณต ์ •์ฑ… ๋˜๋Š” ๊ทผ๋ฆฐ์ƒํ™œ๊ถŒ ํ˜•์„ฑ์œผ๋กœ ํ˜„์žฌ ์ˆ˜๋„๊ถŒ-๊ด‘์—ญ์‹œ์—์„œ ์ฃผ๋กœ ์„ค์น˜๋˜๋Š” ์ง€์—ญ๋‚œ๋ฐฉ ๋„์ž…์„ ๊ณ ๋ คํ•˜์—ฌ ๋‚œ๋ฐฉ์—๋„ˆ์ง€๋ฅผ ์ ˆ๊ฐํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋”ฐ๋ผ์„œ ํ–ฅํ›„ ๊ธฐํ›„๋ณ€ํ™” ๋Œ€์‘์ „๋žต์œผ๋กœ์„œ ๋„์‹œ์˜ ์ง€์—ญ์—๋„ˆ์ง€๊ณ„ํš์„ ์ˆ˜๋ฆฝํ•  ๋•Œ, ์ง€์—ญ๋ณ„ ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ์š”์ธ์˜ ์ฐจ์ด๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๊ฐ ์ง€์—ญ ํŠน์„ฑ์— ์•Œ๋งž์€ ์ ์ ˆํ•œ ์ง€์—ญ๋ณ„ ์—๋„ˆ์ง€ ์ˆ˜์š” ๊ด€๋ฆฌ๋ฐฉ์•ˆ์„ ๋„์ถœํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์˜ ๊ฐ ๋ถ„์œ„์— ๋”ฐ๋ฅธ ๋„์‹œ๊ตฌ์กฐ์™€ ์ง€์—ญ ํŠน์„ฑ ์š”์ธ์„ ๋ฐํž˜์œผ๋กœ์จ ๋„์‹œ ์ฐจ์›์˜ ๊ธฐํ›„๋ณ€ํ™” ๋Œ€์‘์„ ์œ„ํ•œ ์ง€์—ญ์—๋„ˆ์ง€๊ณ„ํš์— ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.์ œ๏ผ‘์žฅ ์„œ๋ก  1 ์ œ๏ผ‘์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ๏ผ’์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ•๊ณผ ๊ตฌ์„ฑ 7 ๏ผ‘. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 7 ๏ผ’. ์—ฐ๊ตฌ ๊ตฌ์„ฑ 8 ์ œ๏ผ’์žฅ ์ด๋ก  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 10 ์ œ๏ผ‘์ ˆ ๋„์‹œ๊ตฌ์กฐ์™€ ์—๋„ˆ์ง€์†Œ๋น„๋Ÿ‰ 10 ์ œ๏ผ’์ ˆ ์ง€์—ญ ํŠน์„ฑ๊ณผ ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰ 12 ์ œ๏ผ“์ ˆ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ๊ณผ ๊ฐ€์„ค 15 ๏ผ‘. ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 15 ๏ผ’. ์—ฐ๊ตฌ์˜ ๊ฐ€์„ค 16 ์ œ๏ผ“์žฅ ์—ฐ๊ตฌ ์„ค๊ณ„์™€ ์ž๋ฃŒ ํ˜„ํ™ฉ 19 ์ œ๏ผ‘์ ˆ ์—ฐ๊ตฌ ์„ค๊ณ„ 19 ๏ผ‘. ์—ฐ๊ตฌ ๊ฐœ์š” 19 ๏ผ’. ๋ณ€์ˆ˜ ๊ตฌ์„ฑ 20 ์ œ๏ผ’์ ˆ ์ง€์—ญ๋ณ„ 1์ธ๋‹น ์—๋„ˆ์ง€์†Œ๋น„๋Ÿ‰๊ณผ ์˜ํ–ฅ ์š”์ธ์˜ ๊ณต๊ฐ„ ํŠน์„ฑ 25 ์ œ๏ผ”์žฅ ๋„์‹œ๊ตฌ์กฐ์— ๋”ฐ๋ฅธ 1์ธ๋‹น ์—๋„ˆ์ง€์†Œ๋น„๋Ÿ‰ ์˜ํ–ฅ์š”์ธ ๋ถ„์„ 31 ์ œ๏ผ‘์ ˆ 1์ธ๋‹น ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰ ๋‹ค์ค‘ํšŒ๊ท€๋ชจํ˜• 31 ๏ผ‘. ์ง€์—ญ๋ณ„ ๋ถ„์„์ž๋ฃŒ์˜ ํŠน์„ฑ 31 ๏ผ’. ์ง€์—ญ๋ณ„ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ 34 ์ œ๏ผ’์ ˆ ๋ถ„์œ„ํšŒ๊ท€๋ชจํ˜•์„ ํ™œ์šฉํ•œ ์—๋„ˆ์ง€์†Œ๋น„๋Ÿ‰ ์˜ํ–ฅ์š”์ธ ๋ถ„์„ 38 ๏ผ‘. ์ „๊ตญ 39 ๏ผ’. ์ˆ˜๋„๊ถŒ๊ด‘์—ญ์‹œ 44 ๏ผ“. ๊ทธ ์™ธ ์ง€์—ญ 49 ์ œ๏ผ“์ ˆ ๋ถ„์„ ๊ฒฐ๊ณผ ์š”์•ฝ ๋ฐ ์ •์ฑ…์  ํ•จ์˜ 53 ์ œ๏ผ•์žฅ ๊ฒฐ๋ก  61 ์ œ๏ผ‘์ ˆ ์—ฐ๊ตฌ์˜ ์š”์•ฝ ๋ฐ ์‹œ์‚ฌ์  61 ์ œ๏ผ’์ ˆ ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ ๊ณผ์ œ 66 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 69 ๋ถ€๋ก 74 ๏ผ‘. ๋ถ„์œ„์— ๋”ฐ๋ฅธ ์‹œ๊ตฐ๊ตฌ ๋ชฉ๋ก 74 Abstract 80์„

    A Study on the Revitalization of Worship by Using Multimedia in Korean Rural Churches with a Particular Attention to Visual Media

    Get PDF
    The purpose of this study is to help to bring about the revitalization of worship in rural churches by using visual media. At present, Korean churches, especially rural churches, are facing great challenges due to the aging of the church members, illiteracy rates, a poor educational environment, the lack of incoming young people, pastorsโ€™ reluctance to minister in rural areas, troubled rural economy and so on. Nonetheless, churches in rural areas should not be abandoned. In this context, one of the most urgent needs in rural churches is healing and restoration through church worship. At any arduous and difficult time in history, churches overcame difficulties through the restoration of a relationship with God. The key to restoring a relationship with God is true worship. The utilization of multimedia, which is a modern convenience available to churches these days, can contribute to church membersโ€™ active participation, commitment, and communication in worship services. This study will investigate the ideas of and methods for the utilization of multimedia among churches that it, incorporating the researcherโ€™s knowledge on multimedia accumulated over 16 years at Gashin Baptist Church, and offer a practical manual on using multimedia in rural churches. With a desire to revitalize worship, he offers a model for pastors of rural churches who attempt to incorporate multimedia in their worship services

    What are the characteristics and motivations of Millennials moving to the County?

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2022.2. ์†ก์žฌ๋ฏผ.As the number of young people concentrated in the metropolitan area continues to increase, the imbalance problem is intensifying throughout the country. In particular, all of the top 10% of the areas at risk of extinction are county areas, and the regional economy and future are expected to be hit hard by the decrease in youth. However, in recent years, the inflow population is increasing, mainly in certain county areas, and the same phenomenon is occurring in millennials. This is a different result from the existing youth movement trend, and research is needed on the characteristics of county areas where the population is increasing. Therefore, this study clusters 77 county regions nationwide according to the net increase/decrease rate of the youth population aged 20 to 39 and analyzes the characteristics according to the type. In addition, case analysis is conducted to more closely analyze the movement characteristics of millennials. As case analysis data, image analysis of each region was conducted through SNS-related search terms analysis in consideration of information acquisition characteristics of millennials, and YouTube videos produced for young people who migrated to the county areas were analyzed. In addition, the final result is derived by conducting policy analysis to examine the effectiveness of the current policy. First, according to cluster analysis, the areas where the population has increased were analyzed in four ways: "Youth Growth Type," "Youth Military Population Type," "Youth in the 30s Type," and "Youth Drastic Reduction Type.โ€œ In the case of the type of increase in the youth population, the size of the urban population is large, the proportion of the urban population is high, and public institutions are relatively high in GRDP per capita and apartment growth. Similar to the results derived from previous studies, it was found that the characteristics of the area with an increased population were selected as county areas with well-equipped employment, wages, and residential environments. Second, the analysis of related search terms showed that the youth increase type and the youth relaxation type in their 30s were recognized as regions with both a high connection with large cities and an advantageous image for employment and start-ups and a rural image of tourism and agriculture. But, the reduction and absolute reduction type are recognized as an area where only rural areas and tourism are developed, indicating that there is no image of connection with adjacent large cities and employment and start-ups, which are major factors in a youth movement. The motivation for movement through YouTube video analysis for young people returning to farming and rural areas was "skepticism about urban life" and "achievement of the vision of life." The Millennial youth examined through case analysis showed that employment and start-ups were the main moving factors, similar to the existing generation, but they also tended to achieve their vision of life and experience new areas in county areas that have a complex image. Third, in the case of analyzing the effectiveness of youth support policies for balanced regional development, it was found that there was no direct correlation between the number of youth policies and the population inflow of millennials. In the case of the currently established policy, it is characterized by limited support to young people living in the region. In the case of excellent policy cases, the characteristics of focusing on online start-ups that have little impact on the openness of support targets, the formation of youth communities, and local market conditions were differentiated from general policies. The implications obtained through this study are as follows. Although the characteristics of millennials can lead to the influx of young people into county areas, most of them choose areas that are advantageous for housing and convenience due to the formation of new housing sites near the metropolitan area or relocation of public institutions are the same as previous studies. However, there are cases in which young people leave large cities and decide to move to county areas to establish and achieve the vision and value of their lives. To increase these cases, local governments need to increase the density around the county base to provide convenience for commercial districts and living and to establish an urban structure that can benefit from the formation of communities. This suggests that policies that provide a good image and experience a new rural space can lead to an opportunity to settle in rural areas beyond rural experiences, rather than establishing policies to increase exchanges with neighboring large cities, expand policy support, and simply transfer young people.์ˆ˜๋„๊ถŒ์œผ๋กœ ์ง‘์ค‘๋˜๋Š” ์ฒญ๋…„๋“ค์˜ ์ˆ˜๊ฐ€ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ๊ตญํ†  ์ „๋ฐ˜์— ๊ฑธ์ณ ๋ถˆ๊ท ํ˜• ๋ฌธ์ œ๊ฐ€ ์‹ฌํ™”๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์†Œ๋ฉธ์œ„ํ—˜์ง€์—ญ ์ค‘ ์ƒ์œ„ 10%์— ํ•ด๋‹นํ•˜๋Š” ์ง€์—ญ์€ ๋ชจ๋‘ ๊ตฐ ์ง€์—ญ์œผ๋กœ ์ฒญ๋…„์˜ ๊ฐ์†Œ๋กœ ์ธํ•œ ๊ตฐ ์ง€์—ญ์˜ ์‚ฌํšŒ, ๊ฒฝ์ œ์  ์ง€์†๊ฐ€๋Šฅ์„ฑ์ด ์œ„ํ˜‘๋ฐ›๊ณ  ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ์ƒํ™ฉ์—์„œ ์ตœ๊ทผ ๋ช‡ ๋…„๊ฐ„ ํŠน์ • ์†Œ์ˆ˜์˜ ๊ตฐ ์ง€์—ญ์„ ์ค‘์‹ฌ์œผ๋กœ ๋ฐ€๋ ˆ๋‹ˆ์–ผ ์„ธ๋Œ€์˜ ์œ ์ž…์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š” ํ˜„์ƒ์ด ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ์ด๋Š” ๊ธฐ์กด ์ฒญ๋…„์˜ ์ด๋™๊ณผ๋Š” ์ƒ์ดํ•œ ํ˜„์ƒ์œผ๋กœ ์ด์— ๋Œ€ํ•œ ๋ฉด๋ฐ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด์™€ ๊ฐ™์€ ๋ฐฐ๊ฒฝ์—์„œ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ „๊ตญ์˜ 77๊ฐœ ๊ตฐ ์ง€์—ญ์„ ๋Œ€์ƒ์œผ๋กœ 20~39์„ธ ์ฒญ๋…„, ์ฆ‰ ๋ฐ€๋ ˆ๋‹ˆ์–ผ ์„ธ๋Œ€์˜ ์ด๋™ํŠน์„ฑ์„ ๊ตฐ์ง‘๋ถ„์„์„ ํ†ตํ•ด ์œ ํ˜•ํ™”ํ•˜๊ณ  ์œ ํ˜•๋ณ„ ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด์™€ ํ•จ๊ป˜, ๋ฐ€๋ ˆ๋‹ˆ์–ผ ์„ธ๋Œ€์˜ ์ด๋™ ํŠน์„ฑ์„ ๋ณด๋‹ค ๋ฉด๋ฐ€ํ•˜๊ฒŒ ๋ถ„์„ํ•˜๊ณ ์ž ์‚ฌ๋ก€๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์‚ฌ๋ก€๋ถ„์„์€ ๋ฐ€๋ ˆ๋‹ˆ์–ผ ์„ธ๋Œ€์˜ ์ •๋ณด์Šต๋“ ํŠน์ง•์„ ๊ณ ๋ คํ•˜์—ฌ SNS ์—ฐ๊ด€ ๊ฒ€์ƒ‰์–ด ๋ถ„์„์„ ํ†ตํ•œ ๊ฐ ์ง€์—ญ์˜ ์ด๋ฏธ์ง€ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€์œผ๋ฉฐ, ๊ตฐ ์ง€์—ญ์œผ๋กœ ์ด์ฃผํ•œ ์ฒญ๋…„์„ ๋Œ€์ƒ์œผ๋กœ ์ œ์ž‘๋œ ์œ ํŠœ๋ธŒ ์˜์ƒ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๊ตฐ์ง€์—ญ์˜ ์ฒญ๋…„์ธต ์ง€์› ์ •์ฑ…์˜ ํšจ๊ณผ์„ฑ์„ ์‚ดํŽด๋ณด๊ธฐ ์œ„ํ•ด ์ •์ฑ… ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๋ถ„์„ ๊ฒฐ๊ณผ, ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ฃผ์š”ํ•œ ๊ฒฐ๊ณผ๊ฐ€ ๋„์ถœ๋˜์—ˆ๋‹ค. ์ฒซ์งธ, ๊ตฐ์ง‘๋ถ„์„์— ๋”ฐ๋ฅด๋ฉด ์ธ๊ตฌ๊ฐ€ ์ฆ๊ฐ€ํ•œ ์ง€์—ญ์€ โ€™์ฒญ๋…„์ฆ๊ฐ€ํ˜•โ€˜, โ€™์ฒญ๋…„๊ฐ์†Œํ˜•โ€˜, โ€™30๋Œ€ ์ฒญ๋…„ ์™„ํ™”ํ˜•โ€˜, โ€™์ฒญ๋…„ ์ ˆ๋Œ€๊ฐ์†Œํ˜•โ€˜ ๋„ค ๊ฐ€์ง€๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ์ฒญ๋…„ ์ธ๊ตฌ๊ฐ€ ์ฆ๊ฐ€ํ•˜๋Š” ์œ ํ˜•์˜ ๊ฒฝ์šฐ ๋„์‹œ์ธ๊ตฌ ๊ทœ๋ชจ๊ฐ€ ํฌ๊ณ , ๋„์‹œ์ง€์—ญ ์ธ๊ตฌ๋น„์œจ์ด ๋†’๊ณ , ๊ณต๊ณต๊ธฐ๊ด€์ด ์ด์ „ํ•˜์—ฌ ๋น„๊ต์  ๋†’์€ 1์ธ๋‹น GRDP์™€ ์•„ํŒŒํŠธ์˜ ์ฆ๊ฐ€์œจ ๋†’์€ ํŠน์ง•์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ธ๊ตฌ๊ฐ€ ์ฆ๊ฐ€ํ•œ ์ง€์—ญ์€ ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ๋„์ถœ๋˜์—ˆ๋˜ ๊ฒฐ๊ณผ์™€ ์œ ์‚ฌํ•˜๊ฒŒ ์ทจ์—…, ์ž„๊ธˆ, ์ฃผ๊ฑฐํ™˜๊ฒฝ์ด ์ž˜ ๊ฐ–์ถ”์–ด์ง„ ์ง€์—ญ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ์—ฐ๊ด€๊ฒ€์ƒ‰์–ด ๋ถ„์„์—์„œ๋Š” ์ฒญ๋…„์ฆ๊ฐ€ํ˜•๊ณผ 30๋Œ€ ์ฒญ๋…„ ์™„ํ™”ํ˜• ๊ตฐ ์ง€์—ญ์˜ ๊ฒฝ์šฐ ๋Œ€๋„์‹œ์™€ ์—ฐ๊ฒฐ์„ฑ์ด ๋†’๊ณ  ์ทจ์—…ยท์ฐฝ์—…์ด ์œ ๋ฆฌํ•œ ์ด๋ฏธ์ง€์™€ ๊ด€๊ด‘ ๋ฐ ๋†์—…์ด ๋ฐœ๋‹ฌํ•œ ๋†์ดŒ ์ด๋ฏธ์ง€๋ฅผ ๋™์‹œ์— ๊ฐ€์ง„ ์ง€์—ญ์œผ๋กœ ์ธ์‹๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ฐ˜๋ฉด, ์ฒญ๋…„ ๊ฐ์†Œ ๋ฐ ์ ˆ๋Œ€๊ฐ์†Œํ˜•์€ ๋†์ดŒ๊ณผ ๊ด€๊ด‘์ด ์ฃผ์š”ํ•œ ์—ฐ๊ด€์–ด๋กœ ๋„์ถœ๋˜์–ด, ์ฒญ๋…„ ์ด๋™์— ์ฃผ์š”ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ธ์ ‘ ๋Œ€๋„์‹œ์™€์˜ ์—ฐ๊ฒฐ์„ฑ๊ณผ ์ทจ์—…๊ณผ ์ฐฝ์—…์— ๊ด€ํ•œ ์ด๋ฏธ์ง€๋Š” ๋ถ€์กฑํ•œ ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๊ท€๋†ยท๊ท€์ดŒํ•œ ์ฒญ๋…„ ๋Œ€์ƒ ์œ ํŠœ๋ธŒ ์˜์ƒ๋ถ„์„์„ ํ†ตํ•œ ์ด๋™ ๋™๊ธฐ๋กœ๋Š” โ€˜๋„์‹œ ์‚ถ์— ๋Œ€ํ•œ ํšŒ์˜๊ฐโ€™, โ€˜์‚ถ์˜ ๋น„์ „ ์„ฑ์ทจโ€™ ๋“ฑ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์‚ฌ๋ก€๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์‚ดํŽด๋ณธ ๋ฐ€๋ ˆ๋‹ˆ์–ผ ์„ธ๋Œ€ ์ฒญ๋…„๋“ค์€ ๊ธฐ์กด์˜ ์„ธ๋Œ€์™€ ๋น„์Šทํ•˜๊ฒŒ ์ทจ์—…๊ณผ ์ฐฝ์—…์ด ์ฃผ์š” ์ด๋™์š”์ธ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์ง€๋งŒ, ๋Œ€๋„์‹œ๊ฐ€ ์•„๋‹Œ ๋†์ดŒ๊ณผ ๋„์‹œ์˜ ์ด๋ฏธ์ง€๋ฅผ ๋ณตํ•ฉ์ ์œผ๋กœ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๊ตฐ ์ง€์—ญ์—์„œ ์ž์‹  ์‚ถ์˜ ๋น„์ „ ์„ฑ์ทจ์™€ ๋„์‹œ๋ฅผ ๋ฒ—์–ด๋‚˜ ์ƒˆ๋กœ์šด ์ง€์—ญ์„ ๊ฒฝํ—˜ํ•˜๊ณ ์ž ํ•˜๋Š” ๊ฒฝํ–ฅ๋„ ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ์…‹์งธ, ์ง€์—ญ๊ท ํ˜•๋ฐœ์ „์„ ์œ„ํ•œ ์ฒญ๋…„์ง€์› ์ •์ฑ…์˜ ํšจ๊ณผ์„ฑ ๋ถ„์„์˜ ๊ฒฝ์šฐ ์ฒญ๋…„ ์ •์ฑ…์˜ ์ˆ˜์™€ ๋ฐ€๋ ˆ๋‹ˆ์–ผ ์„ธ๋Œ€์˜ ์ธ๊ตฌ์œ ์ž… ์‚ฌ์ด์—๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋Š” ์—†๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ˜„์žฌ ์ˆ˜๋ฆฝ๋œ ์ •์ฑ…์˜ ๊ฒฝ์šฐ ์ง€์—ญ์— ๊ฑฐ์ฃผํ•˜๋Š” ์ฒญ๋…„์—๊ฒŒ ๊ตญํ•œ๋˜์–ด ์ง€์›ํ•˜๋Š” ํŠน์ง•์ด ์žˆ๋‹ค. ์šฐ์ˆ˜์ •์ฑ…์‚ฌ๋ก€์˜ ๊ฒฝ์šฐ ์ง€์›๋Œ€์ƒ์˜ ๊ฐœ๋ฐฉ์„ฑ, ์ฒญ๋…„ ์ปค๋ฎค๋‹ˆํ‹ฐ ํ˜•์„ฑ, ์ง€์—ญ ์‹œ์žฅ ์ƒํ™ฉ์— ์˜ํ–ฅ์ด ์ ์€ ์˜จ๋ผ์ธ ์ฐฝ์—…์— ์ง‘์ค‘ํ•˜๋Š” ํŠน์ง•์ด ์ผ๋ฐ˜์ ์ธ ์ •์ฑ…๊ณผ ์ฐจ๋ณ„์„ฑ์ด ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ํ†ตํ•ด ์–ป์€ ์‹œ์‚ฌ์ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋ฐ€๋ ˆ๋‹ˆ์–ผ ์„ธ๋Œ€์˜ ํŠน์„ฑ์œผ๋กœ ๊ตฐ ์ง€์—ญ์œผ๋กœ ์ฒญ๋…„ ์ธ๊ตฌ๊ฐ€ ์œ ์ž…๋  ์ˆ˜๊ฐ€ ์žˆ์ง€๋งŒ, ์ฒญ๋…„ ๋Œ€๋ถ€๋ถ„์€ ์ผ์ž๋ฆฌ๊ฐ€ ํ’๋ถ€ํ•œ ์ˆ˜๋„๊ถŒ ์ธ๊ทผ ํ˜น์€ ๊ณต๊ณต๊ธฐ๊ด€ ์ด์ „์— ๋”ฐ๋ฅธ ์‹ ๊ทœํƒ์ง€๊ฐ€ ํ˜•์„ฑ๋˜์–ด ์ฃผ๊ฑฐ์™€ ์ƒํ™œํŽธ์˜์— ์œ ๋ฆฌํ•œ ์ง€์—ญ์„ ์„ ํƒํ•˜๋Š” ๊ฒƒ์€ ๊ธฐ์กด์˜ ์„ ํ–‰์—ฐ๊ตฌ๋“ค๊ณผ ๋™์ผํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค๋งŒ, ์ฒญ๋…„์ด ์ž๊ธฐ ์‚ถ์˜ ๋น„์ „๊ณผ ๊ฐ€์น˜๋ฅผ ์„ธ์šฐ๊ณ  ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋Œ€๋„์‹œ๋ฅผ ๋– ๋‚˜ ๊ตฐ ์ง€์—ญ์œผ๋กœ ์ด์ฃผ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ์‚ฌ๋ก€๋„ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‚ฌ๋ก€๋ฅผ ๋Š˜๋ฆฌ๊ธฐ ์œ„ํ•ด์„œ ์ง€์ž์ฒด๋Š” ๊ตฐ์„ ๊ฑฐ์  ์ค‘์‹ฌ์œผ๋กœ ๋ฐ€๋„๋ฅผ ๋†’์—ฌ ์ƒ๊ถŒ๊ณผ ์ƒํ™œ์˜ ํŽธ์˜๋ฅผ ์ œ๊ณตํ•˜๊ณ  ์ปค๋ฎค๋‹ˆํ‹ฐ ํ˜•์„ฑ์ด ์œ ๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ๋„์‹œ๊ตฌ์กฐ๋ฅผ ์ˆ˜๋ฆฝํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ธ์ ‘ ๋Œ€๋„์‹œ์™€์˜ ๊ต๋ฅ˜๋ฅผ ๋†’์ด๊ณ  ์ •์ฑ…์˜ ์ง€์› ๋ฒ”์œ„๋ฅผ ๋„“ํžˆ๊ณ  ๋‹จ์ˆœ ์ฒญ๋…„๋“ค์˜ ์ „์ž…์„ ๋ชฉํ‘œ๋กœ ์ •์ฑ…์„ ์ˆ˜๋ฆฝํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹Œ ์ƒˆ๋กœ์šด ๋†์ดŒ์ด๋ผ๋Š” ๊ณต๊ฐ„์„ ์ฒดํ—˜ํ•˜๊ณ  ์ข‹์€ ์ด๋ฏธ์ง€๋ฅผ ์ œ๊ณตํ•˜๋Š” ์ •์ฑ…์ด ์ˆ˜๋ฐ˜๋œ๋‹ค๋ฉด ๋†์ดŒ์ฒดํ—˜์„ ๋„˜์–ด ๋†์ดŒ์— ์ •์ฐฉํ•˜๋Š” ๊ณ„๊ธฐ๋กœ ์—ฐ๊ฒฐ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์„ ์‹œ์‚ฌํ•œ๋‹ค.์ œ1์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ํ๋ฆ„ 5 ์ œ2์žฅ ์ด๋ก  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 6 ์ œ1์ ˆ ์ธ๊ตฌ์ด๋™์˜ ์ •์˜ 6 ์ œ2์ ˆ ์ธ๊ตฌ์ด๋™ ์ด๋ก ๊ณผ ๊ธฐ์ดˆ๋ชจํ˜• 7 ์ œ3์ ˆ ์ธ๊ตฌ์ด๋™ ์‹ค์ฆ์—ฐ๊ตฌ ๊ณ ์ฐฐ 11 ์ œ4์ ˆ ๋ฐ€๋ ˆ๋‹ˆ์–ผ ์„ธ๋Œ€์˜ ํŠน์„ฑ ๋ฐ ์ด๋™ํŠน์„ฑ 21 ์ œ5์ ˆ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 23 ์ œ3์žฅ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 25 ์ œ1์ ˆ ์—ฐ๊ตฌ ๋ฒ”์œ„ 25 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 26 ์ œ4์žฅ ์—ฐ๊ตฌ ๋ถ„์„ 32 ์ œ1์ ˆ ๊ตฐ์ง‘๋ถ„์„ 32 ์ œ2์ ˆ ๊ตฐ ์ง€์—ญ์— ๋Œ€ํ•œ ์ธ์‹ ๋ฐ ์ฒญ๋…„์ธ๊ตฌ ์ด๋™๋™๊ธฐ๋ถ„์„ 45 ์ œ3์ ˆ ์ •์ฑ… ๋ถ„์„ 57 ์ œ5์žฅ ๊ฒฐ๋ก  ๋ฐ ์‹œ์‚ฌ์  64 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 71 Abstract 97์„

    ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ์ œ๋„ ๊ฐœํŽธ์— ๋”ฐ๋ฅธ ํšจ๊ณผ ๋ถ„์„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2020. 8. ๊ตฌ๋ฏผ๊ต.๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ์ œ๋„ ๊ฐœํŽธ๋ฐฉ์•ˆ(๊ธฐํš์žฌ์ •๋ถ€ ๋“ฑ, 2019. 4.) ์ค‘ ์ˆ˜๋„๊ถŒ/๋น„์ˆ˜๋„๊ถŒ ํ‰๊ฐ€ํ•ญ๋ชฉ ๋น„์ค‘ ์ด์›ํ™”์˜ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์ •์ฑ…์ง‘ํ–‰์˜ ํšจ์šฉ์„ฑ์„ ๋ฏธ๋ฆฌ ์˜ˆ์ธกํ•˜๊ณ , ์•ž์œผ๋กœ์˜ ์‹œํ–‰ ๊ณผ์ •์—์„œ ๋ณด์™„ํ•ด์•ผ ํ•  ์‚ฌํ•ญ๋“ค์— ๋Œ€ํ•˜์—ฌ ๊ฒ€ํ† ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ์ œ๋„ ๋„์ž… ์ดํ›„ 20๋…„์ด ๊ฒฝ๊ณผํ•˜๋ฉด์„œ ๊ทธ๊ฐ„์˜ ๋ณ€ํ™”๋œ ๊ฒฝ์ œโ€ค์‚ฌํšŒ์  ์—ฌ๊ฑด์„ ์ œ๋„์— ๋ฐ˜์˜ํ•ด์•ผ ํ•  ํ•„์š”์„ฑ์ด ์ œ๊ธฐ๋จ์— ๋”ฐ๋ผ ์ •๋ถ€๋Š” ๊ตญ๊ฐ€๊ท ํ˜•๋ฐœ์ „๊ณผ ๋‹ค์–‘ํ•œ ์‚ฌํšŒ์  ๊ฐ€์น˜์— ๋Œ€ํ•œ ์‹คํ˜„ ์š”๊ตฌ๊ฐ€ ์ฆ๋Œ€ํ•˜๊ณ  ์žˆ๋‹ค๊ณ  ํŒ๋‹จํ•˜๊ณ  ์žˆ๋‹ค. ์ด์— ์ •๋ถ€๋Š” ์ข…ํ•ฉํ‰๊ฐ€ ๋น„์ค‘์„ ๊ฐœํŽธํ•˜์—ฌ ์ˆ˜๋„๊ถŒ/๋น„์ˆ˜๋„๊ถŒ์˜ ํ‰๊ฐ€๋ฅผ ์ด์›ํ™”ํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•˜์—ฌ ์ง€๋ฐฉ์˜ ๋‚™ํ›„์ง€์—ญ์„ ๋ฐฐ๋ คํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ •๋ถ€์—์„œ ์–˜๊ธฐํ•˜๋Š” ๋น„์ˆ˜๋„๊ถŒ์ด ๊ผญ ๋‚™ํ›„์ง€์—ญ์€ ์•„๋‹ ๊ฒƒ์ด๋ผ๋Š” ์˜๋ฌธ์ด ์žˆ๋Š” ๋งŒํผ, ๊ด€๋ จ ๊ฐœํŽธ๋ฐฉ์•ˆ์˜ ์ˆ˜๋„๊ถŒ/๋น„์ˆ˜๋„๊ถŒ์˜ ์ด์›ํ™”์™€ ํ•จ๊ป˜ ๋ฒ•์  ๊ฐœ๋…์— ๊ทผ๊ฑฐํ•œ ๋‚™ํ›„์ง€์—ญ/๋น„๋‚™ํ›„์ง€์—ญ์˜ ๊ตฌ๋ถ„์„ ํ†ตํ•˜์—ฌ ์ •๋ถ€ ์ •์ฑ…์˜ ๋ชฉํ‘œ ์‹คํ˜„ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฒ€ํ† ํ•ด๋ณด์•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ 2009๋…„ 1์›”๋ถ€ํ„ฐ 2019๋…„ 7์›”๊นŒ์ง€ ๋ฐœ๊ฐ„๋œ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ๋ณด๊ณ ์„œ๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ์ด 377๊ฐœ ํ‘œ๋ณธ ์‚ฌ์—…์— ๋Œ€ํ•œ ๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋จผ์ €, ์ œ๋„ ๊ฐœํŽธ ์ „์˜ AHP ์ข…ํ•ฉํ‰์  ๋ฐ ๊ฒฝ์ œ์„ฑ ์‹œํ–‰ํ‰์ ์„ ์ˆ˜๋„๊ถŒ/๋น„์ˆ˜๋„๊ถŒ์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ์–‘ ๊ถŒ์—ญ ๊ฐ„์— ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ AHP ์ข…ํ•ฉํ‰์ ์€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ์œผ๋ฉฐ, ๊ฒฝ์ œ์„ฑ ์‹œํ–‰ํ‰์ ์€ ์ˆ˜๋„๊ถŒ ์‚ฌ์—…์ด ๋น„์ˆ˜๋„๊ถŒ ์‚ฌ์—…์— ๋น„ํ•˜์—ฌ 17.5% ๋†’์•˜๋‹ค. ์ด๋ฅผ ๋‚™ํ›„์ง€์—ญ/๋น„๋‚™ํ›„์ง€์—ญ์œผ๋กœ ๋‹ค์‹œ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ์–‘ ๊ถŒ์—ญ ๊ฐ„์— ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ AHP ์ข…ํ•ฉํ‰์ ์€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ์œผ๋ฉฐ, ๊ฒฝ์ œ์„ฑ ์‹œํ–‰ํ‰์ ์€ ๋น„๋‚™ํ›„์ง€์—ญ ์‚ฌ์—…์ด ๋‚™ํ›„์ง€์—ญ ์‚ฌ์—…์— ๋น„ํ•˜์—ฌ 55.2% ๋†’์•˜๋‹ค. ์ƒ๋Œ€์ ์œผ๋กœ ์†Œ์™ธ๋œ ์ง€์—ญ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋Š” ๋น„์ˆ˜๋„๊ถŒ์ด๋‚˜ ๋‚™ํ›„์ง€์—ญ์ด ์ˆ˜๋„๊ถŒ์ด๋‚˜ ๋น„๋‚™ํ›„์ง€์—ญ์— ๋น„ํ•˜์—ฌ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ์— ๋”ฐ๋ฅธ AHP ์ข…ํ•ฉํ‰์ ์ด ๊ฒฐ์ฝ” ๋‚ฎ์€ ์ˆ˜์ค€์ด ์•„๋‹ˆ๋ผ๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ๊ฒฝ์ œ์„ฑ ์‹œํ–‰ํ‰์ ์€ ๋น„์ˆ˜๋„๊ถŒ ์ง€์—ญ ๋ณด๋‹ค๋Š” ๋‚™ํ›„์ง€์—ญ์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋” ๋‚ฎ์€ ํ‰์ ์„ ๊ธฐ๋กํ•˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ์ œ๋„ ๊ฐœํŽธ์˜ ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ธฐ์กด์˜ ์‚ฌ์—…๋“ค์„ ์ˆ˜๋„๊ถŒ๊ณผ ๋น„์ˆ˜๋„๊ถŒ, ๋‚™ํ›„์ง€์—ญ๊ณผ ๋น„๋‚™ํ›„์ง€์—ญ์˜ ์‚ฌ์—…์œผ๋กœ ์žฌ๋ถ„๋ฅ˜ํ•˜์—ฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ๋ณด๊ณ ์„œ๋ฅผ ํ†ตํ•˜์—ฌ ๊ฐ ์‚ฌ์—…์˜ ํ‰๊ฐ€ํ•ญ๋ชฉ๋ณ„ ํ‰์ ๊ณผ ๊ฐ€์ค‘์น˜๋ฅผ ์‚ฐ์ถœํ•˜์˜€๊ณ , ์ด๋ฅผ ๊ฐœํŽธ๋œ ์ œ๋„์— ์ ์šฉํ•˜์—ฌ ์ƒˆ๋กœ์šด ๊ฐ€์ค‘์น˜ ๋ถ€์—ฌ ํ›„, ๆ–ฐ AHP ์ข…ํ•ฉํ‰์ ์„ ์‚ฐ์ •ํ•˜์˜€๋‹ค. ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ์ œ๋„ ๊ฐœํŽธ ์ „โ€คํ›„์˜ AHP ์ข…ํ•ฉํ‰์ ์„ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ๋น„์ˆ˜๋„๊ถŒ ์‚ฌ์—…์˜ AHP ์ข…ํ•ฉํ‰์ ์€ ์ œ๋„ ๊ฐœํŽธ ์ „ ๋Œ€๋น„ 4.8% ์ƒ์Šนํ•˜์˜€์œผ๋ฉฐ ์ด๋Š” ํ†ต๊ณ„์ ์œผ๋กœ๋„ ์œ ์˜ํ•œ ๊ฒฐ๊ณผ์˜€๋‹ค. ๋น„์ˆ˜๋„๊ถŒ ๋‚ด ๋‚™ํ›„์ง€์—ญ๊ณผ ๋น„๋‚™ํ›„์ง€์—ญ์„ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋น„๊ตํ•ด๋ณด๋ฉด, ๋‚™ํ›„์ง€์—ญ์€ ์ œ๋„ ๊ฐœํŽธ ์ „ ๋Œ€๋น„ 7.4%, ๋น„๋‚™ํ›„์ง€์—ญ์€ 1.6% ์ƒ์Šนํ•˜์˜€์œผ๋ฉฐ ๋ชจ๋‘ ํ†ต๊ณ„์ ์œผ๋กœ๋„ ์œ ์˜ํ•œ ๊ฒฐ๊ณผ์˜€๋‹ค. ๊ฒฐ๊ตญ, ๊ฒฝ์ œ์„ฑ ํ‰๊ฐ€์˜ ๋น„์ค‘ ์กฐ์ •์€ ๋น„์ˆ˜๋„๊ถŒ๊ณผ ๋‚™ํ›„์ง€์—ญ์— ๋ชจ๋‘ ์œ ๋ฆฌํ•˜๊ฒŒ ์ž‘์šฉํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. AHP ์ข…ํ•ฉํ‰์ ์˜ ๋ณ€๋™์— ๋”ฐ๋ฅธ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ํ†ต๊ณผ์œจ์€ ๋น„์ˆ˜๋„๊ถŒ, ํŠนํžˆ ๋‚™ํ›„์ง€์—ญ์—์„œ ๊ธ‰๊ฒฉํžˆ ์ƒ์Šนํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ •๋ถ€์—์„œ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ํ‰๊ฐ€ํ•ญ๋ชฉ์˜ ์ˆ˜๋„๊ถŒ/๋น„์ˆ˜๋„๊ถŒ ์ด์›ํ™”๋ฅผ ์ถ”์ง„ํ•˜๋Š” ๋ชฉ์ ์€ ๋‚™ํ›„์ง€์—ญ์— ๋Œ€ํ•œ ๋ฐฐ๋ ค์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ์—์„œ ๋‚˜ํƒ€๋‚ฌ๋“ฏ์ด ํ™•์‹คํ•˜๊ณ  ์ •ํ™•ํ•œ ๋ชฉ์ ์˜ ๋‹ฌ์„ฑ์„ ์œ„ํ•ด์„œ๋Š” ์ˆ˜๋„๊ถŒ/๋น„์ˆ˜๋„๊ถŒ์˜ ๊ตฌ๋ถ„๋ณด๋‹ค๋Š” ๋‚™ํ›„์ง€์—ญ/๋น„๋‚™ํ›„์ง€์—ญ์˜ ๊ถŒ์—ญ ๊ตฌ๋ถ„์ด ์ •์ฑ… ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๋Š” ๋ฐ์— ๋ณด๋‹ค ํšจ๊ณผ์ ์œผ๋กœ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.The purpose of this study is to predict the effectiveness of the policy implementation in advance by analyzing the effects of the dualization of the proportion of assessment items in metropolitan/non-metropolitan areas in the plan to reform the preliminary feasibility study system, and to review matters that need to be supplemented in the future implementation process. As 20 years have passed since the introduction of the preliminary feasibility study system, there is a need to reflect the changed economic and social conditions in the system. As a result, the government judges that calls for balanced national development and realization of various social values are increasing. In response, the government intended to dualize the evaluation of metropolitan and non-metropolitan areas by reorganizing the proportion of comprehensive assessment, thereby considering underdeveloped areas in the provinces. However, there is a question that the non-metropolitan area, which the government says, is not necessarily an underdeveloped area. Thus, along with the above-mentioned reform measures, we reviewed the feasibility of realizing the goals of government policies through the classification of underdeveloped/non-underdeveloped areas based on legal concepts. For this purpose, a total of 377 sample projects were analyzed by collecting preliminary feasibility study reports published from January 2009 to July 2019. First of all, I compared AHP's overall score and economic feasibility evaluation score before the system reform by dividing it into metropolitan and non-metropolitan areas. As a result, there was no statistically significant difference in the preliminary feasibility study AHP overall score between the two regions, and the economic feasibility evaluation score was 17.5% higher than that of non-metropolitan area. As a result of comparing them again by dividing them into underdeveloped/non-underdeveloped areas, the preliminary feasibility study AHP overall scores between the two regions showed no statistically significant difference, and the economic feasibility evaluation score was 55.2% higher than that of underdeveloped area. I could see that AHP's overall score based on preliminary feasibility studies in non-metropolitan areas or underdeveloped areas, which are relatively marginalized areas, was never lower than that of metropolitan areas or non-underdeveloped areas. Also, I could see that the economic evaluation score of underdeveloped areas was relatively lower than that of non-metropolitan areas. As another task, the simulation was conducted by reclassifying existing projects into those of metropolitan/non-metropolitan area, and underdeveloped/non-underdeveloped area, to verify the effectiveness of the preliminary feasibility study system reform. The scores and weights for each project were calculated through the existing preliminary feasibility study reports. In addition, the existing weights were applied to the revised system to give new weights to calculate the new AHP overall score. Based on the above process, we compared the AHP overall scores before and after the reorganization of the preliminary feasibility study system. The overall AHP score for non-metropolitan projects rose 4.8% compared to the period before the system was reorganized, which was also a statistically significant. Comparing underdeveloped and non-underdeveloped in non-metropolitan areas, the overall AHP score for projects in underdeveloped areas rose 7.4% compared to before the system was reformed, while that for non-underdeveloped areas rose 1.6%. And they were all statistically significant results. After all, the adjustment of the weight of economic assessment is expected to benefit both non-metropolitan areas and underdeveloped areas. The passing rate of preliminary feasibility studies due to changes in AHP overall scores has been shown to rise sharply in non-metropolitan areas, especially in underdeveloped areas. The purpose of the government's push for the dualization of preliminary feasibility study assessment items between metropolitan and non-metropolitan areas is to provide consideration for underdeveloped areas. As shown in the results of this study, in order to achieve a clear and accurate purpose, the division of areas in underdeveloped/non-underdeveloped areas could contribute more effectively to achieving policy goals than in the metropolitan/non-metropolitan areas.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 5 ์ œ 2 ์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๋ถ„์„ 10 ์ œ 1 ์ ˆ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ์™€ ๋‚™ํ›„์ง€์—ญ ๊ด€๋ จ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 10 1. ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ 10 2. ๋‚™ํ›„์ง€์—ญ 19 ์ œ 2 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  26 ์ œ 3 ์ ˆ ์š”์•ฝ ๋ฐ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 28 ์ œ 3 ์žฅ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 29 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ 29 1. ์—ฐ๊ตฌ๋ชจํ˜•์˜ ์„ค์ • 29 2. ์—ฐ๊ตฌ ๊ฐ€์„ค 35 ์ œ 2 ์ ˆ ์ž๋ฃŒ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• 36 ์ œ 4 ์žฅ ๋ถ„์„๊ฒฐ๊ณผ 37 ์ œ 1 ์ ˆ ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ฐ ๋นˆ๋„ ๋ถ„์„ 37 1. ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ถ„์„ 37 2. ๋นˆ๋„ ๋ถ„์„ 40 ์ œ 2 ์ ˆ ๊ฒฝ์ œ์„ฑ ํ‰๊ฐ€์˜ ์˜ํ–ฅ๋ ฅ ๋ถ„์„ 43 1. ์—ฐ๋„๋ณ„ ํ‰๊ท  B/C๋น„์œจ ๋ถ„์„ 43 2. B/C๋น„์œจ๊ณผ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ํ†ต๊ณผ์œจ ๋ถ„์„ 46 ์ œ 3 ์ ˆ ์ˆ˜๋„๊ถŒ ๊ตฌ๋ถ„๊ณผ ๋‚™ํ›„์ง€์—ญ ๊ตฌ๋ถ„์˜ ๋™์งˆ์„ฑ ๋ถ„์„ 48 1. ์ˆ˜๋„๊ถŒ/๋น„์ˆ˜๋„๊ถŒ ๊ตฌ๋ถ„์— ๋”ฐ๋ฅธ ์‚ฌ์—… ๋ถ„์„ 48 2. ๋‚™ํ›„์ง€์—ญ/๋น„๋‚™ํ›„์ง€์—ญ ๊ตฌ๋ถ„์— ๋”ฐ๋ฅธ ์‚ฌ์—… ๋ถ„์„ 50 3. ์ˆ˜๋„๊ถŒ-๋น„๋‚™ํ›„์ง€์—ญ(๋˜๋Š” ๋น„์ˆ˜๋„๊ถŒ-๋‚™ํ›„์ง€์—ญ) ๊ฐ„ ๋™์งˆ์„ฑ ๋ถ„์„ 52 ์ œ 4 ์ ˆ ํ‰๊ฐ€ํ•ญ๋ชฉ ๋น„์ค‘ ์ด์›ํ™”์˜ ํšจ๊ณผ ๋ถ„์„ 53 1. ๆ–ฐ์ œ๋„ ๊ฐœํŽธ์‹œ ํ‰๊ฐ€ํ•ญ๋ชฉ๋ณ„ ๊ฐ€์ค‘์น˜ ์˜ˆ์ธก 53 2. ๆ–ฐ์ œ๋„ ๊ฐœํŽธ์‹œ AHP ์ข…ํ•ฉํ‰์  ๋ฐ ํ†ต๊ณผ๋น„์œจ ์˜ˆ์ธก 55 ์ œ 5 ์žฅ ๊ฒฐ๋ก  56 ์ œ 1 ์ ˆ ๋ถ„์„๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 56 ์ œ 2 ์ ˆ ์˜ˆ๋น„ํƒ€๋‹น์„ฑ์กฐ์‚ฌ ์ œ๋„ ๊ฐœํŽธ๋ฐฉ์•ˆ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 59 1. ์ˆ˜๋„๊ถŒ/๋น„์ˆ˜๋„๊ถŒ ํ‰๊ฐ€ ์ด์›ํ™” 59 2. ์ข…ํ•ฉํ‰๊ฐ€ ์ฃผ์ฒด์˜ ๋ณ€๊ฒฝ 60 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ–ฅํ›„ ๊ณผ์ œ 62 ์ฐธ๊ณ ๋ฌธํ—Œ 63Maste

    An Analysis on the Effect of C-ITS on Traffic Accident Reduction in Accidental Areas

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2021.8. ์ด์˜์ธ.Abstract An Analysis on the Effect of C-ITS on Traffic Accident Reduction in Accidental Areas Eo, Joong Hyuk ํ•™๊ณผ ๋ฐ ์ „๊ณต Transportation Studies Department of Environmental Planning The Graduate School Seoul National University When C-ITS (Cooperative-Intelligent Transport Systems) was established nationwide to prevent traffic accidents through continuous data sharing with other vehicles or infrastructure installed on roads through vehicle terminals, this paper selects traffic accident analysis information. In addition, based on the penetration rate of C-ITS terminals, the scenario was divided into excellent distribution cases (Scenario1), mandatory installation + AM (Scenario 2) and natural increase (Scenario 3) to calculate the benefits. In addition, local government traffic accident zones were largely divided by administrative unit, road type, and type, and the analysis showed that there were many traffic accident sites in the order of Gyeonggi-do, Seoul, Jeollanam-do, Gyeongsangbuk-do, and Gyeongsangnam-do. In addition, it was analyzed that there were many traffic accidents in the order of intersections (planes), national roads, intersections (rotations), and cities and counties. As a result of calculating the benefits of reducing traffic accidents, Scenario 1, an excellent distribution case, was calculated as the highest benefit value of KRW 7.8556 trillion. The planar intersection, which has 963 out of a total of 1,129 traffic accident multiple points, was 6.7545 trillion won out of 7.8556 trillion won, accounting for about 86 percent of the benefits. In addition, Pearson correlation analysis and Multiple Regression Analysis were used to examine the calculated benefits of reducing traffic accidents and the causality and effects of the factors. Pearson correlation shows the highest positive correlation among correlations, with mortality rates r=.808(p <.05) and p=0.000. Conversely, the injury rate was r=.077 (p<.01) and p=0.010, indicating the lowest positive correlation. Traffic volumes and related numbers of C-ITS terminals, the number of autonomous vehicles, and the number of service compliant vehicles all showed negative correlations. Multiple regression analysis results predicted in this study expected all independent variables to have a significant effect on dependent variables, but the actual analysis showed slightly different results. F = 2122.79 (p<.001) makes the regression model appropriate, adj.=0.653 with 65.3% explanatory power, and the number of injured, dead, and mortality are all non-standardized coefficients. Injury rates, traffic volumes, C-ITS terminals, the number of autonomous vehicles, and the number of service-compatible vehicles were excluded due to a significant probability greater than 0.5. The relative influence on traffic accident reduction benefits among the number of injuries, deaths, and mortality was relatively higher than the two variables with the highest standardization coefficient (ฮฒ=0.777). In this study, we prioritized the construction of C-ITS by utilizing the traffic accident reduction benefits, traffic safety index, and traffic accident risk at the accident site. First, the effect of reducing traffic accidents was analyzed based on the size of the benefits by estimating the benefits that have been valued in currency. The priority of introducing C-ITS was selected as the highest priority and the priority of priority building was divided into 'total benefit' bases, 'benefit by type of road', and 'benefit by city and province' bases. Second, the National Statutory Information Center of the Traffic Safety Act as an indicator of traffic safety assessment because the main purpose of C-ITS is safety (cautionary) driving support. Traffic Safety Assessment Index (Article 291), Enforcement Decree of the Traffic Safety Act [Enforcement No. 2, 2020; Part of the Amendment] has been established by local government. The priorities have been set. As a result of prioritizing the construction, the priority was high around the metropolitan area with many accident points and flat intersections, while the traffic safety index set the lowest priority and the highest traffic accident risk. In this study, C-ITS is analyzed for the effect of reducing traffic accidents at multiple traffic accident sites nationwide, and it can be used as a basis for determining the need to introduce concretely and the efficiency of the construction. It is also expected to be a guide-line for C-ITS deployment expansion based on the analysis of impact relationships of characteristics factors and to be used as useful data for future business expansion and implementation. keywords : ITS, C-ITS, Traffic accident reduction effect, Traffic accident reduction benefits, construct priority Student Number : 2019-28653๊ตญ๋ฌธ์ดˆ๋ก ๋ณธ ๋…ผ๋ฌธ์€ ์ฐจ๋Ÿ‰์— ์žฅ์ฐฉ๋œ C-ITS ๋‹จ๋ง๊ธฐ๋ฅผ ํ†ตํ•ด ์ฃผํ–‰์ค‘์ธ์ฐจ๋Ÿ‰ ๋˜๋Š” ๋„๋กœ์— ์„ค์น˜๋œ ์ธํ”„๋ผ์™€์˜ ์ง€์†์ ์ธ ๋ฐ์ดํ„ฐ ๊ณต์œ ๋ฅผ ํ†ตํ•ด ๊ตํ†ต์‚ฌ๊ณ ๋ฅผ ์˜ˆ๋ฐฉํ•˜๋Š” ์ฐจ์„ธ๋Œ€ ์ง€๋Šฅํ˜•๊ตํ†ต์‹œ์Šคํ…œ C-ITS(Cooperative-Intelligent Transport Systems)๊ฐ€ ์ „๊ตญ์— ๊ตฌ์ถ• ๋˜์—ˆ์„ ๋•Œ C-ITS ์ถ”์ง„ ์„œ๋น„์Šค ์ค‘ โ€˜๋„๋กœ์œ„ํ—˜๊ตฌ๊ฐ„ ์ •๋ณด์ œ๊ณตโ€™ ์„œ๋น„์Šค๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๊ตํ†ต์•ˆ์ „๊ณต๋‹จ ๊ตํ†ต์‚ฌ๊ณ ๋ถ„์„์‹œ์Šคํ…œ์—์„œ ์ œ๊ณตํ•˜๋Š” ์ง€์ž์ฒด๋ณ„ ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์—ญ ๋ถ„์„์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์—ญ ์ง€์ ์„ ์„ ์ •ํ•˜๊ณ  C-ITS๊ฐ€ ๋„์ž… ๋˜์—ˆ์„ ์‹œ 2023-2032๋…„ 10๋…„๊ฐ„์˜ ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์ ์˜ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํšจ๊ณผ๋ฅผ ํ™”ํ๊ฐ€์น˜ํ™”ํ•œ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต์˜ ์‚ฐ์ถœ ์‹์„ ์ƒˆ๋กœ์ด ์ •์˜ํ•˜์—ฌ C-ITS ๋‹จ๋ง๊ธฐ ๋ณด๊ธ‰๋ฅ ์„ ๊ธฐ์ค€์œผ๋กœ ์šฐ์ˆ˜๋ณด๊ธ‰์‚ฌ๋ก€(์‹œ๋‚˜๋ฆฌ์˜ค1), ์˜๋ฌด์žฅ์ฐฉ+AM(์‹œ๋‚˜๋ฆฌ์˜ค2), ์ž์—ฐ์ฆ๊ฐ€(์‹œ๋‚˜๋ฆฌ์˜ค3)๋กœ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ตฌ๋ถ„ํ•ด ํŽธ์ต์„ ์‚ฐ์ถœํ–ˆ๋‹ค. ๋˜ํ•œ, ์ง€์ž์ฒด ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์—ญ์„ ํฌ๊ฒŒ ํ–‰์ •๊ตฌ์—ญ๋‹จ์œ„, ๋„๋กœ์œ ํ˜• ๋ฐ ์ข…๋ฅ˜๋ณ„๋กœ ๊ตฌ๋ถ„ํ•˜์˜€์œผ๋ฉฐ, ๋ถ„์„๊ฒฐ๊ณผ ๊ฒฝ๊ธฐ๋„, ์„œ์šธํŠน๋ณ„์‹œ, ์ „๋ผ๋‚จ๋„, ๊ฒฝ์ƒ๋ถ๋„, ๊ฒฝ์ƒ๋‚จ๋„ ์ˆœ์œผ๋กœ ์‚ฌ๊ณ ๋‹ค๋ฐœ ์ง€์ ์ด ๋งŽ์€ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ๊ต์ฐจ๋กœ(ํ‰๋ฉด), ๊ตญ๋„. ๊ต์ฐจ๋กœ(ํšŒ์ „), ์‹œ๊ตฐ๋„ ์ˆœ์œผ๋กœ ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์ ์ด ๋งŽ์€ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต ์‚ฐ์ถœ๊ฒฐ๊ณผ ์šฐ์ˆ˜๋ณด๊ธ‰์‚ฌ๋ก€์ธ ์‹œ๋‚˜๋ฆฌ์˜ค1์ด ์ด ํŽธ์ต 7์กฐ8,556์–ต์›์œผ๋กœ ๊ฐ€์žฅ ๋†’์€ ํŽธ์ต ๊ฐ’์œผ๋กœ ์‚ฐ์ถœ๋˜์—ˆ์œผ๋ฉฐ, ์ด 1,129๊ฐœ์˜ ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์  ์ค‘ 963๊ฐœ์˜ ์ง€์ ์ด ๋ถ„ํฌ๋˜์–ด์žˆ๋Š” ํ‰๋ฉด๊ต์ฐจ๋กœ๋Š” ์ด 7์กฐ8,556์–ต์› ์ค‘ 6์กฐ7,545์–ต์›์œผ๋กœ ์•ฝ 86%์˜ ํŽธ์ต์˜ ๋น„์ค‘์„ ์ฐจ์ง€ํ•˜๋Š” ๊ฑธ๋กœ ์‚ฐ์ถœ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์‚ฐ์ถœ๋œ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต๊ณผ ๊ทธ ์š”์ธ๋“ค๊ณผ์˜ ์ธ๊ณผ๊ด€๊ณ„ ๋ฐ ์˜ํ–ฅ๋„๋ฅผ ์‚ดํŽด๋ณด๊ธฐ ์œ„ํ•˜์—ฌ Pearson ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„๊ณผ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„(Multiple Regression Analysis)์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. Pearson ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„๊ฒฐ๊ณผ ์‚ฌ๋ง๋ฅ ์ด r=.808(p<.05), p=0.000์œผ๋กœ ์ƒ๊ด€๊ด€๊ณ„ ์ค‘ ๊ฐ€์žฅ ๋†’์€ ์ •(+)์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๋ฐ˜๋Œ€๋กœ ๋ถ€์ƒ๋ฅ ์€ r=.077(p<.01),p=0.010์œผ๋กœ ๊ฐ€์žฅ ๋‚ฎ์€ ์ •(+)์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๊ตํ†ต๋Ÿ‰๊ณผ ๊ทธ์™€ ๊ด€๋ จ๋œ C-ITS๋‹จ๋ง๊ธฐ ๋ณด๊ธ‰๋Œ€์ˆ˜, ์ž์œจ์ž๋™์ฐจ ๋Œ€์ˆ˜, ์„œ๋น„์Šค ์ˆœ์‘์ฐจ๋Ÿ‰์ˆ˜๋Š” ๋ชจ๋‘ ๋ถ€(-)์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„๊ฒฐ๊ณผ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์˜ˆ์ธกํ•˜์˜€๋˜ ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ๋Š” ๋ชจ๋“  ๋…๋ฆฝ๋ณ€์ˆ˜๊ฐ€ ์ข…์†๋ณ€์ˆ˜์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•˜์˜€์ง€๋งŒ ์‹ค์ œ ๋ถ„์„ ๊ฒฐ๊ณผ ์กฐ๊ธˆ ์ƒ์ดํ•œ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์™”๋‹ค. F = 2122.79(p<.001)๋กœ ๋ณธ ํšŒ๊ท€๋ชจํ˜•์ด ์ ํ•ฉํ•˜๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, adj.=0.653์œผ๋กœ 65.3%์˜ ์„ค๋ช…๋ ฅ์„ ๋‚˜ํƒ€๋‚ด์—ˆ์œผ๋ฉฐ, ๋ถ€์ƒ์ž์ˆ˜, ์‚ฌ๋ง์ž์ˆ˜, ์‚ฌ๋ง๋ฅ ์€ ๋ชจ๋‘ ๋น„ํ‘œ์ค€ํ™”๊ณ„์ˆ˜๊ฐ€ ์ •(+)์ ์ธ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์™”๋‹ค. ๋ถ€์ƒ๋ฅ , ๊ตํ†ต๋Ÿ‰, C-ITS๋‹จ๋ง๊ธฐ ๋ณด๊ธ‰๋Œ€์ˆ˜, ์ž์œจ์ž๋™์ฐจ ๋Œ€์ˆ˜, ์„œ๋น„์Šค ์ˆœ์‘์ฐจ๋Ÿ‰์ˆ˜๋Š” ์œ ์˜ํ™•๋ฅ ์ด 0.5๋ณด๋‹ค ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚˜ ์ œ์™ธ๋˜์—ˆ๋‹ค. ๋ถ€์ƒ์ž์ˆ˜, ์‚ฌ๋ง์ž์ˆ˜, ์‚ฌ๋ง๋ฅ  ์ค‘ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต์— ์ƒ๋Œ€์  ์˜ํ–ฅ๋ ฅ์€ ํ‘œ์ค€ํ™”๊ณ„์ˆ˜๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ๋ถ€์ƒ์ž์ˆ˜๊ฐ€(ฮฒ=0.777) ๋‘ ๋ณ€์ˆ˜๋ณด๋‹ค ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์€ ์˜ํ–ฅ์„ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต์— ์ฃผ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์ ์˜ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต๊ณผ ๊ตํ†ต์•ˆ์ „์ง€์ˆ˜, ๊ตํ†ต์‚ฌ๊ณ ์œ„ํ—˜๋„๋ฅผ ํ™œ์šฉํ•˜์—ฌ C-ITS์˜ ๊ตฌ์ถ•์˜ ์šฐ์„ ์ˆœ์œ„๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ, ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํšจ๊ณผ๋ฅผ ํ™”ํ๊ฐ€์น˜ํ™”ํ•œ ํŽธ์ต์„ ์ถ”์ •ํ•˜์—ฌ ํŽธ์ต์˜ ํฌ๊ธฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. C-ITS ๋„์ž…์˜ ์šฐ์„ ์ˆœ์œ„๋กœ ํŽธ์ต์ด ๊ฐ€์žฅ ํฐ ์ง€์ ์„ ์šฐ์„ ์œผ๋กœ ์„ ์ •ํ•˜์—ฌ ํŽธ์ต์šฐ์„ ์ˆœ์œ„๊ธฐ๋ฐ˜์œผ๋กœ โ€˜์ด ํŽธ์ตโ€™๊ธฐ๋ฐ˜, โ€˜๋„๋กœ์ข…๋ฅ˜ยทํ˜•ํƒœ๋ณ„ ํŽธ์ตโ€™๊ธฐ๋ฐ˜, โ€˜์‹œยท๋„๋ณ„ ํŽธ์ตโ€™๊ธฐ๋ฐ˜์œผ๋กœ ์„ธ๋ถ„ํ™” ํ•˜์—ฌ ์šฐ์„ ๊ตฌ์ถ•์˜ ์šฐ์„ ์ˆœ์œ„๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ, C-ITS์˜ ๊ฐ€์žฅ ํฐ ๋ชฉ์ ์ด ์•ˆ์ „(์ฃผ์˜)์šด์ „์ง€์›์ด๊ธฐ ๋•Œ๋ฌธ์— ๊ตํ†ต์•ˆ์ „ ๋Œ€ํ•œ ํ‰๊ฐ€์ง€ํ‘œ๋กœ์จ ๊ตํ†ต์•ˆ์ „๋ฒ• ์‹œํ–‰๋ น[๋ณ„ํ‘œ 3์˜2] ๊ตญ๊ฐ€๋ฒ•๋ น์ •๋ณด์„ผํ„ฐ. ๊ตํ†ต์•ˆ์ „๋„ ํ‰๊ฐ€์ง€์ˆ˜(์ œ29์กฐ์ œ1ํ•ญ๊ด€๋ จ), ๊ตํ†ต์•ˆ์ „๋ฒ• ์‹œํ–‰๋ น[๋ณ„ํ‘œ 3์˜2] [์‹œํ–‰2020.11.27.], [๋Œ€ํ†ต๋ น๋ น ์ œ311189ํ˜ธ, 2020, 11. 24., ์ผ๋ถ€๊ฐœ์ •] ์— ๋ช…์‹œ๋œ โ€˜๊ตํ†ต์•ˆ์ „๋„ ํ‰๊ฐ€์ง€์ˆ˜โ€™(์ œ 29์กฐ์ œ1ํ•ญ ๊ด€๋ จ)๋ฅผ ์ง€์ž์ฒด์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์ ๋ณ„๋กœ ์‚ฐ์ถœํ•ด ๊ตฌ์ถ•์˜ ์šฐ์„ ์ˆœ์œ„๋ฅผ ์„ค์ •ํ•˜์˜€์œผ๋ฉฐ, ๋„๋กœ๊ตํ†ต๋ฒ• ์‹œํ–‰๊ทœ์น™ [๋ณ„ํ‘œ 8์˜2] ๊ตญ๊ฐ€๋ฒ•๋ น์ •๋ณด์„ผํ„ฐ, ๋„๋กœ๊ตํ†ต๋ฒ• ์‹œํ–‰๊ทœ์น™ [๋ณ„ํ‘œ 8์˜2], ๋ฌด์ธ๊ตํ†ต๋‹จ์†์šฉ ์žฅ๋น„ ์„ค์น˜ ๊ธฐ์ค€(์ œ14์กฐ์˜2 ๊ด€๋ จ) ์˜ ์–ด๋ฆฐ์ด ๋ณดํ˜ธ๊ตฌ์—ญ ๋‚ด ๋ฌด์ธ ๊ตํ†ต๋‹จ์†์šฉ ์žฅ๋น„์„ค์น˜ ๊ธฐ์ค€์„ ์ฐธ๊ณ ํ•˜์—ฌ ๋ช…์‹œ๋œ ๊ตํ†ต์‚ฌ๊ณ  ์œ„ํ—˜์ง€์ˆ˜(ARI:Accident Risk Index)๋ฅผ ํ™œ์šฉํ•˜์—ฌ ARI๋ฅผ ์‚ฐ์ถœํ•˜์—ฌ ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์  ๋‚ด C-ITS ๊ตฌ์ถ•์˜ ์šฐ์„ ์ˆœ์œ„๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ๊ตฌ์ถ•์˜ ์šฐ์„ ์ˆœ์œ„ ๊ฒฐ์ •๊ฒฐ๊ณผ ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์ ์ด ๋งŽ์€ ์ˆ˜๋„๊ถŒ๊ณผ ํ‰๋ฉด๊ต์ฐจ๋กœ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋†’๊ฒŒ ๊ฒฐ์ •๋˜์—ˆ์œผ๋ฉฐ, ๊ตํ†ต์•ˆ์ „์ง€์ˆ˜๋Š” ๊ฐ€์žฅ ๋‚ฎ์€ ์ง€์—ญ์„ ์šฐ์„ ์ˆœ์œ„๋กœ ์„ค์ •ํ•˜๊ณ , ๊ตํ†ต์‚ฌ๊ณ  ์œ„ํ—˜์ˆœ์œ„๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ์ˆœ์œผ๋กœ C-ITS๊ตฌ์ถ•์˜ ์šฐ์„ ์ˆœ์œ„๊ฐ€ ๊ฒฐ์ •๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ „๊ตญ ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์ ์— C-ITS๊ฐ€ ๊ตฌ์ถ• ๋˜์—ˆ์„ ๋•Œ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•จ์— ๋”ฐ๋ผ ์‹œ๋ฒ”์‚ฌ์—…๊ณผ ์‹ค์ฆ์‚ฌ์—…์„ ํ† ๋Œ€๋กœ ์ง„ํ–‰๋˜๊ณ  ์žˆ์–ด ๊ตฌ์ถ• ํšจ๊ณผ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋ฏธ๋น„ํ•œ ๋ถ€๋ถ„์— ํ™•์‹คํ•œ ๋„์ž… ํ•„์š”์„ฑ๊ณผ ๊ตฌ์ฒด์ ์ธ ๊ตฌ์ถ•์˜ ์šฐ์„ ์ˆœ์œ„๋‚˜ ํšจ์œจ์„ฑ์˜ ๊ฒฐ์ • ๊ทผ๊ฑฐ๋กœ ํ™œ์šฉ๋˜๋ฉฐ, ํŠน์„ฑ์š”์ธ๋“ค์˜ ์˜ํ–ฅ ๊ด€๊ณ„ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ C-ITS ๊ตฌ์ถ• ํ™•์žฅ์˜ Guide-line์ด ๋˜๋ฉฐ ํ–ฅํ›„ ์‚ฌ์—… ํ™•์žฅ๊ณผ ์ถ”์ง„์˜ ์œ ์šฉํ•œ ์ž๋ฃŒ๋กœ ํ™œ์šฉ๋˜์–ด ์งˆ ๊ฒƒ์ด๋ผ๊ณ  ์˜ˆ์ƒํ•œ๋‹ค. ์ฃผ์š”์–ด : ITS, C-ITS, ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํšจ๊ณผ, ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต, ๊ตฌ์ถ•์šฐ์„ ์ˆœ์œ„โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 2. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 4 1) ์‹œ๊ณต๊ฐ„์  ๋ฒ”์œ„ 4 2) ๋‚ด์šฉ์  ๋ฒ”์œ„ 5 3. ์—ฐ๊ตฌ ํ๋ฆ„๋„ 6 โ…ก. ๋ฌธํ—Œ๊ณ ์ฐฐ 7 1. ์ด๋ก ์ ๋ฐฐ๊ฒฝ 7 1) C-ITS(Cooperation-Intelligent transport system) 7 2) ITS์™€ C-ITS์˜ ์ฐจ์ด์  7 3) ๊ตญ๋‚ด์™ธ C-ITS ์‹ค์ฆ์‚ฌ์—… 8 4) ๊ตญ๋‚ด์™ธ C-ITS ์„œ๋น„์Šค 9 5) ๋„๋กœ์œ„ํ—˜๊ตฌ๊ฐ„ ์ •๋ณด์ œ๊ณต ์„œ๋น„์Šค 10 6) ๋„๋กœํ™˜๊ฒฝ์  ๊ตํ†ต์‚ฌ๊ณ  ๋ฐœ์ƒํ˜„ํ™ฉ 10 2. ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 16 3. ์‹œ์‚ฌ์  ๋ฐ ๋ณธ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 21 1) ๋ถ„์„๋Œ€์ƒ 21 2) ๋ฐฉ๋ฒ•๋ก  21 3) ์ฐจ๋ณ„์„ฑ 23 โ…ข. ๋ฐฉ๋ฒ•๋ก  24 1. ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ์ด์šฉํ•œ C-ITS ๊ตํ†ต์‚ฌ๊ณ  ์ ˆ๊ฐํŽธ์ต๊ณผ ์š”์ธ๋“ค๊ณผ์˜ ์˜ํ–ฅ๋„ ๋ถ„์„ 24 1) ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„ 24 2) ๋ณ€์ˆ˜์„ค์ • 27 2. ์ž๋ฃŒ์ˆ˜์ง‘ ๋ฐ ์š”์ธ์ถ”์ • 28 1) C-ITS ๋‹จ๋ง๊ธฐ ๋ณด๊ธ‰๋ฅ  28 2) ์ž์œจ์ฃผํ–‰์ฐจ ๋ณด๊ธ‰๋ฅ  30 3) ์ˆœ์‘๋„ 34 4) ์‚ฌ๊ณ ์œ ํ˜•๋ณ„ ๊ตํ†ต์‚ฌ๊ณ  ๋น„์šฉ 35 5) ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์ (์‚ฌ์ƒ์ž์ˆ˜) 36 3. ๊ตํ†ต์ˆ˜์š” ์˜ˆ์ธก 38 1) ์ˆ˜์š” ์ถ”์ • ๋ฐฉ๋ฒ• 38 4. C-ITS ๋„์ž… ์‹œ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐ ํŽธ์ต ๊ณ„์‚ฐ์‹ ์ œ์‹œ 39 1) C-ITS ๋„์ž… ์‹œ ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต์˜ ๊ณ„์‚ฐ์‹ 39 โ…ฃ. ์ž๋ฃŒ๋ถ„์„ ๋ฐ ๋ถ„์„ ์ˆ˜ํ–‰ 41 1. ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์  ๊ตฌ์ถ• 41 2. ์‚ฌ๊ณ ๋‹ค๋ฐœ ์ง€์ ๋ณ„ ๊ตํ†ต๋Ÿ‰์‚ฐ์ • 45 3. ์ž๋ฃŒ๋ถ„์„ ๊ฒฐ๊ณผ 46 4. ๊ธฐ์ˆ ํ†ต๊ณ„ 47 5. ํŽธ์ต์‚ฐ์ • ๊ฒฐ๊ณผ 48 1) ํŽธ์ต์‚ฐ์ • ๊ณผ์ • 48 2) ํŽธ์ต์‚ฐ์ • ๊ฒฐ๊ณผ 49 6. ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•œ C-ITS ๊ตํ†ต์‚ฌ๊ณ ์ ˆ๊ฐํŽธ์ต๊ณผ ์š”์ธ๋“ค๊ณผ์˜ ์˜ํ–ฅ๋„ ๋ถ„์„ 53 1) Pearson์ƒ๊ด€๊ด€๊ณ„๋ถ„์„(Correlation Analysis) 53 2) ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„(Multiple Regression Analysis) 55 7. ์‚ฌ๊ณ ๋‹ค๋ฐœ์ง€์  ๊ธฐ๋ฐ˜ C-ITS ๋„์ž… ์šฐ์„ ์ˆœ์œ„ 58 1) ๊ตํ†ต์‚ฌ๊ณ  ์ ˆ๊ฐํŽธ์ต๊ธฐ๋ฐ˜ ์šฐ์„ ์ˆœ์œ„ 59 (1) ์ด ํŽธ์ต ๊ธฐ๋ฐ˜ 59 (2) ๋„๋กœ์ข…๋ฅ˜ํ˜•ํƒœ๋ณ„ ํŽธ์ต ๊ธฐ๋ฐ˜ 59 (3) ์‹œ๋„๋ณ„ ํŽธ์ต ๊ธฐ๋ฐ˜ 60 2) ๊ตํ†ต์•ˆ์ „(์ฃผ์˜)๊ธฐ๋ฐ˜ 64 (1) ๊ตํ†ต์•ˆ์ „๋„ํ‰๊ฐ€์ง€์ˆ˜ ๊ธฐ๋ฐ˜ 64 (2) ๊ตํ†ต์‚ฌ๊ณ ์œ„ํ—˜์ง€์ˆ˜(ARI) ๊ธฐ๋ฐ˜ 65 โ…ค. ๊ฒฐ๋ก  68 1. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ํ•œ๊ณ„์  68 1) ์—ฐ๊ตฌ๊ฒฐ๊ณผ 68 2) ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  72 2. ํ–ฅํ›„๊ณผ์ œ 73 ์ฐธ๊ณ ๋ฌธํ—Œ 74 Abstract 79 [๋ถ€๋ก] 83 1) ๊ตํ†ต์ˆ˜์š” ์ถ”์ •๊ฒฐ๊ณผ ๋ฐ ์ด ํŽธ์ต(10๋…„) ๊ธฐ๋ฐ˜ ๊ตฌ์ถ•์šฐ์„ ์ˆœ์œ„ 83์„

    A Study on Regional Agricultural Policy with the Application of Regionally Specialized Agricultural Products

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†๊ฒฝ์ œ์‚ฌํšŒํ•™๋ถ€ ์ง€์—ญ์ •๋ณด ์ „๊ณต, 2016. 2. ์ด์„ฑ์šฐ.์ค‘์•™์ง‘์ค‘์‹ ๋†์ •์˜ ํ•œ๊ณ„์™€ ํ•จ๊ป˜ ์ง€๋ฐฉ์ž์น˜์ œ๋„์˜ ์„ฑ์ˆ™, ๊ธฐํ›„๋ณ€ํ™”์™€ ๊ฐ์ข… FTA ์ฒด๊ฒฐ ๋“ฑ์œผ๋กœ ์ธํ•œ ๋†์—…์‹œ์žฅ์˜ ๊ฐœ๋ฐฉ์œผ๋กœ ๋†์—… ํ™˜๊ฒฝ์ด ๊ธ‰๋ณ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ์ง€์—ญ์˜ ํŠน์„ฑ๊ณผ ๋น„๊ต์šฐ์œ„ ์š”์†Œ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๊ฒฝ์Ÿ๋ ฅ์ด ์œ ๋งํ•œ ์ž‘๋ชฉ์˜ ํŠนํ™”๋ฅผ ํ†ตํ•œ ๋†์‚ฐ๋ฌผ ์ƒ์‚ฐ์˜ ์ „๋ฌธํ™”์™€ ๋†๊ฐ€์˜ ์†Œ๋“ ์•ˆ์ • ๋ฐ ์ง€์—ญ๋†์—… ๋ฐœ์ „์„ ๋„๋ชจํ•˜๋Š” ์ง€์—ญ๋ณ„ ํŠนํ™”์ž‘๋ชฉ ๊ฐœ๋ฐœ๊ณผ ์ด๋ฅผ ํ†ตํ•œ ํŠนํ™”์‚ฐ์—… ํ™œ์„ฑํ™”๋ฅผ ์š”๊ตฌํ•˜๊ณ  ์žˆ๋‹ค. ํŠนํ™”์‚ฐ์—…์ •์ฑ…์€ ํŠน์ •์ง€์—ญ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ตญ๊ฐ€์  ์ฐจ์›์—์„œ ๊ฒฝ์Ÿ๋ ฅ์„ ์ง€๋…€์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ, ํšจ์œจ์ ์ธ ํŠนํ™”์‚ฐ์—…์ •์ฑ…์„ ์ถ”์ง„ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ง€์—ญ๋ณ„ ๋†์—…ํŠน์„ฑ์„ ๊ณผํ•™์ ์œผ๋กœ ๋ถ„์„ํ•˜์—ฌ ์ง€์—ญ์˜ ํŠนํ™”์ž‘๋ชฉ์— ๋Œ€ํ•œ ๋ณ€ํ™”์™€ ๊ทธ ๋™์ธ ๋“ฑ์„ ํŒŒ์•…ํ•˜๊ณ , ์–ด๋– ํ•œ ์ž‘๋ชฉ์„ ํŠน์„ฑํ™” ํ’ˆ๋ชฉ์œผ๋กœ ์„ ์ •ํ•  ๊ฒƒ์ธ์ง€์— ๋Œ€ํ•œ ํŠนํ™”์‚ฐ์—… ๋ฐœ๊ตด์˜ ๊ธฐ์ดˆ ์ž๋ฃŒ๊ฐ€ ๋งˆ๋ จ๋˜์–ด์•ผ ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ํŠนํ™”์ž‘๋ชฉ์„ ์„ ์ •ํ•˜๊ณ  ์ „๋žต์ž‘๋ชฉ์„ ์œก์„ฑํ•˜๊ธฐ ์œ„ํ•œ ํ•ฉ๋ฆฌ์ ์ธ ์ž๋ฃŒ๋ฅผ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ†ต๊ณ„์ฒญ์—์„œ ์ œ๊ณตํ•˜๋Š” ๋†๋ฆผ์–ด์—…์ด์กฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ž…์ง€์ƒ๊ธฐ๋ฒ•๊ณผ ๋‹ค์ค‘๋ชจํ˜• ํšŒ๊ท€๋ถ„์„ ๋“ฑ์„ ํ™œ์šฉํ•˜์—ฌ ์ž‘๋ชฉ๋ณ„ ํŠนํ™”์ง€์—ญ ๋ฐ ํŠนํ™”์˜ˆ์ƒ์ง€์—ญ์„ ์ถ”์ •ํ•˜๊ณ , ์ง€์—ญ์  ํŠน์„ฑ์— ๊ธฐ๋ฐ˜ํ•œ ํŠนํ™”์‚ฐ์—…์ด ์ง€์†์ ์œผ๋กœ ์œ ์ง€, ๋ฐœ์ „ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ (1) ๋…ผ๋ฒผ์™€ ๊ณผ์ˆ˜ ๋ฐ ์ถ•์‚ฐ๋ถ„์•ผ 9๊ฐœ์˜ ์ž‘๋ชฉ์„ ๋Œ€์ƒ์œผ๋กœ ์ง€์—ญ๋ณ„ ํŠนํ™”ํ˜„ํ™ฉ ๋ฐ ๊ณต๊ฐ„์  ์ง‘์ค‘๋„๋ฅผ ๋ถ„์„ํ•˜๊ณ , (2) ์ž‘๋ชฉ๋ณ„ ํŠนํ™”์ง€์—ญ ๋ฐ ํŠนํ™”์˜ˆ์ƒ์ง€์—ญ์„ ์„ ์ •ํ•˜๊ณ  ๋ณ€๋™์š”์ธ์„ ๋ถ„์„ํ•˜์—ฌ, (3) ํšจ์œจ์ ์ธ ํŠนํ™”์ž‘๋ชฉ์˜ ์„ ์ • ๋ฐ ๋ฐœ๊ตด๊ณผ ํŠนํ™”์‚ฐ์—…์˜ ํ™œ์„ฑํ™” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ๋ฐํ˜€์ง„ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๊ฐ„๋‹จํžˆ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์„ธ๋ถ€ ์ž‘๋ชฉ๋ณ„๋กœ ํŠนํ™”์ง€์—ญ์ด ์ง‘์ค‘๋˜์–ด ์žˆ๋Š” ํ˜„์ƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ํŠนํ™”์ง€์—ญ์ด ์—ฐ๋„๋ณ„๋กœ ์•ˆ์ •์ ์ธ ํ˜•ํƒœ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ž‘๋ชฉ๊ณผ ํŠนํ™”์ง€์—ญ์ด ์—ฐ๋„์— ๋”ฐ๋ผ ์ผ์ •๋ถ€๋ถ„ ๋ณ€ํ™”ํ•˜๋Š” ์ž‘๋ชฉ์ด ์žˆ์—ˆ๋‹ค. ํŠนํ™”์ง€์—ญ์ด ๋ณ€ํ™”ํ–ˆ๋‹ค๋Š” ๊ฒƒ์€ ํŠนํ™”์‚ฐ์—… ์œก์„ฑ์ •์ฑ…์ด ํšจ๊ณผ์ ์œผ๋กœ ์ ์šฉ๋˜์ง€ ๋ชปํ–ˆ์Œ์„ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ํŠนํ™”์ง€์—ญ์˜ ๋ณ€ํ™”๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์ƒˆ๋กœ์šด ํŠนํ™”ํ’ˆ๋ชฉ ๋ฐœ๊ตด ๋ฐ ๊ธฐํ›„๋ณ€ํ™”์™€ ์‹œ์žฅ๊ฐœ๋ฐฉ ๋“ฑ ๋‚ดโ€ค์™ธ๋ถ€์  ์š”์ธ์ด ๋ณตํ•ฉ์ ์œผ๋กœ ์ž‘์šฉํ•˜์—ฌ ๋‚˜ํƒ€๋‚œ ๊ฒฐ๊ณผ๋กœ ์ถ”์ •๋œ๋‹ค. ๋˜ํ•œ, ํŠนํ™”์ž‘๋ชฉ ์„ ์ •๊ณผ์ •์ด ๊ฒฝ์Ÿ๋ ฅ์„ ๋ฐ”ํƒ•์œผ๋กœ ํŠนํ™”๊ฐ€ ๊ฐ€๋Šฅํ•œ ์ž‘๋ชฉ์„ ์„ ์ •ํ•œ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ๊ทธ ์ง€์—ญ์— ์ง€๋ฆฌ์ ์œผ๋กœ ๋ฐ€์ง‘ํ•œ ์ž‘๋ชฉ, ์ฆ‰ ์ƒ์‚ฐ์ด ๋ฐ€์ง‘ํ•œ ์ž‘๋ชฉ์„ ํŠนํ™”์ž‘๋ชฉ์œผ๋กœ ์„ ์ •ํ•œ ๋ฐ ๊ธฐ์ธํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ํŠนํ™”์ž‘๋ชฉ ์„ ์ •๋ฐฉ์‹์€ ๊ทœ๋ชจ์˜ ๊ฒฝ์ œ์™€ ์ง‘์  ํšจ๊ณผ ๋“ฑ์„ ๊ธฐ๋Œ€ํ•  ์ˆ˜ ์žˆ์œผ๋‚˜, ์žฅ๊ธฐ์ ์œผ๋กœ ํ•ด๋‹น ํŠนํ™”์ž‘๋ชฉ์˜ ์—ญ์™ธ ์ง€์—ญ์—์„œ์˜ ๊ฒฝ์Ÿ๋ ฅ ์ €ํ•˜ ๋ฐ ์ „๊ตญ์  ๊ณผ์ž‰์ƒ์‚ฐ ๋“ฑ์œผ๋กœ ์ธํ•ด ํŠนํ™”์ง€์—ญ์˜ ์•ˆ์ •์  ์œ ์ง€์— ์–ด๋ ค์›€์„ ์ดˆ๋ž˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ, ์ง€์—ญ๋ณ„๋กœ๋Š” ๋‹ค์ˆ˜์˜ ์ž‘๋ชฉ์ด ๋™์‹œ์— ํŠนํ™”๊ฐ€ ์œ ๋งํ•œ ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋˜๋Š” ์‹œโ€ค๊ตฐ์ด ๋‹ค์ˆ˜ ์กด์žฌํ•˜๋Š”๊ฐ€ ํ•˜๋ฉด ๋ถ„์„๋Œ€์ƒ 9๊ฐœ ์ž‘๋ชฉ ์ค‘์—์„œ๋Š” ํŠนํ™”ํ’ˆ๋ชฉ์ด ์—†๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜๋Š” ์‹œโ€ค๊ตฐ์ด ์กด์žฌํ–ˆ๋‹ค. ๋˜ํ•œ, ํŠน์ • ์ž‘๋ชฉ์˜ ์ง‘์‚ฐ์ง€๋กœ ํŠนํ™”๋˜์–ด ์žˆ์œผ๋‚˜ ๊ฒฝ์ง€๋ฉด์ ๋‹น ๊ฐœ๋ณ„๋†๊ฐ€์˜ ๊ฒฝ์Ÿ๋ ฅ์€ ๋†’์ง€ ์•Š์€ ์ง€์—ญ์ด ์žˆ์—ˆ๋‹ค. ๋ฐ˜๋ฉด, ํ˜„์ƒ์  ํŠน์ง•๊ณผ๋Š” ๋‹ฌ๋ฆฌ ๊ฐœ๋ณ„ ๋†๊ฐ€์˜ ๊ฒฝ์Ÿ๋ ฅ์ด ์šฐ์ˆ˜ํ•œ ๊ฒƒ์œผ๋กœ ๋“œ๋Ÿฌ๋‚˜๋Š” ์ง€์—ญ๊ณผ ํ˜„์žฌ๋Š” ํŠนํ™”์ง€์—ญ์ด ์•„๋‹ˆ์ง€๋งŒ ํ–ฅํ›„ ํŠนํ™”์ง€์—ญ์œผ๋กœ ์˜ˆ์ธก๋˜๋Š” ์ง€์—ญ์ด ์žˆ์—ˆ๋‹ค. ์ง€์—ญ๋†์—…์˜ ๋ฐœ์ „์„ ์œ„ํ•ด์„œ๋Š” ๊ฐœ๋ณ„์ง€์—ญ์ด ๋ณด์œ ํ•˜๊ณ  ์žˆ๋Š” ์ด๋Ÿฌํ•œ ํŠน์„ฑ๊ณผ ์—ญ๋Ÿ‰์„ ๊ทน๋Œ€ํ™”ํ•˜์—ฌ ์ง€์—ญ ์‹ค์ •์— ์ ํ•ฉํ•˜๋ฉด์„œ๋„ ํƒ€ ์ง€์—ญ๊ณผ ์ฐจ๋ณ„ํ™”๋œ ํŠนํ™”์‚ฐ์—… ์œก์„ฑ์ „๋žต์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ด๋Ÿฐ ์ธก๋ฉด์—์„œ ํŠนํ™”์‚ฐ์—…์˜ ํ™œ์„ฑํ™”๋ฅผ ์œ„ํ•ด ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๋ฐœ์ „๋ฐฉ์•ˆ์„ ์ œ์–ธํ•œ๋‹ค. (1) ํŠนํ™”ํ’ˆ๋ชฉ์˜ ์„ ์ •๊ธฐ์ค€์„ ๋ช…ํ™•ํžˆ ํ™•๋ฆฝํ•ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์ฆ‰, ์‚ฐ์—…๊ตฌ์กฐ๋ถ„์„์„ ํ†ตํ•œ ๋น„๊ต ์šฐ์œ„์— ๋”ฐ๋ฅธ ํŠนํ™”ํ’ˆ๋ชฉ ์„ ์ • ๋“ฑ ๋ช…ํ™•ํ•œ ๊ธฐ์ค€๊ณผ ์ ˆ์ฐจ๋ฅผ ํ™•๋ฆฝํ•ด์„œ ์—ญ์™ธ ๊ฒฝ์Ÿ๋ ฅ์„ ๊ฐ–์ถ˜ ํŠนํ™”ํ’ˆ๋ชฉ์„ ์„ ์ •ํ•˜๊ณ  ์ง‘์ค‘์ ์œผ๋กœ ์œก์„ฑํ•˜๋Š” ๊ฒƒ์ด ๋ฐ”๋žŒ์งํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. (2) ํŠนํ™”ํ’ˆ๋ชฉ์˜ ๊ณ ํ’ˆ์งˆ ์œ ์ง€๋ฐฉ์•ˆ์ด ๋ชจ์ƒ‰๋˜์–ด์•ผ ํ•˜๋ฉฐ, ํŠนํ™”ํ’ˆ๋ชฉ, ํŠนํžˆ ๋†โ€ค์ž„์‚ฐํ’ˆ์˜ ๋‹ค์–‘ํ•œ ๊ฐ€๊ณตํ™”๊ฐ€ ์‹ค์ฒœ๋˜์–ด์•ผ ํ•œ๋‹ค. (3) ๊ณ ํ’ˆ์งˆ์˜ ํŠนํ™”์ž‘๋ชฉ์ด๋ผ๋„ ํŒ๋งค๊ฐ€ ๋ถ€์ง„ํ•˜๊ฑฐ๋‚˜ ๊ฐ€๊ฒฉ๊ฒฝ์Ÿ๋ ฅ์„ ์œ ์ง€ํ•˜์ง€ ๋ชปํ•œ๋‹ค๋ฉด ํŠนํ™”์‚ฐ์—… ์œ ์ง€์— ๋ถ€์ •์ ์ผ ์ˆ˜๋ฐ–์— ์—†์œผ๋ฏ€๋กœ, ์ง€์—ญ์  ํŠน์„ฑ, ์ˆ˜์š”์ž ์š”๊ตฌ ํ’ˆ๋ชฉ ๋“ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ˆ˜์š”์ž์˜ ์š”๊ตฌ์™€ ๋ถ€ํ•ฉํ•˜๋Š” ํŠนํ™”์‚ฐ์—…์„ ์ถ”์ง„ํ•˜๊ณ , ํŠนํ™”์‚ฐํ’ˆ์˜ ์ƒ์„ค ํŒ๋งค์žฅ ๊ฐœ์„ค, ์ธํ„ฐ๋„ท ๊ธฐ๋ฐ˜ ์ „์ž์ƒ๊ฑฐ๋ž˜(C2B ๋“ฑ) ํ™œ์„ฑํ™” ๋“ฑ ์œ ํ†ต๋‹จ๊ณ„์˜ ์ถ•์†Œ์™€ ์œ ํ†ต์ฒด๊ณ„์˜ ๋‹ค์–‘ํ™” ๋“ฑ์˜ ํŒ๋งคโ€ค์œ ํ†ต์ฒด๊ณ„์˜ ๊ฐœ์„ ์ด ์ด๋ฃจ์–ด์ ธ์•ผ ํ•œ๋‹ค. (4) ๊ฐ ์ง€์—ญ๋ณ„๋กœ ์‚ฐ์žฌํ•œ ๊ธฐ์กด์˜ ๊ด€๊ด‘์ž์›์— ๋Œ€ํ•œ ๊ด€๊ด‘์ˆ˜์š”๋ฅผ ํŠนํ™”์‚ฐํ’ˆ๊ณผ ์—ฐ๊ณ„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ๋“ค์„ ๋ชจ์ƒ‰ํ•˜๋Š” ๋“ฑ ํ™•๋Œ€ ์ถ”์„ธ๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋Š” ๋ ˆ์ €โ€ค๊ด€๊ด‘์ˆ˜์š”๋ฅผ ํŠนํ™”์‚ฐ์—…์— ์—ฐ๊ณ„ํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์ •์ฑ…๋“ค์ด ๋ชจ์ƒ‰๋˜์–ด์•ผ ํ•œ๋‹ค. (5) ๋Œ€๋ถ„๋ฅ˜ ๋…ผ๋ฒผ์™€ ๊ณผ์ˆ˜, ์ถ•์‚ฐ์˜ ์ž‘๋ชฉ๋ณ„ ํ™œ์„ฑํ™” ๋ฐฉ์•ˆ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋…ผ๋ฒผ์˜ ๊ฒฝ์šฐ, ๊พธ์ค€ํžˆ ์žฌ๋ฐฐ๋น„์œจ์ด ๊ฐ์†Œํ•  ๊ฒƒ์ด ์˜ˆ์ƒ๋˜๋ฏ€๋กœ ์ƒ์‚ฐ๋Ÿ‰ ์ฆ๋Œ€๋ณด๋‹ค ๊ณ ํ’ˆ์งˆํ™”์™€ ์ฐจ๋ณ„ํ™”๋ฅผ ํ†ตํ•œ ํŠนํ™”์ •์ฑ… ์ถ”์ง„์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๊ณผ์ˆ˜์˜ ๊ฒฝ์šฐ, ๊ธฐ์˜จ ์ƒ์Šน์œผ๋กœ ์žฌ๋ฐฐ์ง€์—ญ ํ™•๋Œ€ ๋ฐ ํŠนํ™”์ง€์—ญ์ด ๋ถ์ƒํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋˜๋Š” ๋™์‹œ์— ๊ธฐํ›„๋ณ€ํ™”์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ธฐ์กด์˜ ์ธํ”„๋ผ ๋“ฑ์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ฒฝ์Ÿ๋ ฅ์ด ์œ ์ง€๋  ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋˜๋Š” ์ง€์—ญ๋„ ์žˆ์—ˆ๋‹ค. ์ƒˆ๋กœ์šด ํŠนํ™”์ง€์—ญ์˜ ์„ ์ • ๋ฐ ์œก์„ฑ๊ณผ ํ•จ๊ป˜ ๊ธฐ์กด ํŠนํ™”์ง€์—ญ์˜ ๊ฒฝ์Ÿ๋ ฅ ์œ ์ง€๋ฅผ ์œ„ํ•œ ์ •์ฑ…๋“ค์ด ์ถ”์ง„๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์ถ•์‚ฐ์˜ ๊ฒฝ์šฐ, ํŠนํ™”๊ฐ€ ์•ˆ์ •์  ํ˜•ํƒœ๋ฅผ ๋ณด์ด๋Š” ์ง€์—ญ์„ ์ค‘์‹ฌ์œผ๋กœ ์ถ•์‚ฐ ์ข…์‚ฌ์ž๋“ค์˜ ์ฐธ์—ฌ๋น„์œจ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š” ์ƒ์‚ฐ์ž์กฐ์ง๊ณผ ์—ฐ๊ณ„ํ•œ ํ’ˆ์งˆ์œ ์ง€์™€ ํŒ๋กœ ํ™•๋Œ€ ๋ฐฉ์•ˆ์ด ์ ๊ทน ๋ชจ์ƒ‰๋˜์–ด์•ผ ํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. (6) ์‚ฌ์—… ์ถ”์ง„ ๊ณผ์ • ๋ฐ ์„ฑ๊ณผ์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ๊ด€๋ฆฌ ์ฒด๊ณ„๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ , ์‚ฌ์—…์„ฑ๊ณผ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ํŒŒ์•…์„ ํ†ตํ•ด์„œ ์‹œโ€ค๊ตฐ๋ณ„๋กœ ์ฐจ๋ณ„ํ™”๋œ ์ „๋žต ๋งˆ๋ จ์˜ ๊ทผ๊ฑฐ๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ด์•ผ ํ•œ๋‹ค.Under the limitations of centralized agricultural policies, the agricultural environment is rapidly changing via maturation of local government systems, climate change, and the liberalization of the agricultural market related to the implementation of Free Trade Agreement(FTA) and other causes. These changes require not only the specialization of agricultural production through specialization in promising competitive crops taking into account regional characteristics and comparative advantage elements but also the activation of specialized industry through the development of regional specialized crops to promote local agricultural development and farmer's income security. Regional agricultural policies need to produce a competitive advantage at the national level as well as at the regional level. Therefore, in order to efficiently construct development strategies for specialized business, regional agricultural characteristics should be scientifically analyzed. Also, in order to determine which crops are desirable and could be specialized, preliminary data for the construction of specialized regional business should be collected on the basis of understanding changes in specialized crops by region and their causes. This study has presumed suitable cultivation areas or potentially suitable cultivation areas by crops and has suggested a continuous maintenance and development scheme for specialized regional business through the application of methods such as the location quotient(LQ) and multiple regression analysis. Agriculture & Forestry and Fishery Census Data from the National Statistical Office were used to construct rational data when selecting specialized crops and promoting strategic crops. The purposes of this study were: (1) to examine regional characteristics and spatial concentrations of nine crops in the rice, fruit trees, and livestock sectors(2) to analyze causes of change as choosing suitable cultivation areas or potentially suitable cultivation areasand (3) to suggest efficient selection, construction and activation methods for specialized regional business. The results of this study are briefly as follows: This study verified that specialized items were spatially concentrated and that hot-spots focused on specific areas/crops existed. Stabilized crops and a little changed crops could be distinguished in suitable cultivation areas on a yearly basis. The change in suitable cultivation areas suggests that specialization has not been applied efficiently. These kinds of changes in specialized regions seem to result from local government discovery of specialized items, climate change, market shifts and a combination of other internal/external factors. Specialized crops are not typically selected based on competition, but rather based on the crops being geographically dense in that region. In other words, it seems like selection is based on the density of specialized crops already being produced. Although this selection method is expected to be affected by economy of scale and an agglomeration effect, it causes specialized crops competitive power to fall out of region and the maintenance of suitable cultivation areas to be destabilized by overproduction in all parts of the country. While some cities had promising crops that could be specialized simultaneously, there were also cities that did not to have any items suitable for specialization among the nine-subject analysis. There were also regions that did not have a high level of competition of cultivation acreage per individual farm, despite being a distribution center of specialized crops. On the other hand, some cities were predicted to have a high level of competition between individual farms even though it differed of its phenomenal characteristics and other cities were predicted to become specialized regions henceforth, although it is not specialized region currently. Therefore, we sought to maximize the capability of each region and to develop a strategy appropriate for the actual state of each region to encourage differentiation. In this respect, I suggest the following regional agricultural policies encouraging specialized business: (1) The selection standard for competitive specialized items outside of the local area should be clearly established. In other words, it is desirable to select a specialized regional business that is competitive and can be intensively promoted while establishing certain comparative standards and procedures and predominance of specialized items through analysis of industrial structure. (2) A high quality maintenance plan for specialized items is a standard requirement for sales and survival. Various processes need to be put into action for specialized items, especially for agricultural and secondary products. (3) Poor sales and disadvantageous price competitiveness can negatively influence the survival of a specialized industry even for high quality items. Therefore, specialization should be performed in accordance with regional characteristics and demand, among other factors. An efficient sales and distribution system with a permanent shop, internet-based e-commerce or other methods is also essential. (4) It is necessary to seek plans for connection with specialized items in each area along with each originally scattered tourist attractions that are in demand. Various policies connecting specialized industries to the demand for leisure and tourism are needed as these industries continue to expand. (5) The plans for vitalizing rice, fruit trees and livestock industries are as follows. The rice cultivation rate is expected to decrease steadily, so instead of increasing output, we should seek specialization, differentiation and better quality. For fruit trees, there are predictions that rising temperatures will gradually lead to expand fruit cultivation areas and to move specialized regions toward the north. At the same time, it has been predicted that some areas will maintain their competition on the basis of the existing infrastructure and etc. Therefore, a policy should be enacted for selecting and promoting new specialized regions while maintaining the competition of existing specialized regions. For livestock, market expansion and quality maintenance of regions where specialization is stable are recommended, and of livestock professions with increases in participation rates and connections with manufacturers. (6) Systematic management should be developed in regard to business procedures and enacted on the basis of differentiation strategies through detailed knowledge of business performance.I. ์„œ๋ก  1 1. ๋ฌธ์ œ ์ œ๊ธฐ ๋ฐ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  3 โ…ก. ๊ด€๋ จ๋ฌธํ—Œ ๊ณ ์ฐฐ 4 1. ์ง€์—ญ๋†์—…๊ณผ ๋†์—…ํŠนํ™” 4 2. ์ง€์—ญํŠนํ™”์‚ฌ์—… 13 3. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  20 โ…ข. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 25 1. ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ 25 2. ์ง€์—ญ๋ณ„ ํŠนํ™” ๋ถ„์„ 26 3. ํŠนํ™”์‚ฐ์—…์˜ ๋ถ„์„ ๋ฐ ์„ ์ • ๋ฐฉ๋ฒ• 29 โ…ฃ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๋ฐ ํ•ด์„ 43 1. ๊ธฐ์ดˆํ†ต๊ณ„ ๋ถ„์„๊ฒฐ๊ณผ 43 2. ๋‹ค์ค‘๋ชจํ˜• ๋ถ„์„๊ฒฐ๊ณผ 49 3. ์ž‘๋ชฉ๋ณ„ ํŠนํ™”์ง€์—ญ ๋ถ„์„๊ฒฐ๊ณผ 55 4. ํŠนํ™”์ง€์—ญ ๋ฐ ํŠนํ™”์˜ˆ์ƒ์ง€์—ญ ์„ ์ • 65 5. ์ง€์—ญ๋ณ„ ํŠนํ™”์ž‘๋ชฉ ํ˜„ํ™ฉ ๋ฐ ๋ณ€ํ™” ์ถ”์ด 88 6. ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์‹œ์‚ฌ์  98 โ…ค. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 106 1. ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  106 2. ์ œ์–ธ 108 3. ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ๋ฐœ์ „๋ฐฉํ–ฅ 110 ์ฐธ๊ณ ๋ฌธํ—Œ 112 Abstract 120Docto

    ํ•œ๊ตญ ์ง€์—ญ ๊ฐ„ ๋ณด๊ฑด์˜๋ฃŒ์ˆ˜์ค€์˜ ์ƒ๋Œ€์  ์œ„์น˜ ๋น„๊ต ์—ฐ๊ตฌ: Position Value for Relative Comparison Index๋ฅผ ํ™œ์šฉํ•˜์—ฌ

    Get PDF
    Background: This study aims to measure regional healthcare differences in Korea, and define relatively underserved areas. Methods: We employed position value for relative comparison index (PARC) to measure the healthcare status of 250 areas using 137 indicators in five following domains: healthcare demand, supply, accessibility, service utilization, and outcome. We performed a sensitivity analysis using t-SNE (t-distributed stochastic neighboring embedding). Results: Based on PARC values, 83 areas were defined as relatively underserved areas, 49 of which were categorized as moderate and 34 as severe. The provincial regions with the most underserved areas were Gyeongbuk (16 areas), Gangwon (13), Jeonnam (13), and Gyeongnam (12). Conclusion: This study suggests a relative comparison approach to define relatively underserved areas in healthcare. Further studies incorporating various perspectives and methods are required for policy implications.ope

    ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด ๊ต์œก ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ตญ์–ด๊ต์œก๊ณผ, 2017. 8. ๊น€์ข…์ฒ .๋ณธ ์—ฐ๊ตฌ๋Š” ์‹ ํ™”์†Œ(็ฅž่ฉฑ็ด , mythmes)๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•™์Šต์ž์˜ ์„คํ™”์— ๋Œ€ํ•œ ๋ช…๋ฃŒํ•˜๊ณ  ๋Šฅ๋™์ ์ด๋ฉฐ ์ „์ด์ ์ธ ์ดํ•ด๋ฅผ ์ด๋Œ์–ด๋‚ด๋Š” ๊ต์œก ๋‚ด์šฉ๊ณผ ๋ฐฉ๋ฒ•์„ ๋ชจ์ƒ‰ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ์‹ ํ™”์†Œ๋ž€ ์‹ ํ™”๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ์ตœ์†Œ์˜ ๋‹จ์œ„๋กœ, ์ด๊ฒƒ์ด ๊ฒฐํ•ฉํ•˜์—ฌ ๋งค๊ฐœํ•ญ(ๅช’ไป‹้ …) ์ค‘์‹ฌ์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ(ๅฐ็ซ‹ๆง‹้€ )๋ฅผ ๊ฐ–์ถ”๊ณ  ์‹ ํ™”์†Œ๋ฅผ ๊ณต์œ ํ•˜๋Š” ๊ตฌ๋น„์„œ์‚ฌ(ๅฃ็ข‘ๆ•ไบ‹)์— ๋Œ€ํ•˜์—ฌ ์‹ ํ™”์˜ ์›ํ˜•์  ์˜๋ฏธ๋ฅผ ๋ฐœํ˜„ํ•œ๋‹ค. ๋ณธ๊ณ ์—์„œ๋Š” ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด๋ฅผ ์œ„ํ•ด ์ฐฝ์„ธ์‹ ํ™”๋กœ๋ถ€ํ„ฐ ์„ธ๊ณ„์ฐฝ์กฐ(ไธ–็•Œๅ‰ต้€ ) ์‹ ํ™”์†Œ, ์ธ๋ฅ˜ํƒ„์ƒ(ไบบ้กž่ช•็”Ÿ) ์‹ ํ™”์†Œ, ์ธ์„ธ(ไบบไธ–)์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ๋ฅผ ์ถ”์ถœํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฐ๊ฐ์˜ ์‹ ํ™”์†Œ์™€ ๋ฐ€์ ‘ํ•œ ๊ด€๊ณ„(้—œไฟ‚)๋ฅผ ๊ฐ–๋Š” ์„คํ™”๋“ค์„ ํƒ์ƒ‰ํ•˜์—ฌ ๊ฑฐ์ธ(ๅทจไบบ) ์„คํ™”, ํ™์ˆ˜(ๆดชๆฐด) ์„คํ™”, ํŠธ๋ฆญ์Šคํ„ฐ๋‹ด(่ญš)์œผ๋กœ ๊ทธ ๋ฒ”์ฃผ๋ฅผ ์„ค์ •ํ•˜๊ณ , ์ด ๊ฐ€์šด๋ฐ ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ๊ด€๊ณ„์˜ ์ „ํ˜•์„ ๋ณด์—ฌ์ค„ ์ˆ˜ ์žˆ๋Š” ์„คํ™”๋ฅผ ๊ฐ๊ฐ ์„ ์ •ํ•˜์—ฌ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์„คํ™”๋ฅผ ์ดํ•ดํ•˜๋Š” ์ฒซ ๋ฒˆ์งธ ๊ณผ์ •์€ ์„คํ™”๋กœ๋ถ€ํ„ฐ ์‹ ํ™”์†Œ์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ, ๋งค๊ฐœํ•ญ, ์›ํ˜•์  ์˜๋ฏธ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋‹ค์Œ์€ ์‹ ํ™”์†Œ์™€ ์„คํ™” ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์ ๊ฒ€ํ•˜๋Š” ๊ณผ์ •์œผ๋กœ, ์‹ ํ™”์†Œ์™€ ๊ด€๋ จ ์„คํ™”์˜ ์›ํ˜•์  ์˜๋ฏธ ๊ฐ„ ์—ฐ๊ด€์„ฑ์„ ๊ฒ€ํ† ํ•˜๊ณ , ๋งค๊ฐœํ•ญ์˜ ์‹ ํ™”์  ํ‘œ์ง€(ๆจ™่ญ˜)๋กœ์˜ ์ „ํ™˜์„ ๊ฒ€ํ† ํ•˜์—ฌ ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๊ด€๋ จ ์„คํ™”๋ฅผ ์‚ดํ•„ ์ˆ˜ ์žˆ๋Š” ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•œ๋‹ค. ์ด์–ด์„œ ์‹ ํ™”์†Œ์™€ ๊ด€๋ จ ์„คํ™”์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๋Œ€์‘ ์—ฌ๋ถ€๋ฅผ ํ™•์ธํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์‹ ํ™”์†Œ์™€ ์„คํ™”์˜ ๊ด€๊ณ„๋ฅผ ํ™•์ •ํ•˜๊ฒŒ ๋œ๋‹ค. ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ์„คํ™” ์ดํ•ด์˜ ๋งˆ์ง€๋ง‰ ๊ณผ์ •์€ ์‹ ํ™”์†Œ์™€ ์„คํ™” ๊ฐ„์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ, ๋งค๊ฐœํ•ญ, ์›ํ˜•์  ์˜๋ฏธ์˜ ๋ณ€์ด๋ฅผ ์‚ดํ”ผ๋Š” ๊ฒƒ์œผ๋กœ, ์ด๋Ÿฌํ•œ ๊ณผ์ •์„ ํ†ตํ•ด ์‹ ํ™”์†Œ์™€ ์„คํ™”๊ฐ€ ๊ตฌ์ฒด์ ์œผ๋กœ ์–ด๋– ํ•œ ์˜๋ฏธ ๊ด€๊ณ„์— ์žˆ๋Š”์ง€ ์‚ดํŽด๋ณด๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ์ด๋Ÿฌํ•œ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด์˜ ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ๊ฐ ์‹ ํ™”์†Œ๊ฐ€ ๋ณ€์ด๋œ ์ „ํ˜•์ ์ธ ๋ฉด๋ชจ๋ฅผ ๋ณด์ด๋Š” , , ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์„คํ™”๋Š” ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ์˜ ํ˜ผ๋ˆ๊ณผ ์งˆ์„œ์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ๋ฅผ ๋ฐ˜๋ณต์ ์œผ๋กœ ๊ตฌํ˜„ํ•˜๋ฉด์„œ๋„ ๊ฑฐ์ธ์‹ ์˜ ์—ฌ์‹ ์  ๋ฉด๋ชจ๋ฅผ ๋ถ€๊ฐํ•˜์—ฌ ์ง€ํ˜• ์งˆ์„œ์˜ ์‹œ์ž‘์— ๋Œ€ํ•ด ํ˜•์ƒํ™”ํ•จ์„ ์‚ดํ•„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์€ ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ์˜ ๋น„๋ฒ”์„ฑ์„ ํ™•์žฅํ•˜์—ฌ ์‹ ๊ณผ ์ธ๊ฐ„์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ๋ฅผ ์žฌํ˜„ํ•˜๊ณ , ์ด๋กœ์จ ์ธ๋ฅ˜์˜ ์žฌํƒ„์ƒ์„ ํ˜•์ƒํ™”ํ•˜์˜€๋‹ค. ๋Š” ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ์˜ ์ž์—ฐ๊ณผ ๋ฌธ๋ช…์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ๋ฅผ ์ƒ์‘์ ์œผ๋กœ ๊ตฌํ˜„ํ•˜๋˜, ์ง€๋žต์„ ์ธ๋ฌผํ™”ํ•˜์—ฌ ๋ฌธ๋ช…์  ์กด์žฌ์˜ ์„ธ๊ณ„์— ๋Œ€ํ•œ ํ”ผ์ƒ์  ์ฐจ์ง€๋ฅผ ์˜๋ฏธํ™”ํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ๊ฐ ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ์„คํ™” ์ดํ•ด์˜ ๊ณผ์ •์„ ๊ฑฐ์ธ ์„คํ™”, ํ™์ˆ˜ ์„คํ™”, ํŠธ๋ฆญ์Šคํ„ฐ๋‹ด์— ์ ์šฉํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด๋ฅผ ์„คํ™”๊ตฐ(็พค)์œผ๋กœ ํ™•์žฅํ•ด ๋‚˜๊ฐˆ ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด์•˜๋‹ค. ์ด์™€ ๊ฐ™์€ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด ๊ต์œก์€ ์„คํ™”๊ฐ€ ๋‹ด์ง€ํ•˜๊ณ  ์žˆ๋Š” ๋…ผ๋ฆฌ์  ์ฒด๊ณ„๋ฅผ ์‚ดํ•„ ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์—ฌ ํ•™์Šต์ž๋“ค์˜ ์„คํ™”์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ์‹ ์žฅ์‹œํ‚ฌ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์‹ ํ™”์†Œ์™€ ๊ด€๋ จ์„ ๊ฐ–๋Š” ์„คํ™”๋“ค์„ ๋ฐœ๊ฒฌํ•˜๊ณ  ์‹ ํ™”์†Œ์™€์˜ ๊ด€๊ณ„ ์†์—์„œ ๊ทธ ์˜๋ฏธ๋ฅผ ํƒ๊ตฌํ•จ์œผ๋กœ์จ ์‹ ํ™”์†Œ์˜ ๊ฐˆ๋ž˜ ๊ฐ„ ์ž‘์šฉ์— ๋Œ€ํ•œ ์‹œ๊ฐ์„ ๊ฒฌ์ง€ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ๋˜ํ•œ ์‹ ํ™”์˜ ์ž‘์šฉ์ด ์„คํ™”์— ๊ทธ์น˜์ง€ ์•Š๊ณ  ํ˜„๋Œ€ ๋ฌธํ™”์—๋„ ์—ฌ์ „ํžˆ ๊ธฐ๋Šฅํ•˜๊ณ  ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜๊ณ , ํ•™์Šต์ž์˜ ํ˜„์žฌ์  ์‚ถ์—์„œ ๋ฌธํ•™์ƒํ™œํ™”๋ฅผ ์‹ค์ฒœํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ์„คํ™” ์ดํ•ด ๊ต์œก์˜ ๋‚ด์šฉ์€ ์‹ ํ™”์†Œ์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ๊ฐ€ ์„คํ™”์— ๋ฐ˜๋ณต์ ์œผ๋กœ ์žฌํ˜„(ๅ†็พ)๋˜๋Š” ๊ฒƒ, ์‹ ํ™”์†Œ์˜ ๋งค๊ฐœํ•ญ์ด ์„คํ™”์— ๋Œ€ํ•˜์—ฌ ๋ณ€์ด๋œ ํ˜•์ƒ์œผ๋กœ ๊ตฌํ˜„๋˜๋Š” ๊ฒƒ, ์‹ ํ™”์†Œ์˜ ์›ํ˜•์ ์ธ ์˜๋ฏธ๊ฐ€ ๊ฐœ๋ณ„ ์„คํ™”๋งˆ๋‹ค ๋งฅ๋ฝ์— ๋งž๊ฒŒ ๋ณ€์šฉ๋˜์–ด ์ƒˆ๋กœ์šด ์˜๋ฏธ๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์œผ๋กœ ์กฐ์ง๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ๊ต์œก ๋‚ด์šฉ์„ ์‹คํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ๊ต์ˆ˜ยทํ•™์Šต ๋ฐฉ๋ฒ•์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ฒซ์งธ, ์ƒํ˜ธํ…์ŠคํŠธ์  ์ฝ๊ธฐ๋ฅผ ํ†ตํ•ด ์„คํ™”๋กœ๋ถ€ํ„ฐ ์‹ ํ™”์  ํ‘œ์ง€๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ณ , ๋‘˜์งธ, ํƒ๊ตฌ ํ•™์Šต์„ ํ†ตํ•ด ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์…‹์งธ, ์†Œ์ง‘๋‹จ ํ˜‘๋™ ํ•™์Šต์„ ํ†ตํ•ด ์‹ ํ™”์†Œ์™€ ๊ด€๊ณ„๋œ ์„คํ™”๊ตฐ์œผ๋กœ ์ดํ•ด์˜ ๋ฒ”์œ„๋ฅผ ํ™•์žฅํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ˜„๋Œ€ ๋ฌธํ™”์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ๊ฐ€๋Šฅํ•จ์„ ๊ตฌ์ฒด์ ์ธ ์‚ฌ๋ก€๋ฅผ ๋“ค์–ด ๋…ผ์˜ํ•˜์˜€๋‹ค. ๊ฐ ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋Š” ํ˜„๋Œ€ ๋ฌธํ™”์˜ ์‚ฌ๋ก€๋กœ ์ƒ์—… ๊ด‘๊ณ , ์žฌ๋‚œ ์˜ํ™”, ์Šคํฌ์ธ ๋ฅผ ๋“ค์–ด ๋ถ„์„ํ•˜์˜€๋‹ค. ํ˜„๋Œ€ ๋ฌธํ™”์— ๋Œ€ํ•œ ์‹ ํ™”์†Œ์˜ ์ž‘์šฉ์„ ์ดํ•ดํ•จ์œผ๋กœ์จ ํ•™์Šต์ž๋Š” ํ˜„๋Œ€ ๋ฌธํ™”์—์„œ์˜ ์‹ ํ™”์†Œ์˜ ์ž‘์šฉ(ไฝœ็”จ)๊ณผ ์—ญ์šฉ(้€†็”จ)์„ ๊ฐ์ง€ํ•˜๊ณ  ๋Œ€์ƒ์˜ ๋ณธ์งˆ์— ํ•œ์ธต ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋‹ค.โ… . ์„œ๋ก  1 1. ๋ฌธ์ œ ์ œ๊ธฐ ๋ฐ ์—ฐ๊ตฌ ๋ชฉ์  1 2. ์—ฐ๊ตฌ์‚ฌ ๊ฒ€ํ†  5 3. ์—ฐ๊ตฌ ๋Œ€์ƒ ๋ฐ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 17 โ…ก. ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด๋ฅผ ์œ„ํ•œ ์ด๋ก ์  ๊ณ ์ฐฐ 30 1. ์‹ ํ™”์†Œ์˜ ๊ฐœ๋… ๋ฐ ์‹ ํ™”์†Œ ์ค‘์‹ฌ ์„คํ™” ์ดํ•ด์˜ ๋ฒ”์œ„ 30 1) ์‹ ํ™”์†Œ์˜ ๊ฐœ๋… ๋ฐ ํŠน์„ฑ 30 โ‘  ์‹ ํ™”์†Œ์˜ ๊ฐœ๋… 30 โ‘ก ์‹ ํ™”์†Œ์˜ ๊ตฌ์กฐ 36 โ‘ข ์‹ ํ™”์†Œ์˜ ์—ญํ•  42 2) ์‹ ํ™”์†Œ์˜ ์ถ”์ถœ๊ณผ ๊ด€๋ จ ์„คํ™”์˜ ์„ ์ • 46 โ‘  ์‹ ํ™”์†Œ์™€ ์ฐฝ์„ธ์‹ ํ™” 46 โ‘ก ์—ฐ๊ตฌ๋Œ€์ƒ ์‹ ํ™”์†Œ์™€ ๊ด€๋ จ ์„คํ™” 49 ใ‰  ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ์™€ 58 ใ‰ก ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ์™€ 62 ใ‰ข ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ์™€ 65 2. ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ์„คํ™” ์ดํ•ด์˜ ๊ณผ์ • 70 1) ๊ด€๋ จ ์„คํ™” ํƒ์ƒ‰์„ ์œ„ํ•œ ์‹ ํ™”์†Œ์˜ ํŒŒ์•… 70 โ‘  ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ํŒŒ์•… 70 โ‘ก ๋งค๊ฐœํ•ญ์˜ ํŒŒ์•… 73 โ‘ข ์›ํ˜•์  ์˜๋ฏธ์˜ ํŒŒ์•… 76 2) ์‹ ํ™”์†Œ์™€ ๊ด€๋ จ ์„คํ™”์˜ ๊ด€๊ณ„ ์ ๊ฒ€ 79 โ‘  ์›ํ˜•์  ์˜๋ฏธ์˜ ์—ฐ๊ด€์„ฑ ๊ฒ€ํ†  79 โ‘ก ๋งค๊ฐœํ•ญ์˜ ์‹ ํ™”์  ํ‘œ์ง€๋กœ์˜ ์ „ํ™˜ ๊ฒ€ํ†  80 โ‘ข ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๋Œ€์‘ ์—ฌ๋ถ€ ํ™•์ธ 82 3) ์‹ ํ™”์†Œ ๋ณ€์ด ๋ถ„์„์— ์˜ํ•œ ๊ด€๋ จ ์„คํ™”์˜ ์˜๋ฏธ ์ดํ•ด 85 โ‘  ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๋ณ€์ด ์ธก๋ฉด 87 โ‘ก ๋งค๊ฐœํ•ญ์˜ ๋ณ€์ด ์ธก๋ฉด 89 โ‘ข ์›ํ˜•์  ์˜๋ฏธ์˜ ๋ณ€์ด ์ธก๋ฉด 90 โ…ข. ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด์˜ ์‹ค์ œ 93 1. ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด 93 1) ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ์˜ ํŒŒ์•… 93 โ‘  ํ˜ผ๋ˆ ๋Œ€ ์งˆ์„œ์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ 93 โ‘ก ๋Œ€๋ฆฝ์˜ ๋งค๊ฐœํ•ญ์œผ๋กœ์„œ ๊ฑฐ์ธ์‹  99 โ‘ข ์›ํ˜•์  ์˜๋ฏธ๋กœ์„œ ์šฐ์ฃผ ์งˆ์„œ์˜ ์‹œ์ž‘ 102 2) ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ์™€ ์˜ ๊ด€๊ณ„ ์ ๊ฒ€ 106 โ‘  ์—์„œ์˜ ์›ํ˜•์  ์˜๋ฏธ ํƒ์ƒ‰ 106 โ‘ก ๊ฑฐ์ธ์‹  ์ค‘์‹ฌ์˜ ํƒ์ƒ‰ 109 โ‘ข ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ์™€ ๊ฐ„ ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๋Œ€์‘ 111 3) ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์˜ ์ดํ•ด 115 โ‘  ํ˜ผ๋ˆ/์งˆ์„œ ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๊ธด์žฅ ๊ฐ•ํ™” 115 โ‘ก ๋งค๊ฐœํ•ญ ๊ฑฐ์ธ์‹ ์˜ ์ฐฝ์กฐ์—ฌ์‹ ์„ฑ ๋ถ€๊ฐ 119 โ‘ข ์ง€ํ˜• ์งˆ์„œ์˜ ์‹œ์ž‘์œผ๋กœ์„œ ์˜ ์ดํ•ด 125 4) ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ๊ฑฐ์ธ ์„คํ™” ์ดํ•ด 129 โ‘  ์ •์น˜์  ํ˜ผ๋ˆ/์ •์น˜์  ์งˆ์„œ์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ๋กœ ๋ณ€์ด 129 โ‘ก ๋งค๊ฐœํ•ญ์˜ '์™•'์œผ๋กœ์˜ ๋Œ€์‘ 131 โ‘ข ์›ํ˜•์  ์˜๋ฏธ๋กœ์„œ '์งˆ์„œ'์˜ ๊ทœ๋ชจ ์ถ•์†Œ 132 2. ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด 136 1) ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ์˜ ํŒŒ์•… 136 โ‘  ์‹  ๋Œ€ ์ธ๊ฐ„์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ 136 โ‘ก ๋Œ€๋ฆฝ์˜ ๋งค๊ฐœํ•ญ์œผ๋กœ์„œ ๋น„๋ฒ”์„ฑ 139 โ‘ข ์›ํ˜•์  ์˜๋ฏธ๋กœ์„œ ์ธ๋ฅ˜์˜ ์‹œ์ž‘ 142 2) ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ์™€ ์˜ ๊ด€๊ณ„ ์ ๊ฒ€ 144 โ‘  ์—์„œ์˜ ์›ํ˜•์  ์˜๋ฏธ ํƒ์ƒ‰ 144 โ‘ก ๋น„๋ฒ”์„ฑ ์ค‘์‹ฌ์˜ ํƒ์ƒ‰ 147 โ‘ข ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ์™€ ๊ฐ„ ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๋Œ€์‘ 151 3) ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์˜ ์ดํ•ด 155 โ‘  ์‹ /์ธ๊ฐ„ ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๊ทน๋‹จ์  ๊ตฌํ˜„ 155 โ‘ก ๋งค๊ฐœํ•ญ ๋น„๋ฒ”์„ฑ์˜ ์ž‘์šฉ ํ™•์žฅ 159 โ‘ข ์ธ๋ฅ˜ ์žฌํƒ„์ƒ์œผ๋กœ์„œ ์˜ ์ดํ•ด 166 4) ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ํ™์ˆ˜ ์„คํ™” ์ดํ•ด 171 โ‘  ์ˆจ์€ ์‹ /๋“œ๋Ÿฌ๋‚œ ์ธ๊ฐ„์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ๋กœ ๋ณ€์ด 171 โ‘ก ๋งค๊ฐœํ•ญ์˜ '๋‚จ๋งคํ˜ผ'์œผ๋กœ์˜ ๋Œ€์‘ 173 โ‘ข ์›ํ˜•์  ์˜๋ฏธ๋กœ์„œ '์‹œ์ž‘'์˜ ์–‘๋ฉด์  ๊ตฌํ˜„ 175 3. ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด 178 1) ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ์˜ ํŒŒ์•… 178 โ‘  ์ž์—ฐ ๋Œ€ ๋ฌธ๋ช…์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ 178 โ‘ก ๋Œ€๋ฆฝ์˜ ๋งค๊ฐœํ•ญ์œผ๋กœ์„œ ์ง€๋žต 185 โ‘ข ์›ํ˜•์  ์˜๋ฏธ๋กœ์„œ ์„๊ฐ€์˜ ์ธ์„ธ ์ฐจ์ง€ 187 2) ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ์™€ ์˜ ๊ด€๊ณ„ ์ ๊ฒ€ 194 โ‘  ์—์„œ์˜ ์›ํ˜•์  ์˜๋ฏธ ํƒ์ƒ‰ 194 โ‘ก ์ง€๋žต ํ–‰์œ„ ์ค‘์‹ฌ์˜ ์˜ ํƒ์ƒ‰ 198 โ‘ข ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ์™€ ๊ฐ„ ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๋Œ€์‘ 201 3) ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์˜ ์ดํ•ด 205 โ‘  ์ž์—ฐ/๋ฌธ๋ช… ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ์ƒ์‘์  ๊ตฌํ˜„ 205 โ‘ก ๋งค๊ฐœํ•ญ ์ง€๋žต์˜ ์ธ๋ฌผํ™” 211 โ‘ข ์„ธ๊ณ„์˜ ๋ถˆ์™„์ „ํ•œ ์ฐจ์ง€๋กœ์„œ ์˜ ์ดํ•ด 215 4) ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ํŠธ๋ฆญ์Šคํ„ฐ๋‹ด ์ดํ•ด 220 โ‘  ํ† ์ฐฉ ์„ธ๋ ฅ/๋„๋ž˜ ๋ฌธ๋ช…์˜ ๋Œ€๋ฆฝ ๊ตฌ์กฐ๋กœ ๋ณ€์ด 220 โ‘ก ๋งค๊ฐœํ•ญ์˜ '๊ฑฐ์ง“๋ง'๋กœ์˜ ๋Œ€์‘ 222 โ‘ข ์›ํ˜•์  ์˜๋ฏธ๋กœ์„œ '์ฐจ์ง€'์˜ ๋Œ€์ƒ ๋‹ค์–‘ํ™” 225 โ…ฃ. ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด ๊ต์œก์˜ ์„ค๊ณ„ 228 1. ์‹ ํ™”์†Œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ์„คํ™” ์ดํ•ด์˜ ๊ต์œก์  ๋ชฉํ‘œ 228 1) ์‹ ํ™”์†Œ๋ฅผ ํ™œ์šฉํ•œ ์„คํ™” ์ดํ•ด ๋Šฅ๋ ฅ์˜ ์‹ ์žฅ 228 2) ์‹ ํ™”์†Œ์˜ ๊ฐˆ๋ž˜ ๊ฐ„ ์ž‘์šฉ์— ๋Œ€ํ•œ ์‹œ๊ฐ์˜ ๊ฒฌ์ง€ 230 3) ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ๋ฌธํ™” ์ดํ•ด๋ฅผ ํ†ตํ•œ ๋ฌธํ•™์ƒํ™œํ™”์˜ ์‹ค์ฒœ 232 2. ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด ๊ต์œก์˜ ์‹ค์ œ 236 1) ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด ๊ต์œก์˜ ๋‚ด์šฉ 236 โ‘  ๋Œ€๋ฆฝ ๊ตฌ์กฐ์˜ ๋ฐ˜๋ณต์  ์žฌํ˜„ 236 โ‘ก ๋งค๊ฐœํ•ญ์˜ ๋ณ€์ด 238 โ‘ข ๋งฅ๋ฝ์— ๋”ฐ๋ฅธ ์›ํ˜•์  ์˜๋ฏธ์˜ ๋ณ€์šฉ 242 2) ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด ๊ต์œก์˜ ๋ฐฉ๋ฒ• 246 โ‘  ์ƒํ˜ธํ…์ŠคํŠธ์  ์ฝ๊ธฐ๋ฅผ ํ†ตํ•œ ์‹ ํ™”์  ํ‘œ์ง€์˜ ๋ฐœ๊ฒฌ 246 โ‘ก ํƒ๊ตฌ ํ•™์Šต์„ ํ†ตํ•œ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์„คํ™” ์ดํ•ด 248 โ‘ข ์†Œ์ง‘๋‹จ ํ˜‘๋™ ํ•™์Šต์„ ํ†ตํ•œ ์„คํ™”๊ตฐ ์ดํ•ด๋กœ์˜ ํ™•์žฅ 252 3) ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ํ˜„๋Œ€ ๋ฌธํ™” ์ดํ•ด๋กœ์˜ ์ ์šฉ 255 โ‘  ์„ธ๊ณ„์ฐฝ์กฐ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์ƒ์—… ๊ด‘๊ณ  ์ดํ•ด 255 โ‘ก ์ธ๋ฅ˜ํƒ„์ƒ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์žฌ๋‚œ ์˜ํ™” ์ดํ•ด 259 โ‘ข ์ธ์„ธ์ฐจ์ง€๊ฒฝ์Ÿ ์‹ ํ™”์†Œ ์ค‘์‹ฌ์˜ ์Šคํฌ์ธ  ์ดํ•ด 263 โ…ค. ๊ฒฐ๋ก  268 ์ฐธ๊ณ ๋ฌธํ—Œ 273 Abstract 285Docto

    ๊ตญ๊ณต๋ฆฝ๋Œ€ํ•™๋„์„œ๊ด€ ํšŒ์› ๋ช…๋ถ€

    Get PDF
    • โ€ฆ
    corecore