8,520 research outputs found

    Be Cool! Staying Open Minded About Climate Policy Development

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    The economic opportunities and constraints of green growth

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    ๋…ธํŠธ : Asie.Visions is an electronic publication dedicated to Asia. With contributions by French and international experts, Asie.Visions deals with economic, strategic, and political issues. The collection aims to contribute to the global debate and to a better understanding of the regional issues at stake. It is published in French and/or in English and upholds Ifriโ€™s standards of quality (editing and anonymous peerreview)

    Investing in the Clean Trillion: Closing the Clean Energy Investment Gap

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    In 2010 world governments agreed to limit the increase in global temperature to two degrees Celsius (2 ยฐC) above pre-industrial levels to avoid the worst impacts of climate change. To have an 80 percent chance of maintaining this 2 ยฐC limit, the IEA estimates an additional 36trillionincleanenergyinvestmentisneededthrough2050โˆ’โˆ’oranaverageof36 trillion in clean energy investment is needed through 2050 -- or an average of 1 trillion more per year compared to a "business as usual" scenario over the next 36 years.This report provides 10 recommendations for investors, companies and policymakers to increase annual global investment in clean energy to at least $1 trillion by 2030 -- roughly a four-fold jump from current investment levels

    Developing future energy performance standards for UK housing: The St Nicholas Court project โ€“ Part 1

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    This paper (and Part 2, to appear in the next issue) set out the results of a housing field trial designed to evaluate the impact of an enhanced energy performance standard for dwellings. The project was designed to inform the next review of Part L of the Building Regulations for England and Wales, which, following the publication of the UK government's white paper on energy policy, is expected in 2005. The project explores the implications of an enhanced standard in the context of timber frame construction. Although for programming reasons it was necessary to terminate the research project at the end of the design phase, the results suggest that the standard investigated is well within the capacity of the industry but it was clear that the whole supply chain will need to take a positive approach to the development of new solutions. The secret to a smooth and cost optimised transition is for the necessary development work to begin immediately, not when regulation changes. ยฉ 2003, MCB UP Limite

    Evaluating the Transition Towards Post-Carbon Cities: A Literature Review

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    To achieve the new European targets concerning CO2 emission reduction, the concept of a post-carbon city has been promoted, which is focused on low-energy and low-emission buildings provided with intelligent heating and cooling systems, electric and hybrid cars, and better public transport. This paradigm entails the inclusion of aspects not strictly related to energy exploitation but referring to environmental, social, and economic domains, such as improvement in local energy security, peopleโ€™s opinion on different energy solutions, economic co-benefits for private users, environmental externalities, and so on. In this domain, it is of particular importance to provide the decision makers with evaluation tools able to consider the complexity of the impacts, thus leading to the choice of the most sustainable solutions. The paper aims to investigate the scientific literature in the context of evaluation frameworks for supporting decision problems related to the energy transition. The review is carried out through the scientific database SCOPUS. The analysis allows for systematizing the contributions according to the main families of evaluation methodologies, discussing to what extent they can be useful in real-world applications. The paper also proposes emerging trends and innovative research lines in the domain of energy planning and urban management. While the energy transition is an important trend, the analysis showed that few studies were conducted on the evaluation of projects, plans, and policies that aim to reach post-carbon targets. The scales of application refer mainly to global or national levels, while few studies have been developed at the district level. Life cycle thinking techniques, such as life cycle assessment and cost-benefit analysis, were widely used in this research field

    ์ „๊ณผ์ • ๋ถ„์„์„ ํ†ตํ•œ ์ƒˆ๋กœ์šด ๊ฒฝ๋Ÿ‰ ์ž๋™์ฐจ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์— ๋Œ€ํ•œ ํ‰๊ฐ€ : ํ•œ๊ตญ์˜ ์‚ฌ๋ก€ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ์†กํ•œํ˜ธ.์ „์„ธ๊ณ„์ ์œผ๋กœ ์ง€๊ตฌ ์˜จ๋‚œํ™” ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•˜๋Š” ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๊ทœ์ œ๊ฐ€ ์‹œํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘์—์„œ๋„ ๋„๋กœ ์ˆ˜์†ก ๋ถ„์•ผ์—์„œ๋Š” ์—ฐ๋น„ ๊ทœ์ œ๋‚˜ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ๋ฅผ ํ†ตํ•ด ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์„ ๊ฐ์ถ•์‹œํ‚ค๊ณ ์ž ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ทœ์ œ์˜ ํŠน์ง•์€ ์ „๊ธฐ ์ฃผํ–‰ ๋ชจ๋“œ์˜ ์ž๋™์ฐจ์— ๋Œ€ํ•ด ๋ฐฐ๊ธฐ๊ตฌ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์„ 0์œผ๋กœ ์‚ฐ์ •ํ•˜๋ฉฐ, ์ด์— ๋”๋ถˆ์–ด ์ถ”๊ฐ€์ ์ธ ์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ๋ถ€์—ฌํ•œ๋‹ค๋Š” ์ ์ด๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์ „๊ธฐ์ž๋™์ฐจ๊ฐ€ ์ฐจ๋Ÿ‰ ์ฃผํ–‰ ์‹œ ์˜จ์‹ค๊ฐ€์Šค๋ฅผ ๋ฐฐ์ถœํ•˜์ง€ ์•Š์ง€๋งŒ, ์ฐจ๋Ÿ‰ ์ฃผํ–‰์„ ์œ„ํ•ด ํ•„์š”ํ•œ ์ „๊ธฐ๋ฅผ ์–ป๊ธฐ ์œ„ํ•œ ๊ณผ์ •์—์„œ ์˜จ์‹ค๊ฐ€์Šค๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋Ÿฌํ•œ ์ƒ๋ฅ˜ ๊ณผ์ •์˜ ์˜จ์‹ค๊ฐ€์Šค๋ฅผ ๋ฌด์‹œํ•œ ์ฑ„ ์ „๊ธฐ ์ฃผํ–‰ ๋ชจ๋“œ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์„ 0์œผ๋กœ ์‚ฐ์ •ํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•ด ์—ฌ๋Ÿฌ ๋…ผ์˜๊ฐ€ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ ๋“ค์–ด ์ด๋Ÿฌํ•œ ๋…ผ์˜๋Š” ๋”์šฑ ๊ตฌ์ฒดํ™”๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์ผ๋ณธ์˜ ์ƒˆ๋กœ์šด ์—ฐ๋น„ ๊ทœ์ œ์—์„œ ์—ฐ๋ฃŒ ์ƒ์‚ฐ ๋‹จ๊ณ„์˜ ํšจ์œจ์„ ์ด์šฉํ•˜์—ฌ ๋ณด์ •ํ•œ ์ž๋™์ฐจ ์—ฐ๋น„๋ฅผ ์‚ฌ์šฉํ•  ๊ฒƒ์ด๋ผ๊ณ  ๋ฐœํ‘œํ•˜์˜€๋‹ค. ์ด์— ๋”ฐ๋ผ ์šฐ๋ฆฌ๋‚˜๋ผ์—์„œ๋„ ์—ฐ๋น„ ๊ทœ์ œ์™€ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์— ์ „๊ณผ์ • ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ์ ์šฉํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•ด ๊ทธ ์˜ํ–ฅ์„ ์˜ˆ์ธกํ•˜๊ณ  ํ‰๊ฐ€ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด์ฒ˜๋Ÿผ ์—ฐ๋ฃŒ์˜ ์ƒ์‚ฐ ๊ณผ์ •, ํŠนํžˆ ์ „๊ธฐ์˜ ์ƒ๋ฅ˜ ๊ณผ์ •์— ๋Œ€ํ•œ ๊ณ ๋ ค์˜ ํ•„์š”์„ฑ์ด ์ด์Šˆํ™”๋˜๋Š” ๊ฒƒ์—๋Š” ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ์ด์œ ๊ฐ€ ์žˆ๋‹ค. ์ฒซ ์งธ๋Š” ๋ฏธ๋ž˜์— ์ „๊ธฐ์ž๋™์ฐจ์˜ ์ˆ˜์š”๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์ „๊ธฐ์˜ ์ˆ˜์š”๊ฐ€ ์ฆ๊ฐ€ํ•  ๊ฒƒ์ด๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋‘˜ ์งธ๋Š” ์ „๊ธฐ ์ƒ์‚ฐ ๊ณผ์ •์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์€ ๋ฐœ์ „์›์˜ ์ข…๋ฅ˜์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง€๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ์ „๊ณผ์ • ๋ถ„์„์€ ๋‹ค์–‘ํ•œ ์—ฐ๋ฃŒ์™€ ์ž๋™์ฐจ์˜ ์นœํ™˜๊ฒฝ์„ฑ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. Well-to-wheel (WTW) ๋ถ„์„์€ ์ž๋™์ฐจ ์—ฐ๋ฃŒ์˜ ์ƒ์•  ์ „๊ณผ์ • ๋ถ„์„์„ ์˜๋ฏธํ•˜๋ฉฐ, ์›์œ  ์‚ฐ์ง€(Well)๋ถ€ํ„ฐ ์ž๋™์ฐจ ์ฃผํ–‰ ๊ณผ์ • (Wheel)์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ์ „์ฒด ๊ณผ์ •์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์ „๊ธฐ์ฐจ์˜ ์ „๊ณผ์ •์—๋Š” ์ž๋™์ฐจ ์ฃผํ–‰ ๊ณผ์ •๊ณผ ๋ฐœ์ „ ๊ณผ์ •, ๊ทธ๋ฆฌ๊ณ  ๋ฐœ์ „ ์›๋ฃŒ์˜ ์ƒ์‚ฐ ๊ณผ์ •์ด ํฌํ•จ๋˜์–ด ์žˆ์œผ๋ฉฐ, ๊ณต์ •ํ•œ ๋น„๊ต๋ฅผ ์œ„ํ•˜์—ฌ ๋‚ด์—ฐ๊ธฐ๊ด€ ์ž๋™์ฐจ๋„ ์ „๊ธฐ์ฐจ์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ํœ˜๋ฐœ์œ , ๋””์ ค ๋“ฑ์˜ ์—ฐ๋ฃŒ ์ƒ์‚ฐ์— ๊ด€ํ•œ ๋ชจ๋“  ๊ณผ์ •์ด ํฌํ•จ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž๋™์ฐจ ์—ฐ๋ฃŒ์˜ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ๊ฐ’์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ทœ์ œํ•˜๋Š” ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ๋ฅผ ์ œ์‹œํ•˜๊ณ , ์ƒˆ๋กœ์šด ๊ทœ์ œ๊ฐ€ ์ž๋™์ฐจ ์‹œ์žฅ๊ณผ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์—๊ฒŒ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ž๋™์ฐจ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ๋ฅผ ํ†ตํ•ด ๊ตญ๊ฐ€์˜ ์—๋„ˆ์ง€ ์ •์ฑ…์ด ์ž๋™์ฐจ ์ •์ฑ…๊ณผ ์—ฐ๊ณ„๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์—ฐ๊ตฌ ์ˆœ์„œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋จผ์ € ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ž๋™์ฐจ ์—ฐ๋ฃŒ์— ๋Œ€ํ•œ ์ „๊ณผ์ • ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ๋ฏธ๋ž˜์˜ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์„ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ „๊ณผ์ • ๊ทœ์ œ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ๊ทœ์ œ์น˜์™€ ๋ฒ”์น™๊ธˆ์„ ์„ค์ •ํ•˜๊ณ , ํ–‰์œ„์ž ๊ธฐ๋ฐ˜ ๋ชจํ˜•์„ ๋ฐ”ํƒ•์œผ๋กœ ์ •๋ถ€์™€ ์†Œ๋น„์ž, ์ž๋™์ฐจ ์ œ์ž‘์‚ฌ ๊ฐ„์˜ ์ƒํ˜ธ ์˜ํ–ฅ์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด์„œ ์–ป์€ ์ž๋™์ฐจ ์‹œ์žฅ ์˜ˆ์ธก ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ์ž๋™์ฐจ ์ œ์ž‘์‚ฌ๊ฐ€ ์ œํ’ˆ์˜ ๊ฐ€๊ฒฉ์„ ์–ด๋–ป๊ฒŒ ์„ค์ •ํ•  ๊ฒƒ์ธ์ง€ ์†Œ๋น„์ž๋Š” ์–ด๋– ํ•œ ์ œํ’ˆ์„ ๊ตฌ๋งคํ•  ๊ฒƒ์ธ์ง€๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ „๊ณผ์ • ๋ถ„์„ ๊ฒฐ๊ณผ์™€ ์ž๋™์ฐจ ์‹œ์žฅ ์˜ˆ์ธก ๋ชจ๋ธ์— ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ๋ฅผ ์ ์šฉํ•˜์—ฌ ๋‚˜ํƒ€๋‚˜๋Š” ์‚ฌํšŒ์  ํ˜„์ƒ์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„ ๋ฒ”์œ„๋Š” 2030๋…„์˜ ์ค€์ค‘ํ˜•์ฐจ ์‹œ์žฅ์„ ๊ฐ€์ •ํ•˜์˜€๋‹ค. ์ด์— ๋”ฐ๋ผ 2030๋…„์˜ ์—ฐ๊ฐ„ ์ค€์ค‘ํ˜• ์ž๋™์ฐจ ํŒ๋งค๋Ÿ‰์€ 50๋งŒ ๋Œ€๋กœ ์ถ”์‚ฐํ•˜์˜€๋‹ค. ์ค€์ค‘ํ˜•์ฐจ ์‹œ์žฅ์˜ ์ฃผ ์†Œ๋น„์ž๋Š” ๊ฐ€๊ฒฉ์— ๋ฏผ๊ฐํ•˜๋ฉฐ, ์ค€์ค‘ํ˜•์ฐจ๋Š” ์ „๊ธฐ ์ž๋™์ฐจ์˜ ๊ธฐ์ˆ ์„ ์ ์šฉํ•˜๊ธฐ ์šฉ์ดํ•œ ํŠน์ง•์ด ์žˆ๋‹ค. ๋˜ํ•œ ๋ณธ ๋ถ„์„์˜ ์ž๋™์ฐจ ์‹œ์žฅ์—๋Š” ํœ˜๋ฐœ์œ  ๋‚ด์—ฐ๊ธฐ๊ด€ ์ž๋™์ฐจ, ํœ˜๋ฐœ์œ  ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ, ํœ˜๋ฐœ์œ  ํ”Œ๋Ÿฌ๊ทธ์ธ ์ž๋™์ฐจ์™€ ์ฃผํ–‰๊ฐ€๋Šฅ๊ฑฐ๋ฆฌ 200 ๋งˆ์ผ์˜ ์ „๊ธฐ์ž๋™์ฐจ๋งŒ ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•˜์˜€๋‹ค. ์ž๋™์ฐจ ์˜จ์‹ค๊ฐ€์Šค ์ „๊ณผ์ • ๋ถ„์„์€ ์›๋ฃŒ ์ถ”์ถœ ๋‹จ๊ณ„๋ถ€ํ„ฐ ์ž๋™์ฐจ์— ์ฃผ์œ  ๋˜๋Š” ์ถฉ์ „ํ•˜๊ธฐ๊นŒ์ง€์˜ ๊ณผ์ •์„ ์˜๋ฏธํ•˜๋Š” Well-to-Tank (WTT) ๊ณผ์ •๊ณผ ์ž๋™์ฐจ ์ฃผํ–‰ ๊ณผ์ •์„ ์˜๋ฏธํ•˜๋Š” Tank-to-Wheel(TTW) ๊ณผ์ •์œผ๋กœ ๋‚˜๋‰œ๋‹ค. ๋ถ„์„์„ ์œ„ํ•ด ๋ฏธ๊ตญ ์•„๋ฅด๊ณค ๊ตญ๊ฐ€ ์—ฐ๊ตฌ์†Œ์˜ ์ „๊ณผ์ • ๋ถ„์„ ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•œ๊ตญ์˜ ์‹ค์ •์— ๋งž๋„๋ก ์ž…๋ ฅ๋ฐ์ดํ„ฐ์™€ ์—ฐ๋ฃŒ ์ƒ์‚ฐ ๊ฒฝ๋กœ๋ฅผ ๋ชจ๋‘ ์ˆ˜์ •ํ•˜์—ฌ, ํ•œ๊ตญ์—์„œ ์‚ฌ์šฉํ•˜๋Š” ์—ฐ๋ฃŒ์— ๋Œ€ํ•œ ์ „๊ณผ์ • ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๋‹ค. 2030๋…„์˜ ์ „๊ณผ์ • ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์š”์†Œ๋Š” ๋ฏธ๋ž˜์˜ ์—ฐ๋น„์™€ ๋ฐœ์ „ ๋ฏน์Šค์ด๋‹ค. ์—ฌ๋Ÿฌ ๊ธฐ๊ด€์˜ ๋ฏธ๋ž˜ ์˜ˆ์ธก ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด ๋‚ด์—ฐ๊ธฐ๊ด€ ์ž๋™์ฐจ์˜ ์—ฐ๋น„ ํ–ฅ์ƒ์œจ์€ ์ „๊ธฐ ์ž๋™์ฐจ์˜ ์ „๋น„ ํ–ฅ์ƒ์œจ๋ณด๋‹ค ๋†’์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ ๋‚˜๋ผ์˜ 2030๋…„ ์ „๋ ฅ ์ˆ˜๊ธ‰๊ณ„ํš์€ ์›์ž๋ ฅ ๋ฐœ์ „๋Ÿ‰์˜ ๊ฐ์ถ•๊ณผ ์‹ ์žฌ์ƒ ์—๋„ˆ์ง€ ๋ฐœ์ „๋Ÿ‰์˜ ์ฆ์ถ•์ด ํ•ต์‹ฌ ๋ชฉํ‘œ์ด๋‹ค. 2030๋…„์˜ ์ „๊ณผ์ • ๋ถ„์„ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ํœ˜๋ฐœ์œ  ์ž๋™์ฐจ, ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ, ํ”Œ๋Ÿฌ๊ทธ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ, ์ „๊ธฐ์ž๋™์ฐจ์— ๋Œ€ํ•ด ๋จผ์ € ์ž๋™์ฐจ ์ฃผํ–‰ ๊ณผ์ •์—์„œ ๋ฐฐ์ถœ๋˜๋Š” ์˜จ์‹ค๊ฐ€์Šค๋Š” ๊ฐ๊ฐ 138.7, 94.6, 13.2, 0 g-CO2-eq./km ์ˆœ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์€ 4๊ฐ€์ง€ ์ž๋™์ฐจ์— ๋Œ€ํ•ด 160.9, 109.9, 89.3, 85.0 g-CO2-eq./km ์ˆœ์œผ๋กœ ๊ณ„์‚ฐ๋˜์—ˆ๋‹ค. ํœ˜๋ฐœ์œ  ์ž๋™์ฐจ์™€ ์ „๊ธฐ ์ž๋™์ฐจ์˜ ์ฃผํ–‰๊ณผ์ •์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ์ฐจ์ด๋Š” 138.7 g-CO2-eq./km์ด์ง€๋งŒ, ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ์ฐจ์ด๋Š” 75.9 g-CO2-eq./km ์ด๋ฉฐ, ๋‘ ์ฐจ์ข… ์‚ฌ์ด์˜ ๊ฐ„๊ทน์ด ์ขํ˜€์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์„ ์ „๊ณผ์ •์ ์œผ๋กœ ๊ณ„์‚ฐํ•˜์˜€์„ ๋•Œ, ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ์™€ ํ”Œ๋Ÿฌ๊ทธ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ์ฐจ์ด๊ฐ€ ํฌ๊ฒŒ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ํ–‰์œ„์ž ๊ธฐ๋ฐ˜ ๋ชจํ˜•์„ ์ด์šฉํ•˜์—ฌ 2030๋…„์˜ ์ž๋™์ฐจ ์‹œ์žฅ์„ ์˜ˆ์ธกํ•˜๋Š” ๋ชจ๋ธ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ํ–‰์œ„์ž ๊ธฐ๋ฐ˜ ๋ชจํ˜•์€ ์‚ฌํšŒ๊ฒฝ์ œ์  ํ™˜๊ฒฝ ์†์—์„œ ์„œ๋กœ ์˜ํ–ฅ์„ ์ฃผ๊ณ  ๋ฐ›๋Š” ํ–‰์œ„์ž๋“ค์˜ ์˜์‚ฌ ๊ฒฐ์ •์„ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์— ์‚ฌ์šฉ๋˜๋Š” ๋ถ„์„ ๊ธฐ๋ฒ•์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž๋™์ฐจ ์‹œ์žฅ์— ์—ฐ๊ด€๋œ ํ–‰์œ„์ž๋กœ ์ •๋ถ€์™€ ์†Œ๋น„์ž, ์ž๋™์ฐจ ์ œ์ž‘์‚ฌ๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ๋จผ์ € ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ž๋™์ฐจ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ๋ฅผ ์‚ดํŽด๋ณด๋ฉด 2020๋…„์˜ ๊ทœ์ œ์น˜๊นŒ์ง€ ๋ฐœํ‘œ๋˜์—ˆ์œผ๋ฉฐ, 2030๋…„์— ๋Œ€ํ•ด์„œ๋Š” ๋ฐœํ‘œ๋œ ๋ฐ” ์—†๋‹ค. ๋”ฐ๋ผ์„œ ๋™์ผ ์„ ์ƒ์˜ ๋น„๊ต๋ฅผ ์œ„ํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฐ€์ •์„ ํ†ตํ•ด ์ •๋ถ€์˜ 2030๋…„ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์˜ ๊ทœ์ œ์น˜์™€ ๋ฒ”์น™๊ธˆ ์š”์œจ์„ ๊ฒฐ์ •ํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ๊ทœ์ œ ๋ฐฉ๋ฒ•์— ๋”ฐ๋ฅธ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์˜ ๊ทœ์ œ์น˜๋Š” 62.2 g/km์ด๋ฉฐ, ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์˜ ๊ทœ์ œ์น˜๋Š” 109.2 g/km์ด๋‹ค. ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์น˜๋ฅผ ๋‹ฌ์„ฑํ•˜์ง€ ๋ชปํ•  ๊ฒฝ์šฐ์— ๋Œ€ํ•œ ๋ฒ”์น™๊ธˆ ์š”์œจ์€ ํ˜„ํ–‰ ๋ฒ•์˜ 2022๋…„ ์ดํ›„ ์‹œํ–‰์•ˆ์„ ์ฐธ๊ณ ํ•˜์—ฌ 1 g/km ์ดˆ๊ณผ ์‹œ 5 ๋งŒ์›์œผ๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์†Œ๋น„์ž์™€ ์ž๋™์ฐจ ์ œ์ž‘์‚ฌ๋Š” ๊ฐ๊ฐ ์ž๋™์ฐจ ๊ตฌ๋งค์— ๋”ฐ๋ฅธ ํšจ์šฉ๊ณผ ์ž๋™์ฐจ ํŒ๋งค์— ์˜ํ•œ ์ˆœ์ด์ต์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ์˜์‚ฌ๊ฒฐ์ •์„ ํ•œ๋‹ค. ์†Œ๋น„์ž๋Š” ์ž๋™์ฐจ์˜ ๊ฐ€๊ฒฉ๊ณผ ์—ฐ๋น„, ์ฃผ์œ ๋น„, ์ถฉ์ „์‹œ๊ฐ„, ์ด์ฃผํ–‰๊ฑฐ๋ฆฌ ๋“ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ํšจ์šฉ์„ ํŒ๋‹จํ•˜๋ฉฐ ์ œํ’ˆ์˜ ํšจ์šฉ์ด ๋†’์„์ˆ˜๋ก ๊ตฌ๋งค ํ™•๋ฅ ์ด ๋†’์•„์ง„๋‹ค. 4๊ฐ€์ง€ ์ž๋™์ฐจ์— ๋Œ€ํ•œ ์†Œ๋น„์ž์˜ ๊ตฌ๋งค ํ™•๋ฅ ์€ ์ž๋™์ฐจ์˜ ํŒ๋งค์œจ๊ณผ ๊ฐ™๋‹ค๊ณ  ๊ฐ€์ •ํ•˜์˜€๋‹ค. ์ž๋™์ฐจ ์ œ์ž‘์‚ฌ์˜ ํŒ๋งค ์ˆœ์ด์ต์€ ํŒ๋งค๊ฐ€์™€ ์ƒ์‚ฐ ๋‹จ๊ฐ€, ๊ทœ์ œ ๋น„์šฉ, ์—ฐ๊ตฌ ๋ฐ ์ƒ์‚ฐ ์‹œ์„ค ๋น„์šฉ์— ๋”ฐ๋ผ ๊ฒฐ์ •๋œ๋‹ค. ์ด ์ค‘์—์„œ ์ž๋™์ฐจ์˜ ํŒ๋งค๊ฐ€๋ฅผ ๊ฒฐ์ •ํ•  ๋•Œ์—๋Š” ๊ฐ€๊ฒฉ์ด ์˜ฌ๋ผ๊ฐˆ์ˆ˜๋ก ํŒ๋งค ์ด์ต์ด ์ฆ๊ฐ€ํ•˜์ง€๋งŒ ์†Œ๋น„์ž์˜ ์ดํƒˆ์ด ์ผ์–ด๋‚˜ ํŒ๋งค์œจ์ด ๊ฐ์†Œํ•  ์ˆ˜ ์žˆ๋‹ค. ์†Œ๋น„์ž์™€ ์ž๋™์ฐจ ์ œ์ž‘์‚ฌ ๊ฐ„์˜ ์ƒํ˜ธ ์˜ํ–ฅ์— ๋”ฐ๋ผ ์ตœ์ ์˜ ์ œํ’ˆ ๊ฐ€๊ฒฉ๊ณผ ์ด์— ๋”ฐ๋ฅธ ์ž๋™์ฐจ ์‹œ์žฅ์˜ ์ ์œ ์œจ์„ ๊ณ„์‚ฐํ•˜๋Š” ๋ชจ๋ธ์„ ์ž‘์„ฑํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์–ป์€ 2030๋…„ ์ค€์ค‘ํ˜• ์ž๋™์ฐจ ํŒ๋งค ๋น„์œจ์€ ๊ธฐ์กด์˜ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ๊ฐ€ ์ ์šฉ๋œ๋‹ค๊ณ  ๊ฐ€์ •ํ•˜์˜€์„ ๋•Œ, ๋‚ด์—ฐ๊ธฐ๊ด€ 27.7%, ํ•˜์ด๋ธŒ๋ฆฌ๋“œ 29.3%, ํ”Œ๋Ÿฌ๊ทธ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ 10.4%, ์ „๊ธฐ์ฐจ 32.6%์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์— ์ „๊ณผ์ • ๋ฐฐ์ถœ๋Ÿ‰์„ ์ ์šฉํ•˜์—ฌ ์‹œ์ค‘์˜ ์ž๋™์ฐจ์— ๋Œ€ํ•œ ๊ทœ์ œ๋ฅผ ์‹œํ–‰ํ•  ๋•Œ ๋‚˜ํƒ€๋‚˜๊ฒŒ ๋  ์˜ํ–ฅ์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๋ฃŒ ์ƒ์‚ฐ ๋‹จ๊ณ„์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์— ์˜ํ•œ ์˜ํ–ฅ์„ ํšจ๊ณผ์ ์œผ๋กœ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ•ด๋‹น ์˜ํ–ฅ์ด ๋‘๋“œ๋Ÿฌ์ง€๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ์ „๊ธฐ ๋ฐœ์ „ ๊ณผ์ •์— ๋Œ€ํ•ด ์ง‘์ค‘ํ•˜์—ฌ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋ฐœ์ „ ์›๋ฃŒ์˜ ์ƒ์‚ฐ ๊ณผ์ •๊ณผ ๋ฐœ์ „, ์†ก๋ฐฐ์ „ ํšจ์œจ์„ ๋ชจ๋‘ ํฌํ•จํ•œ ์ „๊ธฐ์˜ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์€ 2030๋…„์˜ ์ „๋ ฅ ์ˆ˜๊ธ‰๊ณ„ํš์„ ๊ธฐ์ค€์œผ๋กœ 562 g/kWh์ด๋‹ค. ์ „๊ธฐ์˜ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด 0๋ถ€ํ„ฐ 1068 g/kWh๊นŒ์ง€ ๋ณ€ํ™”ํ•  ๋•Œ, ์ฐจ์ข…์— ๋”ฐ๋ฅธ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰๊ณผ ์ด๋กœ ์ธํ•œ ์ž๋™์ฐจ ์‹œ์žฅ์˜ ์ œํ’ˆ ๊ฐ€๊ฒฉ๊ณผ ํŒ๋งค์œจ, ์†Œ๋น„์ž์˜ ์ด ์†Œ์œ  ๋น„์šฉ, ์ •๋ถ€์˜ ์ด ์ˆ˜์ž…์ด ์–ด๋–ป๊ฒŒ ๋‹ฌ๋ผ์ง€๋Š”์ง€ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฃผํ–‰ ๊ณผ์ •์—์„œ ์ฃผ๋กœ ์ „๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ํ”Œ๋Ÿฌ๊ทธ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ์™€ ์ „๊ธฐ ์ž๋™์ฐจ๋Š” ๋ฐœ์ „๋‹จ์˜ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ๋ณ€ํ™”์— ํฐ ์˜ํ–ฅ์„ ๋ฐ›๊ฒŒ ๋œ๋‹ค. ์ „๊ธฐ์˜ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด 700 g/kWh์— ์ด๋ฅด๋ฉด ์ „๊ธฐ์ฐจ์˜ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์€ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ์™€ ๋น„์Šทํ•ด์ง„๋‹ค. ๋˜ํ•œ ์„ํƒ„ 100%์˜ ์ „๋ ฅ ๋ฏน์Šค์—์„œ ์ „๊ธฐ์ฐจ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์€ ํœ˜๋ฐœ์œ  ์ž๋™์ฐจ์˜ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰๊ณผ ๊ฐ™๋‹ค. ๊ธฐ์กด์˜ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ ์ž๋™์ฐจ ์ฃผํ–‰ ๋‹จ๊ณ„์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์— ๋Œ€ํ•ด์„œ๋งŒ ํ‰๊ฐ€ํ•˜์˜€์„ ๋•Œ์—๋Š” ๋ฐœ์ „ ๋ฏน์Šค๊ฐ€ ๋‹ฌ๋ผ์ง€๋”๋ผ๋„ ์ž๋™์ฐจ์—์„œ ๋ฐฐ์ถœ๋˜๋Š” ์˜จ์‹ค๊ฐ€์Šค์—๋Š” ์ „ํ˜€ ์˜ํ–ฅ์ด ์—†๋‹ค. ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ์ž๋™์ฐจ์˜ ์ œํ’ˆ ๊ฐ€๊ฒฉ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ฒŒ ๋œ๋‹ค. ์ž๋™์ฐจ ์ œํ’ˆ ๊ฐ€๊ฒฉ์—๋Š” ๊ทœ์ œ ๋น„์šฉ์ด ํฌํ•จ๋˜์–ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ๋†’์„์ˆ˜๋ก ๋ฒ”์น™๊ธˆ์œผ๋กœ ์ธํ•ด ๊ฐ€๊ฒฉ์ด ๋†’์•„์ง€๋ฉฐ, ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ๋‚ฎ์„์ˆ˜๋ก ํƒ„์†Œ ๋ฐฐ์ถœ๊ถŒ ๊ฑฐ๋ž˜์ œ์— ๋”ฐ๋ฅธ ๋ณด์ƒ์œผ๋กœ ์ œํ’ˆ ๊ฐ€๊ฒฉ์ด ๋‚ฎ์•„์ง„๋‹ค. ์ด๋Š” ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ ๋ฐœ์ „ ๋ฏน์Šค์˜ ๋ณ€ํ™”์— ๋”ฐ๋ผ ์ „๊ธฐ์ฐจ์˜ ๊ฐ€๊ฒฉ์ด ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ๋ฐœ์ „๋‹จ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ์ž‘์„์ˆ˜๋ก ์ „๊ธฐ์ฐจ์˜ ๊ฐ€๊ฒฉ์ด ๋” ๋‚ฎ์•„์ ธ, ์‹œ์žฅ ์ ์œ ์œจ์ด ๋†’์•„์งˆ ๊ฒƒ์ด๋ฉฐ, ๋ฐœ์ „๋‹จ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ์ปค์ง€๋ฉด ์ „๊ธฐ์ฐจ์˜ ๊ฐ€๊ฒฉ์ด ์ƒ์Šนํ•˜๋ฉด์„œ ์‹œ์žฅ ์ ์œ ์œจ์ด ๋‚ฎ์•„์ง€๊ฒŒ ๋œ๋‹ค. ์ฆ‰, ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ๋Š” ์—ฐ๋ฃŒ์˜ ์ƒ์‚ฐ ๊ณผ์ •์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ๋ณ€ํ™”๊ฐ€ ์ž๋™์ฐจ ์‹œ์žฅ์˜ ์ ์œ ์œจ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ์ƒˆ๋กœ์šด ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ ์ค€์ค‘ํ˜• ์ž๋™์ฐจ ์‹œ์žฅ์˜ ํŒ๋งค ๋น„์œจ์€ ๋‚ด์—ฐ๊ธฐ๊ด€ 25.4~37.2%, ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ 28.3~41.5%, ํ”Œ๋Ÿฌ๊ทธ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ž๋™์ฐจ 11.1~5.8%, ์ „๊ธฐ์ฐจ 35.2~15.5%๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฐ ํŒ๋งค์œจ์˜ ๋ฒ”์œ„๋Š” ์ „๊ธฐ์˜ ์ „๊ณผ์ • ๋ฐฐ์ถœ๋Ÿ‰์ด 0 g/kWh์ผ ๋•Œ๋ถ€ํ„ฐ 1068 g/kWh์ผ ๋•Œ๊นŒ์ง€๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ž๋™์ฐจ ์‹œ์žฅ์˜ ๋ณ€ํ™”๊ฐ€ ์†Œ๋น„์ž์™€ ์ •๋ถ€, ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์—ฌ ์ƒˆ๋กœ์šด ๊ทœ์ œ๊ฐ€ ๋ฏธ์น˜๊ฒŒ ๋  ์˜ํ–ฅ์— ๋Œ€ํ•ด ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์†Œ๋น„์ž์˜ ์ด ์†Œ์œ  ๋น„์šฉ์€ ์ž๋™์ฐจ ๊ตฌ์ž… ๊ฐ€๊ฒฉ๊ณผ ์†Œ์œ  ๊ธฐ๊ฐ„๋™์•ˆ์˜ ์ฃผ์œ ๋น„, ์œ ์ง€๋น„์šฉ, ๋ณดํ—˜ ๋“ฑ์„ ํฌํ•จํ•˜๋Š” ๊ฐ’์ด๋‹ค. 2030๋…„์— ์ž๋™์ฐจ๋ฅผ ๊ตฌ๋งคํ•œ ์†Œ๋น„์ž 1๋ช…์˜ ์ด ์†Œ์œ ๋น„์šฉ์€ ๊ธฐ์กด์˜ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ ํ‰๊ท  4750๋งŒ ์›์ด๋ฉฐ, ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ๋Š” 4550~4800 ๋งŒ ์›์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ž๋™์ฐจ ํŒ๋งค์— ๋”ฐ๋ฅธ ์ •๋ถ€์˜ ์ˆœ ์ˆ˜์ž…์€ ์œ ๋ฅ˜์„ธ ์„ธ์ž…๊ณผ ์˜จ์‹ค๊ฐ€์Šค ๋ฒ”์น™๊ธˆ์œผ๋กœ ์ธํ•œ ์„ธ์ž…์˜ ํ•ฉ์— ์ „๊ธฐ์ฐจ ์ถฉ์ „์‹œ์„ค ๊ฑด์„ค์— ๋”ฐ๋ฅธ ์ œํ•œ ๋น„์šฉ์œผ๋กœ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. 2030๋…„์— ์ž๋™์ฐจ 50๋งŒ ๋Œ€๋ฅผ ํŒ๋งคํ–ˆ์„ ๋•Œ, 1๋…„ ๊ฐ„ ์ •๋ถ€์˜ ์ด ์ˆ˜์ž…์€ ๊ธฐ์กด์˜ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ ํ‰๊ท  1์กฐ 6000์–ต ์›์ด๋ฉฐ, ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ๋Š” 1์กฐ 3700์–ต~3์กฐ 3700์–ต ์›์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ž๋™์ฐจ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์€ ํ‰๊ท  ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์œผ๋กœ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ์ด๋Š” 2030๋…„์— ํŒ๋งค๋œ ์ž๋™์ฐจ๊ฐ€ ์ฃผํ–‰ ๊ณผ์ •์—์„œ ๋ฐฐ์ถœํ•˜๋Š” ์˜จ์‹ค๊ฐ€์Šค ์™ธ์—๋„ ์ƒ์‚ฐ, ๋ฐœ์ „, ์ˆ˜์ž…, ์ˆ˜์†ก ๋‹จ๊ณ„์—์„œ ๋ฐฐ์ถœํ•˜๋Š” ๋ชจ๋“  ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์„ ํ•ฉ์‚ฐํ•จ์œผ๋กœ์จ ๊ตญ๊ฐ€ ์ „์ฒด์˜ ์˜จ์‹ค๊ฐ€์Šค ๊ฐ์ถ• ๋ชฉํ‘œ์— ์–ผ๋งˆ๋‚˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€์— ๋Œ€ํ•œ ์ง€ํ‘œ๋กœ์จ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด์˜ ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ 42.8% ์ ์œ ์œจ์„ ์ฐจ์ง€ํ•˜๋Š” ํ”Œ๋Ÿฌ๊ทธ์ธ ์ž๋™์ฐจ์™€ ์ „๊ธฐ์ž๋™์ฐจ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ๋ฐœ์ „ ๋ฏน์Šค์˜ ๋ณ€ํ™”์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง€๊ธฐ ๋•Œ๋ฌธ์—, ํ‰๊ท  ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ๋˜ํ•œ 78.2~145.6 g-CO2-eq./km๋กœ ๋ณ€ํ™”ํ•œ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ๋Š” ํ”Œ๋Ÿฌ๊ทธ์ธ ์ฐจ์™€ ์ „๊ธฐ์ฐจ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰ ๋ณ€ํ™”์™€ ๋”๋ถˆ์–ด, ์ž๋™์ฐจ์˜ ์ ์œ ์œจ์ด ํ•จ๊ป˜ ๋ณ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํ‰๊ท  ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์€ 73.7~139.6 g-CO2-eq./km๋กœ ๋ณ€ํ™”ํ•˜๊ฒŒ ๋œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์—์„œ ๋ฐœ์ „๋‹จ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ๊ฐ์†Œํ•˜๋ฉด ์ „๊ธฐ์ฐจ์˜ ์ ์œ ์œจ์ด ์ฆ๊ฐ€ํ•˜์—ฌ ์˜จ์‹ค๊ฐ€์Šค ๊ฐ์ถ• ํšจ๊ณผ๋ฅผ ์ฆํญ์‹œํ‚ค๋ฉฐ, ๋ฐœ์ „๋‹จ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ์ฆ๊ฐ€ํ•˜๋ฉด ์ „๊ธฐ์ฐจ์˜ ์ ์œ ์œจ์ด ์ค„์–ด๋“ค๋ฉด์„œ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์„ ์ƒ์‡„์‹œํ‚ค๋Š” ํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Š” ์ž๋™์ฐจ ์—ฐ๋ฃŒ์˜ ์ƒ์‚ฐ ๊ณผ์ •์—์„œ์˜ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ๋Ÿ‰์ด ๋‹ฌ๋ผ์ง์— ๋”ฐ๋ผ ์ž๋™์ฐจ ์‹œ์žฅ์ด ์œ ๋™์ ์œผ๋กœ ๋ฐ˜์‘ํ•˜๋Š” ์ „๊ณผ์ • ์˜จ์‹ค๊ฐ€์Šค ๊ทœ์ œ์˜ ์žฅ์ ์„ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.Various regulations are in place around the world to reduce greenhouse gas emissions that cause global warming problems. In the road transportation sector, greenhouse gas emissions are to be reduced through fuel economy standard or greenhouse gas standard. The characteristic of this regulation is that the emission of greenhouse gas emissions from the exhaust port is zero for vehicles in electric driving mode, and additional incentives are provided. However, the electric vehicle does not emit GHG while driving the vehicle, but greenhouse gas is generated in the process of obtaining electricity required for driving the vehicle. Besides, various discussions have been made on estimating GHG emissions in the electric driving mode as 0, ignoring the upstream greenhouse gases. Especially in recent years such discussions have become more specific. In particular, Japan's new fuel economy standards announced that it would use vehicle fuel economy corrected using the Well-to-Tank efficiency of the fuel production stage. Accordingly, in Korea, it is necessary to predict and evaluate the effects of applying life cycle analysis results on fuel economy regulation and greenhouse gas regulation. There are two main reasons why the consideration of the fuel production process, especially upstream of electricity, is needed. First, the demand for electricity will increase as demand for electric vehicles increases in the future. Second, greenhouse gas emissions during the electricity production process are depending on the type of power generation. In this situation, life cycle analysis can be used as a tool to quantitatively evaluate the environmental friendliness of various fuels and vehicles. Well-to-wheel (WTW) analysis refers to the life cycle analysis of automotive fuels and represents the life cycle process, from oil production to the vehicle operation. The life cycle process of the electric vehicle includes the vehicle driving process, the power generation process, and the production process of power generation raw materials. For the sake of a fair comparison, the internal combustion engine car includes all related fuel production processes such as gasoline and diesel, just like electric vehicles. In this study, I proposed the life-cycle GHG regulation regulated based on the life-cycle GHG emission value of automobile fuel and evaluated the effect of the new GHG standards on the vehicle market and stakeholders. It also showed that the national energy policy could be linked to the automobile policy through Well-to-wheel standards. The research order of this study is as follows. First, a life cycle analysis of automobile fuels in Korea was performed, and future life cycle greenhouse gas emissions were predicted. Next, the GHG emission regulations and penalties for life cycle regulation were established, and a model was designed to predict the mutual influence between the government, consumers, and automobile manufacturers based on the actor-based model. Using this model, the vehicle market prediction model can be used to predict how a car manufacturer will set a price for a product and what product a consumer will buy. Third, I analyzed the social phenomena that apply life cycle GHG regulations to the life cycle analysis results and automobile market prediction model. The automotive GHG life cycle analysis is divided into the well-to-tank (WTT) process, which means the process from raw material extraction to refueling or filling the car, and the tank-to-wheel (TTW) process, which means the car driving process. For gasoline cars, hybrid cars, plug-in hybrid cars, and electric cars, the GHGs emitted during the TTW process are 138.7, 94.6, 13.2, and 0 g-CO2-eq./km, respectively. The WTW GHG emissions were calculated for four vehicles in the order of 160.9, 109.9, 89.3, 85.0 g-CO2-eq./km. The difference in TTW GHG emissions between gasoline vehicle and electric vehicle is 138.7 g-CO2-eq./km, but the difference in WTW GHG emissions is 75.9 g-CO2-eq./km. Next, I used an agent-based model to design a model that predicts the automotive market for 2030. An agent-based model is an analytical technique used to predict decision-making of actors that influence and influence each other in socio-economic environments. In this study, the government, consumers, and automobile manufacturers were selected as agents involved in the vehicle market. The goal of the GHG emission regulation is set by comprehensively considering the national GHG reduction target, the potential reduction in the transport sector, and the manufacturers' interests. The GHG standard in Korea has announced its targets by 2020, and no future targets have been announced. Therefore, the average TTW and WTW emissions are inferred from the goal of alternative vehicle supply in Korea in 2030. The target value of original GHG standards is 62.2 g / km, and the target value of proposed GHG standards is 109.2 g / km. Penalty rates for failure to achieve GHG regulations were set at 50,000 won when exceeding 1 g/km. Consumers and manufacturers make decisions to increase the utility of car purchases and the net profit from car sales, respectively. Consumers determine their utility in consideration of the price, fuel economy, fueling cost, charging time, and total driving distance of their vehicles. The automaker's net profit is determined by retail prices, production costs, regulatory costs, and research and production facility costs. The vehicle market prediction model was designed to calculate the optimal product price and the market share according to the mutual influence between consumers and manufacturers. Third, I analyzed the impact that would occur when implementing GHG standards on the vehicle market by applying WTW emissions to GHG regulation. In order to effectively observe the effects of greenhouse gas emissions during the fuel production phase, I have focused on the electricity generation process in which the impact is prominent. Assessing how life-cycle greenhouse gas emissions vary from zero to 1068 g/kWh, resulting in changes in greenhouse gas emissions by vehicle type, resulting in product prices and sales rates in the automotive market, total cost of ownership for consumers, and gross government revenues. As a result, the vehicle market applying the WTW standards has the following characteristics. First, the vehicle market is directly affected by the upstream emissions of the fuel. The original standard regulates the vehicle's Tank-to-Wheel GHG emissions, and the proposed standard regulates the vehicle's Well-to-Wheel GHG emissions. Thus, when the GHG emissions of the electricity production process change, the proposed standard is affected, but the original standard is not. In this study, the regulation cost is determined by the difference between the vehicle's GHG emissions and the GHG target value. The regulation cost is included in the vehicle retail price, which means that the price of the vehicle may change in the proposed standard. As a result, changes in market share due to changes in upstream emissions helped to reduce or offset the increase in total GHG emissions. Sales of PHEV and BEV declined as upstream GHG increased, while sales of PHEV and BEV increased as upstream GHG decreased. In this study, the vehicle market responded flexibly to changes in upstream emission under proposed standards. Second, when the generation mix is the same as Korea's development plan for 2030, the total GHG emissions of the proposed standard will be greater than that of the original standard. This is because the gap between ICEV and BEV is reduced when regulating WTW emissions of vehicles rather than regulating TTW emissions. As a result, sales volume of ICEV and HEV increased, and the sales volume of PHEV and BEV decreased in the proposed standard. In this study, four scenarios are proposed to solve the problem of increasing greenhouse gas emissions under the proposed standard. The four methods are to increase the penalty rate, improve engine efficiency, improve the ratio of PHEV and BEV, and reduce battery price. Besides, this study evaluated the impacts of consumers and governments on four scenarios. The impact of each agent on GHG standards is expressed in terms of TCO and GOV income. The results of this study have the limitation that the total GHG emissions under the WTW standard are higher than those under the TTW standard at the power generation mix level in Korea in 2030. This result arises the concern that the WTW standard are less effective than the TTW standard to reduce the GHG emissions. To solve this concern, this study suggests the development of vehicle technology, reduction of battery price, and increase of penalty rate. However, there are two problems: 1. Difficulty of direct intervention through the policy, 2. GHG reduction effect is greater in TTW regulation with the new technology. Therefore, there is a need to make meaningful suggestions for the phenomenon that seems to increase GHG emission due to the proposed standard. I suggested the two power generation mixes that represent important features.Chapter 1. Introduction 1 1.1. Research background 1 1.2. Research objectives 11 1.3. Research scope 15 Chapter 2. Well-to-Wheel analysis 16 2.1. Introduction 16 2.2. Previous researches 16 2.3. Well-to-Wheel processes approach and methodology 18 2.4. Well-to-Wheel analysis of automotive fuels in Korea 21 2.4.1. Petroleum-based fuel 21 2.4.2. Natural gas 22 2.4.3. Electricity 24 2.4.4. Hydrogen 28 2.5. WTW GHG emissions results in 2017 33 2.6. Future prediction 36 Chapter 3. Agent-based analysis 41 3.1. Introduction 41 3.2. Previous researches 43 3.3. Methodology โ€“ Key parameters and assumptions 45 3.3.1. Policymaker โ€“ Manage the nationwide greenhouse gas emission standard 45 3.3.2. Manufacturer - Decision of vehicle fuel economy and price to maximize profit 47 3.3.3. Consumer โ€“ Select the vehicle with the highest utility 49 3.4. Responses of the agents to the GHG emission standard - Mathematical approach 55 3.4.1. Nash equilibrium 55 3.4.2. Mathematical approach (1) โ€“ Excluding the fixed cost 56 3.4.3. Mathematical approach (2) โ€“ Including the fixed cost 61 3.5. Model validation and sensitivity analysis 65 Chapter 4. Results and Discussion 71 4.1. Evaluation of WTW GHG standards using the WTW results and market prediction model 71 4.1.1. How to read the results graphs 71 4.1.2. Definition of six results parameters - No standard case 73 4.2. Comparison of the effect of original standard (TTW standard) and proposed standard (WTW standard) 80 4.3. How to reduce the total GHG emissions in 2030, with proposed standards 88 Chapter 5. Conclusion 93 Bibliography 97 ๊ตญ๋ฌธ ์ดˆ๋ก 104Docto
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