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Estimation of Energy Consumption and Greenhouse Gas Emissions Considering Aging and Climate Change in the Residential Sector, 2010-2050 : 고령화 특성과 기후변화를 반영한 가정 부문 에너지 사용량 및 온실가스 배출량 변화 예측, 2010-2050

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농업생명과학대학 생태조경·지역시스템공학부
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서울대학교 대학원
AIM/end-use modelHeating degree days and Cooling degree daysEnergy service demandRCP scenariosSocio-economic change
학위논문 (석사)-- 서울대학교 대학원 : 생태조경·지역시스템공학부 생태조경학전공, 2016. 2. 이동근.
The impacts of climate change are a global concern. Rising temperatures are of particular importance, and expected to continue to increase over time. To counter this phenomenon, many nations are aiming to reduce greenhouse gas (GHG) emissions through energy demand management. This key set of policies call upon energy consumption and GHG emissions estimates to combat rising temperatures. Demographic factors such as socio-economic status and age, as well as climate change, are important components of accurate estimations in this regard. The aging demographic is especially noteworthy, because of its prominence worldwide. South Korea is the fastest aging country in the world, and can thus provide a look at the most dramatic aging scenario. Therefore, this study estimates energy consumption and GHG emissions in the residential sector of South Korea through both climate change and demographic characteristics.
This study examines four scenarios from 2010 as the base year, and 2050 as the target year setting four scenarios. The first scenario considers a change in socio-economic elements: GDP and population, the second scenario includes socio-economic elements in scenario 1 plus climate change. The third scenario further adds aging characteristics such as the length of time spent at home and income of the elderly in addition to scenario 2 taking climate change and socio-economic change. Lastly, scenario 4 includes the components of scenario 3 and introduces energy reduction policies. The methodology for evaluating each scenario follows three steps: 1) determining socio-economic factors, climate change factors, and aging factors
2) estimating energy service demand using an estimation formula
and 3) estimating energy consumption and GHG emissions using an adapted AIM/end-use model.
Results show that energy consumption increase from 21.9 Million Tons of Oil Equivalent (MTOE) in 2010 to 26.02 MTOE in scenario 1, 25.05 MTOE in scenario 2, and 29.91 MTOE in scenario 3 in 2050. GHG emissions also increase 68.88 Million Tons of CO2 Equivalent (MT CO2 eq.) in 2010 to 106.38 MT CO2 eq. in scenario 1, 104.36 MT CO2 eq. in scenario 2, and 122.18 MT CO2 eq. in scenario 3 in 2050. This growth is caused by an aging population who stays at home longer and thus increases heating energy use, and an increase in cooling energy demand due to rising temperatures. However, the addition of energy reduction policies in scenario 4 considerably reduced energy consumption and GHG emissions, 23.37 MTOE and 86.44 MT CO eq. in scenario 4 in 2050. In conclusion, this study is useful in preparing energy demand management and establishing and attaining GHG emissions reduction goals.
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