| 摘要 | ABSTRACT
1. Research Purpose
Residential and services including commercial and public sectors have accounted for about 20% of final energy consumption in Korea since 2000 and this share is expected to be maintained at a similar level over the next five years. The government has been conducting some policies and programs to increase energy savings and improve energy efficiency in residential and services sectors ; e. g., building energy labels and certificates, energy efficiency appliance certification programme, e--standby and so on.
It is important to analyze energy consumption by sector and to assess the effect of energy saving policies implemented for efficient and effective implementation of policies, planes, programmes and projects. Detailed energy consumption data is the key to the analysis and Energy Consumption Survey(ECS) conducted by Korea Energy Economics Institute(KEEI) is the most important statistics in energy consumption data. Although this is important for the analysis, it is restricted to open the data to the public so, the analysis was simplified and not detailed.
This study will complement the result of ECS by estimating end-use energy consumption and applying regression analysis and discuss policy suggestions for implementing effectively in residential sector.
2. Summary
The 2011 ECS microdata is used to estimate and analyze end-use energy consumption in residential sector and data of Household Energy Panel Survey(HEPS) by KEEI and Survey on Electricity Consumption Characteristics of Home Appliances by Korea Electric Power Corporation(KEPCO) is used to estimate the consumption for space cooling. The end--use energy consumption is estimated by observation approach rather than consumption model. In this study,we define and estimate three end-uses, such as space heating, space cooling and other. Space heating includes water heating and other covers cooking, lighting and appliances. It is not easy to differentiate more end-use and for this, it is needed to collect more detailed data.
Result shows that household size, climate, region, house type, year built, total area and monthly income are affecting energy consumption of household. Result of OLS regression shows that when other variables are controlled, more energy is consumed in provinces than Seoul and more energy in single-family detached than apartments. While annual energy consumption has similar results with space heating, space cooling and other consumption have different outcomes. Total area and monthly income have more impact on space cooling than household size. Quantile regression estimates calculated at tau=0.1, 0.3, 0.5, 0.7 and 0.9 show that the effect of explanatory variables are greater in the right tail.
3. Research Results and Policy Suggestions
This research finds the following policy implications and suggestions. While the region does not matter for space conditioning in the upper quantiles, energy consumption in province is greater than in Seoul in the lower quantiles. So, it is needed to study what makes the energy-saving household consume more energy in province and to solve the problem. This can be lead to reduction of energy and improvement of energy efficiency in the area.
Climate effect on energy consumption is greater than energy efficiency of housing when weather condition is abnormal, thus, it is needed to improve housing energy efficiency.
Single-family detached is less energy-efficient than apartments thus it is recommended that programmes for improvement of housing efficiency target single-family detached.
The effect of programme of appliances energy efficiency labels on energy consumption is not observed clearly. For more detailed analysis, it could be considered that rebound effect exits.
Furthermore, it could be checked accuracy of observation based approach comparing with model for estimating end-use consumption and it is also investigated whether determinants of energy consumption have changed over time through decomposition analysis on 2002, 2005 and 2008 ECS microdata. |
