The aim of this study is to develop a simplified Life Cycle Assessment. (LCA) model for .... As shown, the households in luxurious houses have much higher ...
日本建築学会技術報告集 第 18 巻 第 40 号，1003-1008，2012 年 10 月 AIJ J. Technol. Des. Vol. 18, No.40, 1003-1008, Oct., 2012
DEVELOPMENT OF A SIMPLIFIED LCA MODEL FOR RESIDENTIAL BUILDINGS IN INDONESIA DEVELOPMENT OF A SIMPLIFIED LCA
インドネシアの住宅への適用を 目的とした簡易型 LCA 手法の ࣥࢻࢿࢩࡢఫᏯࡢ㐺⏝ࢆ 開発
IN INDONESIA
㛤Ⓨ
− A pilot survey in Bandung − BUILDINGS MODEL FOR RESIDENTIAL 㸫A Pilot Survey in Bandung㸫
Usep SURAHMANー ーーー ＊ 1
Tetsu KUBOTAーーーーーー
＊2
Keywords: Life cycle assessment, Energy consumption, CO2 emissions, Inputoutput analysis, Indonesia Usep SURAHMAN 㸨 Tetsu KUBOTA 㸨 Keywords: : キーワード Life cycle assessment, Energy consumption, CO2 emissions, Input-output ライフサイクルアセスメント，エネルギー消費量，CO 2 排出量， analysis, Indonesia 産業連関分析，インドネシア ࣮࣮࢟࣡ࢻ㸸 ࣛࣇࢧࢡࣝࢭࢫ࣓ࣥࢺ㸪 ࢚ࢿࣝࢠ࣮ᾘ㈝㔞㸪 &2 ฟ㔞㸪 ⏘ᴗ㐃㛵ศᯒ㸪 ࣥࢻࢿࢩ
┠ⓗࡋࡓ⡆᫆ᆺ㹊㹁㸿ᡭἲࡢ −バンドンにおけるパイロット調査− 㸫ࣂࣥࢻࣥ࠾ࡅࡿࣃࣟࢵࢺㄪᰝ㸫
スラマン ウセプーーーーー ＊ 1
久保田 徹ーーーーーーーー ＊ 2
The aim of this study is to develop a simplified Life Cycle Assessment (LCA) model for residential buildings in Indonesia, which can be used under relatively poor data availability ࢘ࢭࣉ ࢫ࣐ࣛࣥ ஂಖ⏣ ᚭ conditions. As the initial step, a pilot survey comprising a small number of samples (n=11) was carried out citystudy of Bandung in March 2011 to Cycle initiate the development of The in aimthe of this is to develop a simplified Life Assessment (LCA) model for residential in Indonesia, whichofcan used underdata relatively poor data LCA model. buildings It was found that most thebestatistical required for availability As the initial a pilot survey a small number of LCA wereconditions. only available at thestep, national level comprising in Indonesia, while most samples (n=11) was carried out indothenot cityhave of Bandung March 2011 to initiatethe the of the residential buildings designinrecords. Moreover, developmentenergy of LCAconsumption model. It was found most of thetostatistical data requiredThe for household data that were found be not available. LCA were only available at the national level in Indonesia, while most of the results of initial statistical analyses indicated that there is a potential residential buildings do not have design records. Moreover, the household energy to develop simplified projection methods of The embodied as well consumption data were found to be not available. results ofenergy initial statistical as operational thus totoobtain cycleprojection energy methods and COof2 analyses indicated energy that thereand is a potential developlife simplified emissions basedason explanatory area, embodied energy wellsome as operational energyvariables and thus tosuch obtainaslifetotal cyclefloor energy and lot or household income. COarea based on some explanatory variables such as total floor area, lot area or 2 emissions household income.
1. Introduction One of the obstacles to conducting life cycle assessment (LCA) in developing countries is considered to be relatively poor availability of building, environmental and economic data. This study aims to develop a simplified LCA model for residential buildings in Indonesia, which can be used under relatively poor data availability conditions. As the initial step, a pilot survey comprising a small number of samples was carried out in Bandung city to initiate the development of LCA model using an input-output analysis. This paper discusses the possibility of developing the said simplified model while investigating the availability of required data through the pilot survey. The pilot survey
focuses
especially
on
building
materials,
energy
consumption and their CO2 emissions. 2. Life cycle assessment (LCA) 2.1. LCA methods Previous studies showed that there are mainly three methods commonly used for calculation of energy and environmental impacts, namely process-based, economic input-output (I-O) analysis-based and hybrid-based methods. Firstly, the process-based method models different activities associated with a product or a service using process flow diagrams from downstream to upstream1). All materials and energy used in the process are identified. Thus, the environmental impacts and
*1
Graduate School for International Development and Cooperation (IDEC), Hiroshima Univ., Ph. D., Candidate *2 Assoc. Prof., Graduate School for International Development and Cooperation 㸨 (IDEC), Hiroshima Univ. ᗈᓥᏛᏛ㝔ᅜ㝿༠ຊ◊✲⛉༤ኈㄢ⛬ ࠛ ᗈᓥ┴ᮾᗈᓥᕷ㙾ᒣ 㸨
ᗈᓥᏛᏛ㝔ᅜ㝿༠ຊ◊✲⛉ᩍᤵ
emissions can be estimated accounting for production of the materials and consumption of the energy. By using this method, Utama et al.2) investigated embodied energy of mass housing in Indonesia in order to select a low energy building material for wall in consideration of energy consumption of air-conditioning. Kurdi et al.3) calculated CO2 emissions by using embodied energy during material production and transportation of mass housing in seven large cities in Indonesia. Monahan et al.1) investigated embodied energy in the production phase of housing construction in the United Kingdom. Thomark4) investigated embodied energy for a low energy building in Sweden. This method is specific, detailed and reliable, while generally based on incomplete system boundaries. Secondly, the economic I-O analysis-based method is, literally, the means using the I-O table, which presents the exchanges of goods and services among industrial sectors in matrix form. The I-O table was originated by Wassily Leontief, the 1973 Nobel laureate in economics. In recent years, this method has been frequently used in LCA and various analyses have been conducted for energy consumption and CO2 emissions. By utilizing this method, Suzuki et al.5) estimated CO2 emissions during construction including the manufacturing of building materials. Fujita et al.6) investigated embodied energy and calculated CO 2 emissions of Malaysian housing construction. Norman et al. 7) calculated GHG emissions in construction and operation of residential buildings in USA. This method is more complete in system boundaries but lack of process specificity. 広島大学大学院国際協力研究科 博士課程 （〒 739-8529 広島県東広島市鏡山 1-5-1） 広島大学大学院国際協力研究科 准教授
*1
*2
㸨1
PhD. Candidate, Graduate School for International Development and Cooperation (IDEC), Hiroshima University.
㸨2
Assoc. Professor, Graduate School for International Development and Cooperation (IDEC), Hiroshima University.
1003
Thirdly, the hybrid-based method attempts to overcome the disadvantages of the above two methods while combining their advantages. By using this method, Mithraratne et al. 8) investigated embodied energy and calculated CO 2 emission of a New Zealand house. Crawford et al. 9) investigated energy and calculated CO2 emissions of energy and water embodied in commercial buildings in Australia. Although it is impossible to trace all processes unlike the process-based or the hybrid-based methods, this paper initially uses the input-output analysis-based method because this method is considered the most appropriate under relatively poor data availability conditions such as in Indonesia.
case study houses because these houses account for the largest percentage of housing stocks in Bandung (89%)11). These unplanned houses can be classified into three categories based on the construction cost and lot area, namely simple houses, medium houses and luxurious houses (Fig. 1), having a life span of 20, 35 and 50 years respectively12). The pilot survey was conducted in six simple houses (No. 1-6), three medium houses (No. 7-9) and two luxurious houses (No. 10-11) in March 2011 (see Table 1). As shown in Table 1, the total floor area of simple houses range from 17 m2 to 120 m2, while those of medium houses range from 54 m2 to 142 m2. Luxurious houses have larger area of 254 m2 and 484 m2. The major building materials used are found to be
2.2. Embodied energy intensity Embodied energy intensity is divided into two kinds. The first is imported embodied energy intensity given in the equation: (1) Ei = d (I – A) -1 and the second is domestic embodied energy intensity. Ed = d (I-((I – M) A )) -1 (2) Where Ei is the imported embodied intensity (GJ/price), Ed is the domestic embodied intensity (GJ/price), d is the direct burden per unit production price (GJ/price), I is the identity matrix, A is the coefficient matrix and M is the import coefficient10).
almost the same among the above three categories. Slight differences can be seen in terms of materials for floor and roof. 3.2. Profile of respondents A brief profile of respondents is shown in Table 2. The major ethnic group in West Java is Sundanese at the province level, followed by Javanese, etc. The profile of the respondents shows the similar proportions. The household sizes in this survey vary among the houses, ranging from 3 to 6 persons. The monthly household income was also investigated by a multiple-choice question. As shown, the households in luxurious houses have
3. Pilot survey
much higher incomes than the others as expected.
3.1. Case study houses Bandung city, which is the selected case study city, is located on 791 m above the sea level having humid and relatively cool climate. The survey picked out unplanned individual housing as (a)
3.3. Procedure of LCA LCA generally involves six phases, namely design, material production, construction, operation, refurbishment and demolition phases. However, design, construction and demolition phases are
(b)
(c)
Fig. 1. Case study houses; (a) simple house; (b) medium house; (c) luxurious house. Table 1. Size and major materials of case study houses House category Simple houses 1 2 3 4 5 6 Medium houses 7 8 9 Luxurious houses 10 11
1004
Floor/Lot area (m2)
Foundation
Floor
Walls
Roof
30/33 17/17 120/120 43/43 52/52 70/70
Stone Stone Stone Stone Stone Stone
Cement Cement Cement Cement Cement Cement
Con-block Con-block Clay brick Clay brick Con-block Con-block
Clay tile and zinc Clay tile Clay tile Clay tile Clay tile Clay tile
92/92 54/54 142/142
Stone and concrete Stone and concrete Stone and concrete
Ceramic tile Ceramic tile Ceramic tile
Clay brick Clay brick Clay brick
Clay tile Clay tile Clay tile
484/539
Stone and concrete
Ceramic and granite tile
Clay brick
Concrete tile
254/181
Stone and concrete
Ceramic and granite tile
Clay brick
Concrete tile
Building materials
Table 2. Profile of respondents House category Simple houses 1 2 3 4 5 6 Medium houses 7 8 9 Luxurious houses 10 11
Ethnic group
Household size (person(s))
Monthly income (USD)
Sundanese Sundanese Sundanese Sundanese Sundanese Sundanese
5 4 6 3 4 4
220 - 330 55 - 110 330 - 440 55 - 110 110 - 220 110 - 220
Sundanese Javanese Sundanese
6 4 6
220 - 330 220 - 330 330 - 440
Sundanese
5
>1110
Javanese
5
>1110
Table 3. Data sources and collection methods used in LCA phases Phase
Data
Source
Material production
Material inventory
Design record
Operation
Household energy consumption
Usage of appliances Utility bills
(a)
Collection methods x House owner interview x On-site building measurement x House owner interview x On-site measurement
(b)
Fig. 2. On-site measurement; (a) Building material survey; (b) Household energy consumption survey (a)
Fuel consumption
(b)
Net contribution rate
(c)
Energy direct environmental burden per domestic production
(d)
(e)
Domestic embodied energy intensity
Imported embodied energy intensity
Total embodied energy
CO2 direct environmental burden
Domestic CO2 emission intensity
Imported CO2 emission intensity
Total CO2 emissions
Fig. 3. Flow chart of I-O table analysis
not considered in this paper. This is because most of the housing stocks in Bandung are not designed in formal way but constructed and demolished by manual labor. Thus, the energy consumption and materials used during the above phases are considered negligible. Data sources and collection methods used in respective phases of the present pilot survey are shown in Table 3. The design records such as building drawing are required for the analysis of embodied energy. These data can normally be obtained from the local authorities, developers, consultants, contractors or architects1-4). Some developed countries provide the data in the literatures8)13). Nevertheless, these data were found to be available only for a few medium houses and most of the luxurious houses in Bandung. On the other hand, most of the simple houses and medium houses are constructed not in formal way in practice and therefore the inventory record cannot be obtained. Thus, for simple and medium houses, the actual on-site building measurements were conducted instead in order to acquire the data (Fig. 2a). As shown, detailed dimensions of respective building parts were measured manually and volumes of all the building materials were calculated. The detailed household energy consumption data are necessary for the analysis of operation phase. Few previous investigations on household energy consumption were carried out in Indonesia 2)3) and other developing countries1)5) by interviewing the house owners and measuring the energy on site. Some researches in developed countries utilize literature review for obtaining the data4)8). Since the energy consumption data record is not available in Bandung, the detailed interview was conducted. Ownership level and usage time of all the household appliances were obtained through the interview, while electrical capacity of appliances was measured as shown in Fig. 2b. These data collections were time-consuming and costly. Therefore, simplified projection methods are strongly needed to acquire the necessary data for LCA. Energy used in a building is divided into two categories. The first is embodied energy for raw material extraction, material production, transportation and building construction. The second is energy consumption during the operation phase. The total energy of the above two categories are expressed as life cycle energy, which is given by the following equation. LCE = EE i + EErec + (OE x life span) (3) Where LCE is the life cycle energy, EE i is the initial embodied energy of material, EErec is the recurrent embodied energy of material (maintenance), OE is the annual operational energy and lifespan is the period of building life time 2). 3.4. Embodied energy Fig. 3 illustrates the procedure of the embodied energy analysis. Firstly, the combination of averaged fuel consumption in industrial and transportation sectors during 2000 to 2009 based on the nationwide data14) was calculated (Fig. 3a). Secondly, the net contribution rate was determined by giving figure 0 or 1 for each combination between the fuel type and the sectors indicated in the I-O table, in order to exclude fuel
1005
1006
Medium house
House 11
House 10
House 9
House 8
House 3
House 1
Simple house
House 7
100
House 6
200
House 5
300
House 4
400
House 2
Embodied energy (GJ)
500
Luxurious house
Fig. 4. Embodied energy of each house Table 4. Correlation coefficient between selected variables and embodied energy Variables Lot area Total floor area Household income No. of bedroom Building age Duration of living Household size
1 2 3 4 5 6 7
r-value 0.98 0.98 0.86 0.70 -0.62 -0.53 -0.05
Sig. ** ** ** ** * * -
* = Significant at 5% level; ** = Significant at 1% level 700
y = 1.17 x - 32.16 R² = 0.97
600 500
400 300 200 100 0 0
100
200
300
400
500
600
Lot area (m²)
Fig. 5. Relationship between lot area and embodied energy 45 40
Personal gadgets
35
Cooling
30
Washing/Bathing
25
Entertainment
20
Lighting Cooking
15 10
Simple house
Medium house
House 11
House 10
House 9
House 8
House 7
House 6
House 5
House 4
House 3
0
House 2
5
House 1
4. Results and discussion 4.1. Embodied energy The embodied energy was calculated for respective houses through previously explained calculation methods (Fig. 4). The cement has the largest percentage in all the houses (47-55%), followed by the steel (7-15%), the sand (4-13%) and clay brick (3-12%), etc. Both cement and steel did not account for a large volume percentage, but their embodied energy was found to be large. This is simply because the embodied energy conversion factors for these two materials are high. As shown, the estimated embodied energy largely depends on the size of the house, ranging from 9.1 to 603.5 GJ. Since there is no reliable life-span prediction of building materials in Indonesia, the embodied energy for maintenance is not considered in this study. Although statistical analysis cannot be valid in this case because the sample size is not large enough, the correlation analysis was attempted to examine the possibility of developing a mathematical model that explains the embodied energy. Table 4 shows the correlation coefficients between selected variables and embodied energy. As shown, several variables, including ‘lot area’, ‘total floor area’, ‘household income’ and ‘number of bedroom’,
Clay tile Concrete tile Paint Gypsum Clear glass Wood Ceramic tile Stone Foundation Clay brick Sand Steel Cement
600
0
Embodied energy (GJ)
Energy consumption for respective household appliances was estimated through multiplying number of appliances by their usage time and electrical capacity. The annual mean household energy consumption was calculated by combining consumption for all the appliances. The seasonal changes of air temperature and humidity were considered in the estimation of energy consumption caused by air-conditioning and refrigerator.
700
Energy consumption (GJ/year)
consumption that was converted into another fuel type or used as feedstock (Fig. 3b). Thirdly, the Net Calorific Value (NCV) was obtained from Indonesia15), and fuel consumption was converted into calorific values through multiplying the gross fuel consumption by the net contribution rate and the said NCV for each fuel type in respective sectors (Fig. 3c). The latest Indonesian nationwide I-O table published in 2005 16) consisting of 175x175 sectors were used for calculating the embodied energy and CO2 emissions. Meanwhile, the building material inventory data were investigated as described earlier. Each material was classified into domestic material and imported material respectively, based on the site observation. Then embodied energy intensities for respective materials were calculated by either Eq. (1) or Eq. (2) using the above I-O table. The total embodied energy of respective houses was computed by combining all the energy consumption for respective building materials (Fig. 3d). On the other hand, the CO2 emissions were estimated through multiplying the energy consumption for each fuel type by its corresponding CO2 emission factor obtained from Indonesia17). The total CO2 emissions of respective houses were calculated by adding the emissions for each of the building materials (Fig. 3e). 3.5. Operational energy
Luxurious house
Fig. 6. Annual mean energy consumption of each house have strong relationships with the embodied energy. However, it was found that the above four variables are closely correlated with each other and therefore it is not reasonable to construct a
(3) (Fig. 9). As shown, the operational energy tends to be greater than the embodied energy by 3 to 19 times. Similar correlation analysis was attempted as indicated in Table 6. As shown, five variables show strong relationships with the life cycle energy. This result is almost the same as that of operational energy (see
Operational energy (GJ)
2500
2000
1500
Gas
1000
Electricity
500
Simple house
Medium house
House 11
House 10
House 9
House 8
House 7
House 6
House 5
House 4
House 3
House 2
House 1
0
Luxurious house
Fig. 7. Operational energy of each house Table 5. Correlation coefficient between selected variables and operational energy Variables Total floor area Household income Lot area Building age No. of bedroom Duration of living Household size
1 2 3 4 5 6 7
r-value 0.95 0.95 0.90 -0.70 0.68 -0.60 0.05
Sig. ** ** ** * * ** -
* = Significant at 5% level; ** = Significant at 1% level 2500
Operational energy (GJ)
multiple regression model to explain the embodied energy. Fig. 5 shows the relationship of lot area with embodied energy of the houses as an example. The above results indicate that there is a potential to develop a simplified projection method for embodied energy based on the building size such as lot area or total floor area. 4.2. Operational energy The annual mean energy consumption including electricity and gas was calculated based on the survey data (Fig.6). It was found that cooking accounts for the largest percentage (27-76%), followed by the lighting (7-25%), the entertainment (5-24%) and the washing/bathing (1-26%). The operational energy of the houses was estimated based on the above calculation (Fig. 7). As shown, the operational energy ranges from 152.6 GJ to 2072.7 GJ among the selected houses. As before, the initial correlation analysis was conducted as indicated in Table 5. As shown, five variables, including ‘total floor area’, ‘household income’, ‘lot area’, ‘building age’ and ‘number of bedroom’, were recorded to have high r-values. As before, these five variables were found to be dependent of each other. It can be said tentatively that ‘total floor area’, ‘household income’ or ‘lot area’ is considered to be possible explanatory variable to the household operational energy consumption. Fig. 8 shows the relationship of total floor area with operational energy of the houses as an example. 4.3. Life cycle energy The life cycle energy for each house was obtained through Eq.
y = 4.39 x + 35.08 R² = 0.90
2000
1500
1000
500
Table 5). This is simply because the operational energy accounted for much larger portion in their life cycle energy. As before, it can
0
be said that ‘total floor area’, ‘household income’ or ‘lot area’ is considered to be possible explanatory variable to the life cycle area with life cycle energy of the houses as an example.
to 27 times.
Simple house
Medium house
House 11
House 10
House 9
House 8
House 7
House 6
0
House 5
t-C among the selected houses. The CO2 emissions during operation phase are greater than the embodied CO 2 emissions by 8
Operational Energy
500
House 4
were computed as shown in the same figure. The estimated CO2 emissions of the operational phase range from 21.6 t-C to 371.0
Embodied Energy
1000
House 3
houses. Similarly, the CO2 emissions during the operation phase
600
1500
House 2
emissions range from 1.2 t-C to 52.7 t-C among the selected
500
2000
House 1
corresponding CO2 emission factor (Fig. 11). The estimated CO2
200 300 400 Total floor area (m²)
2500
Life cycle energy (GJ)
multiplying the energy consumption for each fuel type by its
100
Fig. 8. Relationship between total floor area and operational energy
energy of the houses. Fig. 10 shows the relationship of total floor 4.4. Life cycle CO2 emissions The embodied CO2 emissions were calculated through
0
Luxurious house
Fig. 9. Life cycle energy of each house
5. Conclusions (1) The pilot survey was conducted in the city of Bandung in
order to initiate the development of simplified LCA model, which
1007
can be used under relatively poor data availability conditions.
Table 6. Correlation coefficient between selected variables and life cycle energy
(2) It was found that the detailed environmental and economic data were only available at the national level in Indonesia. (3) The actual on-site building measurement was conducted to obtain building material inventory data because most of the residential buildings in Bandung were constructed not in formal way. Moreover, household energy consumption data were acquired by the actual survey because of the unavailability of the energy consumption data record.
the above surveys. Further data collection is particularly required
1000 500
0
in terms of percentages of reused and recycled materials as well as
Acknowledgements
4)
5)
6)
7)
8) 9)
200
Embodied CO2 emission
150
Operational CO2 emission
100 50
Simple house
Medium house
House 11
House 9
House 8
0 House 10
3)
250
House 7
2)
Monahan, et al.: An embodied carbon and energy analysis of modern methods of construction in housing: A case study using a life cycle assessment framework, Energy and Buildings 43, pp.179–188, 2011. Utama N. A. et al.: Life cycle energy of single landed houses in Indonesia, Energy and Buildings 40, pp.1911–1916, 2008. Kurdi et al.: Determining factors of CO2 emission in housing and settlement of urban area in Indonesia, Ministry of Public Work of Indonesia and National Institute for Land, Infrastructure and Management, Japan, 2006. Catarina Thormark: A low energy building in a life cycle—its embodied energy, energy need for operation and recycling potential, Building and Environment 37, pp.429-435, 2002. Suzuki M. et al.: Estimation of life cycle energy consumption and CO2 emissions of office buildings in Japan, Energy and Building 28, pp. 33-41, 1998. Fujita Y. et al.: Assessment of CO2 emissions and resource sustainability for housing construction in Malaysia, International Journal of Low-Carbon Technologies Advance Access, pp. 1-11, March 6, 2009. Norman J. et al.: Comparing high and low residential density: life-cycle analysis of energy use and GHG emissions, Journal of Urban Planning and Development 132, pp.10-21, 2006. Mithraratne N. and Vale Brenda: Life cycle analysis for New Zealand houses, Building and Environment 39, pp.483-492, 2004. Crawford H.R. et al.: An assessment of energy and water embodied in commercial building construction, The 4th Australian Conference,
300
House 6
1)
350
House 1
References
600
400
CO2 emissions (t-C)
Foundation for Construction Materials Industry Promotion.
400
Fig. 10. Relationship between total floor area and life cycle energy
more accurate material inventory, thus CO 2 emission predictions.
Scientists (B) (N0. 23760551) and a grant from the TOSTEM
200
Total floor area (m²)
durable periods of building materials. This would help develop
This research was supported by a JSPS Grant-in-Aid for Young
0
House 5
comprising mathematical prediction models would be finalized by
1500
House 4
survey shown in this paper. The simplified LCA model
2000
House 3
Jakarta using the initial LCA model developed through the pilot
y = 5.65 x - 8.60 R² = 0.93
2500
House 2
Large-scale surveys would be carried out in Bandung and
Life cycle energy (GJ)
such as total floor area, lot area or household income.
Sig. ** ** ** ** ** * -
3000
a potential to develop simplified projection methods of embodied energy and CO2 emissions based on some explanatory variables
r-value 0.96 0.94 0.93 0.69 -0.69 -0.59 0.03
* = Significant at 5% level; ** = Significant at 1% level
(4) The results of initial statistical analyses indicated that there is energy as well as operational energy and thus to obtain life cycle
Variables Total floor area Household income Lot area No. of bedroom Building age Duration of living Household size
1 2 3 4 5 6 7
Luxurious house
Fig. 11. Life cycle CO2 emissions of each house Sydney, 2005. 10) Nansai K. et al.: An input-output analysis of total requirements of energy using input-output table, CGER-report, ISSN, 2002. 11) Bandung: Bandung in figures, Statistic Centre of West Java, 2010. 12) Indonesia: The Ministerial Decree of Public Work no. 45, 2007. 13) Tove M. and Mauritz: Life cycle assessment in buildings: The ENSLIC simplified method and guidelines, Renewable and Sustainable, Energy Reviews, pp.1341-4356, 2010. 14) Indonesia: Handbook of energy and economic statistics of Indonesia, Ministry of Energy and Mineral Resources of Indonesia, 2010. 15) Indonesia: Comprehensive assessment of different energy sources for electricity generation in Indonesia, Indonesian expert teams and IEAE, 2002. 16) Indonesia: Input-output table of Indonesia, Statistic Centre, 2005. 17) Indonesia: Indonesian national GHG emissions inventory status and needs, Asian least cost GHG abatement strategy, 1997. ［2012 年 2 月 20 日原稿受理 2012 年 4 月 24 日採用決定］
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