benchmarks as a tool for free allocation through comparison with similar project.pdf

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Benchmarks as a tool for free allocation through comparison with similar

projects: Focused on multi-family housing complex

Taehoon Hong, Choongwan Koo, Sungug Lee

Department of Architectural Engineering, Yonsei University, Seoul 120-749, Republic of Korea

h i g h l i g h t s

We propose the model for establishing benchmarks for free allowance allocation. The model can preliminarily estimate the amount of allowances in construction site.

The prediction performance of the proposed model is superior in all classification.

For the concrete, prediction accuracy and standard deviation are 93.45% and 6.01.

For the steel bar (94.20%; 4.34) and for the formwork (94.28%; 4.67), respectively.

a r t i c l e i n f o

Article history:

Received 14 May 2012

Received in revised form 12 September 2012

Accepted 13 October 2013

Keywords:

Emission trading schemeProcess-based LCA

Product-level LCA

Multi-family housings

Reinforced concrete frame

Case based reasoning

a b s t r a c t

A multilateral effort to reduce greenhouse gas (GHG) emissions has been implemented around the world.

In particular, the Emissions Trading Scheme (ETS) emerged as a market-based approach used to control

GHG emissions by providing carbon credits (or allowances). One of the most controversial issues in the

ETS is the question of how the allowances will be distributed. Therefore, this research aimed to develop a

decision support model for establishing benchmarks as a tool for free allocation in the construction

industry. It can be used in the pre-design phase to estimate the amount of allowances in a given construc-

tion site. In this study, a total of 147 types of data on the reinforced concrete frame in multi-family hous-

ing projects in South Korea were collected and used to develop the advanced Case-Based Reasoning

(CBR), which can be used to establish benchmarks as a tool for free allocation.

Results showed that the prediction performance of the advanced CBR model was superior (prediction

accuracy; standard deviation) in all classifications: concrete (93.45%; 6.01), steel bar (94.20%; 4.34), and

formwork (94.28%; 4.67). In the case study, a total of 60 possible combinations were evaluated in terms of

the economic and environmental impact simultaneously with the retrieved cases. The results of this

study could be expanded into other areas including new renewable energy, rehabilitation projects, and

demolition projects.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

The rapid growth of cities and subsequent industrialization has

led to the rise of various environmental issues, such as globalwarming and depletion of resources. With the Kyoto Protocol in

1997, however, a multilateral effort to reduce greenhouse gas

(GHG) emissions has been implemented around the world [1,2].

Under the treaty, Annex I Parties (which consist of 37 industrial-

ized countries and the European Community) commit themselves

to binding targets for GHG emissions. Toward this end, the protocol

defines three flexibility mechanisms that can be used by Annex I

Parties[3]. The three flexibility mechanisms are Emissions Trading

Scheme (ETS), Clean Development Mechanism (CDM), and Joint

Implementation (JI). Among these, the ETS (or cap-and-trade) is a

market-based approach used to control GHG emissions by provid-

ing carbon credits (or allowances) as economic incentives for

achieving the emissions reduction target. That is to say, nationsthat emit less than their quota will be able to sell the emission

credits to nations whose emissions exceed their quota [4,5].

One of the most controversial issues in the ETS is the question of

how the allowances will be distributed. Since the ETS creates a sig-

nificant value, decisions about the allocation of allowances in es-

sence result in arguable issues. It involves whether or not to

freely allocate the allowances, whether or not to auction the allow-

ances, or whether or not to use a combination of free allocation and

auctioning[6]. Emerging programs have changed in the transition

from free allocation to auction over time. A combination of both

free allocation and partial auction offers flexibility in order to

achieve environmental and economic objectives[7,8]. For example,

0306-2619/$ - see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.apenergy.2013.10.035

Corresponding author. Tel.: +82 2 2123 5788; fax: +82 2 2248 0382.

E-mail address:[email protected](T. Hong).

Applied Energy 114 (2014) 663675

Contents lists available at ScienceDirect

Applied Energy

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a p e n e r g y
http://dx.doi.org/10.1016/j.apenergy.2013.10.035mailto:[email protected]://dx.doi.org/10.1016/j.apenergy.2013.10.035http://www.sciencedirect.com/science/journal/03062619http://www.elsevier.com/locate/apenergyhttp://www.elsevier.com/locate/apenergyhttp://www.sciencedirect.com/science/journal/03062619http://dx.doi.org/10.1016/j.apenergy.2013.10.035mailto:[email protected]://dx.doi.org/10.1016/j.apenergy.2013.10.035http://-/?-http://-/?-http://-/?-http://-/?-http://crossmark.crossref.org/dialog/?doi=10.1016/j.apenergy.2013.10.035&domain=pdfhttp://-/?-


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in the first and second trading periods of the European Union Emis-

sions Trading Scheme (EU-ETS), the majority of emission allow-

ances have been freely given to entities covered under the

program, according to historical emissions. In the third trading per-

iod of the EU-ETS, free allocation of emission allowances will be

progressively replaced by auctioning of the allowances by 2020.

Yet, free allocation will surely continue to play a significant

role up to 2020. The proposal is being negotiated in the EuropeanParliament[9].

Within a free allocation, there may be a variety of acceptable

ways to distribute allowances: (i) grandfathering, allowances

based on historical emissions; and (ii) benchmarking, allowances

based on energy input or product output [9]. When allowances are

freely given to entities, the following requirements should be met.

They should be allocated in a manner that is fair, transparent, and

ambitious. In other words, the allocation approach needs to allow

entities getting a strong incentive for the achieving GHG emissions

reduction target. In this regard, free allocation based on historical

emissions called grandfathering is potentially problematic. Un-

der the free allocation method of grandfathering, most allowances

are assigned to the entities that have emitted most. To make the

ETS more efficient and effective, however, free allocation should

levy penalty on those who have emitted most. This can be achieved

through free allocation based on energy input or product output

called benchmarking. Theoretically, this can find the optimal

solution for allowance allocation that is fair, transparent, and

ambitious. Yet there remain considerable challenges in designing

an allocation scheme and in determining concrete values as the ac-

tual benchmarks [9,10].

South Koreas National Assembly passed legislation, The Act on

Allocation and Trading of Greenhouse Gas Emissions Allowances,

which would set the GHG emissions reduction target starting in

2015. It applies both to entities that emit more than 125,000 tCO2-eq./yr and to factories or buildings that produce more than 25,000

tCO2eq./yr. About 95% of allowances will be allocated for free to

companies, factories, or buildings for the first period (2015

2017) and the second period (20182020)[11]. To keep pace withthe current trend, the construction industry has taken various ac-

tions to reduce GHG emissions in buildings. The South Korean gov-

ernment has conducted a variety of research to establish the

allocation methods that are appropriate for the characteristics of

the construction industry. In particular, due to the uniqueness of

the construction site, which is substantially different from the

characteristics of the manufacturing industry, both policymakers

and construction entities are becoming more interested in the

benchmarking approach for allowance allocation.

Therefore, this research aimed to develop a decision support

model for establishing benchmarks as a tool for free allocation in

the construction industry. It can be used in the pre-design phase

to estimate the amount of allowances for each product (e.g., con-

crete, steel bar, or formwork) that is produced, transported, andconstructed in a given construction site. Along with this, it can pre-

liminarily estimate the construction cost that is required to achieve

the level of benchmark for allowance allocated to a given

construction site. Using the model developed in this study, both

policymakers and construction entities can establish in advance

the level of benchmark for allowance allocation specified to a given

construction site and negotiate it with each other. Also, construc-

tion entities can assess eco-friendly technologies under budget

constraints.

The scope of this study is limited to conduct the economic and

environmental impact assessment at the sites of construction pro-

jects, especially the collection of materials, which are assembled

into a reinforced concrete frame in multi-family housing complex

projects. Toward this end, the product-level LCA method wasadopted to conduct environmental impact assessment. The

product-level LCA method is one of the four-level methods (i.e.,

material-level, product-level, building-level, and industry-level)

to conduct an LCA, and is calculated as a collection of materials,

which are assembled into a final product. After a quantity takeoff

of the product is completed, the amount of the emissions from

each component of the product is determined. The detailed infor-

mation on the product-level LCA method can be founded in [12].

It has a limitation in analyzing all materials, and, thus, the mainmaterials that occupy a considerable amount of the total environ-

mental load should be determined. As proposed by [13], the

environmental load evaluation of a standard apartment unit in

Korea shows that the total ratio of CO2emissions by concrete, steel

bar, and formwork accounts for 70.12% of total CO2 emissions

generated during the construction phase of a reinforced concrete

frame in a multi-family housing complex. As provided by [12],

the ATHENA

Impact Estimator covers around 1200 assemblies,

consisting mainly of concrete, steel, and wood products used in

foundations and structural assemblies. Accordingly, this study se-

lected concrete, steel bar, and formwork as the main materials

for the reinforced concrete frame of a multi-family housing project.

Also, the process-based LCA method was implemented as a cra-

dle-to-gate approach for assessing the environmental load from

the material manufacturing through the on-site construction of

the building project. The process-based LCA method is one of the

two methods to conduct an LCA, and focuses on a specific product

rather than a sector. Accordingly, the major advantage of this

method is the ability to compare two products that have the same

function. The detailed information on the process-based LCA meth-

od can be founded in[12]. Since collection of the detailed design

information for performing energy simulation is limited in the

pre-design phase, an analysis of the operational environmental

load during the operation and maintenance phases was excluded

from this study.

Meanwhile, Case-Based Reasoning (CBR), one of the data-min-

ing methods, was adopted to establish the level of benchmark for

allowance allocation specified to a given construction site. CBR

has a powerful advantage because it cannot only present the pre-dicted value, but also historical data as references. Based on this

feature, policymakers or construction entities can estimate the le-

vel of benchmark for a given project by comparison with similar

projects that are retrieved through the CBR algorithm. In other

words, the CBR is characterized by suggesting the prediction re-

sults with a high explanatory power based on historical data. De-

spite such advantages of CBR, its prediction accuracy is inferior

to that of the other methodologies, such as Multiple Regression

Analysis (MRA) and Artificial Neural Network (ANN). To improve

prediction accuracy, MRA and ANN were integrated to filtering

the prediction results generated by CBR. Also, Genetic Algorithm

(GA) was used to apply the concept of optimization. The research

team names a series of processes in the advanced CBR model.

Additional information on the advanced CBR model can be foundin previous studies conducted by the research team [14,15].

In this study, a total of 147 project characteristics and quantity

data were collected on the reinforced concrete frame in multi-fam-

ily housing projects in South Korea. This study was carried out in

three steps: (i) the collected data were analyzed at the level of

the main materials (i.e., concrete, steel bar, and formwork) to

establish the case base; then, by using the advanced CBR model,

the quantity of the main materials is estimated; (ii) using the esti-

mated quantity, the construction costs and CO2 emissions in the

material manufacturing through on-site construction were esti-

mated; and (iii) based on the estimated construction costs and

CO2 emissions, the study proposed possible combinations on

which the economic and environmental impact assessment was

performed. The detailed input data can be found in Table S1 ofthe supplementary data.

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The advanced CBR model for economic and environmental

impact assessment in the pre-design phase, which this study pro-

poses, can be characterized as follows:

Using this model, both policymakers and construction enti-

ties can establish in advance the level of benchmark for

allowance allocation specified to a given construction site

and negotiate it with each other. This model estimates the construction costs and CO2emis-

sions based on the available information at the pre-design

phase, so it can save time and effort in terms of efficiency. This model improves prediction accuracy on material quan-

tity by using the advanced CBR model with a combination

of various methodologies, including MRA, ANN, and GA,

as well as CBR in terms of effectiveness.

The proposed model suggests not only the quantity as the

result of a simple prediction, but also shows the character-

istics of the projects performed in the past, because users

can compare several design options by changing the project

characteristics or selecting more optimal options among

the retrieved cases. For example, if high-strength concrete

is used, its CO2 emissions per unit quantity can be applied

to analyze the change in the final result. By using a Microsoft Excel-based VBA, the proposed model

systemizes the complicated process and equation to calcu-

late CO2emissions; and finally. It is expected that more products or assemblies can be eval-

uated using the proposed model.

2. Literature review

2.1. Life cycle assessment for buildings

Many studies have assessed the environmental load to whole

buildings and assemblies by using the internationally recognized

Life Cycle Assessment (LCA) methodology. First, various studies

have been conducted to assess CO2 emissions from buildings.These assessments evaluated greenhouse gas emissions based on

classification standards, such as structural type, plan type, activity,

construction method, or structural strength. A detailed examina-

tion of the energy and greenhouse emissions associated with the

construction of alternative structural systems, such as wood, steel,

and concrete structural assemblies, was analyzed [16]. The com-

parison of the environmental effects of steel- and concrete-framed

buildings has been analyzed according to all life-cycle phases [17].

The environmental performance of high-strength concrete used in

super tall buildings was evaluated using the suitable LCA method

by life-cycle phases[18]. The economic and environmental assess-

ment of green roof systems or energy-saving techniques for build-

ings was conducted by using life cycle cost (LCC) and life cycle CO2

(LCCO2) analysis [1922]. Several studies on the effect of energy-saving and CO2 emissions reduction in the operation and mainte-

nance phase were carried out by using LCC, LCCO 2, or LCA[2328].

Second, there have been several studies that were focused on

developing the model for conducting LCA. A simple life cycle CO2assessment system was proposed to assess GHG emissions in the

life cycle phases of a standard Korean apartment[13,29]. A hybrid

approach, combining both process-based LCA (P-LCA) and eco-

nomic inputoutput LCA (EIO-LCA), has also been proposed

[30,31]. A new LCA approach for buildings, called region-based life

cycle impact assessment (R-LCIA), has likewise been proposed,

which consists of the local environmental burden (EB) and at-

tached EB[32]. The relevance of simplified LCA of building compo-

nents, which aims at providing results of similar quality as

comprehensive assessments with less effort, has been analyzed[33]. The LCI model was formed as a global methodology that

combined advanced optimization techniques, LCI, and cost-benefit

assessment, including boundary conditions for thermal comfort,

indoor air quality, and legal requirements for energy performance

[34].The integrated model for assessing the cost and CO2emission

(IMACC) was developed for sustainable structural design in ready-

mix concrete[35].

Third, the environmental assessment tools were examined and

analyzed in terms of their characteristics and limitations in con-ducting building environmental assessment. The role and limita-

tions of current environmental building assessment methods in

identifying building sustainability were analyzed from the per-

spective of different countries[36]. The foundations for the devel-

opment of an LCA program for buildings were established with a

comparison of domestically and foreign designed programs [37].

A review of recent developments of LCA methods was carried

out, focusing on areas where there has been significant methodo-

logical development in recent years[38]. The field of building envi-

ronmental assessment tools was clarified and discussed in terms of

the differences of the tools as a group rather than as individual fac-

tors. For example, a classification system, Assessment Tool Typol-

ogy, was introduced by the ATHENA Institute, which has three

levels: (i) Level 1 was defined as product comparison tools and

information sources (e.g.,BEES); (ii) Level 2, whole building design

or decision support tools (e.g.,ATHENA); and (iii) Level 3, whole

building assessment frameworks or systems (e.g., LEED

)[39]. The

detailed analysis on the tools can be found in Table S2 of the

supplementary data.

As mentioned above, most of the previous studies have focused

on assessing the environmental load of entire buildings or assem-

blies, but not on estimating it in the early stage of a project. In

other words, the current environmental assessment tools for build-

ings could be used after completing the detailed design, based on

the bill of quantity. Accordingly, it is determined that they have

shortcomings that could demand a considerable amount of time

and effort in assessing the environmental load, and could not be

also used in the early phase of a project. Additionally, they could

not be used to establish benchmarks as a tool for free allocationsof GHG emissions permits in the construction industry.

2.2. Preliminary estimation in the early stages of the construction

project

There are many studies on preliminary estimation in the early

stages of the construction project. Most of the previous studies

have focused on cost estimation or energy consumption prediction.

Detailed literature reviews on cost estimation can be found in the

previous studies conducted by the research team[15,40]and those

on energy consumption prediction have been also conducted

[14,25,26]. The estimation of the material quantities in the early

stages was often carried out by using data-mining methodologies,

such as MRA, ANN, and CBR in previous studies. First, the MRAmethod was used to estimate material quantities by parametric

statistical equations. Then[41]developed the early cost estimating

models for road construction projects and[42]proposed a concep-

tual cost-estimate model for bridge foundations based on the esti-

mation of materials quantities.

Second, the ANN method was used to estimate the material

quantities by repetitively performing machine learning.[43]devel-

oped a system to assist in the early cost estimation of road tunnels;

[44] established the relationship between the quantities of con-

crete and form-work required for the structural elements of

high-rise commercial buildings;[45]proposed the logarithm-neu-

ron network to improve its efficiency and accuracy in quantity esti-

mation of steel and RC buildings; and[46]developed the model for

identifying and controlling the variances in the quantity of anywork package of building construction projects.

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ton/m2 under the ratio scale. Then, the study collected related data

on the project characteristics and the quantity of the reinforced

concrete frame of 147 multi-family housing projects. Based on

the collected data, a database was established.

4.2. Retrieval of similar cases

There were various methodologiesCBR, MRA, ANN, or decision

treethat could be applied to the quantity estimation of the rein-

forced concrete frame of a multi-family housing project during the

pre-design phase. However, the construction materials and meth-

ods can be changed, depending on the characteristics of a project.

Moreover, if new techniques or methods are applied to a project, it

will be difficult to produce an accurate result with a simple estima-

tion. In other words, even with an identical material quantity, the

construction costs and CO2 emissions may differ. To reflect such

changes in the characteristics of a project, it is necessary to propose

a model that can present not only the prediction result, but also the

characteristics of previous projects. This study selected CBR as a

methodology to satisfy such requirements. To improve prediction

accuracy, the study also used the advanced CBR model that com-

bined various methodologies including MRA, ANN, and GA.

The CBR methodology consists of three phases: the attribute

similarity, the case similarity, and the prediction performance.The attribute similarity can be calculated by the differences in

the independent variables between the test and the retrieved

cases, which can be shown in Eq.(1). The case similarity can be cal-

culated by using the attribute similarity and attribute weight of all

attributes, which can also be shown in Eq.(2). Finally, the predic-

tion performance can be calculated by the differences in the

dependent variables between the test and retrieved cases, as

shown in Eqs.(3) and (4).

fASx 100 jAVTest CaseAVRetrieved Case j

AVTest Case 100

if fASxPMCAS

0 if fASx


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PVANNis the predicted value of the ANN model, and SERANN is the

standard error rate of the ANN model.

MaxMinPRMRA; MinPRANN 6 CRMA6 MinMaxPRMRA; MaxPRANN

7

MinCRMA 1 TRCRMA100

6 CRMA

6 MaxCRMA 1 TRCRMA100

8

whereCRMA is the cross-range between the predicted value of the

MRA and ANN models, TRCRMAis the tolerance range of CRMA, and

CRMA is the filtering range in which TRCRMA was applied to CRMA.

5. Calculation of construction costs and CO2 emissions

5.1. Calculation of construction costs

Construction cost can be calculated by multiplying the esti-

mated quantity by unit cost of each main material. This study se-lected concrete, steel bar, and formwork as the main materials

[13,29]. With the process in Section 4, the study estimated the

quantity of the main materials as well as the construction cost

by using the unit cost of each main material, as shown in following

equation:

Construction Cost Xni1

EMQi UCi 9

whereEMQ is the estimated material quantity, UCis the unit cost,

and n is the number of main materials including concrete, steel

bar, and formwork in this research.

5.2. Calculation of CO2 emissions

CO2 emissions can be calculated as material manufacturing,

material transportation, and on-site construction according to the

material life cycle [12]. (i) In the material manufacturing phase,

CO2 emissions are calculated by using the estimated quantity and

Life Cycle Inventory (LCI), as shown in Eq. (10). In the material

transportation phase, CO2 emissions are calculated by using the

estimated quantity, the load capacity and the fuel efficiency of

the transportation vehicle, transportation distance, and the CO2conversion factor, as shown in Eq.(11). In the on-site construction

phase, CO2emissions are calculated by the estimated quantity, the

energy consumption per unit quantity of the construction equip-

ment, and the CO2conversion factor, as shown in Eq.(12). A Micro-

soft Excel-based VBA was used in this series of processes toproduce the final result in a systematic, accurate, easy and quick

manner.

CMXni1

EMQi CCFi 10

where CM is the carbon dioxide emission in material manufactur-

ing, EMQ is the estimated material quantity, CCF is the carbon

dioxide conversion factor, and n is the number of materials.

CTXni1

EMQiELCi

TDi 2EFEi

CCFi 11

whereCTis the carbon dioxide emission in material transportation,

EMQis the estimated material quantity, ELC is the equipment loadcapacity, TD is the one-way transportation distance, EFE is the

equipment fuel efficiency,CCFis the carbon dioxide conversion fac-

tor, and n is the number of materials.

CCXni1

EMQi ECUQi CCFi 12

where CCis the carbon dioxide emission in on-site construction,

EMQis the estimated material quantity, ECUQ is the energy con-sumption per unit quantity, CCF is the carbon dioxide conversion

factor, and n is the number of materials.

6. Prediction performance

Table 2shows that the prediction performance of the advanced

CBR model is superior (prediction accuracy; standard deviation) in

all classifications: concrete (93.45%; 6.01); steel bar (94.20%; 4.34);

and formwork (94.28%; 4.67). In the case of the steel bar, the pre-

diction accuracy of the advanced CBR was somewhat smaller

(94.20%) than that of the ANN model (94.60%). However, this re-

mains an excellent result, an improvement over the prediction

accuracy of the CBR model. This result is identical to the resultsverified in previous studies that used the advanced CBR model

[14,15,25,26]. The advanced CBR model is a sophisticated model

that offers both higher explanatory power, which is an advantage

of the CBR methodology, and higher prediction accuracy, which

is an advantage of MRA, ANN, and other methodologies.

The proposed advanced CBR model is based on the CBR model,

and since more cases are accumulated in the case base, prediction

performance will be more accurate.

7. Case study

A case study was conducted to verify the reliability and applica-

bility of the proposed advanced CBR model for the economic and

environmental impact assessment in the pre-design phase.

To acquire the representability of the case study, the study se-

lected a case that is close to the average quantity per unit area of

each main materialconcrete, steel bar, and formworkas its

test case. The test case has the following characteristics: rein-

forced concrete as the type of structure; 0.6407 m3/m2 as the

quantity of concrete per unit area (14.25% less than 0.7472 on

average); 0.0795 ton/m2 as the quantity of steel bar per unit area

(1.23% more than 0.0785 on average); 5.3885 m2/m2 as the quan-

tity of formwork per unit area (8.85% less than 5.9116 on aver-

age); 4251 m2 of the total floor area (17.21% less than 5135 m 2

on average); and 56 households (4.66% less than 58.74 on

average).

Table 2

Prediction performance by model.

Classification Methodology Prediction accuracy Standard deviation

Concrete MRA 89.40 6.27

ANN 91.75 5.95

CBR 91.97 9.57

Advanced CBR 93.45 6.01

Steel bar MRA 93.03 5.23

ANN 94.60 4.37

CBR 92.69 7.02


Formwork MRA 90.95 4.91

ANN 94.18 4.15

CBR 93.07 7.59




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In this research, the streamlined LCA process was conducted by

following four steps stipulated by ISO 14040: (1) goal and scope

definition; (2) inventory analysis; (3) impact assessment; and (4)

results and interpretations[12].

Step 1. Goal and scope definition: based on the information

available in the pre-design phase, the CO2emissions gener-

ated from the material manufacturing phase to the on-siteconstruction phase are calculated and analyzed along with

the estimated construction costs. An economic and envi-

ronmental impact assessment is subsequently performed. Step 2. Inventory analysis: the CO2 emissions per unit

quantity of the main materials of the reinforced concrete

frame of a multi-family housing are calculated using an

inter-industry analysis (domestic and overseas).

Step 3. Impact assessment: to assess global warming

potential, CO2 emissions produced in step 2 are used as a

representative index. Other greenhouse gases, such as

methane (CH4), nitrous oxide (N2O), are included in this

category, thus the term CO2 equivalent is implemented

to express an impact, and not an emission.

Step 4. Results and interpretations: using the estimated

construction cost and CO2 emissions, an economic and

environmental impact assessment is performed.

Fig. 3 shows the diagram of the scenario for the use of LCA,

based on the above four-phase LCA process in this study. The dia-

gram signifies a system boundary that dictates the breadth and

depth of the LCA process. It consists of five categories, including life

cycle phase, building systems, life cycle inventory, life cycle im-

pact, and the phase during which LCA is conducted.

7.1. Estimation of material quantities using the advanced CBR model

Tables 35 show the estimations of material quantities using

the advanced CBR model: (i) in the case of concrete (refer toTable 3), two cases were retrieved. The average prediction accuracy

was 91.43%, at which the average quantity per unit area was

0.6956 m3/m2; (ii) in the case of the steel bar (refer to Table 4), five

cases were retrieved. The average prediction accuracy was 96.78%,

at which the average quantity per unit area was 0.0791 ton/m2;

and (iii) in the case of the formwork (refer to Table 5), six cases

were retrieved. The average prediction accuracy was 95.43%, at

which the average quantity per unit area was 5.6314 m2/m2.Tables

35show the detailed description of each retrieved case.

7.2. Calculation of construction costs

Construction costs can be calculated by multiplying the

estimated quantity by the unit cost (refer to Eq.(9)).Table 6shows

the detailed explanation of the description, unit, and unit cost

of each main material[50]. For example, the quantity of concrete

is 2724.06 m3. Considering that the unit cost of concrete is

US$52.49/m3, and then the construction cost of concrete is

US$142997.85. The same process can be applied to the steel bar

and the formwork, so the construction cost for each of the two

materials is US$227308.86 and US$590983.74, respectively.

7.3. Calculation of CO2emissions

7.3.1. Material manufacturing phase

In the material manufacturing phase, the CO2emissions for the

estimated quantity based on the advanced CBR model can be calcu-

lated using the LCI (refer to Eq. (10)). Table 7 shows the carbon

dioxide conversion factor of the main materialsconcrete, steel

bar, and formwork. This study established the CO2 emissions per

unit quantity by main material through an inter-industry analysis

(domestic and overseas) [18] and a detailed description by main

material based on the data applied to the test case.

7.3.2. Material transportation phase

In the material transportation phase, the CO2 emissions for the

estimated quantity based on the advanced CBR model can becalculated using the characteristics of each transportation vehicle

(e.g., load capacity, fuel efficiency, transportation distance, and

Fig. 3. Scenario for the use of LCA.

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CO2 conversion factor (diesel, 2.5841 kgCO2/))[17],[29]and[32]

(refer to Eq. (11)).Table 8 shows the characteristics of the trans-portation vehicle used in this study. Based on the data applied to

the test case, this study established the characteristics of the trans-

portation vehicles (such as the type of transportation vehicles ortransportation distances).

Table 3

The retrieved cases using the advanced CBR model (concrete).

Variable Case

No.

Total

floor

area

(m2)

Exclusive

use area

(m2)

Common

use area

(m2)

Building

area

(m2)

Ground

floor

area

(m2)

Standard

floor area

(m2)

No. of

stories

No. of

households

Size of

households

Case

similarity

score

Quantity

per unit

area (m3/

m2)

Prediction

accuracy

(%)

Test case 84 4251.78 3349.75 966.56 340.22 173.80 299.12 15 56 59 0.6407

Retrieved

case 1

6 4282.81 3349.64 1095.92 391.82 173.74 298.09 15 57 59 0.96 0.6916 92.05

Retrieved

case 2

37 3979.24 3110.49 897.52 340.22 37.49 299.12 15 52 59 0.96 0.6996 90.81

Average quantity per unit area 0.6956 91.43

Table 4

The retrieved cases using the advanced CBR model (steel bar).

Variable Case

No.

Total

floor

area

(m2)

Exclusive

use area

(m2)

Common

use area

(m2)

Building

area

(m2)

Ground

floor

area

(m2)

Standard

floor area

(m2)

No. of

stories

No. of

households

Size of

households

Case

similarity

score

Quantity

per unit

area

(ton/m2)

Prediction

accuracy

(%)

Test case 84 4251.78 3349.75 966.56 340.22 173.80 299.12 15 56 59 0.0795

Retrieved

case 1

85 4692.62 3351.04 964.88 380.32 210.22 329.48 15 56 59 0.92 0.0773 97.23

Retrieved

case 2

103 4823.90 3470.72 1133.32 376.20 211.18 329.48 15 58 59 0.90 0.0760 95.56

Retrieved

case 3

102 4823.90 3470.72 1133.32 376.20 211.18 329.48 15 58 59 0.90 0.0779 97.98

Retrieved

case 4

108 3807.54 2692.80 879.30 376.20 211.18 265.27 15 45 59 0.87 0.0828 95.81

Retrieved

case 5

82 4005.64 3110.82 897.52 466.28 248.69 299.12 15 52 59 0.83 0.0816 97.31


Table 5

The retrieved cases using the advanced CBR model (formwork).

Variable Case

No.

Total

floorarea

(m2)

Exclusive

use area(m2)

Common

use area(m2)

Building

area(m2)

Ground

floorarea

(m2)

Standard

floor area(m2)

No. of

stories

No. of

households

Size of

households

Case

similarityscore

Quantity

per unitarea (m2/

m2)

Prediction

accuracy(%)

Test case 84 4.251.78 3.349.75 966.56 340.22 173.80 299.12 15 56 59 5.3885

Retrieved

case 1

105 4282.81 3.349.64 1.095.92 391.82 173.74 298.09 15 57 59 0.98 5.8145 92.10

Retrieved

case 2

85 4.692.62 3.351.04 964.88 380.32 210.22 329.48 15 56 59 0.95 5.3780 99.81

Retrieved

case 3

99 4347.00 3.469.27 1.135.06 427.32 173.74 298.09 15 58 59 0.94 5.7707 92.91

Retrieved

case 4

103 4.823.90 3.470.72 1.133.32 376.20 211.18 329.48 15 58 59 0.92 5.4436 98.98

Retrieved

case 5

102 4.823.90 3.470.72 1.133.32 376.20 211.18 329.48 15 58 59 0.92 5.6421 95.29

Retrieved

case 6

108 3.807.54 2.692.80 879.30 376.20 211.18 265.27 15 45 59 0.90 5.7397 93.48


Table 6

Unit cost by main material.

Main materials Description Unit Quantity Unit cost (US$) Construction cost (US$)

Concrete Ready mixed concrete m3 2724.06 52.49 142997.85

Steel bar Deformed steel bar ton 337.94 672.64 227308.86

Formwork Plywood m2 22910.88 25.79 590983.74

Note: The exchange rate (KRW/USD) is 1116.5 won to a US dollar (as of 5 March 2012). The quantity is based on the actual value of the main materials of Case No. 84, which is

the test case of the case study.



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7.3.3. On-site construction phase

In the on-site construction phase, the CO2emissions for the esti-

mated quantity based on the advanced CBR model can be calcu-

lated using the characteristics of each construction equipment

(e.g., energy consumption per unit quantity, CO2conversion factor

(electricity, 0.4705 kgCO2/kW h; diesel, 2.5841 kgCO2/)) [29]

(refer to Eq. (12)). Table 9 shows the characteristics of the

construction equipment used in this study, which was limited to

the equipment commonly used in the reinforced concrete frame,

the tower crane for lifting materials, the hoist for lifting workers,

the pump car for pouring concrete, and the vibrator for consolidat-

ing concrete.

7.3.4. Total amount of CO2emissionsTables 1012show the total amount of CO2emissions from the

material manufacturing phase to the on-site construction phase by

using the estimated quantity based on the advanced CBR model.

The results of the analysis on the main materials (concrete, steel

bar, and formwork) are shown inTables 10, 11 or 12, respectively.

7.4. Economic and environmental impact assessment

As illustrated in Section7.1, the quantity per unit area for the

main materials (i.e., concrete, steel bar, and formwork) in the rein-

forced concrete frame of a multi-family housing project was esti-

mated. Based on these results, the construction costs and CO2emissions by main material were calculated in Sections 7.2 and

7.3. However, when more than one retrieved case is presenteddue to the characteristics of the CBR methodology, the final result

can be presented as a range instead. Table 13shows the range of

the estimates of the construction cost and CO2 emissions by main

material. Here, the unit of the construction cost is $1K, and that of

the CO2emissions is tCO2.

As shown inTable 13, the best estimate of the construction cost

for the concrete was 155.26 with a range of 154.37156.14. The

best estimate of the CO2 emissions for the concrete was 567.65

with a range of 564.40570.92. The same type of result was pre-

sented for the steel bar and the formwork. Finally, the estimated

results of each main material were summed together to calculate

the total construction cost and CO2 emissions. As mentioned in

Section 1, however, this study analyzed the main materials (i.e.,

concrete, steel bar, and framework) that occupy a considerable

amount of the total environmental load of a standard apartment

unit in Korea, which accounts for 70.12% of total CO2 emissions

generated from a reinforced concrete frame in a multi-family hous-

ing complex. Therefore, the results mentioned above have been

converted into a percentage. The best estimate of the totalconstruction cost resulted in 1424.87 with a range of 1371.11

1469.87. The best estimate of the total CO2 emissions resulted in

3314.97 with a range of 3195.543428.95.

The final decision-maker (i.e., policymakers or construction

entities) can establish the target construction costs and CO2emis-

sions of a given project in the pre-design phase by using the result

presented. However, the result may be different depending on the

decision made by the final decision-maker (refer to Fig. 4).Fig. 4

shows 60 possible combinations of the estimated construction

costs and CO2 emissions in the scatter diagram. In the case study

of this research, two similar cases were retrieved for the concrete,

five cases for steel bar, and six cases for formwork. Thus, the case

study had 60 possible combinations (2 5 6; refer toTables 35).

As shown inFig. 4, the low and high estimates are located in the

bottom left-hand corner and the upper right-hand corner of the

scatter diagram, respectively, while the best estimate is in the cen-

ter of the scatter diagram because it is the average value. However,

Fig. 4shows that the actual value of the test case (Case No. 84) is

located very closely to the low estimate. This result may differ on

a case-to-case basis. Thus, depending on the criteria of the final

decision-maker, the difference between the target value and the

actual value of the construction costs and CO2 emissions may be

changed.

Figs. 5 and 6show the difference between the target and actual

values (test case) of the construction costs and CO2 emissions,

depending on the final decision-maker. Figs. 5 and 6 also show

the most similar case proposed by the advanced CBR model: the

case with the highest case similarity score (Scenario 1). In terms

of the economic impact, the construction cost showed an error of+3.94%, +0.01%, +7.22% and +5.39% for the best estimate, the low

estimate (Scenario 8; here, Scenario 8 means a combination of the

1st ranked retrieved cases for the concrete, the 2nd ranked for the

steel bar, and the 2nd ranked for the formwork, which is abbrevi-

ated as C1 + S2 + F2), high estimate (scenarios 49; C2 + S4 + F1),

and the 1st case similarity (scenario 1; C1 + S1 + F1), respectively.

In terms of the environmental impact, CO2 emissions showed an

error of +3.21%, 0.51%, +6.75%, and +3.29%, respectively (refer to

Fig. 6).

Such results can be changed depending on the attributes of var-

ious factors, such as estimated quantity, the type of material and

Table 7

CO2emission per unit quantity by main material in the material manufacturing stage.

Main materials Detailed description CO2 emission per unit quantity

Co ncre te Ready mi xed con cre te 18 6. 493 kg CO2/m3

Steel bar Deformed s teel bar 3.052 kgCO2/kg

Formw ork W at erproof plywood 1.516 kg CO2/kg

Table 8

The characteristics of transportation vehicle by main material in material transportation stage.

Main materials Transport vehicle Load capacity Fuel efficiency (km/) Tra nsport ation d istance s ( km) Powe r sourc e

Concrete Ready mixed concrete truck 6 m3 (13.11 ton) 2.44 8.66 Diesel

Steel bar 20 ton trailer 20 ton 3.1 82.85 Diesel

Formwork 8 ton truck 8 ton 4.5 43.92 Diesel

Table 9

The characteristics of the construction equipment by main material in the on-site construction stage.

Main materials Construction equipment

Tower crane (kW h/ton, electricity) Hoist (kW h/ton, electricity) Pump car (/ton, diesel) Vibrator (/ton, diesel)

Concrete 0.553 0.238 0.183

Steel bar 1.354 0.553

Formwork 1.354 0.553

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equipment, unit cost, or carbon dioxide conversion factor. There-

fore, the economic and environmental assessment should consider

the characteristics of a given project. The result should be used to

establish the target criteria for the construction costs and CO2

emissions of the given project.

8. Conclusions and discussion

The objective of this study is to develop a decision support

model for establishing benchmarks as a tool for free allocation in

the construction industry, which can be used in the pre-design

phase. The scope of this study is limited to conduct the economic

and environmental impact assessment at the sites of construction

projects, especially the collection of materials that are assembled

into a reinforced concrete frame in multi-family housing complex

projects.

The process-based LCA method was implemented as a cradle-

to-gate approach to assess the environmental load from the mate-

rial manufacturing phase to the on-site construction phase of abuilding project. The product-level LCA method was also adopted

to calculate the collection of materials, which are assembled into

the reinforced concrete frame in a multi-family housing project.

Material quantity was estimated using the advanced CBR model

that was proposed in this study. A total of 147 project characteris-

tics and quantity data were collected on the reinforced concreteframe in multi-family housing projects in South Korea. This study

was carried out in three steps: (i) the collected data were analyzed

at the level of the main materials (i.e., concrete, steel bar, and form-

work) to establish the case base and the quantity of the main mate-

rials is estimated using the advanced CBR model; (ii) using the

estimated quantity, the construction costs and CO2 emissions in

the material manufacturing phase to the on-site construction

phase were estimated; and (iii) based on the estimated construc-

tion costs and CO2 emissions, the study proposed possible

combinations on which the economic and environmental impact

assessment was performed.

A case study was conducted to verify the reliability and applica-

bility of the advanced CBR model, which was proposed for the eco-

nomic and environmental impact assessment in the pre-design

phase of the construction project. The results are discussed below.

Table 10

Total amount of CO2 emission (concrete).

Variable Case

No.

Quantity per unit area (m3/

m2)

Amount of CO2 emission (kgCO2)

Material

manufacturing

Material

transportation

On-site

construction

Total amount of CO2emission

Test case 84 0.6407 508028.46 8346.0076 6489.31 522863.78

Retrieved case 1 6 0.6916 548388.46 9006.3510 7004.84 564399.65

Retrieved case 2 37 0.6996 554731.87 9098.0654 7085.87 570915.81

Average 0.6956 551560.17 9043.0368 7045.36 567648.56

Note: If the quantity per unit area (0.6916 m3/m2) of the concrete in Retrieved Case 1 (Case No. 6) is applied to the total floor area of the test case (Case No. 84) (4251.78 m 2)

(refer toTable 3), the total quantity of the concrete will be 2940.53 m3. Applying this result to the CO2emission per unit quantity (186,493 kg of CO2/m3) in the manufacturing

stage of concrete, which is shown inTable 7, produces 548388.49 kg of CO2.Tables 8 and 9can be used to calculate the total amount of CO2emissions in the stages of material

transportation and on-site construction. As a result, the total amount of CO2emission of Retrieved Case 1 (Case No. 6) is 564399.65 kg of CO2. Such a process can be equally

applied to the steel bar (refer to Table 11) and formwork (refer toTable 12).

Table 11

Total amount of CO2 emission (steel bar).

Variable Case

No.

Quantity per unit area

(ton/m2)


Material

manufacturing

Material

transportation

On-site

construction


Test case 84 0.0795 1031626.39 2348.1133 239.81 1034214.31Retrieved case 1 85 0.0773 1003078.24 2348.1133 233.18 1005659.53

Retrieved case 2 103 0.0760 986208.87 2348.1133 229.25 988786.24

Retrieved case 3 102 0.0779 1010864.10 2348.1133 234.99 1013447.20

Retrieved case 4 108 0.0828 1074448.62 2486.2376 249.77 1077184.62

Retrieved case 5 82 0.0816 1058876.90 2486.2376 246.15 1061609.28

Average 0.0791 1026435.82 2348.1133 238.61 1029022.53

Table 12

Total amount of CO2 emission (formwork).

Variable Case

No.

Quantity per unit area

(m2/m2)


Material

manufacturing

Material

transportation

On-site

construction


Test case 84 5.3885 691965.38 2925.6147 345.41 695236.41Retrieved case 1 105 5.8145 752680.99 3228.2644 375.24 756284.50

Retrieved case 2 85 5.3780 684307.93 2875.1730 342.08 687525.19

Retrieved case 3 99 5.7707 748351.68 3127.3812 372.98 751852.04

Retrieved case 4 103 5.4436 697713.27 2925.6147 348.38 700987.27

Retrieved case 5 102 5.6421 729053.32 3076.9396 363.57 732493.82

Retrieved case 6 108 5.7397 734998.11 3127.3812 367.05 738492.55

Average 5.6314 738331.49 3127.3812 368.45 741827.32



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First, the study applied the advanced CBR model to each of the

main materials of the reinforced concrete frame of a multi-family

housing project. The result showed that the prediction perfor-

mance of the advanced CBR model was superior to that of the other

models (i.e., MRA, ANN, or CBR) (refer toTable 2). The result proved

that the advanced CBR model is a sophisticated model that offers

both higher explanatory power, which is the advantage of the

CBR methodology, and higher prediction accuracy, which is the

advantage of MRA, ANN, and other methodologies.

Second,using theestimatedquantity bythe advancedCBR model,

thestudy could estimatethe constructioncosts andCO2 emissionsof

each main material. Particularly, CO2 emissions were subdivided

into the material manufacturing phase, the material transportation

phase, and the on-site construction phase so as to systematically

and accurately calculate the result. Toward this end, the Microsoft

Excel-based VBA was used in this series of processes.

Third, possible combinations were constructed based on the

estimated construction costs and CO2 emissions, which were the

object of the economic and environmental impact assessment.

The result was presented both in a scatter diagram and in a bar

chart. In this way, the study offered visible and concrete references

to support final decisions. In other words, policymakers and

Table 13

Range of estimates of the construction cost and CO 2 emission by main material.

Main materials Evaluation index Estimates range (70.12%) Estimates range (100%)

Best Low High Best Low High

Concrete Construction cost 155.26 154.37 156.14 221.42 220.15 222.68

CO2 emission 567.65 564.4 570.92 809.54 804.91 814.20

Steel bar Construction cost 226.24 217.22 236.83 322.65 309.78 337.75

CO2 emission 1029.02 988.79 1077.18 1467.51 1410.14 1536.20

Formwork Construction cost 617.62 589.83 637.7 880.80 841.17 909.44

CO2 emission 727.79 687.53 756.28 1037.92 980.50 1078.55

Total Construction cost 999.12 961.42 1030.67 1424.87 1371.11 1469.87

CO2 emission 2324.46 2240.71 2404.38 3314.97 3195.54 3428.95

Note: The unit of the construction cost is US$1K and the unit of the CO 2 emissions is tCO2.

Fig. 4. Scatter diagram of estimated construction costs and CO2emissions.

Fig. 5. Comparison of estimated construction costs in terms of economic impact.

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construction entities can establish in advance the level of bench-

mark for allowance allocation specified to a given construction site

and negotiate it with each other. Also, it is expected that construc-

tion entities can assess the eco-friendly technologies under budget

constraints by changing the project characteristics or selecting

more optimal choices among the retrieved cases.

With the reinforced concrete frame of a multi-family housing

project, this study limited its scope to performing the economic

and environmental impact assessment during the pre-design

phase. To complement this studys limitation, the following re-

search efforts are currently being conducted by the research

team:

Extended research on finishing and mechanical electrical

plumbing, other than the reinforced concrete frame of a

multi-family housing project.

Research on the establishment of assembly costs and CO2

emissions data at the level of building components (e.g.,wooden wall, concrete roof, etc.) to perform economic

and environmental impact assessment in the schematic

design phase. Research on performing an economic and environmental

impact assessment according to changes in the structural

type, plan type, activity, construction method, or structural

strength. Research on multilateral impact categories based on the

factors affecting life cycle impact assessment, such as glo-

bal warming potential, acidification potential, eutrophica-

tion potential, and ozone depletion potential. Research related to expansion into other areas, such as new

renewable energy sources, rehabilitation projects, and

demolition projects.

Acknowledgements

This research was supported by Basic Science Research Program

through the National Research Foundation of Korea (NRF) funded

by the Ministry of Education, Science and Technology (Nos.

2012-004376 and 2012-0001247).

Appendix A. Supplementary material

Supplementary data associated with this article can be found, in

the online version, at http://dx.doi.org/10.1016/j.apenergy.2013.10.035.

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Fig. 6. Comparison of estimated CO2 emissions in terms of environmental impact.

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