Hybrid LCA model for assessing the embodied environmental impacts of buildings in South Korea

Environmental Impact Assessment Review 50 (2015) 143–155

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Environmental Impact Assessment Review journal homepage: www.elsevier.com/locate/eiar

Hybrid LCA model for assessing the embodied environmental impacts of buildings in South Korea Minho Jang a, Taehoon Hong b,⁎, Changyoon Ji b a b

Asset Management Division, Mate Plus Co., Ltd., 9th Fl., Financial News Bldg. 24-5 Yeouido-dong, Yeongdeungpo-gu, Seoul, 150-877, Republic of Korea Department of Architectural Engineering, Yonsei University, Seoul, 120-749, Republic of Korea

a r t i c l e

i n f o

Article history: Received 19 June 2014 Received in revised form 15 September 2014 Accepted 17 September 2014 Available online xxxx Keywords: Life cycle assessment Hybrid model Embodied environmental impact Building construction

a b s t r a c t The assessment of the embodied environmental impacts of buildings can help decision-makers plan environment-friendly buildings and reduce environmental impacts. For a more comprehensive assessment of the embodied environmental impacts of buildings, a hybrid life cycle assessment model was developed in this study. The developed model can assess the embodied environmental impacts (global warming, ozone layer depletion, acidification, eutrophication, photochemical ozone creation, abiotic depletion, and human toxicity) generated directly and indirectly in the material manufacturing, transportation, and construction phases. To demonstrate the application and validity of the developed model, the environmental impacts of an elementary school building were assessed using the developed model and compared with the results of a previous model used in a case study. The embodied environmental impacts from the previous model were lower than those from the developed model by 4.6–25.2%. Particularly, human toxicity potential (13 kg C6H6 eq.) calculated by the previous model was much lower (1965 kg C6H6 eq.) than what was calculated by the developed model. The results indicated that the developed model can quantify the embodied environmental impacts of buildings more comprehensively, and can be used by decision-makers as a tool for selecting environment-friendly buildings. © 2014 Elsevier Inc. All rights reserved.

Abbreviations: ADP, Abiotic depletion potential; AMOqj, The annual monetary output produced in commodity sector j included in industry sector q; AP, Acidification potential; APiq, The physical amount of pollutant i emitted directly per year from industry sector q, including relevant commodity sector j; CF, The characterization factor matrix (p × m) whose element cfki represents thecontribution of emission i to environmental impactcategory k (k = 1…p, and i = 1…m); ECnl, The energy consumption of construction equipmentn using energy sourcel per hour; EEI, The embodied environmental impact vector (p × 1) whose element eeik represents the embodied environmental impact on impact category k (k = 1…p); EEml, The energy efficiency of vehicle m using energy source l; EP, Eutrophication potential; EU, The energy use matrix (r × n) whose element eulj represents the physical amount of energy source l used directly for producing one monetary unit of goods in productive sector j (l = 1…r, and j = 1…n); eudir_Cl, The physical amount of energy source l used directly by construction equipment; EUdir_M, The direct energy use vector (r × 1) whose element eudir_Ml represents the physical amount of energy source l used directly for manufacturing materials (l = 1…r); EUdirT&C, The direct energy use vector (r × 1) whose elementeudirT&Clrepresents the physical amount of energy source l used directly for transportation and construction (l = 1…r); eudir_Tl, The physical amount of energy source l used directly by a transportation vehicle;eudirT&Cl, The physical amount of energy source l used directly by transportation vehicles and construction equipment (l = 1…r); EUind_M, The indirect energy use vector (r × 1) whose element euind_Ml represents the physical amount of energy source l used indirectly for manufacturing materials (l = 1…r); eulj, The physical amountof energy source l used directly for producing one monetary unit of goods in commodity sector j; G-SEED, Green Standard for Energy and Environmental Design; GWP, Global warming potential; HTP, Human toxicity potential; IClj, The input coefficient representing the monetary amount of energy source l used for producing one monetary unit of goods in commodity sector j; (I-A)− 1, The Leontief inverse matrix (n × n); Idir_M, The direct inventory vector (m × 1) whose element idir_Mi represents the physical amount of emission i generated directly from material manufacturing (i = 1…m); IdirT&C, The direct inventory vector (m × 1) whose element idirT&Ci represents the physical amount of emission i generated directly from transportation and construction (i = 1…m); Iind_M, The indirect inventory vector (m × 1) whose element iind_Mi represents the physical amount of (i) emission i generated indirectly from material manufacturing (i = 1…m) or (ii) abiotic resourcei used indirectly for material manufacturing (i = 1…m);IindT&C, The indirectinventory vector (m × 1) whose elementiindT&Cirepresents the physical amount of (i) emission i generated indirectly from transportation and construction (i = 1…m) or (ii) abiotic resource i used for transportation and construction (i = 1…m); ILCA, The inventory vector (m × 1) whose element iLCAi represents the physical amount of (i) emission i generated directly and indirectly from building construction or (ii) abiotic resource i used directly and indirectly for building construction (i = 1…m); KSIC, Korean Standard Industrial Classification; LCjm, The load capacity of vehicle m for loading material j; LEED, Leadership in Energy and Environmental Design; MC, The material cost vector (n × 1) whose element mcj represents the material cost in productive sector j; ME, Ministry of Environment; MEC, The matrix (m × r) whose element mecil represents the emission i generated directly from the combustion of one unit of energy source l (i = 1…m, and l = 1…r); MEP, The matrix (m × r) whose element mepil represents (i) the emission i generated directly from the production of one unit of energy source l (i = 1…m, and l = 1…r) or (ii) the abiotic resource i used directly for the production of one unit of energy source l (i = 1…m, and l = 1…r);MEUdirT&C, The monetary energyuse vector(r × 1) whose elementmeudirT&Clrepresents themonetary amount ofenergy source l used directly by the transportation vehicle and construction equipment; MKE, Ministry of Knowledge and Economy; ODP, Ozone-layer depletion potential; P, The pollutant matrix (m × n) whose element pij representstheemission i emitteddirectlyforproducing onemonetary unitof goods inproductive sector j (i = 1…m, and j = 1…n); pij, The amount ofpollutant i emitteddirectlyfor producing one monetary unit of goods in commodity sector j; POCP, Photochemical ozone creation potential; QMj, The quantity of material j; Rqj, The ratio of annual monetary output produced in commodity sector j included in industry sector q; SIP, The soil-cement injected precast pile; TDjm, The transportation distance of the vehicle m loading material j; UPl, The unit price of energy source l; WCjn, The work capacity of construction equipment n representing the amount of work done per hour. ⁎ Corresponding author at: Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea. Tel.: +82 2 2123 5788; fax: +82 2 2248 0382. E-mail addresses: [email protected] (M. Jang), [email protected] (T. Hong), [email protected] (C. Ji).

http://dx.doi.org/10.1016/j.eiar.2014.09.010 0195-9255/© 2014 Elsevier Inc. All rights reserved.

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M. Jang et al. / Environmental Impact Assessment Review 50 (2015) 143–155

Introduction The building construction industry consumes 40% of the materials entering the global economy and generates 40–50% of the global greenhouse gas (GHG) emissions. In South Korea, the government has been making efforts to reduce the environmental impacts of the building industry by establishing regulations such as the “Act on the Promotion of Green Buildings” (MOLIT, 2013), and by implementing the greenbuilding rating system, such as “Leadership in Energy and Environmental Design (LEED)” or “Green Standard for Energy and Environmental Design (G-SEED)” (MOLIT, 2013; USGBC, 1999). To reduce the environmental impacts of the building industry, it is important to quantify the environmental impacts caused by buildings. Some researchers have developed methods of evaluating the environmental impacts of buildings based on life cycle assessment (LCA) (Bilec et al., 2006; Chang et al., 2013; Guggemos and Horvath, 2006; Lee et al., 2009; Li et al., 2010; Lippiatt and Boyles, 2001; Sartori and Hestnes, 2007; Sharrard et al., 2008). Generally, 75–90% of a building's energy consumption is from the operation phase, 10–20% is consumed by material extraction and production, and less than 1% is consumed by the transportation, construction, and end-of-life (Chang et al., 2013; Sartori and Hestnes, 2007). For such reasons, most of the previous researches focused on the environmental impacts during the operation phase of a building's life cycle. Although the embodied environmental impacts of buildings are smaller than the environmental impacts in the operation phase, the embodied environmental impacts may be significant when the different time frame is considered. With the growing interest in sustainable buildings such as the zero-energy building, the embodied environmental impacts of buildings will become increasingly important. In South Korea, most of the research, which has been conducted to assess the embodied environmental impacts of buildings, concentrated on GHG emissions like carbon dioxide (CO2) (Hong et al., 2012, 2013; Lee et al., 2009; Nässén et al., 2007; Ng et al., 2013; Song and Lee, 2010; Tae et al., 2011). Not only global warming, however, but also ozone layer depletion, acidification, eutrophication, photochemical ozone creation, abiotic depletion, and human toxicity are significant environmental impacts (Blengini, 2009; Chan et al., 2012; Dreyer et al., 2003; Malmqvist et al., 2011). Accordingly, this study aims to develop a hybrid LCA model that is capable of more comprehensively evaluating the embodied environmental impacts of buildings. Methods Overview The LCA methodology has been comprehensively used to evaluate the environmental impacts occurring during the life cycle of a building and to present objective and transparent results (Crawford, 2008; Tukker, 2000). Generally, there are three types of LCA methodologies: process-based LCA, input–output LCA (I-O LCA), and hybrid LCA. Process-based LCA can present more accurate results when there are accurate data available since it directly determines the quantities of resources used in the manufacturing process of goods and evaluates the environmental impacts (Treloar, 1997). However, process-based LCA is systemically incomplete because it is impossible to consider all the upstream stages for the goods due to the complexity of upstream processes (Hendrickson et al., 1997; Lave et al., 1995). Therefore, it is impossible to use process-based LCA to evaluate the environmental impacts of complex products such as a building. Input–output analysis is a well-established tool in economic analysis developed by Wassily Leontief in the 1930s, where the interdependencies across the different sectors of the economy are represented by a set of linear equations (Leontief, 1970). Input–output analysis has been attempted for environment-related analysis since the 1960s, and has been attempted to be applied in LCA since the early 1990s (Suh and Huppes, 2002). I-O

LCA is based on the input–output analysis developed. As I-O LCA has a systemically complete system boundary, it can solve the major weakness of process-based LCA (Crawford, 2008). I-O LCA, however, has a limitation: the same output is generated when producing one monetary unit of goods in each productive sector because it uses the national average data for each productive sector of the economy (Lenzen, 2000; Suh et al., 2004). Hybrid LCA has been proposed, which combines the advantages of both process-based and I-O LCA (Suh and Huppes, 2002; Suh et al., 2004). The hybrid LCA helps reduce the truncation error in the process-based LCA and increases the resolution of the I-O LCA (Suh et al., 2004). Thus, hybrid LCA has been considered appropriate for evaluating the environmental impacts of a complex product such as a building (Goggins et al., 2010). In this study, a hybrid LCA model capable of evaluating the embodied environmental impact of buildings was developed. Instead of considering all of the building materials, some researchers evaluated the embodied environmental impacts of a building only by considering some components and materials of buildings (Raynolds et al., 2000a,b; Shin et al., 2011; Tae et al., 2011). Although ISO 14040 stipulates the cut-off criteria such as in terms of mass, energy flow, and environmental relevance (ISO, 2006), the validity of the cut-off criteria is determined on a case-by-case basis (Hunt et al., 1998). Therefore, this study aims to develop the hybrid LCA model considering all building materials. The hybrid LCA model has several different characteristics compared to the previous LCA models developed in South Korea: (i) most of previous LCA models developed in South Korea considered only the GHG emissions generated from the combustion of energy sources used in the material manufacturing, transportation and construction phases (Hong et al., 2012, 2013; Shin et al., 2011; Tae et al., 2011). However, there are the emissions affecting various environmental impacts such as ozone layer depletion, acidification, etc., as well as global warming. In addition, the abiotic resources used for buildings also affect an environmental impact (i.e., abiotic resource depletion). Especially, the Ecolabeling (Type III ED) implemented by Ministry of Environment (ME) in South Korea considers GWP, ODP, AP, EP, POCP, and ADP as the environmental impacts (ME, 2012). Therefore, the hybrid LCA model can assess the global warming potential (GWP), ozone layer depletion potential (ODP), acidification potential (AP), eutrophication potential (EP), photochemical ozone creation potential (POCP), and abiotic depletion potential (ADP) considering both the emissions generated from the production and combustion of energy sources and the abiotic resources used for the production of energy sources; (ii) whereas the previous LCA models considered energy sources used directly and indirectly in the material manufacturing phase to evaluate the embodied environmental impacts of buildings, they considered only the energy sources used directly in the transportation and construction phases (Hong et al., 2012; Jeong et al., in press; Ji et al., 2014; Lee et al., 2009). The logical consistency of system boundary should be considered to improve the reliability of the LCA model. Therefore, the hybrid LCA model considers energy sources used directly and indirectly in the material manufacturing, transportation and construction phases; (iii) various toxic pollutants affecting human health are generated directly from the manufacturing process for goods as well as the production and combustion of energy sources. Although the improvement of the human health is most important issue in modern society, however, the previous LCA models developed in South Korea did not consider toxic pollutants generated directly from the manufacturing process of goods. The hybrid LCA model can assess the human toxicity potential (HTP) of buildings considering the toxic pollutants generated from the manufacturing process. Fig. 1 shows the system boundary of the hybrid LCA model for evaluating the embodied environmental impacts of buildings. The hybrid LCA model provides GWP, ODP, AP, EP, POCP, ADP, and HTP as the embodied environmental impacts of buildings, by considering not only the emissions and the abiotic resources from the production and

M. Jang et al. / Environmental Impact Assessment Review 50 (2015) 143–155

145

Fig. 1. System boundary of the hybrid LCA model.

combustion of energy sources used directly and indirectly in the material manufacturing, transportation, and construction phases, but also toxic pollutants generated directly and indirectly in each phase. Hybrid LCA model for buildings Life cycle inventory analysis As shown in Fig. 1, the developed model evaluates the embodied environmental impacts of buildings by considering the material manufacturing, transportation, and construction phases. The embodied environmental impacts generated directly and indirectly in the material manufacturing phase are calculated based on the cost of building materials. As shown in Eq. (1), the direct inventory including emissions generated directly from material manufacturing (Idir_M) can be estimated by

multiplying a pollutant matrix (P), which includes the pollutant data emitted directly for producing one monetary unit of goods in a productive sector, by the material cost. The coefficient of Leontief inverse matrix signifies the output change in all the relevant productive sectors as one unit of change in a productive sector (Cellura et al., 2011). Therefore, the output of the relevant productive sectors is calculated by multiplying the coefficient of Leontief inverse matrix by the material cost. As shown in Eq. (2), the indirect inventory including emissions generated indirectly from material manufacturing (Iind_M) are estimated by multiplying P by the output which is calculated by multiplying the Leontief inverse matrix by the material cost (refer to Eq. (2)).

I

dir M

¼ P  MC;

ð1Þ

146

I

ind M

M. Jang et al. / Environmental Impact Assessment Review 50 (2015) 143–155 ‐1

¼ P  ðI‐AÞ  MC;

ð2Þ

where Idir_M is the direct inventory vector (m × 1) whose element idir_Mi represents the physical amount of emission i generated directly from material manufacturing (i = 1…m); P is the pollutant matrix (m × n) whose element pij represents the emission i emitted directly for producing one monetary unit of goods in productive sector j (i = 1…m, and j = 1…n); MC is the material cost vector (n × 1) whose element mcj represents the material cost in productive sector j; Iind_M is the indirect inventory vector (m × 1) whose element iind_Mi represents the physical amount of a specific emission i generated indirectly from material manufacturing (i = 1…m); and (I-A)−1 is the Leontief inverse matrix (n × n). The various emissions are generated from the combustion of energy sources. Therefore, the emissions from the material manufacturing can be calculated based on the energy sources used for material manufacturing. As shown in Eq. (3), using an energy use matrix (EU), which includes the energy consumption data used for producing one monetary unit of goods in a productive sector, instead of P, the amounts of energy sources used directly for manufacturing materials (EUdir_M) can be estimated. As shown in Eq. (4), Idir_M is estimated by multiplying a matrix whose element represents the emissions generated directly from the combustion of one unit of energy sources (MEC) by EUdir_M. The amount of energy source used indirectly for manufacturing materials (EUind_M) can be estimated using the Leontief inverse matrix, similar to the calculation of Idir_M in Eq. (2) (refer to Eq. (5)). As the emissions are generated from the production of energy sources as well as the combustion of energy sources, both the emissions generated from the combustion and the production of energy sources should be considered in estimating Iind_M, as shown in Eq. (6). In addition, for assessing the ADP, Iind_M should include abiotic resources used for the production of energy sources.

EU

I

dir M

EU

I

dir M

¼ MEC  EU

ind M

ind M

¼ EU  MC;

ð3Þ

dir M

;

ð4Þ

‐1

¼ EU  ðI‐AÞ  MC;

¼ ðMEC þ MEP Þ  EU

ind M

ð5Þ

;

ð6Þ

where EUdir_M is the direct energy use vector (r × 1) whose element eudir_Ml represents the physical amount of energy source l used directly for manufacturing materials (l = 1…r); EU is the energy use matrix (r × n) whose element eulj represents the physical amount of energy source l used directly for producing one monetary unit of goods in the productive sector j (l = 1…r, and j = 1…n); MC is the material cost vector (n × 1) whose element mcj represents the material cost in productive sector j; Idir_M is the direct inventory vector (m × 1) whose element idir_Mi represents the physical amount of emission i generated directly from material manufacturing (i = 1…m); MEC is the matrix (m × r) whose element mecil represents the emission i generated directly from the combustion of one unit of energy source l (i = 1…m, and l = 1…r); EUind_M is the indirect energy use vector (r × 1) whose element euind_Ml represents the physical amount of energy source l used indirectly for manufacturing materials (l = 1…r); Iind_M is the indirect inventory vector (m × 1) whose element iind_Mi represents the physical amount of (i) emission i generated indirectly from material manufacturing (i = 1…m) or (ii) abiotic resource i used indirectly for material manufacturing (i = 1…m); and MEP is the matrix (m × r) whose element mepil represents (i) the emission i generated directly

from the production of one unit of energy source l (i = 1…m, and l = 1…r) or (ii) the abiotic resource i used directly for the production of one unit of energy source l (i = 1…m, and l = 1…r). The embodied environmental impacts generated directly and indirectly in the transportation and construction phases are calculated based on the amounts of energy sources used by transportation vehicles and construction equipment. The direct inventory including emissions generated directly from the transportation and construction phases (Idir T&C ) can be estimated by multiplying MEC by the direct energy use vector (EU dir T&C ) whose element eudir T&C l represents the physical amount of energy source l used directly by transportation vehicles and construction equipment, as shown in Eq. (7). The physical amount of energy source l used directly for transportation and construction (eudir_Tl and eudir_Cl, respectively) are estimated using Eqs. (8) and (9) presented by Hong et al. (2012, 2013).

I

dir T&C

dir T

eu

l

¼ MEC  EU

¼2

dir C l

¼

dir T&C l

ð7Þ

LC jm  EEml

X X Q M j  EC nl WC jn

n¼1 j¼1

eu

;

X X QM j  TD jm

m¼1 j¼1

eu

dir T&C

¼ eu

dir T

dir C

l

þ eu

;

ð8Þ

;

ð9Þ

l;

ð10Þ

dir T&C

is the direct inventory vector (m × 1) whose element where I idir T&C i represents the physical amount of emission i generated directly from transportation and construction (i = 1…m); MEC is the matrix (m × r) whose element mecil represents the emission i generated directly from the combustion of one unit of energy source l (i = 1…m, and l = 1…r); EU dir T&C is the direct energy use vector (r × 1) whose dir T&C element eu l represents the physical amount of an energy source l used directly for transportation and construction (l = 1…r); eudir_Tl is the physical amount of energy source l used directly by a transportation vehicle; QMj is the quantity of material j; TDjm is the transportation distance of the vehicle m loading material j; LCjm is the load capacity of vehicle m for loading material j; EEml is the energy efficiency of vehicle m using energy source l; eudir_Cl is the physical amount of energy source l used directly by construction equipment; ECnl is the energy consumption of construction equipment n using energy source l per hour; WCjn is the work capacity of construction equipment n representing the amount of work done per hour; and eudir T&C l is the physical amount of energy source l used directly by transportation vehicles and construction equipment (l = 1…r). As shown in Eq. (11), the indirect inventory including (i) emissions generated indirectly from the transportation and construction phases and (ii) abiotic resources used indirectly for the transportation and construction phases (I ind T&C) can be estimated based on the indirect energy use vector (EU ind T&C ) whose element euind T&C l represents the physical amount of energy source l used indirectly for producing the energy sources used for transportation and construction. EU ind T&C is estimated using the Leontief inverse matrix, EU, and the monetary energy use vector (MEU dir T&C ), as shown in Eq. (12). As the Leontief inverse matrix is based on the monetary unit, the physical amount of energy source  dir T&C  eu  l shouldbe converted into a monetary amount of energy source meudir T&C l by multiplying it by the unit price of energy source l (UPl), as shown in Eq. (13).

I

ind T&C

¼ ðMEC þ MEP Þ  EU

ind T&C

;

ð11Þ

M. Jang et al. / Environmental Impact Assessment Review 50 (2015) 143–155

EU

ind T&C

‐1

¼ EU  ðI‐AÞ  MEU

dir T&C

meu

l

¼ eu

dir T&C l

dir T&C

;

ð12Þ

 UP l ;

ð13Þ

where Iind T&C is the indirect inventory vector (m × 1) whose element ind T&C i i represents the physical amount of (i) emission i generated indirectly from transportation and construction (i = 1…m) or (ii) abiotic resource i used for transportation and construction (i = 1…m); MEC is the matrix (m × r) whose element mecil represents the emission i generated directly from the combustion of one unit of energy source l (i = 1…m, and l = 1…r); MEP is the matrix (m × r) whose element mepil represents (i) the emission i generated directly from the production of one unit of energy source l (i = 1…m, and l = 1…r) or (ii) the abiotic resource i used directly for the production of one unit of energy source l (i = 1… m, and l = 1…r); EU dir T&C is the direct energy use vector (r × 1) dir T&C whose element eu l represents the physical amount of energy source l used directly for transportation and construction (l = 1…r); EU is the energy use matrix (r × n) whose element eulj represents the physical amount of energy source l used directly for producing one monetary unit of goods in productive sector j (l = 1…r, and j = −1 1…n); (I-A) is the Leontief inverse matrix (n × n); MEU dir T&C is the monetary energy use vector (r × 1) whose element meudir T&C l represents the monetary amount of energy source l used directly by the transportation vehicle and construction equipment; eudir T&C l is the physical amount of energy source l used directly by transportation vehicles and construction equipment; and UPl is the unit price of energy source l. Life cycle impact assessment Life cycle impact assessment converts the result of life cycle inventory analysis into environmental impacts (ISO, 2006). According to ISO 14040, life cycle impact assessment consists of the following elements: classification, characterization, normalization, and weighting. Classification and characterization are mandatory while normalization and weighting are optional (Crawford, 2008). The developed model considers classification and characterization using the characterization factors as shown in Eq. (14). The characterization factors represent the potential of each emission to contribute to the respective environmental impacts (ISO, 2006). For example, the GWP is derived from CO2, CH4, N2O, CFC-11, etc., multiplied by their respective characterization factors (1 for CO2, 25 for CH4, 298 for N2O, and 4750 for CFC-11). As shown in Eq. (14), the embodied environmental impacts are estimated by multiplying the emissions resulting from the life cycle inventory analysis by the characterization factors.

EEI ¼ C F  I

LCA

 dir ¼ CF  I

M

þI

ind M

þI

dir T&C

þI

ind T&C

 ;

ð14Þ

where EEI is the embodied environmental impact vector (p × 1) whose element eeik represents the embodied environmental impact on impact

Table 1 Reference for the characterization factors. Impact category

Reference substance

Reference

Global warming potential Ozone layer depletion potential Acidification potential Eutrophication potential Photochemical ozone creation potential Abiotic depletion potential Human toxicity potential

CO2 CFC-11 SO2 PO43 C2H4

(IPCC, 2007), 100 year time horizon (Daniel et al., 2007) (Hauschild and Wenze, 1998) (Guinee et al., 2001) (Jenkin and Hayman, 1999)

sb C6H6

(Guinee et al., 2001) (Itsubo and Inaba, 2003)

147

category k (k = 1…p); CF is the characterization factor matrix (p × m) whose element cfki represents the contribution of emission i to environmental impact category k (k = 1…p, and i = 1…m); ILCA is the inventory vector (m × 1) whose element iLCAi represents the physical amount of (i) emission i generated directly and indirectly from building construction or (ii) abiotic resource i used directly and indirectly for building construction (i = 1…m); Idir_M is the direct inventory vector (m × 1) whose element idir_Mi represents the physical amount of emission i generated directly from material manufacturing (i = 1…m); Iind_M is the indirect inventory vector (m × 1) whose element iind_Mi represents the physical amount of (i) emission i generated indirectly from material manufacturing (i = 1…m) or (ii) abiotic resource i used indirectly for material manufacturing (i = 1…m); Idir T&C is the direct inventory vecdir T&C tor (m × 1) whose element i i represents the physical amount of emission i generated directly from transportation and construction ind T&C is the indirect inventory vector (m × 1) (i = 1…m); and I whose element iind T&C i represents the physical amount of a specific (i) emission i generated indirectly from transportation and construction (i = 1…m) or (ii) abiotic resource i used indirectly for transportation and construction (i = 1…m). Previous researches have proposed various life cycle impact assessment methodologies, including the characterization factors based on scientific analysis (Bare et al., 2003; Frischknecht et al., 2007; Guinee et al., 2001; Hauschild and Potting, 2005; Jenkin and Hayman, 1999; Jolliet et al., 2003). The developed hybrid LCA model used the characterization factors for GWP, ODP, AP, EP, POCP, ADP, and HTP proposed by the previous researches as shown in Table 1. Data sources Leontief inverse matrix The input–output table presented by Bank of Korea includes the Leontief inverse matrix, which categorizes the economic system in South Korea into 403 commodity sectors. Therefore, to develop the hybrid LCA model, this study used the Leontief inverse matrix (403 × 403) in the 2010 input–output table proposed by Bank of Korea (2012). P According to the “General Guidance on the Use and Limitations of Korea's Pollutant Release and Transfer Registers (PRTR),” South Korea's National Institute of Environmental Research (NIER) has established the annual national pollutant release data since 2001 (NIER, 2012). In this study, the P was established using the national pollutant release data proposed by NIER. The 2010 pollutant release data proposed by NIER (2012) was used to make it identical to the time point of the 2010 input–output table. The Korean Standard Industrial Classification (KSIC) divides South Korea's economic system into 76 industry sectors (Korea National Statistical Office, 2008). The national pollutant release data include the pollutant release data emitted from 34 of the 76 industry sectors categorized based on the KSIC. As the Leontief inverse matrix divides the economic system into 403 commodity sectors, the national pollutant release data were matched with the 403 commodity sectors by reviewing KSIC's categorization standard. For example, as shown in Fig. 2, “11 beverage manufacture” industry sector in KSIC was matched with six commodity sectors in the input–output table (“077 Ethyl alcohol for beverages,” “078 Blended and distilled sojoo,” “079 Beer,” “080 Other liquors,” “081 Soft drinks,” and “082 Spring water and manufactured ice”). “12 Tobacco product manufacture” industry sector in KSIC was matched with one commodity sector (“084 Tobacco products”) in the input–output table. Table S1 of the supplementary data shows the relationship between the commodity sectors of the input– output table and the industry sectors of KSIC. The national pollutant release data presented by NIER (2012) present the total amount of pollutants emitted by each industry sector for 1 year. For inventory analysis, the national pollutant release data

148

M. Jang et al. / Environmental Impact Assessment Review 50 (2015) 143–155

Fig. 2. Calculation of the physical amounts of pollutants emitted directly for producing one monetary unit of goods in each commodity sector.

should be converted into the pollutant release data per one monetary unit of goods (pij) in each industry sector. The element (pij) in P can be calculated using Eq. (15). For example, according to the national pollutant release data presented by NIER (2012), the “11 Beverage manufacture” industry sectors, including “079 Beer” commodity sector emitted 134 kg of sulfur in 2010. As shown in Fig. 2, the total monetary output and the ratio of the “079 Beer” commodity sector were USD3.249 million and 27.90%, respectively. Therefore, the amount of sulfur emitted directly from the production of one monetary unit of goods in “079 Beer” commodity sector was estimated as 1.15E− 08 kg/USD (= 134 kg/year × 27.90% ÷ USD3.249 million/year.). Table S2 of the supplementary data shows the total monetary output and the ratios of

the relevant commodity sectors in each industry sector. According to NIER (2012), since a total of 213 pollutants were emitted by 34 industry sectors, P (213 × 403) was established. Table S3 of the supplementary data shows P.

pi j ¼

AP iq  Rq j ; AMOq j

ð15Þ

where pij is the amount of pollutant i emitted directly for producing one monetary unit of goods in commodity sector j; APiq is the physical amount of pollutant i emitted directly per year from industry sector q,

M. Jang et al. / Environmental Impact Assessment Review 50 (2015) 143–155

149

Fig. 3. Calculation of the physical amount of an energy source used directly for producing one monetary unit of goods in the each commodity sector.

including relevant commodity sector j; Rqj is the ratio of annual monetary output produced in commodity sector j included in industry sector q; and AMOqj is the annual monetary output produced in commodity sector j included in industry sector q. EU The input–output table presented by Bank of Korea includes the input coefficient matrix as well as the Leontief inverse matrix. The input coefficient signifies the monetary input to the relevant commodity sectors to produce one monetary unit of goods in a commodity sector. The input coefficient matrix includes the commodity sectors related to the 14 energy sources including “Anthracite,” “gasoline,” and “light oil” Table 2 Reference source of the emission data of the energy sources. Energy source

Reference

Coal (anthracite, bituminous coal, coal briquette), gasoline, light oil, liquefied petroleum gas, electricity (hydroelectric power generation, fire power generation, nuclear power generation, other generation) Natural gas, manufactured gas supply, kerosene, heavy oil

MKE

ME

among others. Therefore, EU can be estimated by dividing the input coefficient (IClj) of the commodity sector related to energy source l by UPl, as shown in Eq. (16). For example, as shown in Fig. 3, the input coefficient of the “134 Gasoline” commodity sector related to the “001 Unmilled rice” commodity sector is 1.77E−03. As the unit price of gasoline is USD1.58/L, the physical amount of gasoline used directly for producing one monetary unit of goods in the “001 Unmilled rice” commodity sector is calculated as 1.12E−003 L/USD (=1.77E−03 ÷ USD1.58/L). Table S4 of the supplementary data shows EU (14 × 403). At this point, the average supply price of energy sources in 2010 presented by the Korea Energy Statistics Information System (KESIS, 2010) was used as the unit price of the energy sources (UPl) (refer to Table S5 of the supplementary data).

eul j ¼

IC l j ; UP l

ð16Þ

where eulj is the physical amount of energy source l used directly for producing one monetary unit of goods in commodity sector j; IClj is the input coefficient representing the monetary amount of energy

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source l used for producing one monetary unit of goods in commodity sector j; and UPl is the unit price of energy source l. MEP The ME and Ministry of Knowledge and Economy (MKE) in South Korea established the life cycle inventory of energy sources including the emissions and the abiotic resources (KEITI, 2004). As shown in Table 2, this study used the life cycle inventories of energy sources presented by the ME and MKE to establish MEP. As the life cycle inventory of anthracite, bituminous coal, and coal briquette production has not been established, the life cycle inventory of coal established by the MKE was equally applied to anthracite, bituminous coal, and briquette. There is no life cycle inventory of hydroelectric power generation, fire power generation, nuclear power generation, and other generation. Therefore, the life cycle inventory of electricity established by the MKE was equally applied to hydroelectric power generation, fire power generation, nuclear power generation, and other generation. Table S6 of the supplementary data shows MEP (323 × 14). MEC Wang (1999) presented the emission data from the combustion of energy sources including “coal,” “liquefied natural gas,” “gasoline,” and “light oil” among others (Wang, 1999). Based on the emission data presented by Wang (1999), MEC (8 × 14) was established in this study (refer to Table S7 of the supplementary data). Results and discussion To demonstrate the application and validity of the developed model, a case study was conducted targeting an elementary school building with a reinforced concrete structure and a total floor area of 7906.00 m2. The embodied environmental impacts (GWP, ODP, AP, EP, POCP, ADP and HTP) of the case building were first assessed to demonstrate the application of the developed model. To demonstrate the validity of the developed model, the embodied environmental Table 3 Material cost by commodity sector.

a

Rank

Code

Name

Cost (USD)a

Ratio (%)

1 2 3 4 5 6

191 209 180 148 179 181

461,461.82 269,323.14 178,828.70 120,491.15 72,727.35 71,633.75

23.92% 13.96% 9.27% 6.25% 3.77% 3.71%

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Total

178 183 117 114 172 192 159 177 038 210 161 116 128 214 182 216 167 215 115 403

Steel rods and bars Metal products for construction Ready mixed concrete Synthetic resins Cement Concrete blocks, bricks, and other concrete products Clay products for construction Cut stone and stone products Wooden products for construction Lumber Sheet glass and primary glass products Section steel Paints, varnishes, and allied products Refractory ceramic products Sand and gravel Metal products for structure Adhesives, gelatin and sealants Reconstituted and densified wood Other paper products Bolts, nuts, screws, rivets, and washers Lime, gypsum, and plaster products Fastening metal products Industrial plastic products Fabricated wire products Plywood Non-classifiable activities

56,977.55 54,763.27 48,036.65 41,964.52 29,855.19 29,790.63 25,062.88 15,207.40 14,499.21 4,670.75 3,492.43 3,304.61 2,158.52 1,981.45 1,416.98 1,050.04 570.31 477.07 275.84 316,723.49 1,929,117.91

2.95% 2.84% 2.49% 2.18% 1.55% 1.54% 1.30% 0.79% 0.75% 0.24% 0.18% 0.17% 0.11% 0.10% 0.07% 0.05% 0.03% 0.02% 0.01% 16.42% 100.00%

The KRW1,079.50/USD exchange rate was used; the prevailing rate on January 24, 2014.

Table 4 Embodied environmental impacts of building materials. Impact category

Unit

GWP ODP AP EP POCP ADP HTP

t CO2 eq. g CFC-11 eq. 10 kg SO2 eq. kg PO3− 4 eq. kg C2H4 eq. 10 kg sb eq. kg C6H6 eq.

Embodied environmental impact Direct

Indirect

Total

572.29 (10.1%a) 2.63 (3.1%) 102.37 (12.3%) 189.96 (13.1%) 76.34 (1.9%) 0.00 (0.0%) 499.16 (25.5%)

5088.69 (89.9%) 82.43 (96.9%) 729.06 (87.7%) 1262.11 (86.9%) 3925.65 (98.1%) 2750.42 (100.0%) 1456.52 (74.5%)

5660.98 85.06 831.44 1452.07 4001.99 2750.42 1955.68

a

The value listed within the parentheses is the proportion of direct and indirect impacts.

impacts calculated using the developed model were compared with those calculated using the LCA model developed by a previous research.

Embodied environmental impacts of material manufacturing To evaluate the embodied environmental impacts of material manufacturing, the material cost in the bill of quantity was first categorized into relevant commodity sectors in the input–output table. For example, the deformed bar was categorized as belonging to the “191 Steel rods and bars” commodity sector, while the cement was categorized as belonging to the “179 Cement” commodity sector. As shown in Table 3, the building materials were categorized into 26 commodity sectors. Theoretically, the cost value input to the developed hybrid LCA model should be the direct production cost, but the material cost in the bill of quantity is the comprehensive cost, including the retailer's profit, transportation fee, and overhead fee. Therefore, this case study set 90% of the material cost in the bill of quantity as the direct production cost of the materials, as in Chang et al. (2012). By applying the cost value in Table 3 to Eqs. (1)–(6), the inventory including (i) the emissions generated directly and indirectly from the building material manufacturing and (ii) the abiotic resources used directly and indirectly for the building materials manufacturing was calculated. By applying the inventory results to Eq. (14), the embodied environmental impacts of materials were calculated. Table 4 shows the embodied environmental impacts of materials. As shown in Table 4, the direct impacts accounted for 0.0–25.5% of the total embodied environmental impacts whereas the indirect impacts accounted for 74.5–100.0%. Comparing with the direct impacts (10.1–13.1%) in other impact categories, direct ODP (3.1%) and POCP (2.2%) were low while direct HTP (25.6%) was high. The direct ADP was zero since the developed model did not consider the abiotic resources input for the production of energy sources used directly in manufacturing materials. As shown in Table 5, more embodied environmental impacts were generated by a commodity sector with a higher construction cost ratio. For instance, the “191 Steel rods and bars” commodity sector, with 23.92% of construction cost ratio (refer to Table 3), resulted in 34.3% of GWP, 23.4% of AP, 23.3% of EP, 27.0% of POCP, 35.0% of ADP, and 46.1% of HTP. ODP of “191 Steel rods and bars” commodity sector (14.7%) was shown to be lower than the construction cost ratio (23.92%) whereas the “172 Sheet glass and primary glass products” commodity sector generated much higher ODP (10.0%) than the construction cost ratio (1.55%). As shown in Table 5, each environmental impact was influenced by different commodity sectors. For instance, GWP and ADP were significantly affected by “191 Steel rods and bars” and “179 Cement” commodity sectors, whereas ODP was significantly affected by “191 Steel rods and bars,” “209 Metal products for construction,” and “148 Synthetic resins” commodity sectors. AP and EP were influenced by “191 Steel rods and bars” and “180 Ready mixed concrete” commodity sectors, while POCP and HTP were influenced by “191 Steel rods and bars” and “209 Metal products for construction” commodity sectors.

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151

Table 5 Embodied environmental impacts of building materials by commodity sector. Code

Commodity sector

GWP (t CO2 eq.)

ODP (g CFC-11 eq.)

AP (10 kg SO2 eq.)

EP (kg PO3− eq.) 4

POCP (kg C2H4 eq.)

ADP (10 kg sb eq.)

HTP (kg C6H6 eq.)

191 209 180 179 178 181

Steel rods and bars Metal products for construction Ready mixed concrete Cement Clay products for construction Concrete blocks, bricks, and other concrete products Synthetic resins Section steel Sheet glass and primary glass products Other materials

1941 (34.3%a) 570 (10.1%) 574 (10.1%) 1116 (19.7%) 193 (3.4%) 220 (3.9%)

12.5 (14.7%) 10.2 (12.0%) 5.9 (6.9%) 3.6 (4.2%) 4.3 (5.1%) 5.6 (6.6%)

195 (23.4%) 92 (11.1%) 137 (16.5%) 71 (8.5%) 61 (7.4%) 40 (4.9%)

339 (23.3%) 153 (10.5%) 251 (17.3%) 128 (8.8%) 105 (7.2%) 72 (5.0%)

1082 (27.0%) 818 (20.4%) 219 (5.5%) 149 (3.7%) 315 (7.9%) 136 (3.4%)

1109 (35.0%) 313 (9.9%) 293 (9.3%) 698 (22.0%) 97 (3.0%) 119 (3.8%)

900 (46.1%) 317 (16.2%) 149 (7.6%) 57 (2.9%) 43 (2.2%) 71 (3.6%)

98 (1.7%) 123 (2.2%) 66 (1.2%) 759 (13.4%)

7.0 (8.2%) 1.0 (1.1%) 8.5 (10.0%) 26.5 (31.2%)

21 (2.5%) 13 (1.6%) 16 (1.9%) 185 (22.3%)

35 (2.4%) 22 (1.5%) 27 (1.9%) 320 (22.0%)

125 (3.1%) 73 (1.8%) 77 (1.9%) 1,012 (25.3%)

48 (1.5%) 70 (2.2%) 38 (1.2%) 386 (12.2%)

63 (3.2%) 58 (2.9%) 24 (1.2%) 274 (14.0%)

148 192 172 a

The value listed within the parentheses is the proportion of each commodity sector.

Embodied environmental impacts of transportation The embodied environmental impacts of transportation were calculated based on the amount of energy source used by transportation vehicles. To estimate the amount of energy source used for transportation, the materials either with significant physical features (weight or volume) or high costs were considered. By applying the physical amounts of the materials in the bill of quantity to Eq. (8), the amounts of energy sources used for transportation vehicles were estimated, as shown in Table 6. The load capacity and energy efficiency of the transportation vehicles were based on data from the previous researches (CAK, 2011; Hong et al., 2013; Lee et al., 2004). The transportation distance was set at 30 km, which was used in developing the life cycle inventory database in South Korea (KEITI, 2004). For example, 18,663.9 l (= 4554 m3 × 30 km ÷ (6 m3 × 2.44 km/L)) of diesel was used to transport 4554 m3 of ready mixed concrete. As shown in Table 6, the total amount of diesel used directly for transportation was estimated as 28,224.4 L. By applying the total amount of diesel (28,224.4 L) to Eq. (12), the amounts of energy sources used indirectly for transportation were calculated. The amounts of directly and indirectly used energy sources were applied to Eqs. (7) and (11), respectively, to estimate the inventory including (i) the emissions generated directly and indirectly from transportation and (ii) the abiotic resources used directly and indirectly for transportation. Then, by applying the inventory results to Eq. (14), the embodied environmental impacts of transportation were calculated. As considerable amounts of emissions affecting GWP and POCP are generated from the diesel combustion, the direct GWP and POCP were determined to be high (43.8% and 28.0%, respectively) as shown in Table 7. Negligible emission affecting ODP, ADP, and HTP was generated

from diesel combustion, but direct ODP, ADP, and HCP of transportation was almost zero. The proportion of direct AP and EP was similar to that of the material manufacturing phase. Embodied environmental impacts of construction In the construction phase, most of the energy sources are consumed by work done with heavy construction equipment including temporary work, earth work, piling work, and frame work (Hong et al., 2013). Therefore, this case study considered the construction process from the temporary work to the frame work to assess the embodied environmental impacts in the construction phase. To estimate the amounts of energy sources used for construction, the quantity data of excavation, refilling, concrete, etc., in the bill of quantity were applied to Eq. (9). The work capacity and energy consumption data of the construction equipment proposed by the previous researches (CAK, 2011; Hong et al., 2013; Lee et al., 2004) were used. Table 8 shows the amounts of energy sources used for construction. For example, according to the bill of quantity of the case building, 4398 m3 of ready mixed concrete (slump value: 15) was planned to be poured at more than 300 m3 per day. By applying 4398 m3 of ready mixed concrete to Eq. (9), the physical amount of diesel used by the concrete pump car was estimated as 3376.2 L (=4,398 m3 × 31.0 L/h. ÷ 40.3 m3/h.). As shown in Table 8, the total amount of diesel used directly for construction was estimated as 36,192.2 L. By applying the total amount of diesel (36,192.2 L) to Eq. (12), the amounts of energy sources used indirectly for construction were calculated. The amounts of directly and indirectly used energy sources were applied to Eqs. (7) and (11), respectively, to estimate the inventory including (i) the emissions generated directly and

Table 6 Amounts of energy sources used for transportation. Material

a

Vehicle

Energy consumption

Type

Quantity

Type

LCa

EEb (km/L)

Type

Quantity (L)

Ready mixed concrete Cement Steel rebar Section steel PHC pile Formwork Brick Tile Stone products Sand and gravel Glass products Steel door, wood door, glass door, and fireproof rolling shutter door Total

4,554 m3 14,433 bags 699.3 t 41.0 t 4747 m 8,023 m2 1,129,484 ea 1,952 m2 91.17 m2 871 m3 3,085 m2 178 ea

Concrete mixer truck 11-t truck 20-t truck 25-t trailer 20-t truck 8-t truck 11-t truck 8-t truck 5-t truck 8-t truck 8-t truck 11-t truck

6 m3 275 bags 20 t 25 t 135 m 600 m2 5,300 ea 660 m2 92 m2 3.08 m3 930 m2 16 ea

2.44 4.0 3.1 2.5 3.1 4.5 4.0 4.5 6.0 4.5 4.5 4.0

Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel

18,663.9 787.3 647.6 39.3 680.6 178.3 3,196.7 39.4 9.6 3,770.6 44.2 166.9

LC is the load capacity of the transportation vehicle. bEE is the energy efficiency of the transportation vehicle.

28,224.4

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Table 7 Embodied environmental impacts of transportation. Impact category

GWP ODP AP EP POCP ADP HTP

Unit

t CO2 eq. g CFC-11 eq. 10 kg SO2 eq. kg PO3− eq. 4 kg C2H4 eq. 10 kg sb eq. kg C6H6 eq.

Table 9 Embodied environmental impacts of construction.

Embodied environmental impact Direct

Indirect

Total

75.60 (43.8% a) 0.00 (0.0%) 19.83 (12.0%) 36.83 (12.0%) 12.78 (28.0%) 0.00 (0.0%) 0.03 (0.7%)

99.25 (56.8%) 1.30 (100.0%) 145.47 (88.0%) 269.00 (88.0%) 39.39 (75.5%) 61.72 (100.0%) 4.25 (99.3%)

174.85 1.30 165.30 305.83 52.17 61.72 4.28

Impact category

Unit

Embodied environmental impact

GWP ODP AP EP POCP ADP HTP

t CO2 eq. g CFC-11 eq. 10 kg SO2 eq. kg PO3− eq. 4 kg C2H4 eq. 10 kg sb eq. kg C6H6 eq.

Direct

Indirect

Total

96.95 (43.8%a) 0.00 (0.0%) 25.43 (12.0%) 47.23 (12.0%) 16.39 (28.0%) 0.00 (0.0%) 0.04 (0.7%)

127.27 (56.8%) 1.66 (100.0%) 186.54 (88.0%) 344.94 (88.0%) 50.51 (75.5%) 79.15 (100.0%) 5.45 (99.3%)

224.21 1.66 211.97 392.16 66.90 79.15 5.49

a

a

indirectly from construction and (ii) the abiotic resources used directly and indirectly for construction. Then, by applying all the inventory results to Eq. (14), the embodied environmental impacts of construction were calculated. Since diesel was used as an energy source in the construction phase, the proportions of direct and indirect embodied environmental impacts of construction were identical to those of the transportation phase, as shown in Table 9.

et al. (in press) was used for comparison. Fig. 4 shows the results of the comparison. As shown in Fig. 4, GWP, ODP, AP, EP, POCP, and ADP calculated by Jeong et al.'s (in press) model were lower than those of the developed model by 4.6–25.2%. In particular, compared to the HTP of the developed model (1965 kg C6H6 eq.), that of Jeong et al.'s (in press) model was considerably lower (13 kg C6H6 eq.). This gap is caused by the difference between the system boundary of the developed model and Jeong et al.'s (in press) model. Because the developed model considers the energy source used indirectly and directly for the transportation and construction phases while Jeong et al.'s (in press) model considers only the energy source used directly for the transportation and construction phases, the results of the developed model were higher than those of Jeong et al.'s (in press) model. In addition, since there were few emissions affecting the HTP from the production and combustion of energy sources, HTP of Jeong et al.'s (in press) model was as low as negligible. However, HTP of the developed model was significantly higher than that of Jeong et al.'s (in press) model since the developed model considers the national pollutant release data that represent the toxic pollutants significantly affecting the HTP as well as emissions from the production and combustion of energy sources. By considering not only all the emissions (including toxic pollutants) generated directly and indirectly in the material manufacturing, transportation, and construction phases but also the abiotic resources used directly and indirectly in each phases, the developed model could evaluate the embodied environmental impacts of buildings more comprehensively than previous LCA models. In particular, since the developed model is more logically consistent compared to previous LCA model, it can obtain more reliable results. Therefore, it is expected that the

The value listed within the parentheses is the proportion of direct and indirect impacts.

Discussion By summing up the results of the material manufacturing, transportation, and construction phases, the total embodied environmental impacts of the case building were estimated. As shown in Table 10, 93.4% of GWP was generated in the material manufacturing phase, and 2.9% and 3.7% of GWP were generated in the transportation and construction phases, respectively. The proportion of AP and EP in the transportation phases was high (13.7% and 14.2%, respectively), and the result was similar to the proportion of AP and EP in the construction phase since a large amount of emissions were generated from the combustion of the diesel. Most of ODP, POCP, ADP, and HTP, however, were generated from the material manufacturing phase. To demonstrate the validity of the results, the embodied environmental impacts which were determined using the developed model were compared with the findings of the LCA model presented by a previous study. Most of LCA models for assessing buildings developed in South Korea only concentrated on GHG emissions. Jeong et al. (in press), however, has presented a state of art LCA model that considers various environmental impacts (i.e., GWP, ODP, AP, EP, POCP, ADP) as well as GHG emissions. Therefore, the LCA model presented by Jeong

The value listed within the parentheses is the proportion of direct and indirect impacts.

Table 8 Amounts of energy sources used by construction. Construction work

Construction equipment

Energy consumption b

c

Work

Detailed information

Quantity

Type

Size

WC (unit/h)

EC (L/h)

Type

Quantity (L)

Excavation Loading soil Refilling Compaction PHC piling

Normal Normal Normal – SIPa

3800 m3 3800 m3 2221 m3 2221 m3 375 ea

Pile cutting Concrete pouring Concrete pouring Soil transportation Soil transportation Total

Reinforced concrete, slump value: 15, over 300 m3 Plain concrete, slump value: 15, 300 m3 30 km (excavated soil) 30 km (refilled soil)

375 ea 4389 m3 120 m3 3800 m3 2221 m3

Backhoe Loader Bulldozer Plate compactor Excavator Crawler crane Hydraulic hammer Forklift Backhoe Truck crane Excavator Concrete pump car Concrete pump car 25-t truck 25-t truck

1 m3 1.34 m3 19 t 1.5 t 0.2 m3 50–80 t 5t 5t 0.2 m3 50 t 0.2 m3 80 m3 80 m3 25 t 25 t

55.14 50.80 96.91 9.70 100.00 1.25 1.25 4.17 3.13 1.25 100.00 40.30 31.45 12 m3 d 12 m3

19.5 11.3 25.0 1.0 5.0 17.2 23.1 5.7 5.0 4.7 6.0 31.0 31.0 2.0 l/km e 2.0 l/km

Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel

1,343.7 845.2 573.0 229.1 18.8 5,160.0 6,930.0 513.0 600.0 1,410.0 22.5 3,376.2 118.3 9,500.0 5,552.5 36,192.2

a SIP is the soil-cement-injected precast pile. bWC is the work capacity of the construction equipment. cEC is the energy consumption of construction equipment per hour. d12 m3 is the load capacity of the 25-t truck. e2.0 L/km is the fuel efficiency of the 25-t truck.

M. Jang et al. / Environmental Impact Assessment Review 50 (2015) 143–155

153

Table 10 Embodied impacts of the case building. Impact category

GWP (t CO2 eq.) ODP (g CFC-11 eq.) AP (10 kg SO2 eq.) EP (kg PO3− 4 eq.) POCP (kg C2H4 eq.) ADP (10 kg sb eq.) HTP (kg C6H6 eq.)

Embodied environmental impact Material manufacturing

Transportation

Construction

Total

5660.98 (93.4% a) 85.06 (96.6%) 831.44 (68.8%) 1452.07 (67.5%) 4001.99 (97.1%) 3170.84 (95.7%) 1955.68 (99.5%)

174.85 (2.9%) 1.30 (1.5%) 165.30 (13.7%) 305.83 (14.2%) 52.17 (1.3%) 61.72 (1.9%) 4.28 (0.2%)

224.21 (3.7%) 1.66 (1.9%) 211.97 (17.5%) 392.16 (18.2%) 66.90 (1.6%) 79.15 (2.4%) 5.49 (0.3%)

6060.05 88.02 1208.71 2150.06 4121.07 3311.71 1965.45

a

The value listed within the parentheses is the proportion of each phase.

developed model will be used by various stakeholders of construction (i.e., the owner, architecture, engineer, government officer, construction manager, etc.) in evaluating the embodied environmental impacts of buildings and in selecting an environment-friendly design or in evaluating the environmental value of a building. Conclusions In this study, a hybrid LCA model that is capable of more comprehensively evaluating the embodied environmental impacts of buildings was developed. In addition, the dataset required for developing a hybrid LCA model was established based on the input–output table, the national pollutant release data, the unit prices of energy sources, the life cycle inventory of energy sources, and the emission data from the combustion of energy sources, consisting of the following: the Leontief inverse matrix, P, EU, MEP, and MEC. The hybrid LCA model evaluates GWP, ODP, AP, EP, POCP, ADP, and HTP as environmental impacts. The hybrid LCA model has the characteristics as follows: (i) the developed model can assess HTP of buildings by considering the toxic pollutants emitted directly and indirectly from the manufacturing process, which the previous LCA models cannot do; (ii) the developed model considers the emissions generated from the production and combustion of the energy sources used directly and indirectly in the material manufacturing, transportation, and construction phases while the previous LCA models consider only the emissions generated from the production and combustion of the energy sources used directly and indirectly in the material manufacturing phase and the emissions from the combustion of the energy sources used directly in transportation and construction phases; (iii) the developed model considers the abiotic

resources used for the production of the energy sources used directly and indirectly in the material manufacturing, transportation, and construction phases. The embodied environmental impact of an elementary school building was assessed to demonstrate the application as a case study. The result was then compared with that obtained using Jeong et al.'s (in press) model to demonstrate the validity of the developed model. As a result, GWP, ODP, AP, EP, POCP, and ADP calculated by Jeong et al.'s (in press) model were lower than those of the developed model by 4.6– 25.2%. Particularly, compared to the HTP of the developed model (1965 kg-C6H6 eq.), those of Jeong et al.'s (in press) model were considerably lower (13 kg C6H6 eq.). The results of the case study indicate that the developed model can quantify the embodied environmental impacts of buildings more comprehensively. In particular, the developed model can present the HTP of buildings, which the previous models cannot do, by considering the national pollutant release data. In addition, the system boundary of the developed model is logically consistent compared to that of the previous LCA models. Therefore, the developed model has potential use as a tool of decision-makers for selecting environment-friendly buildings. Evaluation of the developed model is limited to the embodied environmental impacts of the buildings, but it considers only the material manufacturing, transportation, and construction phases and not the maintenance, operation, and disposal phase, which are generated after completion of construction. Accordingly, the developed model cannot evaluate the environmental impacts during the life cycle of buildings. Therefore, further research should be planned to develop a model that can consider the complete life cycle of buildings including the operation, maintenance and repair, and disposal phases.

Fig. 4. Comparison results.

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Acknowledgment This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (NRF-2012R1A2A1A01004376).

Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.eiar.2014.09.010.

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Taehoon Hong is an associate professor at the department of architectural engineering in Yonsei University, Seoul. He is the director of the Sustainable Construction Management (SUSCOM) Laboratory at Yonsei University. He has diverse experience in construction management, mainly in the following research areas: Life Cycle Cost Analysis, Life Cycle Assessment, Sustainable Construction Development, MultiFamily Housing, Construction Simulation, Infrastructure Management, Facility management, Productivity, Construction Project Cost Control, Project Cycle Time, and Decision Support Systems.

Changyoon Ji is a graduate research assistant at the Department of Architectural Engineering in Yonsei University, Seoul. He is working at the SUSCOM laboratory and interested in the following research areas: Life Cycle Cost Analysis, Life Cycle Assessment, Construction Project Cost Control, and Sustainable Construction.