Establishing environmental benchmarks to determine the environmental performance of elementary school buildings using LCA

Energy and Buildings 127 (2016) 818–829

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Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild

Establishing environmental benchmarks to determine the environmental performance of elementary school buildings using LCA Changyoon Ji, Taehoon Hong ∗ , Jaewook Jeong, Jimin Kim, Minhyun Lee, Kwangbok Jeong Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03772, Republic of Korea

a r t i c l e

i n f o

Article history: Received 18 December 2015 Received in revised form 14 June 2016 Accepted 14 June 2016 Available online 16 June 2016 Keywords: Life cycle assessment Benchmark Environmental impacts Elementary school building Green building

a b s t r a c t Benchmarks are required to determine the environmental performance of new buildings. Through LCA, this study assessed the environmental impacts of 23 elementary school buildings in South Korea. By conducting statistical analysis (Pearson correlation analysis, partial correlation analysis, and Mann-Whitney test), gross floor area, latitude, and longitude were influence factors which cause the differences in environmental impacts among buildings. The differences in environmental impacts by gross floor area can be considered by defining the functional unit as one square meter of floor area. The differences in environmental impacts by region can be considered after dividing 23 elementary school buildings according to latitude and longitude. Based on the results of the two-step cluster analysis and the Mann-Whitney test on latitude and longitude, the environmental impacts of 23 elementary school buildings were divided into two clusters, with the exception of human carcinogenic potential and human non-carcinogenic potential. Therefore, this study presented the benchmarks in two clusters. For instance, the benchmarks for global warming potential in Clusters One and Two were 3.70E + 03 and 2.53E + 03 kg-CO2 eq./m2 , respectively. The benchmark for the human carcinogenic potential was 8.63E − 08 casescan /m2 . The benchmarks are expected to be used in determining the environmental performance of new elementary school buildings. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Buildings are key factors in energy consumption and global warming, consuming as much as 40% of the resources entering the global economy [1–3]. Thus, many researchers are studying ways in which the environmental impacts of buildings can be reduced [4–10]. The South Korean government has also exerted efforts to reduce the environmental impacts of their buildings by using environment-friendly materials and encouraging “green” buildings [11,12]. There are many definitions of green building. For instance, according to the U.S. Green building Council (USGBC), the green building is defined as the design and construction practices that reduce the negative impacts of buildings on the environment and occupants [13]. Common elements of these definitions include life cycle perspective, impacts on the environment, and health issues [14]. Generally, green buildings have higher initial costs, but have low environmental impacts compared to conventional buildings. Thus, it is imperative to determine how small the environmental impacts of green buildings are compared to those of conventional buildings. Therefore, the benchmark, which is used to determine

∗ Corresponding author. E-mail address: [email protected] (T. Hong). http://dx.doi.org/10.1016/j.enbuild.2016.06.042 0378-7788/© 2016 Elsevier B.V. All rights reserved.

the environmental performance of green buildings, must be presented to promote the adoption of such buildings. Life Cycle Assessment (LCA) is a representative method that assesses the potential environmental impacts of products, services, and systems by considering their life cycles [15]. Thus, in many previous studies, LCA has been used in evaluating environmental impacts, including global warming, ozone layer depletion, and resource depletion, which ensue during the building’s life cycle. A number of studies used LCA to determine the most environment-friendly building by comparing the environmental impacts of several buildings [16–23]. Jonsson et al. (1998) compared concrete and steel building frames. Other studies assessed the environmental impacts of a particular building, and identified the part of the building that should be considered important in reducing the environmental impacts of the said building [24–27]. Although many studies assessed the environmental impacts of buildings in China, Finland, France, New Zealand, Scotland, South Korea, Sweden, Thailand, and the United States, they assessed the environmental impacts of either one or less than three buildings [24–26,28–32]. Characteristics, such as building types, regions, and size, contribute to the differences in environmental impacts among buildings [33–37]. According to Ramesh et al., although all the buildings that have been assessed in previous LCA studies are residential buildings, the life cycle primary energy consumption of the

C. Ji et al. / Energy and Buildings 127 (2016) 818–829

buildings fall in the range of 150–400 kwh/m2 per year [38]. Thus, it is not reasonable to determine a benchmark by only considering the LCA result of a single building. Therefore, it is necessary to evaluate the environmental impacts of many existing buildings using the LCA, and then determine the benchmark to compare the environmental impacts of the new building based on the results of the LCA. In particular, the factors affecting the environmental impacts of buildings should be considered in establishing a benchmark. For example, the larger the gross floor area (GFA) of a building is, the more significant its environmental impacts become. Thus, evaluating environmental impacts based on unit area (i.e., one square meter) is more adequate in obtaining the reasonable environmental impacts of a building. Therefore, this study aims to assess the environmental impacts of several existing buildings by using LCA. Then, after conducting statistical analysis based on LCA results, influential factors on the environmental impacts of buildings will be determined, and a benchmark based on such factors will be presented in this study. There are various types of buildings, including residential buildings, office buildings, school buildings, etc. Thus, the environmental impacts of all building types should be evaluated. However, it is impossible to conduct such an evaluation because enormous data, such as the bill of quantity, energy consumption data, number of residents, etc., must be collected. According to the 2010 Educational Statistical Yearbook, over 1600 educational facilities have been constructed every year since 2000 [39]. Moreover, the South Korean government has implemented the Green School Project, which aims to repair and renovate existing elementary school buildings to reduce the environmental impacts of elementary school buildings and to improve the educational environment [35,40]. Considering that significant cost and efforts are consistently invested in the construction of elementary school facilities, this study is primarily focusing on elementary school buildings in South Korea.

2. Materials and methods 2.1. Data collection The energy consumption and material data of the elementary school buildings should be collected to assess the environmental impacts of the elementary school buildings. Furthermore, the data on the characteristics of the elementary school buildings should be collected for statistical analysis to determine the attributes that influence each of the environmental impacts. The Ministry of Education Science and Technology (MEST) has established not only the energy consumption data, but also the number of students, location, year of construction, building area, total floor area, and heating and cooling system types of elementary schools in South Korea [39]. This study collected the energy consumption data and the characteristics of the elementary school buildings from the 2010 Educational Statistical Yearbook published by MEST. However, the Educational Statistical Yearbook does not include the material data of elementary school buildings. Therefore, the material data of elementary school buildings were collected from the Office of Education in South Korea. However, although the Office of Education in South Korea has managed the material data on elementary school buildings, it is impossible to obtain the material data on buildings built more than 10 years ago. In particular, the material data collected from the Office of Education in South Korea did not contain all of the necessary material data, such as architectural, civil, landscaping, mechanical, and electrical construction information. Eventually, the material data of 23 elementary school buildings that were built since 2007 were collected from the Office of Education in South Korea, as shown in Fig. 1. The latitude and longitude of 23 elementary school buildings have

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Fig. 1. Locations of the 23 elementary school buildings.

been obtained by using Google maps. 23 elementary school buildings are located in five administrative districts, and their total floor area ranges from 6372 to 14,555 m2 . In addition, all 23 elementary school buildings have excellent grade in Korea Green Building Certification System (G-SEED). The detailed information on the 23 case buildings is presented in Table S1 of the Supplementary material. 2.2. Life cycle assessment 2.2.1. System boundary, goal and scope definition The main objective of this study is to establish the benchmark for comparing the environmental impacts of new elementary schools based on the environmental impacts of 23 elementary school buildings. In this context, the LCA aims to quantify the environmental impacts of 23 elementary school buildings. The functional unit for analysis, in reference to the elementary school buildings, was defined as one square meter of floor area for a period of the life cycle (1 m2 ); this enables comparisons with similar studies. The system boundary includes the building’s life cycle, consisting of material manufacturing, transportation, construction, operation, and disposal phases, as shown in Fig. 2. In order to calculate environmental impacts in maintenance phase, the quantity of building material for repair and replacement should be calculated. However, the quantity of building material for repair and replacement can be calculated based on several assumptions since there is no reliable data on the repair and replacement cycle. Thus, the maintenance phase is excluded from this study. The life time of the elementary school buildings was defined as 40 years, which is the expected service life of a reinforced-concrete building defined in the Enforcement Rule of the Corporate Tax Act [41]. Furthermore, this study covers the direct and indirect environmental impacts of elementary school buildings. Direct environmental impacts result from the emissions and abiotic resources within the building’s

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Fig. 2. Scope of life cycle assessment.

boundary. Indirect environmental impacts result from the emissions and abiotic resources in the supply chain of materials and energy sources that are used during the building’s life cycle. Emissions affect various environmental impacts, such as global warming, ozone layer depletion, acidification, eutrophication, and photochemical ozone creation. Resources such as fossil fuel and minerals used during the building’s life cycle affect another environmental impact (i.e., abiotic resource depletion) [42–46]. Therefore, this study assesses 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). Moreover, toxic emissions can adversely affect human health (i.e., cancer and non-cancer diseases) [47,48]. For instance, Benzene could cause cancer such as Leukemia while Formaldehyde could cause noncancer diseases such as pneumonia and diarrhea. The improvement of the quality of life is the most important issue in modern society. Therefore, this study assesses human carcinogenic potential (HCP), and human non-carcinogenic potential (HNCP) as environmental impacts of buildings. HCP and HNCP are the impact categories which indicate human cancer risk and non-cancer risk due to the toxic pollutants from building life cycle. Furthermore, this study also assesses the environmental cost of 23 elementary school buildings, which is a single index calculated by integrating eight environmental impacts. The environmental cost represents the avoidance, prevention, or damage cost of the environmental impacts. 2.2.2. Inventory analysis This study assesses the environmental impacts of elementary school buildings by using the LCA models developed in previous studies, instead of developing a new LCA model. Jang et al. developed a hybrid LCA model, which is based on the input-output table

proposed by the Bank of Korea (BOK) [49], annual national pollutant release data presented by the National Institute of Environmental Research (NIER) [50], the life cycle inventory of energy sources presented by the Ministry of Environment (ME) and the Ministry of Knowledge and Economy (MKE) [51], and the emission data from the combustion of energy sources presented by Wang [52]. This hybrid LCA model could quantify the emissions generated directly and indirectly from the materials manufacturing, transportation, and construction phases and the abiotic resources used directly and indirectly in three phases [53]. Therefore, this study quantified the direct and indirect emissions and the abiotic resources in three phases by applying the material data of 23 elementary school buildings to the hybrid LCA model developed by Jang et al. (2015). For instance, the material cost of ready mixed concrete (RMC) for “A” school is $ 354,513. By applying $ 354,513 of the material cost to Equation (1), the amount of CO2 emission due to the diesel directly used in the RMC manufacturing was calculated at 26,828 kg-CO2 (=(2.66710 kg-CO2 /l + 0.05615 kgCO2 /l) × 0.03165 l/$ × $ 354,513). The energy use coefficient of RMC to diesel was 0.03165 l/$ which is calculated by applying the exchange rate (KRW1, 204.30/USD on September 7, 2015) to the energy use coefficient in the hybrid LCA model presented by Jang et al. (2015). The CO2 emission from the combustion and production of one unit (one liter) of diesel (mec and mep) is 2.66710 kg-CO2 /l and 0.05615 kg-CO2 /l, respectively. Then, the amount of CO2 emission due to the diesel indirectly used in the RMC manufacturing is calculated using Equation (2). According to the Leontief inverse matrix included in the hybrid LCA model, the production of $ 354,513 of RMC leads to the production of $ 93,359 of cement (=0.26335 × $ 354,513). The production inducement coefficients of RMC to cement in Leontief inverse matrix is 0.26335. Since the energy use coefficient of cement to diesel, which is included in the hybrid LCA model presented by Jang et al.

C. Ji et al. / Energy and Buildings 127 (2016) 818–829

(2015), is 0.01279 l/$, the amount of diesel used for manufacturing $ 93,359 of cement was calculated at 1194 l (=0.01279 l/$ × $ 93,359). By considering all the industry related to the RMC manufacturing according to the same way, the amount of diesel indirectly used for manufacturing $ 354,513 of RMC was calculated at 20,344 l. As mentioned above, since the mec and mep of diesel is 2.66710 kg-CO2 /l and 0.05615 kg-CO2 /l, respectively, the amount of CO2 emission due to the diesel indirectly used in the RMC manufacturing was calculated at 55,402 kg-CO2 (=((2.66710 kgCO2 /l + 0.05615 kg-CO2 /l) × 20,344 l).

in transportation and construction phases related to RMC was 28,120 kg-CO2 and 6668 kg-CO2 , respectively.

 QMj × TDjm

eul = 2

m=1 j=1

eul =

(1)

I ind = (MEC + MEP) × (EU × (I − A)−1 × MC)

(2)

where Idir is the emissions directly emitted and abiotic resource directly used; MEC is the matrix whose element mecil represents the emission i generated directly from the combustion of one unit of energy source l; MEP is the matrix whose element mepil represents (i) the emission i generated directly from the production of one unit of energy source l or (ii) the abiotic resource i used directly for the production of one unit of energy source l; EU is the energy use matrix whose element eulj represents the energy use coefficient of material j to energy source l; MC is the material cost of material j; Iind is the emissions emitted and abiotic resource indirectly used; and (I-A)−1 is the Leontief inverse matrix. The emissions from transportation were calculated based on the amount of energy sources used by transportation vehicles. First, the amounts of energy sources used by transportation vehicles and construction equipment were estimated using Equations (3) and (4) [54]. For instance, “A” school used 9522 m3 of RMC. By applying 9522 m3 of RMC to Equation (3), the amount of diesel directly used for transportation is calculated at 39,025 l (= 2 × 9522 m3 × 30 km/(6 m3 /ea × 2.44 km/l)). Here, the load capacity and energy efficient of RMC truck is 6.0 m3 /ea and 2.44 km/l, respectively [54]. The transportation distance was set at 30 km, which has been used in developing the life cycle inventory database in South Korea [51]. In addition, by applying 9522 m3 of RMC to Equation (4), the amount of diesel directly used by concrete pump car is calculated at 9253 l (= 9522 m3 × 31 l/h/31.9 m3 /h). Here, the energy consumption per hour and work capacity of concrete pump car is 31 l/h and 31.9 m3 /h, respectively [54]. Then, by applying the amount of diesel calculated to Equation (5), the amount of CO2 emissions directly emitted from transporting and pouring 9522 m3 of RMC was 106,275 kgCO2 (=(2.66710 kg-CO2 /l + 0.05615 kg-CO2 /l) × 39,025 l) and 25,199 kg-CO2 (=(2.66710 kg-CO2 /l + 0.05615 kg-CO2 /l) × 9253 l), respectively. By applying the amount of diesel calculated to Equation (6), the amount of CO2 emissions indirectly emitted

(3)

LCjm × EEml

 QMj × ECnl n=1 j=1

(4)

WCjn

I dir = (MEC + MEP) × eu I

I dir = (MEC + MEP) × EU × MC

821

ind

= (MEC + MEP) × (EU × (I − A)

(5) −1

× (eu × UP))

where eul is the physical amount of energy source l used directly by a transportation vehicle or construction equipment; 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; 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 UP is the unit price of energy sources. The 2010 Educational Statistical Yearbook suggests the energy consumption of elementary school buildings including the electricity and gas energy for warming, cooling, lighting and appliances [39]. Thus, the energy consumption of elementary school buildings during the operation phase was calculated by multiplying the actual data from the 2010 Educational Statistical Yearbook by the building’s life time. As mentioned above, the life time of the elementary school buildings was defined as 40 years. By applying the amount of the energy consumption during the operation phase to Equations (5) and (6), the direct and indirect emissions and the abiotic resources in operation phase was calculated. For instance, “A” school building used 662,708 kWh of electricity in 2010, according to 2010 Educational Statistical Yearbook. Thus, the amount of operational energy consumption of “A” school was calculated at 26,508,320 kWh of electricity (=662,708 kWh/year × 40 years). Then, the amount of CO2 emission directly emitted in operational phase was calculated 12,915,384 kg-CO2 by applying 26,508,320 kWh of electricity to Equation (5) (=0.48722 kg-CO2 /kWh × 26,508,320 kWh). The mep of electricity is 0.48722 kg-CO2 /kWh [53]. Jeong et al. provided the model for estimating the amount of energy sources directly used in the disposal phase [37]. Thus, by using the model developed by Jeong et al. (2015), the amount of energy sources directly used during the disposal phase was estimated. Then, by applying the amount of energy sources directly used during the disposal phase to Equations (5) and (6), the emissions emitted and the abiotic resources used directly and indirectly in the disposal phase were quantified. For instance, according to the model provided by Jeong et al. (2015), 170,255 l of diesel and 7788 kWh of electricity is directly required for the disposal and landfill of the waste materials due to 9522 m3 of RMC. By applying the amount of diesel and electricity directly required dur-

Table 1 Reference for the characterization factors. Impact category

Unit

References

Global warming potential Ozone layer depletion potential Acidification potential Eutrophication potential Photochemical ozone creation potential Abiotic depletion potential Human carcinogenic potential Human non-carcinogenic potential

kg CO2 eq. kg CFC-11 eq. kg SO2 eq. kg PO3 4− eq. kg C2 H4 eq. kg sb eq. casecan a casenonc b

[55], 100 year time horizon [56] [57] [58] [59] [58] [47] [47]

a b

casecan is the unit of HCP that refers to cancer incidences. casenonc is the unit of HNCP that refers to non-cancer disease incidences.

(6)

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Table 2 The damage factors for each impact category. Impact category

GWP ODP AP EP POCP ADP HCP HNCP a b c

Unit

kg CO2 eq. kg CFC-11 eq. kg SO2 eq. kg PO4 3− eq. kg C2 H4 eq. kg Sb eq. casecan casenonc

Safeguard subject

Refs.

Human health (DALYa /unit)

Social assets (USDb /unit)

Biodiversity (EINESc /unit)

Primary production (kg/unit)

1.23E − 07 1.35E − 03 2.38E − 04 – 3.22E − 05 – 11.5 2.7

2.26E − 03 1.08E + 00 4.23E + 00 1.93E + 00 6.83E − 01 1.18E − 02 – –

– – – – – 8.90E − 14 – –

– 2.79E + 02 2.61E + 01 – 2.64E + 01 1.53E − 01 – –

[61] [61] [61] [61] [61] [61] [61] [61]

DALY is the disability adjusted life years. USD is the United States Dollar (The KRW1,204.30/USD exchange rate was used; the prevailing rate on September 7, 2015). EINES is the expected increase in number of extinct species.

ing the disposal phase to Equation (5), the direct CO2 emission is quantified at 467,441 kg-CO2 (=(2.66710 kg-CO2 /l + 0.05615 kgCO2 /l) × 170,255 l + 0.48722 kg-CO2 /kWh × 7788 kWh). 2.2.3. Impact assessment Life cycle impact assessment (LCIA) represents the environmental impacts of elementary school buildings by converting the result of the life cycle inventory analysis to environmental impacts. LCIA consists of the classification, characterization, normalization, and weighing steps. Classification and characterization are mandatory, while normalization and weighing are optional [15]. This study considered classification and characterization. The characterization factors represent the potential of each emission to contribute to the respective environmental impacts. Thus, the environmental impacts are estimated by multiplying the amount of emissions (or abiotic resources) resulting from the inventory analysis by the characterization factors, as shown in Equation (7). Previous studies presented the characterization factors for eight environmental impacts (i.e., GWP, ODP, AP, EP, POCP, ADP, HCP, and HNCP), as shown in Table 1. Therefore, this study used the characterization factors presented by previous studies for LCA. EI = I × CF

(7)

where EI is the environmental impact; I is the emissions or abiotic resources calculated by inventory analysis; and CF is the characterization factor. LCIA methods are generally divided into the midpoint approach and the endpoint approach. The midpoint approach focuses on the problems at an early stage in the cause-effect chain and quantifies environmental impacts, such as GWP, ODP, AP, EP, POCP, and ADP. The endpoint approach tries to model the cause-effect chain up to the endpoint [60]. Of the existing LCIA methodologies based on the endpoint approach, the Korean Life Cycle Impact Assessment Index based on a Damage-oriented Modeling (KOLID) provides the damage factors and integration factors. By using damage factors, it is possible to determine the environmental damages to four safeguard subjects (i.e., human health, social assets, biodiversity, and primary production), which could be affected by the environmental impacts, such as GWP, ODP, AP, EP, POCP, and ADP. The damage factors provided by KOLID are suitable for use in South Korea as they reflect the environmental conditions in the country. The integration factors indicate the willingness to pay for the prevention of damages to safeguard subjects. The integration factors provided by KOLID reflect the public preferences over the damages in South Korea, as the conjoint analysis was conducted based on the face-to-face survey conducted with 400 respondents in South Korea [61]. Meanwhile, the World Health Organization (WHO) has established the DALY factors of cancer and non-cancer diseases [62,63]. It is possible to use the DALY factors as the damage factors for HCP and HNCP. Eventually, this study calculated the environ-

Table 3 The integration factors for each safeguard subject. Safeguard subject

Unit

Integration factor

Human health Social assets Biodiversity Primary production

USD/DALY USD/USD USD/EINES USD/ton

2.24E + 04a 1.00E + 00 4.72E + 02 4.09E − 02

a The KRW1,204.30/USD exchange rate was used; the prevailing rate on September 7, 2015.

mental cost by using the damage factors and integration factors provided by KOLID and WHO, as shown in Tables 2 and 3. For instance, 1 kg-CO2 eq. of GWP causes 1.23E − 07 DALY of damage on human health and 2.26E − 03 USD of damage on social asset, as shown in Table 2. 1 DALY of damage on human health represents 2.24E + 04 USD of the monetary value, as shown in Table 3. Thus, 1 kg-CO2 eq. of GWP was converted to 5.02E − 03 USD of the environmental cost using the damage and integration factors in Tables 2 and 3 5.02E − 03 USD = (1 kg-CO2 eq. × (1.23E − 07 DALY/kg-CO2 eq. × 2.24E + 04 USD/DALY + 1.26E − 03 USD/kg-CO2 eq.)). 3. Results and discussion 3.1. Environmental impacts of elementary school buildings Fig. 3 shows the summary of the results after assessing the environmental impacts of 23 elementary school buildings. As shown in Fig. 3, the GWP, ODP, AP, EP, POCP, ADP, HCP, HNCP, and environmental cost of 23 elementary school buildings are about 3.17E + 03 kg-CO2 eq./m2 , 3.23E − 05 kg-CFC11 eq./m2 , 4.26E + 00 kg-SO2 eq./m2 , 6.93E − 01 kg-PO4 3− eq./m2 , 4.51E + 00 kg-C2 H4 eq./m2 , 1.65E + 01 kg-sb eq./m2 , 8.71E − 08 casescan /m2 , 1.48E − 06 casesnonc /m2 , and 9.11E + 04 $/m2 on average, respectively. However, the environmental impacts of the elementary school buildings were different, depending on the school buildings. For instance, the GWP of the elementary school buildings ranged from 1.58E + 03 to 5.36E + 03 kg-CO2 eq./m2 , as shown in Fig. 4. The detailed results of all impact categories are presented in Tables S2 to S10 of the Supplementary material. Table 4 shows the average environmental impacts in each phase of the building’s life cycle. Most of the environmental impacts were caused by the operation and material manufacturing phases. As shown in Table 4, however, the influence of each phase on the environmental impacts differed, depending on the impact category. First, most of the GWP was generated in the operation (72.22%) and the material manufacturing (24.99%) phases, whereas few GWP was caused in the transportation, construction, and disposal phases. Like GWP, the ODP, AP, EP, POCP, and ADP are generally generated in the operation phase. Unlike six environmental impacts,

C. Ji et al. / Energy and Buildings 127 (2016) 818–829

Fig. 3. Summary of the environmental impacts of 23 elementary school buildings.

Fig. 4. GWP of elementary school buildings.

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Table 4 Average environmental impacts of elementary school buildings in each phase of the building’s life cycle. Environmental impacts

Building’s life cycle

Total

Materials

Transportation

Construction

Operation

Disposal

7.92E + 02 (24.99%)

1.33E + 01 (0.42%)

9.94E + 00 (0.31%)

2.29E + 03 (72.22%)

6.49E + 01 (2.05%)

3.17E + 03 (100%)

OPD (kg-CFC-11 eq./m )

1.10E − 05 (34.06%)

1.74E − 07 (0.54%)

1.30E − 07 (0.40%)

2.02E − 05 (62.53%)

7.99E − 07 (2.47%)

3.23E − 05 (100%)

AP (kg-SO2 eq./m2 )

1.38E + 00 (32.33%)

3.26E − 02 (0.76%)

2.43E − 02 (0.57%)

2.68E + 00 (62.98%)

1.43E − 01 (3.35%)

4.26E + 00 (100%)

GWP (kg-CO2 eq./m2 ) 2

2.41E − 01 (34.70%)

5.89E − 03 (0.85%)

4.41E − 03 (0.64%)

4.17E − 01 (60.14%)

2.54E − 02 (3.67%)

6.93E − 01 (100%)

POCP (kg-CO2 eq./m2 )

6.79E − 01 (15.05%)

5.42E − 03 (0.12%)

4.06E − 03 (0.09%)

3.78E + 00 (83.81%)

4.19E − 02 (0.93%)

4.51E + 00 (100%)

ADP (kg-C2 H4 eq./m2 )

4.61E + 00 (27.84%)

8.28E − 02 (0.50%)

6.19E − 02 (0.37%)

1.14E + 01 (68.94%)

3.89E − 01 (2.35%)

1.65E + 01 (100%)

HCP (casescan /m )

8.62E − 08 (98.99%)

3.26E − 11 (0.04%)

2.44E − 11 (0.03%)

6.97E − 10 (0.80%)

1.30E − 10 (0.15%)

8.71E − 08 (100%)

HNCP (casesnonc /m2 )

1.47E − 06 (99.84%)

6.75E − 11 (0.00%)

5.05E − 11 (0.00%)

1.95E − 09 (0.13%)

2.69E − 10 (0.02%)

1.48E − 06 (100%)

EC ($/m2 )

2.58E + 04 (28.29%)

5.40E + 02 (0.59%)

4.04E + 02 (0.44%)

6.19E + 04 (67.97%)

2.46E + 03 (2.70%)

9.11E + 04 (100%)

EP (kg-PO4

3−

2

eq./m )

2

Fig. 5. Influence of each environmental impact category to the environmental cost.

98.99% of HCP and 99.84% of HNCP were caused in the material manufacturing phase, whereas four other phases had minimal effect on the HCP and HNCP. Since the manufacturing process uses not only the energy sources, but also various chemical substances, a large amount of the toxic emissions causing the HCP and the HNCP are generated from the manufacturing process of building materials. It is the reason why most of HCP and HNCP were caused in the material manufacturing phase. In addition, as much as 67.97% of the environmental cost was caused in the operation phase because the HCP and HNCP had minimal effect on the environmental cost while the GWP, AP, and POCP affected the environmental cost by more than 90%, as shown in Fig. 5.

3.2. Direct and indirect environmental impacts This study assessed not only the direct environmental impacts of elementary school buildings, but also the indirect environmental impacts that result from the supply chain of materials and energy sources. Although the environmental impacts significantly depended on the elementary school buildings (refer to Fig. 4), the proportions of direct and indirect impacts were similar in all elementary school buildings. Fig. 6 shows the proportion of direct and indirect impacts to the GWP. Whereas the GWPs of 23 elementary school buildings showed a difference of up to 3.4 times (refer to Fig. 4), the proportion of indirect GWP showed a difference of up to 1.12 times at most (refer to Fig. 6) (i.e., the GWPs ranged from 1.58E + 03 to 5.36E + 03 kg-CO2 eq./m2 in Fig. 4, whereas the proportion of indirect GWP ranged from 70.5 to 79.0% in Fig. 6).

As shown in Fig. 7, however, the proportions of direct and indirect impacts varied, depending on the impact categories. For instance, the proportions of direct and indirect GWPs were 26.4% and 73.6%, respectively. However, in the case of the ODP and POCP, indirect environmental impacts accounted for about 99% of the total environmental impacts. The detailed result of all impact categories is presented in Table S11 of the Supplementary material.

3.3. Benchmark for determining the environmental performance of new buildings According to the results of the LCA, the environmental impacts of 23 elementary school buildings differed, depending on the condition of the elementary school buildings. Therefore, the attributes that affect environmental impacts of building should be defined, and the difference in the environmental impacts depending on the defined attributes should be considered in establishing the environmental benchmarks for determining the environmental performance of elementary school buildings. Thus, this study conducted statistical analysis to determine the attributes affecting the environmental impacts of elementary school buildings. Referring to the previous studies [32,36], the structure type, heating system type, cooling system type, and energy source type were defined as the nominal attributes, and the GFA, building area, number of students, stories, and year of construction were defined as the numerical attributes. Furthermore, the latitude and the longitude were defined as numerical attributes in consideration of the regional characteristics. Then, statistical analysis was performed

C. Ji et al. / Energy and Buildings 127 (2016) 818–829

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Fig. 6. Influence of direct and indirect environmental impacts to the GWP.

Table 5 Significance probability in the Mann-Whitney Test. Attributes

Groups GWP

ODP

AP

EP

POCP

ADP

HCP

HNCP

EC

Heating type

Aa , Bb

0.230

0.196

0.196

0.230

0.230

0.268

0.573

0.404

0.230

Cooling type

Cc , Dd C, Ee D, E

0.714 0.400 0.444

0.868 0.400 0.444

0.616 0.400 0.444

0.664 0.400 0.444

0.616 0.400 0.222

0.764 0.400 0.444

0.482 0.800 0.889

0.525 0.933 0.889

0.714 0.400 0.444

Energy type

Ff , Gg F, Hh G, H

0.187 0.317 0.133

0.573 0.462 0.533

0.187 0.362 0.133

0.187 0.410 0.133

0.047 0.317 0.133

0.292 0.362 0.133

0.105 0.829 0.133

0.047 1.000 0.133

0.187 0.317 0.133

Ii , Jj

0.587

0.290

0.491

0.403

0.638

0.446

0.446

0.857

0.587

Structure type a b c d e f g h i j

Impact category

A is for individual heating. B is for central heating. C is for individual cooling. D is for central cooling. E is for mixed cooling. F is for electricity. G is for gas. H is for electricity and gas. I is for reinforced concrete structure. J is for steel reinforced concrete structure.

by using the software program SPSS 20.0 (SPSS 20.0 for Windows; SPSS Inc., Chicago, IL). First, a homogeneity test was conducted to determine the influence of nominal attributes on environmental impacts. Generally, a homogeneity test of samples employs analysis of variance (ANOVA), but a non-parametric test is more appropriate for a smaller number of samples [64,65]. Since the data on 23 elementary school buildings was used in this study, the Mann-Whitney test, which could be used to verify the amount of overlapping between the two independent samples, was conducted [64]. As shown in Table 5, the p-values for all nominal attributes are over 0.05, which mean that they are insignificant to the environmental impacts of elementary school buildings. Second, Pearson’s correlation analysis was conducted to determine the influence of numerical attributes on environmental

impacts. As shown in Table 6, the GFA and the number of stories appeared to be correlated with the nine environmental impacts. The number of students, latitude, and longitude, however, were shown to have a correlation with the seven impact categories, except for HCP and HNCP. Furthermore, there was no correlation between “the building area and the construction year” and all impact categories. Meanwhile, a larger GFA means it can accommodate a higher number of students and increase its stories. Partial correlation is a measure of the strength and direction of a linear relationship between two continuous variables while controlling the effect of one or more continuous variables. Thus, the partial correlation analysis was conducted to accurately determine the influence of the three attributes (i.e., GFA, the number of students, and the number of stories) on environmental impacts. As a result, the GFA had a

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C. Ji et al. / Energy and Buildings 127 (2016) 818–829

Fig. 7. Influence proportion of direct and indirect environmental impacts.

Table 6 Results of Pearson’s correlation coefficient. Attributes

Impact category

GFA Building area Number of students Number of stories Construction year Latitude Longitude

GWP

ODP

AP

EP

POCP

ADP

HCP

HNCP

EC

0.000 0.874 0.004 0.007 0.182 0.000 0.026

0.000 0.974 0.004 0.003 0.214 0.000 0.037

0.000 0.877 0.003 0.005 0.166 0.000 0.038

0.000 0.914 0.003 0.004 0.174 0.000 0.045

0.001 0.695 0.004 0.030 0.148 0.000 0.019

0.000 0.930 0.004 0.004 0.192 0.000 0.035

0.000 0.738 0.054 0.005 0.194 0.039 0.412

0.000 0.777 0.035 0.002 0.209 0.056 0.395

0.000 0.842 0.003 0.008 0.163 0.000 0.030

Table 7 Partial correlation analysis between attributes and environmental impacts. Impact category

GWP ODP AP EP POCP ADP HCP HNCP EC * ** a

Attributes Gross floor area

Number of students

Number of stories

0.509* (0.019)a 0.509* (0.018) 0.557** (0.009) 0.570** (0.007) 0.459* (0.036) 0.519* (0.016) 0.770** (0.000) 0.734** (0.000) 0.532* (0.013)

0.261 (0.252) 0.235 (0.304) 0.269 (0.239) 0.253 (0.268) 0.320 (0.157) 0.236 (0.303) −0.213 (0.354) −0.153 (0.509) 0.280 (0.219)

0.210 (0.360) 0.298 (0.189) 0.214 (0.351) 0.244 (0.287) 0.060 (0.796) 0.259 (0.256) 0.350 (0.120) 0.412 (0.063) 0.186 (0.420)

The correlation coefficient is significant at the 0.05 level. The mean difference is significant at the 0.01 level. The value listed within the parentheses is the significance probability.

correlation with all impact categories, but the number of students and stories do not (refer to Table 7). Eventually, the GFA, latitude, and longitude were defined as the influence factor on the environmental impacts of elementary school buildings. As mentioned above, this study defined one square meter of the floor area for the duration of the building’s life cycle as the functional units with considering the GFA as a key factor affecting the environmental impacts of elementary school buildings. Thus, the benchmark for comparing the environmental impacts of new elementary school buildings was developed to consider the difference in environmental impacts depending on regions. The cluster analysis divides a large group into sub-groups by maximizing the similarity among the observations within a cluster, and maximizing the differences between the clusters. There are several methods used in cluster analysis, including hierarchical, nonhierarchical, k-means, and two-step. The two-step cluster analysis method could consider both continuous and categori-

Table 8 Results of the two-step cluster analysis. No.

School

Region

Cluster

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

“A” school “B” school “C” school “D” school “E” school “F” school “G” school “H” school “I” school “J” school “K” school “L” school “M” school “N” school “O” school “P” school “Q” school “R” school “S” school “T” school “U” school “V” school “W” school

Seoul Seoul Seoul Seoul Seoul Seoul Seoul Chungnam Chungnam Chungnam Chungnam Daegu Daegu Daegu Daegu Gwangju Gwangju Gwangju Gwangju Gwangju Busan Busan Busan

#1 #1 #1 #1 #1 #1 #1 #1 #1 #1 #1 #2 #2 #2 #2 #2 #2 #2 #2 #2 #2 #2 #2

cal variables, and automatically determine the number of clusters based on statistical criteria [66,67]. Thus, the two-step cluster analysis was conducted for the latitude and longitude of 23 elementary school buildings. As a result, 23 elementary school buildings were divided into two clusters, as shown in Table 8. Cluster One included the 11 elementary school buildings in Seoul and Chungnam. Cluster Two included the 12 elementary school buildings in Daegu, Gwangju, and Busan. The Mann-Whitney test was conducted to verify whether or not the environmental impacts of 23 elementary school buildings

C. Ji et al. / Energy and Buildings 127 (2016) 818–829 Table 9 The results of Mann-Whitney Test. Impact category

P-value

Decision

GWP ODP AP EP POCP ADP HCP HNCP Environmental Cost

0.000 0.001 0.000 0.000 0.000 0.000 0.235 0.118 0.000

null hypothesis is rejected null hypothesis is rejected null hypothesis is rejected null hypothesis is rejected null hypothesis is rejected null hypothesis is rejected null hypothesis is correct null hypothesis is correct null hypothesis is rejected

are divided into two clusters. As a result, the p-value in the seven impact categories, except for the HCP and HNCP, was less than 0.05 (refer to Table 9). It was shown that the seven environmental impacts of 23 elementary school buildings were divided into two clusters. Therefore, in case of the seven impact categories, this study established the benchmarks for determining the environmental performance of a new elementary school building by dividing the environmental impacts of 23 elementary school buildings into two clusters. The median value is more appropriate than the mean value when the distribution of the data is skewed [9,68,69]. In particular, since the sample size is at most 23, the Central Limit Theorem is not valid in this study. Thus, this study defined the median values of environmental impacts of 23 elementary school buildings as the benchmarks, which could be used for determining the environmental performance of new elementary school buildings. As shown in Fig. 8, the benchmarks for the GWP, ODP, AP, EP, POCP, ADP, and environmental cost in Cluster One are larger than those of Cluster Two. For instance, the benchmark for GWP in Cluster One was 3.70E + 03 kg-CO2 eq./m2 , whereas that of Cluster Two was 2.53E + 03 kg-CO2 eq./m2 . The benchmarks for HCP and HNCP were 8.63E − 08 casescan /m2 and 1.46E − 06 casesnonc /m2 , respectively. 3.4. Discussions This study presented the environmental benchmarks to be used for determining the environmental performance of elementary school buildings in terms of nine environmental impacts. The environmental benchmarks for seven impact categories (i.e., GWP, ODP, AP, EP, POCP, ADP, and environmental cost) were divided into two clusters depending on regions. Cluster One includes Seoul and Chungnam while Cluster Two includes Gwanju, Daegu, and Busan. Thus, when determining the environmental performance of elementary school buildings in Seoul and Chungnam in terms of GWP, ODP, AP, EP, POCP, ADP, and environmental cost, the environmental benchmarks for Cluster One could be used. The environmental benchmarks for Cluster Two could be used to determine the environmental performance of elementary school buildings in Gwanju, Daegu, and Busan. However, since the environmental benchmarks for HCP and HNCP were not divided into two clusters depending on regions, they could be used in all regions, South Korea. In order to establish the environmental benchmarks, this study assessed the environmental impacts of 23 elementary school buildings considering their life cycle, including material manufacturing, transportation, construction, operation, and disposal phases using LCA model in detail. Then, the environmental benchmarks were established based on the LCA results of 23 elementary school buildings. However, the results considering the influence factors on the environmental impacts of elementary school buildings instead of the mean value of 23 elementary school buildings are suggested as the benchmarks. That is, the results of the statistical analysis showed that the region as well as the GFA affects the environmental impacts of elementary school buildings. Based on the results of

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the statistical analysis, the median values of environmental impacts in terms of seven impact categories, which were divided into two clusters, were established as the environmental benchmarks. If the environmental benchmarks are established without the consideration of the influence factor (i.e., region), they could lead to incorrect results. For instance, the benchmark for GWP is calculated at 3.05E + 03 kg-CO2 eq./m2 if it is not divided into two clusters depending on regions. The environmental performance of elementary school buildings in Cluster One (i.e., Seoul and Chungnam) will be poorer than those in Cluster Two (i.e., Gwanju, Daegu, and Busan) if the benchmark without the consideration of region is used. For this reason, the environmental benchmarks established in this study are expected to determine the environmental performance of new elementary school buildings more accurately. However, the established benchmarks are based on only the LCA results of 23 elementary school buildings in five regions of South Korea. More samples are required in order to improve the reliability of the statistical analysis. Therefore, additional LCA should be conducted on lots of elementary school buildings outside the aforementioned five regions.

4. Conclusions The degree of environmental impact reduction with the use of green buildings must be presented to further promote the construction of such type of buildings. Therefore, the benchmark for comparing the environmental impacts of green buildings is required to determine actual environmental performance. The benchmark was established based on the environmental impacts of existing conventional buildings. As characteristics, including building type, region, and size, contribute to the differences in environmental impacts among buildings, the environmental impacts of only one building are not appropriate as benchmark. Although previous studies provided the environmental impacts of buildings, most of them relied on the LCA of a building. Therefore, this study assessed the environmental impacts (i.e., GWP, ODP, AP, EP, POCP, ADP, HCP, HNCP, and environmental cost) of 23 elementary school buildings in five regions (i.e., Seoul, Chungnam, Daegu, Gwangju, and Busan) in South Korea by using LCA, and aimed to establish the benchmarks based on the results of LCA. As a result, the mean value of GWP, ODP, AP, EP, POCP, ADP, HCP, HNCP, and environmental cost were calculated at 3.05E + 03 kg-CO2 eq./m2 , 3.20E − 05 kg-CFC11 eq./m2 , 4.19E + 00 kg-SO2 eq./m2 , 6.82E − 01 kg-PO4 3− eq./m2 , 4.47E + 00 kg-C2 H4 eq./m2 , 1.59E + 01 kg-sb eq./m2 , 8.63E − 08 casescan /m2 , 1.46E − 06 casesnonc /m2 , and 8.93E + 04 $/m2 , respectively. The influence factors, which cause the differences in the environmental impacts among buildings, should be considered in determining a reliable benchmark. The GFA, latitude, and longitude were determined as influence factors on the environmental impacts of elementary school buildings after conducting a statistical analysis. The differences in environmental impacts, depending on GFA, can be considered by defining the functional unit as one square meter of the floor area. The differences in environmental impacts, depending on region, can be considered by dividing 23 elementary school buildings according to latitude and longitude. Based on the results of the two-step cluster analysis and the MannWhitney test on latitude and longitude, the seven environmental impacts (i.e., GWP, ODP, AP, EP, POCP, ADP, and environmental cost) of 23 elementary school buildings were divided into two clusters. The HCP and HNCP were not included in the two clusters. Therefore, this study established the benchmarks for seven impact categories by dividing the environmental impacts of the 23 elementary school buildings into two clusters. The benchmarks

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Fig. 8. The benchmark for determining the environmental impact reduction of new elementary school buildings.

for GWP in Clusters One and Two were calculated at 3.70E + 03 and 2.53E + 03 kg-CO2 eq./m2 , respectively. On the other hand, the benchmarks for HCP and HNCP, which were not included in the two clusters, were 8.63E − 08 casescan /m2 and 1.46E − 06 casesnonc /m2 , respectively. The established benchmarks are expected to be applied in determining the environmental performance of new elementary school buildings with reinforced concrete structure in South Korea. Compared to previous studies, this study evaluated the environmental impacts of many existing buildings, and established the benchmarks based on such evaluation. However, the 23 elementary school buildings were limited to only five regions in South Korea, and only represent a small sampling of elementary school buildings in the country. Therefore, additional LCA should be conducted on elementary school buildings outside the aforementioned five regions. Acknowledgements 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) (NRF2015R1A2A1A05001657). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.enbuild.2016.06. 042. References [1] E.W.L. Cheng, Y.H. Chiang, B.S. Tang, Exploring the economic impact of construction pollution by disaggregating the construction sector of the input–output table, Build. Environ. 41 (2006) 1940–1951. [2] U.S. Energy Information Administration (EIA), Table 2.1a Energy Consumption Estimates by Sector, 1949–2010, EIA, US, 2011. [3] U.S. Energy Information Administration (EIA), Emissions of Greenhouse Gases in the United States 2009. Table 7 U.S. Energy-Related Carbon Dioxide Emissions by End-Use Sector, 1990–2009, EIA, US, 2011. [4] T. Hong, H. Kim, T. Kwak, Energy-saving techniques for reducing CO2 emissions in elementary schools, J. Manage. Eng. 28 (1) (2012) 39–50. [5] T. Hong, C. Koo, T. Kwak, Framework for the implementation of a new renewable energy system in an educational facility, Appl. Energy 103 (2013) 539–551.

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