Analysis of embodied carbon in the building life cycle considering the temporal perspectives of emissions: A case study in China

Energy and Buildings 155 (2017) 404–413

Contents lists available at ScienceDirect

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

Replication Studies

Analysis of embodied carbon in the building life cycle considering the temporal perspectives of emissions: A case study in China Xiaocun Zhang a , Fenglai Wang b,c,∗ a

School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China b c

a r t i c l e

i n f o

Article history: Received 8 April 2017 Received in revised form 13 September 2017 Accepted 18 September 2017 Available online 20 September 2017 Keywords: Embodied emission Life-cycle assessment Temporal perspective Scenario analysis Carbon reduction

a b s t r a c t The building sector contributes substantially to worldwide greenhouse gas emissions, and efforts to meet emission reduction targets have been gaining importance. Accordingly, the present study investigates the importance of building embodied emissions to the entire life cycle and potential approaches for lowcarbon development in China. Life-cycle assessment was proposed for the analysis of building emissions, dividing the life cycle into production, construction, operation, and disposal phases. The temporal perspectives of emissions were considered, including the potential improvements to energy efficiency and the weighted average impacts for delayed emissions in the operation and disposal phases. A case study of a residential building in a cold region was analyzed, and scenario analyses were conducted. The results indicated that the relative contribution of embodied emissions (10551 tCO2e ) considering the temporal perspectives could be twice that of conventional calculations. Further discussion revealed that the payback time of constructing a new building could be 45 years compared to the current regional average buildings. Hence, with respect to the high costs and technical limits of passive houses, renovating old buildings with energy saving measures might be the most appropriate approach for implementing the short-term low-carbon development target. Overall, the present study is helpful to better understand the importance of embodied emissions and for policy-making in the regional building sector. © 2017 Elsevier B.V. All rights reserved.

1. Introduction The greenhouse effect has been regarded as the most significant challenge to the relationship between humanity and nature owing to its serious consequences of global temperature increase and sea level rise [1,2]. Greenhouse gas emissions originating from human activities (especially, carbon dioxide [3]) have been reported to be the most likely reason for climate change [1]. As a result, people all around the world are making efforts on the issue of energy conservation and carbon reduction [4,5]. As indicated by previous studies, the building sector contributes approximately 36% of total emissions worldwide [6], and it is considered to have more potential and lower costs in the near future for reducing emissions compared to other sectors [7,8]. In this context, research on the life-cycle emissions of buildings has recently been highlighted [9]. Life-cycle assessment (LCA) has been recommended for analyzing the various environmental impacts from a comprehensive view

∗ Corresponding author at: Room 521, School of Civil Engineering, Harbin Institute of Technology, Haihe Road #202, Nangang District, Harbin 150090, Heilongjiang Province, China. E-mail address: fl[email protected] (F. Wang). http://dx.doi.org/10.1016/j.enbuild.2017.09.049 0378-7788/© 2017 Elsevier B.V. All rights reserved.

[10], and two main approaches—the process-based method and input–output analysis—are widely used in the LCA of building emissions [11]. Input–output analysis combines economic input–output tables and relevant environmental data to convert monetary values into carbon emissions, consequently capturing the carbon footprint from the entire supply-chain [12,13]. However, this method applies the average emission coefficient of a sector, which makes it inaccurate for assessing a detailed industrial process [14]. In this context, process-based LCA is more popular in the research relevant to emissions from individual buildings. For example, many researchers have analyzed the life-cycle emissions of residential buildings [15–18]; Wang et al. [5] and Cheung et al. [19], for example investigated the life-cycle emission reduction of public buildings based on case studies. Peng [20] and Zhao et al. [21] estimated the emissions from each life-cycle stage based on building information modeling. Chau et al. [22] and Islam et al. [23] summarized the various concepts and equations for calculation of building emissions. These studies have provided good knowledge for process-level emission assessment. Nevertheless, concerns about the truncation error and possible underestimation of emissions by process-based methods have been raised owing to the subjective definition of the system boundary of the calculations [24,25]. Hence, a combi-

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413

nation of process-based and input–output methods, i.e., a hybrid method, might be a more efficient approach [26,27]. For Example, Suh and Lippiatt [28] proposed a framework for hybrid life-cycle inventory databases; Stephan and Crawford [29,30] investigated the life-cycle energy and emissions of residential buildings based on input–output–based hybrid analysis; Dixit [31] proposed an improved hybrid method for analyzing the embodied energy of building materials. Typically, a building life cycle is divided into several processes—materials manufacturing, transportation, building construction, operation, maintenance, and demolition [32,33]—of which the operational emissions are regarded as the largest contributor to the life-cycle impacts [34,35]. However, the importance of building embodied emissions has been emphasized in recent studies [36–39]. On the one hand, for new buildings, the application of energy efficiency techniques could significantly decrease operational emissions; accordingly, embodied emissions are crucial to realizing low-carbon buildings [40]. On the other hand, construction-related emissions are considerable for developing countries such as China, which is conducting extensive construction work every year [41]. Furthermore, the temporal perspectives of carbon emissions should also be noted. First, the construction of new buildings generates a significant amount of greenhouse gas emissions in a very short time horizon. This sudden increase of emissions can raise the level of carbon concentration in the atmosphere in a short time, which might lead to irreversible climate change, making the benefits of future energy efficiency useless [7,14]. Second, the LCA of building emissions based on current technology might overestimate operational emissions owing to potential future improvements in energy efficiency; therefore, the contribution of embodied emissions might have been underestimated [42,43]. Finally, emissions in early life-cycle stages are present in the atmosphere for a longer time during the assessment period, and accordingly weighted average impacts have been suggested for the delayed emissions from the operation and disposal phases [44,45]. However, despite the recognized temporal perspectives of life-cycle emissions as summarized above, limited studies have accounted for these effects in their case studies of building emissions. Säynäjoki et al. [7] and Heinonen et al. [42] compared the life-cycle emissions of a residential area with different building energy efficiencies. Oldfield [46] investigated the importance of embodied emissions to the life-cycle impacts of buildings, and discussed the influences of increasing energy efficiency. Jones [47] indicated that future improvement in electricity generation could benefit the reduction of building operational emissions. With consideration of this knowledge gap, the present study aims to (1) apply hybrid LCA and scenario analyses to compare building lifecycle emissions with respect to the temporal perspectives, (2) investigate the importance of embodied emissions in the building life cycle, and (3) propose suggestions for low-carbon development. Accordingly, the remainder of the paper is organized as follows. Section 2 introduces the research scope and the hybrid method for emission assessment. Section 3 presents the information of the case study and scenario decisions. Section 4 analyzes the life-cycle emissions in different scenarios based on a case study building and discusses the importance of embodied emissions from a comprehensive view. Section 5 concludes the study, identifies its limitations, and suggests prospects for future research. 2. Methodology 2.1. Research scope The total emissions from buildings can be divided into operational emissions and embodied emissions [48]. Operational

405

Fig. 1. Scope and system boundary of building life-cycle emissions.

emissions refer to the energy-related emissions for daily running of buildings such as powering heating, cooling, lighting, and appliances [49]. In contrast, embodied emissions are the total emissions from the processes of manufacturing building products, transportation, building construction, maintenance, renovation, and demolition [50]. The scope and system boundary of the life-cycle emission sources are illustrated in Fig. 1. Both the process-based method and input–output analysis were applied for emission assessment in order to achieve comprehensive results. The fundamental outcome of the process-based method can be interpreted as the product of engineering quantities (or energy consumption) and emission factors, whereas input–output analysis applies a coefficient matrix between environmental impacts and economic flows [51,52] to calculate emissions based on the Leontief quantity model [53]: EFIO = ε · (I-A)−1

(1)

where EFIO is the total (both direct and supply chain) emission intensity for the unit economic cost of sectoral products, ␧ is a row vector containing the sectoral direct emission coefficients, and (IA)−1 represents the Leontief inverse square matrix. Furthermore, the input-output method for emission assessment can be expressed as “Emission = Cost × Intensity”, where “Intensity” represents the sectoral emission intensity, and “Cost” represents the consumption of products in monetary values. More detailed information is presented in previous studies [14,26,52]. The process-based method incorporates energy consumption data to assess the operational emissions; however, embodied emissions are sourced from various processes throughout the life cycle, and it is nearly impossible to characterize each single activity owing to information scarcity and high costs. In this context, emissions from the key processes are calculated based on processlevel data, and the others are estimated by input–output analysis according to the monetary values as suggested by previous studies [10,26]. It should be noted that the proposed hybrid method was process-based; input–output analysis was applied to estimate the emission factors of products and activities, for which no process-level data was available. Previous researchers have also proposed other hybrid methods [54]. These methods could assess

406

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413

the emissions from upstream activities such as services, and therefore broaden the system boundary [55,56]. However, the upstream emissions were not included in the present study with the following considerations: first, the inconsistency in classification of sectors between input-output tables and statistical data of energy use officially published in China could result in uncertainty for hybrid analysis [57], and researchers [59,60] have made efforts to improve these original data; and second, the input-output components of hybrid analysis could be significantly affected by the changes of prices [58]. 2.2. Building embodied emissions As introduced above, the building embodied emissions (Eemb ) can be calculated as Eemb = Em + Et + Ec + Er + Ed + Ew

Table 1 Maintenance frequency for main building components. Building components

Lifetime (years)

Maintenance count

Main structure Thermal insulation layer Water supply and ventilation pipe Wire and cable Decoration board Ceramic tile Roofing Plastic–steel window Drain pipe Painting Heating radiator Central heating pipeline Central air-conditioner Household air-conditioner

50 50 50 50 30 30 25 25 30 10 15 50 20 10

0 0 0 0 1 1 1 1 1 4 3 0 2 4

(2)

where Em , Et , Ec , Er , Ed , and Ew represent the emissions from materials manufacturing, materials transportation, building construction, maintenance, demolition, and waste transportation, respectively. With respect to the scope of application and relevant consumption of materials, they were classified into three types: (1) primary materials, incorporating materials with considerable consumption and process-level emission factors; (2) auxiliary materials, incorporating materials essential to on-site construction, but not components of the actual buildings, such as templates and scaffolds; and (3) other materials, incorporating all other materials involved in building construction, of which the consumption is usually small, and the process-level emission factors are not available. For primary and auxiliary materials, the process-based method was applied for emission assessment, however, the turnover frequency should be considered for the latter. For other materials, input–output analysis was applied to estimate the emissions according to the monetary costs. The emission factors of materials for both the process-level and economic sectors in China have already been discussed in previous research [26], which serves as the data basis. For emissions relevant to transportation, the process method, which considers freight in terms of metric ton–kilometers and its emission factor as that due to fuel combustion per unit of freight, was suitable for primary materials, auxiliary materials, and demolition wastes. The emission factors of railway and road transportation average 91.3 × 10−4 kgCO2e /(tkm) and 2841.4 × 10−4 kgCO2e /(tkm), respectively, according to the fuel combustion data provided by the China Statistical Yearbook [61]. However, the consumption of “other materials” in terms of weight or volume was usually not available, which made the above method impossible. Hence, transportation costs were instead used to estimate the transportation emissions of “other materials” based on the sectoral emission intensity (2.47 × 10−4 tCO2e /CNY [26]). The energy consumption of construction machinery, temporary lighting, and office works are the main sources of emissions from construction activities. The total energy use can be obtained from the billed quantities for the assessed project, and the relevant emission factor of energy can be acquired from international and governmental documents. For diesel and gasoline, the factors are 3.1063 kgCO2e /kg and 2.9355 kgCO2e /kg, respectively [12]. Furthermore, the indirect emissions from the production and repair of machinery should also be included. Input–output emissions were assessed based on the depreciation and repair costs of machinery, and the sectoral emission intensities of production and repair are 2.26 × 10−4 tCO2e /CNY and 2.64 × 10−4 tCO2e /CNY, respectively [26]. The emissions related to building maintenance are usually ignored owing to the difficulty of data acquisition and its low contribution to life-cycle emissions. However, some researchers have

argued that emissions from maintenance could be significant in light of embodied carbon [5]. The emissions sourced from materials, transportation, and energy use for renovation could be assessed in the same manner as that for Em , Et , and Ec . Furthermore, the maintenance frequency is related to the lifetimes of the building and its components [6]. Considering a typical building lifespan of 50 years, the maintenance frequency of main building components as proposed by Wang [62] are summarized in Table 1. It should be noted that the emissions could be also estimated based on the construction-embodied emissions as a proportion of the repair cost to the initial investment [63] for a quick but less accurate calculation. Similar to construction work, the emissions from building demolition could be assessed according to the energy use. However, it is difficult to handle such calculations owing to data scarcity for new and in-use buildings. In this study, the emissions are estimated according to the essential activities, including removal of elements, ground leveling, and crane handling, and the relevant emission factors for these works are 7.8 kgCO2e /m2 (building area), 0.62 kgCO2e /m2 (site area), and 2.85 kgCO2e /t (removed elements), respectively [17]. Overall, the methods for assessing the building embodied emissions and relevant limitations are summarized in Table 2. 2.3. Building operational emissions As indicated above, the operational emissions (Eope ) can be calculated as Eope =





Uheat,x + Ucool,x + Ulight,x + Uapps,x · EFex

(3)

x

where Uheat ,x , Ucool ,x , Ulight ,x , and Uapps,x represent the consumption of type-x energy by heating, cooling, lighting, and appliances, respectively, and EFex represents the emission factor of type-x energy. The energy consumption can be obtained from the following sources: (1) directly measured or recorded data, such as utility bills [64,65]; (2) regional average data from statistical investigation [22]; (3) simulated data from software packages [66,67]; and (4) calculated or recommended data based on standards and codes. Different sources should be applied corresponding to the scope of the assessment and the availability of data. For example, directly recorded data might be the most accurate, but it is difficult to collect and nonexistent for buildings in the design phase. Regional data might not be accurate for a specified building, but it could reflect the average level for the region, making it useful for policy-making. Simulated data is especially suitable for the design phase, which aims to balance economic costs and environmental impacts. Finally, calculated

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413

407

Table 2 Methods for assessing the building embodied emissions. Source

Method

Equation

Epm

Process-based

Epm =

Eam

Process-based

Eam =

Eom

Input–output

Eom =

Ept

Process-based

Ept =

Process-based

Eat



i  j 

Input–output

Process-based

Eec

Eoc

Input–output

Consumption of materials includes the loss during the transportation and construction phases

Qaj · EFaj /TFaj

Turnover frequency of steel templates, timber formwork, steel pipe, and fasteners are 50, 10, 100, and 20 times, respectively

IO Comk · EFmk

The monetary costs of other materials are collected from bills of quantities



Eat = Eot = Eec = Eoc = Er =

Hybrid

Er

Qpi · EFpi

k  

l

i



j

l

Eot

Assumptions and limitations

The transport distance is estimated according to the real locations of the project site and suppliers

Qaj · Dai · EFtl

Bidirectional transportation is considered between project site and warehouse

IO Cotk · EFtk

k 

Ueq · EFeq

q 

l 

Qpi · Dpi · EFtl

Cocl · EFclIO

Erh · Trh

 h

Ed

Process-based

Ed =

Wds · EFds

 s

Process-based

Ew

Ewt =

Qwt · EFtl

Transport costs are taken as 5% of the total price of materials as indicated by construction quota

Energy use for construction is collected from bills of quantities, combining the working time with energy use per hour by quota Depreciation and repair cost of machinery are sourced from construction quota

Emission factors for materials, transportation, and energy are assumed to be the same as those in the pre-use phase Three main activities are considered, including removal of elements, ground leveling, and crane handling The amount of waste is assumed as the self-weight of the assessed building

l

In the equations, “E” represents embodied emissions; “EF” represents emission factors; “Q” represents quantities of materials; “C” represents monetary cost; “TF” represents turnover frequency; “D” represents transportation distance; “U” represents consumption of energy; “T” represents times for maintenance; “W” represents amount of demolition works. Subscripts “m”, “t”, “c”, “r”, “d”, and “w” represent material, transportation, construction, building repair, demolition, and waste, respectively; “p” and “a”, represent primary and auxiliary materials, respectively; “o” represents others; “e” represents energy.

or recommended data could provide good approximations with reduced time-cost that are helpful for policy-making. It should be noted that some researchers [15,18] have considered that the emissions related to energy consumption of appliances should be included to make a comprehensive assessment; nevertheless, others [5] have argued that those emissions are user-specific and not essential for regular operation of a building. With respect to the research goals, the present study suggests that emissions related to appliances should be included when investigating the total level and potential reduction of energy consumption from buildings, but these emissions should be excluded when designing new buildings and comparing different schemes. 2.4. Temporal perspectives of carbon emissions In light of possible improvements in energy efficiency, the future operational emissions might decrease compared to the current level. Generally, electricity use and energy demand for heating (especially in cold regions) are two prominent sources of operational emissions. In this context, the temporal perspectives of energy efficiency were assessed using scenario analyses accounting for different heating supply and electricity generation systems. Furthermore, weighted average impacts of delayed emissions were considered for the emissions arising from the building operation and final disposal phases, indicating that the emissions released in the early stage of the building life cycle could be present in the atmosphere for a longer time during the assessment period. The weighting factors for delayed emissions proposed by PAS 2050 [45] are applied as follows: FW =

100  y=1





y 100 − ty /100

(4)





FW ∗ = 100 − 0.76ty /100

(5)

where FW represents the weighting factor, ty is the number of years between the formation of the building and the year (y) in which emissions occur; y is the proportion of total emissions occurring in year y; and FW∗ is the weighting factor for the specific case of a delayed single release within 25 years of the formation of the building. It should be noted that, the method proposed by PAS 2050 was a simplification of the approach outlined in IPCC 2007. Full implementation of the approach in IPCC could provide more precise results, but is only applicable to CO2 emissions. Consequently, the approximation is less accurate when the total emissions include significant non-CO2 components. 3. Case study and scenarios 3.1. Basic information for the assessed building A 17-story (including one story of basement) residential building located in Harbin, China (longitude: 125◦ 42 E–130◦ 44 E, latitude: 44◦ 04 N–46◦ 40 N), was selected for the case study and scenario analysis. The building, which employs a cast-in-situ concrete shear wall structure, was constructed in 2012 and put into use in the following year. The designed service life of the building is 50 years. The total floor area and building height are 17558.72 m2 and 49.2 m, respectively. For the embodied emissions, the original data for the production and construction phases were collected from the bills of quantities. Relevant emissions of building maintenance and demolition were calculated according to the proposed method. The consumption of materials for the main body of the case building was provided in previous research [26], and detailed information about the other

408

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413

Table 3 Conditions and assumptions of the scenarios in the present study. Type

Scenario No.

Conditions and assumptions

Base case (BS) Efficient heating system (HS)

BS01 HS01 HS02 HS03 HS04 ES01 ES02 WS01

Cogeneration heating system was assumeda Coal-fired boiler for heating Oil-fired boiler for heating Gas-fired boiler for heating Solar-assisted heat pump for heating Efficient electricity generation system gradually replaces the current system within 20 years On the basis of ES01, clean energy is gradually applied within the following 30 years Weighted average impacts are considered for delayed emissions in the operation and disposal phases based on the basic case Desired scenario: gas-fired boiler is applied for heating, clean energy is gradually applied as in ES02, and weighted average emissions are considered

Efficient electricity generation (ES)b Weighted average impacts (WS)

WS02 a

For all scenarios, unspecified conditions are the same as the basic case. Emission factors of electricity generation using current technology and an efficient system are 1.13 kgCO2e /kWh and 0.43 kgCO2e /kWh, respectively [68], and the emission factor of clean energy (wind turbines) is 0.02 kgCO2e /kWh [17]. b

elements and activities are introduced in the Appendix A of this study. For the operational emissions, a centralized heating system was assumed for the building, and heat consumption was simulated at 0.2537 GJ/(m2 ·year) based on the Chinese code for building energy efficiency in cold regions. It should be emphasized that the residential building is located in a cold region such that the summertime temperatures are not very high. As a result, the energy demand for cooling strongly depends on the occupants’ habits; in fact, few families use or even own air-conditioners. Hence, the regional average energy use was considered for cooling in addition to the electricity demands of lighting and household appliances. Furthermore, consumption of gaseous fuels for cooking was also estimated based on the regional statistical data. The Chinese statistical yearbook [61] provides the energy use for regional residential buildings, relevant data and total building area could be combined to estimate the average level for gaseous fuels consumption [35]. 3.2. Scenario analysis Different scenarios were analyzed as summarized in Table 3, in order to investigate the importance of building embodied emissions to the total life-cycle impacts considering the temporal perspectives of emissions. The assessed building introduced in Section 3.1 was taken as the base case (BS01). Scenarios HS01–HS04 aimed at comparing improvements to heating systems, and the relevant fuels and efficiencies of the five systems for BS01 and HS01–HS04 are presented in Table 4. Scenarios ES01 and ES02 were designed to assess the possible promotion of power generation systems. Finally, Scenarios WS01 and WS02 accounted for the weighted average impacts of delayed emissions. 4. Results and discussion 4.1. Life-cycle emissions of the case building The life-cycle emissions for the assessed building in the base case (BS01) are summarized in Table 5, and detailed results are presented in the Appendix A. As shown in Table 5, for a typical residential building without energy recovery, the life-cycle emissions were calculated as 62.1 ktCO2e , in which the operational emissions contributed 83.0% of total emissions throughout the life cycle. Similar results were also obtained in previous research [16,22]. Heating was shown to be the main source of operational emissions (61.6%), followed by electricity use (26.4%). Considering only building embodied emissions, the production phase contributed 74% of the total. Manufacturing of primary materials was the largest contributor, accounting for more than 90% of production emissions, and steel and concrete were significant emission sources (nearly 70% together). Transportation

Fig. 2. Comparison of building life-cycle emissions with different heating systems.

related emissions contributed 4.5% of the production phase emissions. However, if instead no local products were consumed, those emissions could increase greatly owing to the longer transportation distance. In contrast, emissions from the construction phase contributed only 7.8% of embodied emissions; furthermore, efficient construction techniques, such as prefabrication, might reduce materials consumption [69]. It should be noted that the emissions from the HVAC and electric systems were 248.3 tCO2e in the preuse phase, which are usually ignored. Additionally, emissions from building maintenance and demolition contributed 18.3% and 3.1% to the embodied and life-cycle emissions, respectively, which illustrates the small impacts on the LCA results as indicated in previous studies; however, these are non-negligible sources of embodied emissions. 4.2. Comparative analysis of different scenarios Four kinds of heating systems, as defined in HS01–HS04, were compared to the base case, and the results are illustrated in Fig. 2. As shown in Fig. 2, the solar-assisted heat pump has the lowest emissions for heating, whereas the traditional coal-fired boiler has the highest emissions. The operational emissions could experience a decrease of 25000 tCO2e (28.5 kgCO2e /(m2 ·year)) owing to improvements to heating systems, and the corresponding contribution of embodied emissions to the total life cycle would increase from 17% to 28.5%. However, the initial investment for the solarassisted heat pump is currently much higher (3–5 times) than the other schemes. Hence, with further consideration of the balance between economic and environmental factors, the gas-fired boiler

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413

409

Table 4 Characteristics of different heating systems. Characteristics

Cogeneration

Coal-fired boiler

Oil-fired boiler

Gas-fired boiler

Solar-assisted heat pump

Fuel types Unit of fuels Calorific value of fuels (kJ/unit) Emission factor of fuels (kgCO2e /unit) Thermal efficiency Pipeline efficiency

Coal kg 20,908 1.9901 72% 90%

Coal kg 20,908 1.9901 68% 90%

Fuel oil kg 41,816 3.1806 88% 90%

Natural gas m3 38,931 2.1671 90% 90%

Solar energya and natural gas m3 38,931 2.1671 90% 90%

a

For the solar-assisted heat pump, solar energy was assumed to satisfy 50% of the total heat demand.

Table 5 Building life-cycle carbon emissions in the base case (BS01). Source

Emissions (ktCO2e )

Emissions per area (kgCO2e /m2 )

Proportion in life cycle (%)

Production phase Manufacturing of primary materials Manufacturing of auxiliary materials Manufacturing of other materials Transportation of materials Construction phase Operation of construction machinery Temporary lighting and office works Production and repair of machinery Operation phase Centralized heating Electricity consumption Gaseous fuels consumption Building maintenance Disposal phase Building demolition Waste transportation Total life-cycle emissions Total embodied emissions Total operational emissions

7.80 7.14 0.02 0.29 0.35 0.82 0.65 0.06 0.11 53.09 32.71 14.04 4.84 1.50 0.42 0.23 0.19 62.14 10.55 51.59

444.4 406.8 1.0 16.7 19.8 46.9 37.0 3.6 6.3 3023.8 1862.9 799.4 275.8 85.7 24.0 13.2 10.7 3539.0 600.9 2938.1

12.6% 11.5% 0.0% 0.5% 0.6% 1.3% 1.0% 0.1% 0.2% 85.4% 52.6% 22.6% 7.8% 2.4% 0.7% 0.4% 0.3% 100.0% 17.0% 83.0%

Fig. 3. Building life-cycle emissions considering the promotion of electricity generation systems.

system might be more appropriate for reducing operational emissions, especially for cold regions such as Heilongjiang Province in China, whose economic development is relatively slow. Electricity is the most commonly used energy type in residential buildings. The potential promotion of power generation systems was considered in ES01 and ES02, and relevant emissions were assessed, as presented in Fig. 3. The results indicate that operational emissions will respectively decrease by approximately 6950 tCO2e and 8520 tCO2e if efficient system and clean energy could be gradually adopted. It should be noted that in addition to the temporal perspectives of power generation, enhancing awareness and pop-

Fig. 4. Building life-cycle emissions (tCO2e ) considering the weight averaged impacts.

ularization of energy-saving appliances in China would also show benefits. Furthermore, the weighted average impacts of delayed emissions in the operation and disposal phases were taken into consideration as defined in Section 2.4. Fig. 4 illustrates the annual emissions in the building life cycle comparing WS01 and WS02 with the base case; this figure shows that production, construction, maintenance, and demolition activities can manifest a sharp increase of annual emissions, especially in the pre-use phase of building. Comparing BS01 to WS01 demonstrates that separately accounting for the weighted averaged impacts increases the con-

410

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413

tribution of embodied emissions to the total life cycle from 17% to 20.6%. Finally, if both possible improvements of heating and power generation systems and weighted averaged impacts are considered for a typical residential building in China, embodied emissions could contribute 33.7% of the life-cycle emissions, which is nearly twice the result obtained using the conventional calculation method. It should be emphasized that if a longer lifespan was assumed for the assessed building, the contribution of embodied emissions could be lower, but the fact that the contribution was underestimated without temporal considerations would not be changed. Overall, the above analyses show that operational emissions, while controlled, are presently the largest contributor to life-cycle impacts, but they also indicate that the contribution of building embodied emissions to the entire life cycle might be largely underestimated if temporal perspectives of emissions are not included. Although embodied emissions could be reduced by using lowcarbon materials, it is nearly impossible to reduce them to zero. However, by applying PV systems and other improved techniques, the energy production from renewable energy sources on site could be equal to or more than annual primary energy consumption at the building operation stage, i.e., net energy production [40]. In this context, the control of embodied emissions is also crucial to achieving net-zero life-cycle emissions, because the embodied emissions at the construction stage must be offset by the net energy production at the building operation stage [70].

4.3. Discussion on low-carbon development China has a great responsibility and has made commitments to reduce emissions, which are among the highest all around the world owing to the development of economy and civilization. In light of the large population, a large amount of buildings are required for commercial and residential use; consequently, saving energy and reducing emissions in the building sector is of particular importance. In fact, China has large amounts of “old” buildings (constructed during the 1970s to 1990s) that generally lack energy saving measures (e.g., insulation) owing to the historical economic level and living conditions of that time. Consequently, those buildings have much higher operational emissions than buildings constructed under the latest standards and codes, and they have caused heavy burdens to the low-carbon development of the building sector. However, it is impossible to demolish all these old buildings and replace them with new ones owing to various economic, social, and environmental reasons such as extremely high investments, problems of resettlement, and interference to the surroundings. Furthermore, the reconstruction works could lead to a spike of carbon emissions in the short term, which might cause irreversible climate change and hinder the implementation of the emission reduction target. In this context, how to deal with the “old” buildings is becoming a crucial topic. To address the above issue, the present study discussed a case of a residential building, and four more scenarios were compared with consideration of temporal perspectives of emissions. The first scenario (S1) refers to the aforementioned residential building, which corresponds to the Chinese code for building energy efficiency in cold regions. The second scenario (S2) refers to the regional average buildings in use at present. The third scenario (S3) refers to ordinary buildings in the 1980s without energy saving measures. The last scenario (S4) refers to those ordinary buildings but with energy-saving renovations. Potential improvements to electricity generation systems were considered for all scenarios, whereas different heating demands were estimated based on the building types. The cumulative emissions over 50 years for the four scenarios were assessed, and the results are illustrated in Fig. 5.

Fig. 5. Cumulative building emissions (tCO2e ) over 50 years for different scenarios. (The emissions for maintenance and demolition are not considered in the figure.).

As indicated in Fig. 5, the emissions of the production and construction phases are taken as zero in scenarios S2 and S3, because the buildings already exist before the assessment period. For scenario S4, the renovation activities mainly include roof maintenance, transformation of external thermal insulation, wall painting, and replacement of windows, pipes, and radiators; the corresponding embodied emissions were calculated as 922 tCO2e . Furthermore, with respect to emissions for heating, scenarios S1 and S4 incorporated the heat demand using calculations according to the building conditions; scenario S2 applied the regional average level of 51.6 kgCO2e /m2 [35]; scenario S3 calculated the emissions based on the typical energy consumption of 40 kg of coal equivalent per building area for heating in Harbin. Overall, the total emissions over 50 years for scenarios S1–S4 were 41.3 tCO2e , 42.0 tCO2e , 120.9 tCO2e , and 38.6 tCO2e , respectively. Compared to the regional average level as indicated by scenario S2, the emissions for heating in scenarios S1 and S4 were approximately 28% and 13% lower, respectively, whereas scenario S3 (the original building in the 1980s without renovation) increased heating emissions by three times. According to Fig. 5, the emission payback time, the period within which the cumulative emissions from the new building are equal to the emissions of an existing average building, is approximately 45 years. The results indicate that a typical building under new energy saving standard could only benefit the emission reduction target after several decades (nearly the end of the designed lifetime of 50 years), compared to the current regional average. It should be also noted that passive houses have been proposed that have very little energy consumption and might show benefits in a much shorter time. However, it is hardly possible to adopt large-scale application in China at present because of the high costs and technical limits. In this context, it was better to renovate the old buildings rather than replace them with new ones in order to implement short-term emission reduction. 5. Conclusions The present study investigated the importance of embodied emissions to total building life-cycle impacts based on the proposed hybrid LCA method. The life cycle was divided into production, construction, operation, and disposal phases; temporal perspectives of emissions were considered in the assessment. A case study of a typical residential building newly constructed in a cold region of China was conducted, and the life-cycle emissions were shown to be 62.1 ktCO2e , of which embodied emissions contributed 17%. Then, eight scenarios were analyzed considering potential improvements to the heating and electricity generation systems and the weighted

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413

averaged impacts for delayed emissions in the operation and disposal phases. Relevant results indicated that the contribution of embodied emissions to the life cycle could be nearly twice that of the base case when accounting for the temporal perspectives. Furthermore, with respect to existing old buildings without energy saving measures, the issue of achieving low-carbon development for the building sector was discussed. Cumulative emissions over a time horizon of 50 years were compared for different scenarios corresponding to typical new buildings, the regional average situation, old buildings from the 1980s, and renovated old buildings. The analyses revealed that the renovation of old buildings might be the most appropriate approach for cold regions because the benefits of emission reduction by constructing a new building would only be realized after 45 years, which might be too long for implementing the short-term target for emission reduction. Besides, although passive houses might have shorter emission payback time, the high costs and technical limits make them unsuitable for economically sluggish regions in China. Overall, the present case study and scenario analyses could be helpful for understanding the importance of building embodied emissions from a comprehensive view. Relevant results could provide useful information for policy-making on the implementation of the short-term emission reduction target. Furthermore, limitations and possible research prospects should also be mentioned. First, a case study of a residential building was conducted in this study, and cases of other building types may be investigated in future research. Second, the results of the scenario analyses correspond to cold and economically sluggish regions in China, where heating is the leading source for operational emissions. Further discussion on other climatic and economic regions should be made in order to achieve comprehensive understanding. Finally, upstream emissions calculated using improved hybrid methods, which were not considered in the present study, could be discussed in future research along with the enrichment of relevant data.

411

Table A1 (Continued) Material

Unit

Quantities

Emissions (tCO2e )

Wood products Gypsum powder Talcum powder Paint Fire resistance steel door Plastic-steel door and window Welding rod Polyamides safety net Water Steel template Steel pipe for scaffold Fastener Timber formwork Welded steel pipe Lining plastic pipe Iron drain-pipe PVC pipe Cast-iron radiator Copper conductor Socket and switch Electricity meter box Luminaire Other electrical appliances Other metal products Adhesive Glass fiber products Other plastic products Other special chemicals Cloth Chemical solvent Other cement and plaster products Other wood products

m3 t t t m2 m2 t t m3 t t t m3 t t t t t km 104 104 104 104 104 104 104 104 104 104 104 104 104

9.68 43.42 115.68 26.08 1794.72 4590.48 7.29 0.74 5721.23 44.00 52.41 4.39 395.67 14.70 0.36 7.73 8.64 5.26 61.89 1.50 11.64 0.36 0.90 42.89 25.51 2.80 2.72 2.09 3.05 2.14 0.78 0.18

3.51 9.99 144.60 93.90 109.48 146.90 149.52 6.86 1.72 1.54 0.94 0.39 14.24 25.79 0.70 10.63 40.19 7.23 9.57 3.43 26.67 0.76 2.16 143.16 78.32 10.53 6.84 6.39 5.91 4.73 4.56 0.32

CNY CNY CNY CNY CNY CNY CNY CNY CNY CNY CNY CNY CNY

Note: the quantities of some materials and products are presented in terms of “104 CNY”. The emission factors of these materials are derived by the input-output method; and in contrast, the others are process-based. The emission factors were discussed in previous research [25].

Appendix A. See Tables A1–A6 Table A2 Carbon emissions from materials transportation.

Table A1 Carbon emissions from materials manufacturing. Material

Unit

Quantities

Emissions (tCO2e )

Steel bar Shape steel Light shape steel Steel wire Galvanized wire Iron nail Iron castings Sand Gravel Premixed lime-soil 3:7 Premixed composite mortar M5 Premixed cement mortar M7.5 Premixed composite mortar M10 Premixed cement mortar M10 Clay brick Concrete block Premixed concrete C10 Premixed concrete C20 Premixed concrete C25 Premixed concrete C30 Premixed concrete C35 Premixed superfluid concrete C30 Cement 32.5 MPa Cement paste Polystyrene board SBS waterproof roll

t t t t t t t m3 m3 m3 m3 m3 m3 m3 103 piece m3 m3 m3 m3 m3 m3 m3 t t m3 103 m2

897.46 1.38 2.92 0.05 14.13 6.23 2.22 303.74 6.33 66.18 367.94 166.85 916.31 635.03 52.29 2127.29 123.64 160.99 151.37 5765.00 2494.59 1044.69 0.90 268.51 1343.55 4.64

2151.20 2.41 4.03 0.14 19.42 8.57 3.05 2.13 0.01 26.34 74.32 30.37 197.01 127.64 30.12 255.28 20.65 42.34 44.20 1827.51 910.53 348.93 0.60 127.54 124.95 3.58

Material

Unit

Weight (t)

Distance Emissions (km) (tCO2e )

Premixed lime-soil, mortar, and concrete Brick and block steel products (road) steel products (railway) Sand and gravel cement Door and window Scaffold and template Gypsum and talcum powder Polystyrene board Paint and coating Iron products SBS waterproof roll Welding rod Polyamides safety net wood products PVC pipe Iron drainpipe Steel pipe Cast-iron radiator Copper conductor Electrical appliances and Luminaire Pipe fittings others

t

27251.73

30

232.30

t t t t t t t t t t t t t t t t t t t t 104 CNY

2690.28 901.75 898.83 450.30 189.03 183.82 298.64 159.09 40.31 26.08 22.62 19.51 7.29 0.74 4.84 8.64 7.73 15.06 5.26 2.39 0.72

80 50 650 80 50 50 60 50 30 30 30 30 50 30 30 30 30 50 30 30

61.15 12.81 5.33 10.24 2.69 2.61 5.09 2.26 0.34 0.22 0.19 0.17 0.10 0.01 0.04 0.07 0.07 0.21 0.05 0.02 1.78

104 CNY 104 CNY

1.18 2.93

2.92 7.24

412

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413

Table A3 Carbon emissions from building construction. Source

Unit

Quantities

Emissions (tCO2e )

Electricity Diesel Gasoline Temporary office work Temporary lighting Machinery transportation Machinery repair Machinery production

MWh t t MWh MWh 104 CNY 104 CNY 104 CNY

513.10 20.39 2.20 6.00 50.00 1.40 17.95 26.46

579.34 63.33 6.46 6.77 56.46 3.46 47.33 59.91

[11]

[12]

[13]

[14]

[15] Table A4 Carbon emissions from building operation. [16] Source

Unit

Quantities

Emissions (ktCO2e )

Heating Electricity Liquefied petroleum gas Natural gas

GJ MWh t m3

222691 12432 1041 744128

32.7 14.0 3.2 1.6

[17] [18]

[19] Table A5 Carbon emissions from building maintenance. Source

Roof maintenance Replacement of windows Replacement of drain-pipes Wall painting Replacement of radiators

[20]

Times for maintenance

Emissions for Total a single time emissions (tCO2e ) (tCO2e )

Point-in-time of occurrence (year)

[21]

1

60.28

60.28

25

[22]

1

208.49

208.49

25

1

91.51

91.51

25

4 3

252.38 45.05

1009.53 135.14

10, 20, 30, and 40 15, 30, and 40

[23]

[24]

[25] [26]

Table A6 Carbon emissions from building demolition. Source

Unit

Quantities

Emissions (ktCO2e )

Removal of elements Ground leveling Crane handling Waste transportation

m2 (building area) m2 (site area) t t·km

17559 1033 33184 663679

0.14 6 × 10−4 0.09 0.19

[27]

[28]

[29] [30]

References [1] Intergovernmental Panel on Climate Change, Climate change 2014: synthesis report, summary for policymakers, 2014. http://www.ipcc.ch/pdf/ assessment-report/ar5/syr/AR5 SYR FINAL SPM.pdf. [2] Q. Shi, T. Yu, J. Zuo, What leads to low-carbon buildings? A China study, Renew. Sustain. Energy Rev. 50 (2015) 726–734. [3] G. Ali, V. Nitivattananon, Exercising multidisciplinary approach to assess interrelationship between energy use, carbon emission and land use change in a metropolitan city of Pakistan, Renew. Sustain. Energy Rev. 16 (1) (2012) 775–786. [4] M.G. Carvalho, M. Bonifacio, P. Dechamps, Building a low carbon society, Energy 36 (2011) 1842–1847. [5] T. Wang, S. Seo, P.C. Liao, D.P. Fang, GHG emission reduction performance of state-of-the-art green buildings: review of two case studies, Renew. Sustain. Energy Rev. 56 (2016) 484–493. [6] C.K. Chau, W.K. Hui, W.Y. Ng, G. Powell, Assessment of CO2 emissions reduction in high-rise concrete office buildings using different material use options, Resour. Conserv. Recycl. 61 (2012) 22–34. [7] A. Säynäjoki, J. Heinonen, S. Junnila, A scenario analysis of the life cycle greenhouse gas emissions of a new residential area, Environ. Res. Lett. 7 (2012) 034–037. [8] E. Cubi, G. Doluweera, J. Bergerson, Incorporation of electricity GHG emissions intensity variability into building environmental assessment, Appl. Energy 159 (2015) 62–69. [9] C.K. Anand, B. Amor, Recent developments, future challenges and new research directions in LCA of buildings: a critical review, Renew. Sustain. Energy Rev. 67 (2017) 408–416. [10] J. Heinonen, A. Säynäjoki, J.M. Junnonen, A. Pöyry, Pre-use phase LCA of a multi-story residential building: can greenhouse gas emissions be used as a

[31]

[32]

[33]

[34]

[35]

[36]

[37] [38] [39]

[40]

more general environmental performance indicator? Build. Environ. 95 (2016) 116–125. F.H. Abanda, J.H.M. Tah, F.K.T. Cheung, Mathematical modelling of embodied energy, greenhouse gases, waste, time-cost parameters of building projects: a review, Build. Environ. 59 (2013) 23–37. X.C. Zhang, F.L. Wang, Hybrid input-output analysis for life-cycle energy consumption and carbon emissions of China’s building sector, Build. Environ. 104 (2016) 188–197. Y.A. Huang, C.L. Weber, H.S. Matthews, Categorization of Scope 3 emissions for streamlined enterprise carbon footprinting, Environ. Sci. Technol. 43 (2009) 8509–8515. N.C. Onat, M. Kucukvar, O. Tatari, Scope-based carbon footprint analysis of U.S. residential and commercial buildings: a input-output hybrid life cycle assessment approach, Build. Environ. 72 (2014) 53–62. D.Z. Li, H.X. Chen, E.C.M. Hui, J.B. Zhang, Q.M. Li, A methodology for estimating the life-cycle carbon efficiency of a residential building, Build. Environ. 59 (2013) 448–455. A. Atmaca, N. Atmaca, Life cycle energy (LCEA) and carbon dioxide emissions (LCCO2 A) assessment of two residential buildings in Gaziantep Turkey, Energy Build. 102 (2015) 417–431. X.C. Zhang, F.L. Wang, Life-cycle assessment and control measures for carbon emissions of typical buildings in China, Build. Environ. 86 (2015) 89–97. F. You, D. Hu, H.T. Zhang, Z. Guo, Y.H. Zhao, B.N. Wang, et al., Carbon emissions in the life cycle of urban building system in China—a case study of residential buildings, Ecol. Complex. 8 (2011) 201–212. M. Cheung, J. Fan, Carbon reduction in a high-density city: a case study of Langham place hotel Mongkok Hong Kong, Renew. Energy 50 (2013) 433–440. C.H. Peng, Calculation of a building’s life cycle carbon emissions based on Ecotect and building information modeling, J. Cleaner Prod. 112 (2016) 453–465. Y. Zhao, W. Pana, Y. Ning, Challenges for modeling carbon emissions of high-rise public residential buildings in Hong Kong, Procedia Eng. 118 (2015) 614–621. C.K. Chau, T.M. Leung, W.Y. Ng, A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings, Appl. Energy 143 (2015) 395–413. H. Islam, M. Jollands, S. Setunge, Life cycle assessment and life cycle cost implication of residential buildings—a review, Renew. Sustain. Energy Rev. 42 (2015) 129–140. J. Nässén, J. Holmberg, A. Wadeskog, M. Nyman, Direct and indirect energy use and carbon emissions in the production phase of buildings: an input-output analysis, Energy 32 (2007) 1593–1602. M. Lenzen, Errors in conventional and input-output-based life-cycle inventories, J. Ind. Ecol. 4 (4) (2001) 127–148. X.C. Zhang, F.L. Wang, Assessment of embodied carbon emissions for building construction in China: comparative case studies using alternative methods, Energy Build. 130 (2016) 330–340. J. Guan, Z.H. Zhang, C.L. Chu, Quantification of building embodied energy in China using an input?output-based hybrid LCA model, Energy Build. 110 (2016) 443–452. S. Suh, B. Lippiatt, Framework for hybrid life cycle inventory databases: a case study on the building for environmental and economic sustainability (BEES) database, Int. J. Life Cycle Assess. 17 (5) (2012) 604–612. R.H. Crawford, Post-occupancy life cycle energy assessment of a residential building in Australia, Archit. Sci. Rev. 57 (2) (2014) 114–124. A. Stephan, R.H. Crawford, A multi-scale life-cycle energy and greenhouse-gas emissions analysis model for residential buildings, Archit. Sci. Rev. 57 (1) (2014) 39–48. M.K. Dixit, Embodied energy analysis of building materials: an improved IO-based hybrid method using sectoral disaggregation, Energy 124 (2017) 46–58. L. Gustavsson, A. Joelsson, R. Sathre, Life cycle primary energy use and carbon emission of an eight-storey wood-framed apartment building, Energy Build. 42 (2010) 230–242. X.Z. Gong, Z.R. Nie, Z.H. Wang, S.P. Cui, F. Gao, T.Y. Zuo, Life cycle energy consumption and carbon dioxide emission of residential building designs in Beijing: a comparative study, J. Ind. Ecol. 16 (4) (2012) 576–587. M. Sandanayake, G. Zhang, S. Setunge, C.Q. Li, J. Fang, Models and method for estimation and comparison of direct emissions in building construction in Australia and a case study, Energy Build. 126 (2016) 128–138. X.C. Zhang, F.L. Wang, Life-cycle carbon emission assessment and permit allocation methods: a multi-region case study of China’s construction sector, Ecol. Indic. 72 (2017) 910–920. M. Sandanayake, G. Zhang, S. Setunge, Environmental emissions at foundation construction stage of buildings—two case studies, Build. Environ. 95 (2016) 189–198. Z.X. Luo, L. Yang, J.P. Liu, Embodied carbon emissions of office building: a case study of China’s 78 office buildings, Build. Environ. 95 (2016) 365–371. G. Kang, T. Kim, Y.W. Kim, H. Cho, K.I. Kang, Statistical analysis of embodied carbon emission for building construction, Energy Build. 105 (2015) 326–333. X.D. Li, F. Yang, Y.M. Zhu, Y.X. Gao, An assessment framework for analyzing the embodied carbon impacts of residential buildings in China, Energy Build. 85 (2014) 400–409. Y. Dutil, D. Rousse, G. Quesada, Sustainable buildings: an ever evolving target, Sustainability 3 (2011) 443–464.

X. Zhang, F. Wang / Energy and Buildings 155 (2017) 404–413 [41] Z.Y. Zhang, B. Wang, Research on the life-cycle CO2 emission of China’s construction sector, Energy Build. 112 (2016) 244–255. [42] J. Heinonen, A. Säynäjoki, S. Junnila, A longitudinal study on the carbon emissions of a new residential development, Sustainability 3 (2011) 1170–1189. [43] M.K. Dixit, J.L. Fernandez-Solis, S. Lavy, C.H. Culp, Identification of parameters for embodied energy measurement: a literature review, Energy Build. 42 (8) (2010) 1238–1247. [44] Intergovernmental Panel on Climate Change, Climate Change 2007: Synthesis Report. Contribution of Working Groups I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Cambridge University Press, Cambridge, 2007. [45] British standards institution, PAS 2050:2011: Specification for the Assessment of the Life Cycle Greenhouse Gas Emissions of Goods and Services, British Standards Institution, 2011. [46] P. Oldfield, Embodied carbon and high rise, council on tall buildings and urban habitat (CTBUH), 9th World Congress, Shanghai (2012), Available at http:// global.ctbuh.org/resources/papers/download/941-embodied-carbon-andhigh-rise.pdf. [47] C. Jones, Embodied Carbon: A Look Forward, Technical report, Sustain, 2011. [48] S. Seo, J. Kim, K. Yum, J. McGregor, Embodied carbon of building products during their supply chains: case study of aluminum window in Australia, Resour. Conserv. Recycl. 105 (2015) 160–166. [49] M.M. Sesana, G. Salvalai, Overview on lifecycle methodologies and economic feasibility for nZEBs, Build. Environ. 67 (2013) 211–216. [50] M.K. Dixit, J.I. Fernandez-Solis, S. Lavy, C.H. Culp, Need for an embodied energy measurement protocol for buildings: a review paper, Renew. Sustain. Energy Rev. 16 (2012) 3730–3743. [51] X. Tian, M. Chang, H. Tanikawa, F. Shi, H. Imura, Structural decomposition analysis of the carbonization process in Beijing: a regional explanation of rapid increasing carbon dioxide emission in China, Energy Policy 53 (2013) 279–286. [52] S. Guo, L. Shao, H. Chen, Z. Li, J.B. Liu, F.X. Xu, et al., Inventory and input–output analysis of CO2 emissions by fossil fuel consumption in Beijing 2007, Ecol. Inform. 12 (2012) 93–100. [53] W. Leontief, Environmental repercussions and the economic structure: an input–output approach, Rev. Econ. Stat. 52 (3) (1970) 262–271. [54] S. Suh, G. Huppes, Methods for life cycle inventory of a product, J. Cleaner Prod. 13 (7) (2005) 687–697. [55] M.K. Dixit, C.H. Culp, J.L. Fernandez-Solis, System boundary for embodied energy in buildings: a conceptual model for definition, Renew. Sustain. Energy Rev. 21 (2013) 153–164. [56] S. Suh, M. Lenzen, G.J. Treloar, H. Hondo, A. Horvath, G. Huppes, et al., System boundary selection in life-cycle inventories using hybrid approaches, Environ. Sci. Technol. 38 (3) (2004) 657–664.

413

[57] B. Su, H.C. Huang, B.W. Ang, P. Zhou, Input-output analysis of CO2 emissions embodied in trade: the effects of sector aggregation, Energy Econ. 32 (2010) 166–175. [58] M.K. Dixit, C.H. Culp, J.L. Fernandez-Solis, Embodied energy of construction materials: integrating human and capital energy into an IO-based hybrid model, Environ. Sci. Technol. 49 (3) (2015) 1936–1945. [59] Y. Wang, An industrial ecology virtual framework for policy making in China, Econ. Syst. Res. 29 (2) (2017) 252–274. [60] S. Liang, T. Feng, S. Qu, A.S.F. Chiu, X. Jia, M. Xu, Developing the Chinese environmentally extended input-output (CEEIO) database, J. Ind. Ecol. 21 (4) (2017) 953–965. [61] National Bureau of Statistics, China statistical yearbook, China Statistics Press, Beijing, 2008–2015. Available from: http://www.stats.gov.cn/tjsj/ndsj/. [62] X. Wang, Life Cycle Assessment for Carbon Emission of Residential Building (in Chinese) (M.Ph. Thesis), Tianjin University, 2011. [63] X.L. Li, Z.Y. Ren, D. Lin, An investigation on life-cycle energy consumption and carbon emissions of building space heating and cooling systems, Renew. Energy 84 (2015) 124–129. ˜ [64] G. Escrivá-Escrivá, C. Álvarez-Bel, E. Penalvo-López, New indices to assess building energy efficiency at the use stage, Energy Build. 43 (2011) 476–484. [65] F. Ascione, N. Bianco, R.F. De Masi, G.P. Vanoli, Rehabilitation of the building envelope of hospitals: achievable energy savings and microclimatic control on varying the HVAC systems in Mediterranean climates, Energy Build. 60 (2013) 125–138. [66] L. Aye, T. Ngo, R.H. Crawford, R. Gammampila, P. Mendis, Life cycle greenhouse gas emissions and energy analysis of prefabricated reusable building modules, Energy Build. 47 (2012) 159–168. [67] C. Baek, S.H. Park, M. Suzuki, S.H. Lee, Life cycle carbon dioxide assessment tool for buildings in the schematic design phase, Energy Build. 61 (2013) 275–287. [68] Department of Climate Change, National Development and Reform Commission of China. 2015 baseline emission factors for regional power grids in China (in Chinese), 2016. Available from: http://cdm.ccchina.gov.cn/Detail. aspx?newsId=61599&TId=19. [69] C. Mao, Q. Shen, L. Shen, L. Tang, Comparative study of greenhouse gas emissions between off-site prefabrication and conventional construction methods: two case studies of residential projects, Energy build. 66 (2013) 165–176. [70] R.H. Crawford, Towards a comprehensive approach to zero-emissions housing, Archit. Sci. Rev. 54 (4) (2011) 277–284.