Life-cycle carbon emission assessment and permit allocation methods: A multi-region case study of China’s construction sector

Ecological Indicators 72 (2017) 910–920

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Life-cycle carbon emission assessment and permit allocation methods: A multi-region case study of China’s construction sector Xiaocun Zhang a , Fenglai Wang b,∗ a b

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

a r t i c l e

i n f o

Article history: Received 29 April 2016 Received in revised form 14 September 2016 Accepted 15 September 2016 Keywords: Life-cycle assessment Carbon emission Construction sector Multi-region analysis Gini coefficient

a b s t r a c t China is making efforts to reduce carbon emissions from the building industry, and carrying out an allocation and trading system for building emissions. However, to date, methods for using existing statistical data to assess the emissions of the construction sector and to make decisions affecting permit allocation are still unclear. In this context, a process is proposed in this study to calculate the life-cycle emissions of regional construction sectors in China, and a multi-criteria Gini coefficient is introduced as an indicator for emission permit allocation. Statistical data of the construction sector for 2004–2013 were analyzed. The results indicated an overall trend of increased emissions from China’s construction sector, of which the production phase of buildings was shown to be the largest contributor. Various characteristics for different life-cycle sub-processes were also discussed at the provincial level. Finally, a case study of emissions from the construction sector was conducted on the basis of a multi-criteria Gini coefficient. Relevant analyses revealed the major regions in carbon reduction practices from a comprehensive view of efficiency and equality. In addition, suggestions were provided for allocating emissions for regional construction sectors. Overall, the present study would be helpful in the calculation, assessment, and allocation of emissions from China’s construction sector. It should also provide insight into decision-making about low-carbon development policy of the building industry. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction The issue of global climate change has attracted increasing attention in recent years, because of its serious consequences to the natural and human environments (IPCC AR5, 2014). The construction sector accounts for nearly 36% of the worldwide carbon emissions (Chau et al., 2012), of which building operation is regarded as the main source in developed countries (Nässén et al., 2007; Onat et al., 2014). However, in tandem with the projected development of China’s economy and urbanization, nearly 1.5 billion square meters of new buildings are being constructed annually according to the China Statistical Yearbook (National Bureau of Statistics, 2005b). This results in a dramatic growth of CO2e (carbon dioxide equivalent) emissions from construction works. Consequently, both production and use phases of buildings are crucial

∗ 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 addresses: fl[email protected], fl[email protected] (F. Wang). http://dx.doi.org/10.1016/j.ecolind.2016.09.023 1470-160X/© 2016 Elsevier Ltd. All rights reserved.

factors for China’s low-carbon development as promised at the United Nations Climate Change Conference (Gong et al., 2012). The process method and input-output analysis (IOA) are two essential approaches for carbon emission analysis (Huang et al., 2009). For micro-level research relevant to individual buildings, process-based analysis could achieve the desired level of details of a target process, and consequently is usually applied in life-cycle carbon assessments (LCCA). Various process-level studies pertaining to production of materials, on-site construction, building operation, demolition, and waste treatment were conducted by previous researchers (Biswas, 2014; Gustavsson and Joelsson, 2010; Li et al., 2013; Mahapatra, 2015; You et al., 2011). Overall, analytical methods for micro-level LCCA have been adequately investigated and reviewed (Abanda et al., 2013; Chau et al., 2015; Islam et al., 2015). With respect to macro-level assessment, there is relatively less process-based research relevant to the carbon footprint of the building sector (Zhang and Wang, 2016b). For this, IOA is usually applied considering its advantages to account for entire supply chains. For example, Acquaye and Duffy (2010), Chang et al. (2016), and Nässén et al. (2007) analyzed the embodied emissions of the Swedish, Irish, and Chinese construction sectors, respectively. Onat et al. (2014) investigated the life-cycle emissions of U.S. buildings,

X. Zhang, F. Wang / Ecological Indicators 72 (2017) 910–920

dividing the emission sources into three scopes. However, IOA has certain limitations for carbon assessment. First, IO tables in China are formally updated every five years. Second, converting monetary values to emissions could introduce inevitable uncertainties. Finally, the desired level of detail might not be achievable based on the statistical data available for the specified analysis of building sector. As the largest emitter of GHGs (greenhouse gases) (Zhang et al., 2014a,b), China is making great efforts to control its emissions. Recently, an allocation and trading system for carbon emission permits (CEP) is being created for industrial producers (e.g., steel and cement plants) to encourage low-carbon techniques (Xiong et al., 2015). Meanwhile, a similar system from the perspective of consumers might reduce the total material consumption and related emissions. Accordingly, CEP allocation for buildings, among the largest consumers of materials and energy, have also been discussed and tried out in Shenzhen, Shanghai, and other places in China (Zhao et al., 2016). Although there have been attempts in previous studies to analyze provincial GHG emissions in China (Huang et al., 2015; Su and Ang, 2014; Tian et al., 2014; Wang et al., 2013), and permit allocation of national emissions among regions (Zhang et al., 2014a,b, 2016), research specifically relevant to multi-regional construction sectors, based on statistical data, are relatively few (Hong et al., 2016; Liu and Lin, 2016). However, such analyses were preconditions for studying emission characteristics and building up the allocation and trading system. In this context, CEP allocation for regional construction sectors is facing a great challenge considering the currently used principles (Zhang et al., 2015). On the other hand, once a statistically relevant method for determining carbon emissions of regional buildings is provided, the assessment of results becomes another key point. Gini coefficient was previously indicated as a practical indicator for the analysis of environmental issues, and could enable balance between efficiency and equality for permit distribution (Druckman and Jackson, 2008; Groot, 2010; Pilla et al., 2016; Liang et al., 2016; Sun et al., 2010; Wang et al., 2015; Xiao et al., 2012). China’s building industry is involved in an ongoing low-carbon path to development, and compliance with different reduction targets should occur in various regions considering their social and economic background. In this context, a multicriteria Gini that could balance these factors in allocating building carbon-emissions might be a possible approach, and would have potential advantages. Overall, the proposed Gini approach should offer new insight into the target decomposition of carbon reduction policy in the building industry. With consideration of the above-mentioned knowledge gaps, the present study aimed to achieve the following: (1) propose a process-based approach for life-cycle carbon emission assessment of regional construction sectors in China, and (2) introduce an environmental Gini coefficient as an indicator for building carbon analysis and for permit allocation in multiple regions. Accordingly, the contributions of the study could be summarized from three aspects. First, proposal of relevant methods would be good practice for emission analysis of building life-cycles based on statistical data. Second, time-series emissions of the building sector in 30 regions (including 22 provinces, four autonomous regions, and four municipalities) of China from 2004 to 2013 would provide good knowledge of the current situation of regional building life-cycles. Finally, a case study of carbon permit allocation for regional building sectors would have potential application for decision-making about relevant carbon-reduction targets. The remainder of the paper is organized as follows: Section 2 provides an introduction of the scope of research, analytical methods, and collection of data. In Section 3, the results of case studies are analyzed, and some policy implications are offered. Finally, in

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Section 4, the study is summarized and specific significance and limitations are presented. 2. Methodology 2.1. Research scope In accordance with the life-cycle of individual buildings (Sandanayake et al., 2016), the life-cycle of the construction sector consisted of three fundamental components. First was the materialization stage (MAT): incorporating materials production, transportation, and on-site construction of new buildings. Second was the operation stage (OPE): the daily operation of existing buildings. Third and last, was the disposal stage (DIS): demolition of buildings no longer useful and related waste transportation. It should be emphasized that the “life-cycle” here pertains to the entire inventory of the construction sector based on annual statistical data, which is very different from that of an individual building (Onat et al., 2014). The process-based approach was applied for the carbon emission assessment, and was primarily aimed at improving the details and accuracy of the analytical results. The essential concept of this technique can be explained as “Emissions = EQ × EF” (Hong et al., 2016), where EQ and EF indicate the engineering quantities and associated emission factors, respectively. Accordingly, carbon emissions of the six sub-processes involved in the three life-cycle stages could be evaluated, with further consideration of the various characteristics of statistical data relevant to each process. 2.2. Life-cycle carbon emission assessment of construction sector 2.2.1. Carbon emissions from the materialization stage Regarding the production of materials, process-based emissions can be calculated as follows: Epro =

n  

ms × EFpro,s



(1)

s=1

where Epro represents the emissions from production of materials, n is the total number of material types, and ms and EFpro,s are the quantity and CO2e emission factor of type s material, respectively. In light of the details and availability of data, five kinds of materials (steel, cement, wood, glass, and aluminum) were considered in the present study. Relevant consumption data for regional building sectors were obtained from the “China Statistical Yearbook on Construction” (National Bureau of Statistics, 2005c). However, in consideration of the existence of double counting and possible errors in statistical quantities, certain modifications were made based on previous research (Lin et al., 2015). Furthermore, an accessional 10% of the emissions from the above primary materials were added, assumed to be the emissions of “others”. For building-materials transportation, fuel combustion is the most pertinent source of emissions, and was estimated by: Etran =

n 3  

(Wdsr × EFtran,r )

(2)

r=1 s=1

where Etran represents the emissions from transportation, Wdsr is the freight turnover (material weight multiplied by transport distance) of type s material transported via method r, and EFtran,r is the emission factor of the transport method r. Here, Wdsr was calculated based on the above materials consumption, and the average transport distance was deduced from National Bureau of Statistics, 2005b and the “Yearbook of China Transportation & Communications” (Association of China Transportation and Communications, 2016). The three main methods of transportation considered were railway,

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Table 1 Energy consumption at the building operation stage.

2.3. Allocation of carbon emission permits based on the Gini coefficient

Building group

Sectors in energy balance tables

Energy consumption datab

Urban public buildings

Wholesale, Retail trade, Hotel, Restaurants, and othersa Urban households

Coal and coke, gaseous fuels, LNG, purchased electricity and heat, 5% gasoline, 35% kerosene and diesel Coal and coke, gaseous fuels, LNG, purchased electricity and heat, 5% kerosene and diesel Coal and coke, gaseous fuels, LNG, purchased electricity and heat, 5% kerosene and diesel

Urban residential buildings Rural residential buildings

Rural households

2.3.1. Gini coefficient for building carbon emissions The Gini coefficient, proposed by Corrado Gini in 1912, is the most commonly used measurement of income inequality. The Gini coefficient could be calculated based on the Lorenz curve (Drezner et al., 2009). This could be described as the ratio of the area between the Lorenz curve and the straight “equity” line to the area below the “equity” line. It could be calculated as: Gini = 1 −

a

Energy consumed by sector of transport, storage, and post were not considered, because energy use of this sector mainly occurred outside the boundary of buildings. b A large amount of fuel oils was consumed for transportation and non-energy use by relevant sectors. Thus, these oils were excluded according to previous research (World Resources Institute, WRI, 2015).

shipping, and road, and their emission factors were 8.64, 37.28, and 284.14 gCO2e /(t·km), respectively (Zhang and Wang, 2015). Carbon emissions of on-site work mainly originated from energy consumption by construction machinery. Therefore, the construction emissions, Econs were assessed as follows: Econs =

l  

uq × EFe,q



(3)

q=1

where l is the total number of energy types, uq and EFe,q represent the consumption and emission factor of type q energy, respectively. Here, uq was provided by the regional energy balance tables in the “China Energy Statistical Yearbook” (National Bureau of Statistics, 2005a). It should be noted that the energy consumption data above included energy use for building demolition, which accounted for about 9% of the construction work of new buildings (Zhang and Wang, 2016a), and should be excluded. 2.2.2. Carbon emissions from the operation stage Energy-related emissions at the operation stage could also be calculated using Eq. (3). Based on the classification of relevant statistical data (National Bureau of Statistics, 2005a), existing buildings were divided into three groups, namely urban public buildings, urban residential buildings, and rural residential buildings. The energy consumption of each group is indicated in Table 1. The main applications for five types of energy can be summarized as: (1) purchased heat for centralized heating; (2) coal and coke for household heating (and cooking in rural buildings), (3) gaseous fuels (including coal gas, LPG, natural gas, and LNG) for cooking and hot water, (4) electricity for lighting, equipment, and household appliances, and (5) fuel oils for temporary power generation. 2.2.3. Carbon emissions from the disposal stage As indicated in Section 2.2.1, carbon emissions for the demolition of buildings were about 9% of the on-site construction work of new buildings. On the other hand, waste transportation was another crucial aspect of emissions at this stage, and this could be estimated using Eq. (2). Building wastes were mainly sourced from on-site construction and building demolition, and had an average transport distance of 20 km (Zhang and Wang, 2016b). Furthermore, an area-based method (Ding and Xiao, 2014) was applied to estimate the total quantity of waste from constructed building-area (0.055 t/m2 ) and demolished building-area (1.3 t/m2 ). Relevant building area was calculated based on National Bureau of Statistics (2005c).

n 

(xi − xi−1 )(yi + yi−1 ) ∈ [0, 1]

(4)

i=1

where xi and yi represent the cumulative proportion of population and income (for i = 1, xi−1 = yi−1 = 0), respectively. Recently, the Gini coefficient has come to be used in environmental assessment, for the implementation of balance between efficiency and equality. In the present study, the Gini coefficient was introduced into the assessment of construction sector, aimed at reflecting the characteristics of building carbon emissions, and allocating the initial permits among regional construction sectors. In this context, xi and yi in Eq. (4) should represent the allocation criteria and emissions, respectively, of the construction sector. Furthermore, a normalized emission burden coefficient (EBC) was applied to indicate the carbon efficiency of a regional construction sector. As defined in Eq. (5), EBCi > 1 means that the carbon efficiency of region i is below the nationwide average, and vice versa. EBCi =



Ei Criteriai

  /





Ei

Criteriai



(5)

where Ei and Criteriai represent emissions from the construction sector, and the relevant value of the allocation criteria of region i, respectively. 2.3.2. Permit allocation scheme based on a multi-criteria Gini coefficient In order to make comprehensive considerations in decisionmaking about emission permit allocation, a multi-criteria Gini technique (Sun et al., 2010) was applied. The multi-criteria Gini expanded the original single-criterion Gini to provide broader insight that can reflect a variety of factors relevant to the efficiency and equality of allocation. In the present study, four indicators were considered: gross domestic product (GDP), disposable income of residents (RDI), population (POP), and urban area (URA). The reasons for selecting these indicators are briefly summarized. First, the GDP-based carbon Gini can indicate local economic development, which means that regions would share equal emission permits for their construction sectors based on the quality of their economies. Second, the RDI-based carbon Gini implies that regions with low consumption levels, should not allow extensive development of their building sectors. Third, the POP-based carbon Gini suggests that every resident should have equal emission permits relevant to buildings (living and entertainment). Finally, the URA-based carbon Gini can account for the future potential emissions from urbanization. As illustrated in Fig. 1, the permit allocation scheme is in fact an optimization analysis based on the grandfathering principle. Furthermore, Fig. 1 indicates that the allocation scheme can be resolved into the following five steps: (1) Collect data on the current circumstances as initial conditions, including emissions of regional construction sectors and values of relevant allocation indicators;

X. Zhang, F. Wang / Ecological Indicators 72 (2017) 910–920

Single-criterion based Gini0,j Multi-criteria based Gini0,multi

Calculate the initial Gini coefficients Set the nation-level carbon reduction target

Carbon reduction rate compared to current level

Year

Steel

Cement

Aluminum

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

1.962 1.934 1.926 1.901 1.880 1.810 1.814 1.800 1.798 1.770

0.823 0.800 0.788 0.781 0.754 0.689 0.669 0.654 0.635 0.617

11.303 11.224 10.922 10.696 9.863 9.689 9.780 9.941 9.464 9.402

Note: Recycling rates for steel and aluminum were assumed to be 10% and 30%, respectively.

Calculate multi-criteria Ginimulti(k) of step k

Judgment |Ginimulti(k)-Ginimulti(k-1)|≤u u=10

Table 3 Emission factors of steel, cement, and aluminum in China (tCO2e /t).

Historical emissions of regional construction sectors relevant allocation indictors: GDP, RDI, POP, URA

Data input

913

No

-8

Yes

Reallocate carbon emission permits for each region, constrained by: Total permit equal to the nation-level target Regional reduction rate within the predetermined lower and upper limits Single-criteria Gini less than initial values

is the initial (current level) carbon Gini coefficient based on criterion j;

Total permit constraint : E0,tot · (1 − Rtot ) =

30 

E0,i · (1 − Ri )

(9)

i=1

Stop the optimization Output the results Fig. 1. The permit allocation technique based on the multi-criteria Gini coefficient.

(2) Calculate the current Gini based on each single criterion (Ginij , j = 1–4), and propose a principle for integration of multi-criteria Gini (Ginimulti ). Three simple and representative modes of integration were performed as follows:

(6)

(7)

(8) (3) Set the national target for carbon reduction compared to the current level; (4) Based on the objective to minimize Ginimulti , optimize the regional carbon permits with constraints as defined in Eq. (9)–(11), where E0,tot and E0,i represent the current emissions of the construction sector nationwide and for region i, respectively; Rtot and Ri denote the emission reduction rate nationwide and for region i, respectively; Rlow ,i and Rup,i represent the lower and upper limits of Ri , respectively; and Gini0,j

Reduction rate constraint : Rlow,i ≤ Ri ≤ Rup,i

(10)

Coeifiecent constraint : Ginij ≤ Gini0,j

(11)

(5) Discuss the optimization results and make decisions for permit allocation. 2.4. Data collection 2.4.1. Statistical data source As indicated above, all statistical data were sourced from official published yearbooks of China within the period of 2004–2013. A total of 30 administrative regions in China were included in the following analyses (see Table A1, other regions were not considered because of the unavailability of data). The data sources were further summarized in Table 2 for conciseness and clarity. 2.4.2. Carbon emission factors of building materials Both industrial process emissions and energy-related emissions were considered for the production of building materials. In accordance with the statistical data, annual production energy use for steel, cement, and aluminum was taken from National Bureau of Statistics (2005a), and default direct emissions were calculated based on the “Guideline for Provincial Greenhouse Gas Inventories in China” (National Development and Reform Commission, 2011). Relevant factors are presented in Table 3. In addition, the average emission factors for timber and glass (Zhang and Wang, 2015) were taken as 0.270 tCO2e /m3 and 0.070 tCO2e /weight case (one weight case equals 50 kg).

Table 2 Summary of the statistical data sources. Process

Required data

Source (2005–2014)

Note

Materials production Transportation

Quantity of materials Transport distance

Modified according to previous research Calculated based on existing data

Building construction Building operation Building demolition Waste transportation

Energy consumption Energy consumption Energy consumption Building area

National Bureau of Statistics (2005b) National Bureau of Statistics (2005b) and Association of China Transportation and Communications (2016) National Bureau of Statistics (2005a) National Bureau of Statistics (2005a) National Bureau of Statistics (2005a) National Bureau of Statistics (2005b) and National Bureau of Statistics (2005c)

1/1.09 of the whole construction sector Exclude energy unrelated to buildings 0.09/1.09 of the whole construction sector Constructed area and demolished area

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Table 4 Carbon emission factors of related fuels.

consumption in China” (World Resources Institute, 2013), and the results are shown in Table 5. Carbon emission factor

Raw coal Cleaned coal Other washed coal Briquette Coke Coke oven gas Blast furnace gas Converter gas Gasoline Kerosene Diesel Fuel oil LPG Natural gas LNG

1.9901 tCO2e /t 2.4166 tCO2e /t 0.9590 tCO2e /t 2.3244 tCO2e /t 2.8648 tCO2e /t 8.2543 tCO2e /104 m3 9.6731 tCO2e /104 m3 14.3091 tCO2e /104 m3 2.9355 tCO2e /t 3.0439 tCO2e /t 3.1063 tCO2e /t 3.1806 tCO2e /t 3.1041 tCO2e /t 21.6714 tCO2e /104 m3 3.1817 tCO2e /t

3. Results and discussion 3.1. Analysis of total carbon emissions of the construction sector

2.4.3. Carbon emission factors of energy and fuels Emission factors for related fuels in the present study are summarized in Table 4. To be consistent with China’s situation, carbon content, oxidation rate, and calorific value were derived from Chinese guidelines (National Development and Reform Commission, 2011), and the default emissions of CH4 and N2 O were from IPCC (2006). Emission factors for purchased heat were calculated based on the “Input and output of energy transformation (Part 2. Heating supply)” in the energy balance tables (National Bureau of Statistics, 2005a) as follows:

EFheat,i =

m  



Ifuel,q · EFfuel,q /Oheat,i

(12)

q=1

where m is the total number of energy types for heat generation; Oheat ,i and EFheat ,i are the output of purchased heat and relevant emission factor for region i, respectively; Ifuel ,q and EFfuel ,q are the fossil fuel inputs and relevant emission factors, respectively. Moreover, the National Development and Reform Commission of China provided the Baseline Emission Factors for regional power grids annually, aimed at Clean Development Mechanism (CMD) projects. These baseline factors are subject to several conditions. First, the emission factors for fuels were taken as the lower limits of the 95% confidence interval. Second, clean power generation such as nuclear and hydroelectricity was not considered. Finally, power transmission loss was not included. Given these conditions, certain modifications should be (and were) made to assess the emissions for terminal electricity use according to “GHG protocol tool for energy

Carbon emissions (billion tCO2e)

6.0 5.0

3.1.1. Total emissions of the nationwide construction sector Fig. 2 illustrates the life-cycle carbon emissions of China’s construction sector, and relevant contribution to nationwide emissions during 2004–2013. As can be seen from Fig. 2, the life-cycle emissions of the construction sector have increased significantly since 2004, with relatively rapid growth after 2008. The total emissions in 2013 reached 5.28 billion tCO2e (46.9% of the national emissions). Furthermore, the materialization stage is the most influential factor on the emissions of construction sector, increasing from 53.5% in 2004 to 65.4% in 2013. The operation stage is the second largest contributor, with a descending share of 40.7% on average. On the other hand, emissions of the disposal stage were below 20 million tCO2e during the assessed period, and therefore could be neglected. It should be noted that the above results emphasized the materialization stage of construction sector in China. Hence, the components of emissions are very different from those of developed countries (Nässén et al., 2007; Onat et al., 2014), where emissions from the operation stage usually dominate. Detailed emissions from each life-cycle process are presented in Table 6, and will be further discussed in Section 3.2. 3.1.2. Life-cycle emissions of regional construction sectors Fig. 3 indicates the life-cycle emissions of the construction sectors in 30 regions. As illustrated in Fig. 3, with the projected development of China’s economy and urbanization, an overall trend of increased emissions is shown, with especially dramatic growth in HE (Hebei), JS (Jiangsu), SD (Shandong), HB (Hubei), and SC (Sichuan) during 2010–2013. Furthermore, average emissions in 2012–2013 were taken as an example to show the range among different regions. It can be observed that JS (Jiangsu) had the most construction-sector emissions (486 million tCO2e ), while HI (Hainan) contributed the least (13 million tCO2e ). An intuitive imbalance can be concluded between relatively developed regions and underdeveloped regions. 3.2. Analysis of carbon emissions from each stage 3.2.1. Analysis of the materialization stage As shown in Table 6, Materials production is the largest contributor to emissions at the materialization stage, with shares over

Disposal Operation Materialization

60% 50%

4.0

40%

3.0

30%

2.0

20%

1.0

10%

0.0

E P E P E P E P E P E P E P E P E P E P 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

0%

Proportion of national emissions

Fuel

Fig. 2. Carbon emissions of the construction sector in China: The proportion in the chart indicates the contribution of the construction sector to the national emissions of all sectors. “E” and “P” denote the emission and relevant proportion, respectively.

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Table 5 Carbon emission factors for regional purchased electricity (tCO2e /MWh). Power grid

Region

2004

2005

2006

2007

2008

2009

2010

2011

2012b

North Northeast East Central Northwest South Hainana

BJ, TJ, HE, SX, SD, IM LN, JL, HL SH, JS, ZJ, AH, FJ HA, HB, HN, JX, SC, CQ SN, GS, QH, NX, XJ GD, GX, YN, GZ, (HI) HI

1.2255 1.2781 0.9496 0.8911 0.9616 0.7933 0.8167

1.2513 1.2416 0.9486 0.8379 0.9251 0.8041 0.8815

1.1621 1.2671 0.9255 0.8313 0.8771 0.8114 0.8187

1.1448 1.1668 0.9035 0.8308 0.9116 0.7930 0.8166

1.1628 1.1235 0.8579 0.6749 0.8912 0.6659 0.8134

1.1517 1.1520 0.8529 0.6691 0.8751 0.6953 –

1.1761 1.1380 0.8258 0.6895 0.8501 0.6971 –

1.1563 1.1898 0.8334 0.7231 0.8545 0.7070 –

1.1454 1.1553 0.8118 0.6260 0.8410 0.6358 –

a b

Hainan power grid was integrated into the South China power grid since 2009. Emission factors of 2013 were taken as the values of 2012, because of the unavailability of data.

Table 6 Components of emissions from the nationwide construction sector (million tCO2e ). Sub-process

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Materials production Transportation Construction The materialization stage Urban public building Urban residence Rural residence The operation stage Building demolition Waste transportation The disposal stage Life-cycle

934.8 18.7 50.6 1004 274.5 339.6 252.1 866 4.6 2.6 7 1877

1027.1 20.5 55.8 1103 329.4 384.9 284.2 999 5.0 3.1 8 2110

1186.6 23.4 61.2 1271 355.2 401.7 281.4 1038 5.5 3.6 9 2319

1284.9 28.1 65.8 1379 388.4 433.8 279.2 1101 5.9 4.2 10 2490

1635.2 59.9 71.6 1767 463.9 469.6 291.9 1225 6.4 4.6 11 3003

1745.9 75.7 80.7 1902 530.3 499.8 316.9 1347 7.3 5.1 12 3262

2184.7 101.6 97.4 2384 601.0 560.7 341.2 1503 8.8 6.1 15 3901

2765.5 121.4 108.2 2995 673.5 611.3 381.4 1666 9.7 7.3 17 4678

3134.0 151.5 108.4 3394 725.2 650.8 392.8 1769 9.8 8.4 18 5181

3205.5 127.4 116.1 3449 773.2 629.7 403.6 1806 10.4 9.6 20 5275

The bold values were total emissions of the three stages and the whole life-cycle.

Fig. 3. Life-cycle carbon emissions of regional construction sectors (million tCO2e ): Average emissions for each two adjacent years are presented for conciseness. The links between abbreviations and relevant regions are presented in Table A1.

80% in nearly all regions (92.6% on average), followed by on-site construction work (4.2%), and transportation (3.1%). the materialization stage is also the driving factor of the emission increase, that emissions in 2013 were 3.4 times those in 2004. However, statistical data indicated that emission factors of major building materials (e.g., steel and cement) dropped about 10–20% in the interval from 2004 to 2013. Thus, the increasing emissions mainly resulted from the expansion of construction nationwide. Regarding the regional construction sectors, the carbon emissions at this stage are illustrated in Fig. 4. A trend of increased emissions can be observed for all regions, especially in HE (Hebei), JS (Jiangsu), and SC (Sichuan) after 2010. Moreover, these provinces also have relatively high construction emissions among all the assessed regions. Furthermore, it should be pointed out that the emissions from on-site building construction activities were calculated based on the energy consumption of the whole construction sector (which includes other subsections such as railways, highways and subways), and might lead to possible overestimation. However, this potential overestimation could be acceptable, because on-site con-

struction work was not the major contributor (only shares 2.5% of the life-cycle emissions) as indicated in Table 6. 3.2.2. Analysis of the operation stage Emissions at the operation stage have increased significantly for all three building types, as indicated in Table 6. The emission components have changed within the decade. Urban public buildings gradually became the dominated component with a 42.8% share in 2013. Meanwhile, the emission shares of urban and rural residences dropped by 4.3% and 6.8%, respectively. Furthermore, with consideration of the carbon efficiency, energy-related emissions per existing building area were analyzed (Fig. 5). Heating and electricity use were the main factors, with a total contribution of 90% on average. Emission intensity in public buildings was shown to be much higher than for residences. The emission intensity of rural residences was only about 23.4% that of public buildings, but increased gradually after 2008. It should be emphasized that, the emission intensity of urban buildings initially appeared to increase before 2010, and then fell during 2011–2013. This indicates that energysaving policies have recently shown positive results in urban China.

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Fig. 4. Carbon emissions at the materialization stage in 30 regions of China (million tCO2e ): Average emissions for each two adjacent years are presented for conciseness. The links between abbreviations and relevant regions are presented in Table A1.

Fig. 5. Energy-related carbon emissions per building area at the operation stage (kgCO2e /m2 ): UP, UR, and RR represent urban public buildings, urban residences, and rural residences, respectively.

Fig. 6. Carbon emissions at the operation stage in 30 regions of China (million tCO2e ): Average emissions for each two adjacent years are presented for conciseness. The links between abbreviations and relevant regions are presented in Table A1.

Fig. 6 presents the building operation emissions from the aspect of different regions. As shown in Fig. 6, the operation emissions are increasing, and are relatively high, in the following regions: (1) provinces with large population, including SD (Shandong), HE (Hebei), and GD (Guangdong); (2) cold regions with more requirements for heating, such as IM (Inner Mongolia), LN (Liaoning), and HL (Heilongjiang); and (3) highly developed municipalities, even those with small area, including Beijing and Shanghai. 3.2.3. Analysis of the disposal stage Regional building disposal emissions, including those from demolition and waste transportation, are illustrated in Fig. 7. Waste treatment was not considered because of the present low recycling rate in China. It can be seen from Fig. 7, East China appears to have more disposal emissions during the assessed period. Although the disposal stage presently makes a negligible contribution, emphasis should be given to this stage when considering future low-carbon

development. Because with the progress of technology, recycling and reuse will significantly reduce production-related emissions. 3.3. Optimization analysis of carbon emission permits The emissions of the construction sector’s life-cycle in 2013 were taken as an example for the analysis based on the Gini coefficient. As indicated in Fig. 8, the Lorentz curve was applied for the calculations. GDP, RDI, POP, and URA were taken as criteria, with Gini of 0.2144, 0.2201, 0.2630, and 0.2957, respectively. Although the curves in Fig. 8 are similar in shape, the sequences of the regions are very different from each other. The EBCs of regional construction sectors based on population are presented in Fig. 9. Relevant results could be helpful for decision-making of emission policy, considering an EBC exceeding 1.0 means lower carbon efficiency. Fig. 9 also illustrates that, regional emissions from various life-cycle stages might have differ-

X. Zhang, F. Wang / Ecological Indicators 72 (2017) 910–920

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Fig. 7. Carbon emissions at the disposal stage in 30 regions of China (million tCO2e ): Average emissions for each two adjacent years are presented for conciseness. The links between abbreviations and relevant regions are presented in Table A1.

Table 7 Multi-criteria carbon Gini coefficients of construction sectors.

Initial Additive Subduplicate Reciprocal

Single-criteria Gini

Multi-criteria Gini

GDP

RDI

POP

URA

Initial

Optimized

0.2144 0.1688 0.1691 0.1683

0.2201 0.1751 0.1751 0.1748

0.2630 0.2341 0.2338 0.2349

0.2957 0.2644 0.2644 0.2647

– 0.2483 0.2505 0.2440

– 0.2106 0.2144 0.2031

ent characteristics, and should be considered separately in terms of carbon reduction. To verify the feasibility of emission permit allocation methods based on a multi-criteria Gini coefficient, the following scenario was analyzed. The national emission reduction target for the construction sector was assumed to be a 5% decrease, and the upper and lower limits of the regional reduction target were confined within the range 1–20%. The allocation process was programmed using MATLAB, all the three principles for integration were assessed, and the results are presented in Table 7. As presented in Table 7, the multi-criteria Gini declined by 17–20% after optimization, which indicates the effectiveness of the allocation technique; however, relatively small differences could be observed for the three principles. Expanded analyses were done, and demonstrated that in terms of optimizing multi-criteria Gini, the reciprocal principle was better at reducing lower rank components (e.g., GDP-based Gini here). Conversely, the subduplicate principle was better for higher rank components. In this context, when considering different emphasis in optimization, corresponding principles for integration should be applied. An example of allocating emission permits among regions (additive principle) is illustrated in Fig. 10. It can be concluded that regions with higher EBCs are generally required to reduce more emissions in the present technique, which is conceptually accept4.0 3.0

Cumulative percentage of emissions

Principle

100%

The "equity" line Urban area based Population based Income based GDP based

80%

60%

40%

20%

0% 0%

20% 40% 60% 80% Cumulative precentage of criteria

100%

Fig. 8. Lorentz curves of carbon emissions from regional construction sectors in 2013.

able and reasonable. Detailed information of relevant data and analytical results are summarized in Table A2. 3.4. Policy implications The present study introduced a statistical analysis technique for emission assessment of regional building sectors in China. Compared with the current emission trading system to allocate the national emission target (Zhang and Hao, 2015), the proposed Gini coefficient aimed at the reduction target decomposition of the regional building industry. The results would be important as basic data and potential approaches for policy-making of low-carbon development from the insight of building life-cycles.

Materialization Operation Disposal Life-cycle

2.0

0.0

BJ TJ HE SX IM LN JL HL SH JS ZJ AH FJ JX SD HA HB HN GD GX HI CQ SC GZ YN SN GS QH NX XJ

1.0

Fig. 9. Emission burden coefficients of regional construction sectors based on population in 2013: The links between abbreviations and relevant regions are presented in Table A1.

918

X. Zhang, F. Wang / Ecological Indicators 72 (2017) 910–920

NX

BJ XJ 1.0

TJ

4. Conclusions HE SX

QH

IM

GS SN

LN

0.9

JL

YN

HL

GZ 0.8

SH

SC

JS

CQ HI

ZJ

GX

AH GD

FJ HN

HB

HA

SD

JX

Fig. 10. Emission permits allocation of regional construction sectors based on multi-criteria Gini coefficient (using additive principle for integration): The rates of allocated emissions to initial emissions in 2013 are indicated. The links between abbreviations and relevant regions are presented in Table A1.

Based on the above-mentioned results and analysis, the following policy implications can be suggested. First, the calculability and accuracy of regional building carbon-emissions depend upon relevant consumption data of materials and energy. Hence, statistical system should be enhanced by the government to better quantify emissions and potential reductions of the construction sector. Second, the building industry in China is facing great challenges to cut emissions. Different reduction rates should be proposed for different regions due to their various social and economic situations. The unbalanced development of regional construction sectors should be given attention, and appropriate polices should be made to encourage the technical progress of underdeveloped regions. Furthermore, cap control should also be emphasized for regions with rapid development and large populations. Finally, the results also provide some detailed strategies for emission mitigation. On the one hand, as a developing country, extensive construction work is being conducted in China. Consequently, policy support is expected to control the emissions from the construction process in addition to the use phase (previously of primary concern). On the other hand, reduction policies should be shaped for urban public buildings due to their higher emissions intensities compared to residences. The central and local governments should make efforts to raise public consciousness of building energy-savings and carbon-reduction, which are also crucial perspectives. It should be noted that, the proposed life-cycle emission allocation scheme for building sector might have double counting problem to combine with the current emission trading system (ETS). Because, the production emission of materials are also be covered by the ETS. Hence, the proposals and results of this study might not be used directly in the ETS, but would serve as essential basic data and an independent assessment method for policy-making and low-carbon development of building industry. Furthermore, possible research extensions could include continuous research on more efficient and accurate approaches to assess building emissions, potential application of the analytical methods and results in the current emission trading system, and the exploration of driving factors and regional differences of building carbon-reduction.

In the present study, a process-based method was first applied to analyze the life-cycle emissions of regional construction sectors in China, which could contain the entire inventory of building materialization, operation, and disposal. Then, the multi-criteria Gini coefficient was introduced as an indicator attempted to assess the emissions and to allocate the permits for regional building sectors. Overall, an analytical approach for calculation, assessment, and optimization of emissions was established from the perspective of regional construction sectors. Statistical data relevant to regional construction sectors within 2004–2013 were analyzed, and the results indicated that emissions from the materialization stage dominated in China, which was very different from developed countries. Detailed discussion on regional perspectives from each life-cycle stage was also provided. An overall trend of increase was observed, but different characteristics were revealed for various regions and sub-processes. Finally, a case study of the regional construction life-cycle emissions in 2013, based on the Gini coefficient, was conducted. Relevant analysis demonstrated the feasibility of the proposed permit allocation technique, and suggested relevant carbon reduction policies aimed at regional construction sectors. The methods presented here would contribute a good practice, and could be applied in future life-cycle carbon analyses of China’s regional construction sectors. The life-cycle analysis would be helpful for understanding the regional characteristics of emissions from the construction sector. Furthermore, relevant results for emission allocation could be applied for decision-making about low-carbon development and on-going emission permit allocation of the building industry. However, certain limitations of this study have to be mentioned. First, some simplifications and assumptions were made in the calculations due to the lack of relevant statistical data. This should be improved along with the enrichment of data source. Second, a case study was presented for permit allocation of provincial construction sectors, whereas more detailed analysis for each lifecycle stage at county, or building group level, should be made in future research to make the technique more practical. Appendix A.

Table A1 Abbreviations of the 30 study regions in China. Region

Abbreviation

Region

Abbreviation

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong

BJ TJ HE SX IM LN JL HL SH JS ZJ AH FJ JX SD

Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

HA HB HN GD GX HI CQ SC GZ YN SN GS QH NX XJ

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Table A2 Initial data and relevant results for analysis based on the Gini coefficient. Region

BJ TJ HE SX IM LN JL HL SH JS ZJ AH FJ JX SD HA HB HN GD GX HI CQ SC GZ YN SN GS QH NX XJ

Emissions of construction sector in 2013 (104 tCO2e )

GDP

POP

RDI

URA

Ratio of allocated emission permits to the levels in 2013 (%)

PRO

OPE

DIS

LC

108 CNY

104

108 CNY

km2

Additive

Subduplicate

Reciprocal

6569 6860 22512 4656 2489 20516 11722 2718 5039 35547 45421 9588 15469 6555 15016 11500 25844 10083 12585 5782 850 8419 26701 4287 13300 7064 3073 519 1218 2991

9074 3808 13319 6953 10157 9815 4555 9570 5639 9138 8372 4896 4390 2445 14666 6464 5857 5740 11445 2517 669 2564 5388 6875 2830 5084 2275 920 887 4333

71 54 76 52 58 87 49 21 63 210 237 69 75 34 120 64 106 90 89 23 8 52 77 30 45 52 28 9 16 33

15714 10722 35906 11661 12704 30417 16326 12310 10741 44895 54029 14553 19935 9033 29803 18029 31808 15912 24119 8322 1527 11035 32166 11192 16176 12200 5376 1448 2121 7358

19501 14370 28301 12602 16832 27078 12981 14383 21602 59162 37568 19039 21760 14339 54684 32156 24668 24502 62164 14378 3146 12657 26261 8007 11721 16045 6268 2101 2565 8360

2115 1472 7333 3630 2498 4390 2751 3835 2415 7939 5498 6030 3774 4522 9733 9413 5799 6691 10644 4719 895 2970 8107 3502 4687 3764 2582 578 654 2264

8635 3881 11138 5488 4669 9139 4402 6099 10186 19671 16370 9138 8008 6828 18501 13370 9552 10708 24929 6645 1409 4921 11537 3882 5895 5409 2829 748 953 3095

12187 2334 6478 2999 8356 13974 3596 2766 6341 14308 10992 5852 4299 2113 21635 4658 7349 4312 16136 6104 1265 6134 6433 1828 3337 1555 1450 560 2106 1620

99.00 83.66 80.00 99.00 99.00 80.00 80.00 99.00 99.00 99.00 80.00 99.00 86.22 99.00 99.00 99.00 80.00 99.00 99.00 99.00 99.00 99.00 80.00 80.00 81.23 98.83 99.00 99.00 99.00 97.08

99.00 83.42 80.00 99.00 99.00 80.00 80.00 99.00 99.00 99.00 80.00 99.00 85.50 99.00 99.00 99.00 80.00 99.00 99.00 99.00 99.00 99.00 80.09 80.00 82.34 99.00 99.00 99.00 99.00 96.27

99.00 85.62 80.00 99.00 97.90 80.00 80.00 99.00 99.00 99.00 80.00 99.00 87.93 99.00 99.00 99.00 80.00 99.00 99.00 99.00 99.00 99.00 80.00 80.00 80.00 99.00 99.00 99.00 99.00 93.93

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