Energy-related GHG Emissions of the Textile Industry in China

Resources, Conservation and Recycling 119 (2017) 69–77

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Energy-related GHG emissions of the textile industry in China Beijia Huang a,b,∗ , Juan Zhao a , Yong Geng c , Yihui Tian d , Ping Jiang e a

College of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China Department of Environment and Low Carbon Science, University of Shanghai for Science and Technology, Shanghai 200093, China c School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240,China d Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, China e Department of Environmental Science & Engineering, Fudan University, Shanghai 200433, China b

a r t i c l e

i n f o

Article history: Received 21 February 2016 Received in revised form 16 May 2016 Accepted 17 June 2016 Available online 2 July 2016 Keywords: Textile industry Greenhouse gas Energy saving Scenario analysis

a b s t r a c t As the sixth largest energy consuming industry sector in China, the textile industry is encountering great challenges in reducing Greenhouse Gas (GHG) emissions. Considering the existing studies have the limitation of lacking updated data and include limited energy sources, this study will conduct a comprehensive analysis of the GHG emissions in China’s textile industry and analyze the emission characteristics. The results show that coal consumption is the main source of GHG emissions in China’s textile industry. The second largest GHG emission source is electricity consumption, which is primarily from the Eastern China, Central China and Northern China Power Grids. A driving force factor analysis reveals that the order of intensity for the driving forces is the production scale, the energy intensity, the energy structure and the emission factors. In particular, the increasing scale of production is the main factor driving increasing GHG emissions; however, energy intensity reduction and energy structure optimization can effectively reduce GHG emissions. This study also summarizes the main energy saving measures being used by the textile industry in China. The measures used in the spinning, weaving and wetting processes are found to have high energy saving potential and a short payback period. Scenario analysis indicates that under the optimal technology application scenario, GHG emissions would be 34.3% less than emissions under the baseline scenario in 2030. Furthermore, GHG emissions per unit output value would be 0.18 t/million RMB, which approaches the advanced international level of 0.14 t/million RMB. Corresponding polices for reducing GHG emission in the textile industry need to be considered based on the implications indicated in this study. © 2016 Elsevier B.V. All rights reserved.

1. Introduction With the growing demand for textile products, global textile production has increased rapidly in recent years. According to WTO statistics, global textile production was approximately 140.84 million tons in 2012, an increase of 25.7% compared with 2008 (Zhu and Zhu, 2012). It is expected that global textile production will continue to grow at a rate of approximately 6.5% in the near future (Xu and Jiang, 2009). The textile industry is one of China’s traditional pillar industries and has maintained rapid growth after China’s reform and opening. China is currently the world’s largest textile production country (Chen and Fu, 2011), with total textile production of 79.29 million tons in 2012, representing approxi-

∗ Corresponding author at: College of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China. E-mail address: [email protected] (B. Huang). http://dx.doi.org/10.1016/j.resconrec.2016.06.013 0921-3449/© 2016 Elsevier B.V. All rights reserved.

mately 56.3% of global production. The textile industry is also a high energy consumption sector in China, after the ferrous metal smelting industry, the chemical manufacturing industry, the nonmetallic mineral manufacturing industry, the hydroelectric power industry, the mining industry, petroleum processing and the coking industry. The “twelfth five-year GHG emission control program” issued by the State Council emphasizes GHG emission reduction in the textile industry (Wang and Xu, 2011). Thus, it is highly necessary to analyze the GHG emissions of the textile industry and to seek potential energy saving measures. There are a number of related studies concerning GHG emissions in the textile industry, particularly focusing on specific products and production processes. In terms of GHG emission accounting, certain large cloth production companies such as Levi Strauss Co. and Continental Clothing Co. have calculated the life cycle carbon footprint of shirts, jackets, T-shirts and other clothing styles (Mahler et al., 2012; Norbert, 2009). Li et al. (2011) measured the life cycle carbon footprint of black cotton products and indicated

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that the proportion of carbon emission raw materials is the highest (64.54%), the carbon emissions resulted from wastewater discharge and energy consumption account for 34.33%. Germany’s Systain International Environmental Consulting Firm calculated the life cycle GHG emissions of three types of clothing for Germany’s Otto Group (BASF, 2009). Researchers have conducted influence factor analysis for GHG emissions in the textile industry. For instance, Wang et al. (2011) established a factorization model of China’s textile industry using the LMDI (Logarithmic Mean Division Index) method. His results show that the expansion in industrial scale was the main reason for the increase in GHG emissions from the textile industry. Zhao (2012) analyzed the carbon footprint of the textile supply chain and declared that electricity consumption and thermal energy consumption are the main sources of GHG emissions. Regarding the research on GHG emission reduction measures for the textile industry, Li et al. (2013) mainly discussed the accounting boundary setting methodology for the carbon footprint calculation. Yao (2014) explored calculation methods for the carbon footprint of cotton fiber based on Publicly Available Specification (PAS) 2050 and proposed carbon emission reduction measures for textile production at the raw materials stage. Zabaniotou and Andreou (2010) analyzed energy consumption in the cotton ginning process of Greek National textile and discussed the feasibility of applying alternative energy options in the ginning process. Hong et al. (2010) conducted an investigation regarding textile enterprises’ energy consumption efficiency, energy consumption structure and utilization in Taiwan and proposed several energy saving strategies. Bevilacqua et al. (2010) calculated the carbon footprint of a Merino wool sweater based on the Life Cycle Assessment (LCA) theory and indicated that strengthening industry clusters could effectively reduce the carbon footprint of textile products caused by transportation. Ma and Lu (2015) estimated the factors influencing carbon emissions of the textile and garment industry and found the main influencing factor is the GDP of the textile industry and the energy consumption per unit of GDP. M.H. Wang et al. (2015) and Z.Z. Wang et al. (2015) calculated the CO2 emission of China’s textile industrial. His results show that the carbon intensity of China’s textile industry is decreasing. The literature review finds that there are several related studies concerning the carbon footprint calculation and GHG emission reduction for the textile industry; however, most of them targeted specific textile products or typical enterprises. Few studies have been conducted to investigate the GHG emissions of the China’s textile industry. The only three studies calculating the GHG emission in China’s textile industry (M.H. Wang et al., 2015; Z.Z. Wang et al., 2015; Wang and Wu, 2011) have the limitation of lacking updated data, and consider limited energy sources. In order to overcome these research gaps, this study will conduct a comprehensive analysis of the GHG emissions in China’s textile industry and analyze its emission characteristics. Emission predictions will also be conducted to foresee the GHG emission under an optimal technology scenario in the near future. Suggestions for reducing the GHG emissions of China’s textile industry will be proposed based upon our findings.

2. Methodology To calculate GHG emissions for the textile industry, it is critical to first establish a defined boundary. The boundary definition in our study refers to “The Greenhouse Gas Protocol – A Corporate Accounting and Reporting Standard” (Bhatia and Ranganathan, 2004). In this report, GHG emissions are divided into direct emissions and indirect emissions. Direct GHG emissions are from sources owned or controlled by textile industry companies, termed Scope 1 (see Table 1). The energy-related GHG emission sources and

the non-energy related GHG emissions of Scope 1 are emissions in the industrial process caused by chemical reaction, such as nitrous oxide (N2 O), methane (CH4 ) from the chemical treatment of chemical products, and wastewater treatment. Because the non-energy related GHG emissions contribute less than 1% of the total GHG emissions, they are not included in our study (Tong et al., 2012). Indirect GHG emissions are caused by company activities but occur from sources owned or controlled by other companies in the textile industry. Indirect emissions can be further divided into Scope 2 and Scope 3. Scope 2 emissions are emissions from the generation of purchased electricity, heat, steam and other energy consumption. Scope 3 emissions are a consequence of textile industry activities but occur from sources not owned or controlled by textile industry companies (refer to Table 1). Because scope 3 carbon emission behavior is very different and the energy consumption proportion is limited, our research solely considers the energy-related GHG emissions of scope 1 and scope 2. Furthermore, because of statistical data limitations, the indirect energy-related GHG emissions of scope 2 solely include GHG emissions from purchased electricity. 2.1. GHG emission calculation 2.1.1. Direct GHG emissions The energy-related GHG emissions of the textile industry can be estimated by referring to the “IPCC national GHG inventory guidelines”(Institute for Global Environmental Strategies, 2006) and the “GHG protocol tool for energy consumption in China” (Song and Yang, 2011). Although the textile industry’s energy-related GHG emissions are generated in various chemical processes, the carbon content of different fuels and materials are very different. Therefore, the three types of GHG emissions, including carbon dioxide (CO2 ), methane (CH4 ) and nitrous oxide (N2 O), are investigated by converting them into carbon dioxide equivalents (CO2 e). The carbon footprint of the textile industry is indicated by its global warming potential (GWP), which means the global warming effect from a per unit mass of certain GHG emissions in a fixed period (Reisinger et al., 2011). The total direct energy-related GHG emissions within the textile industry’s boundary are estimated based upon energy consumption and emission factors (EF) (Tian et al., 2013; Yue et al., 2015). The following equation presents the calculation method: Ctotal-direct = Ei EFi Oi

(1)

i

Where Ctotal-direct represents the total direct energy-related GHG emissions (in million tons, t), subscript i represents energy fuel type i; Ei represents the energy consumption (in million tons, t) of fuel type i; EFi represents the Emission Factors (EF) of fuel type i; and Oi represents the oxidation rate of fuel type i. According to China Statistics Yearbook (NBS, 2015) and China Energy Statistics Yearbook (NBS, 2001–2015), there are eight types of energy resources for the textile industry: coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil and natural gas. Their emission factors are shown in Table 2 (Tian et al., 2013). 2.1.2. Indirect GHG emission Indirect energy-related GHG emissions in China’s textile industry mainly originate from electricity consumption. Because of different technology levels and energy structure mixes in different periods and regions, the emission factors for electricity generation change significantly over time and across regions. In this study, total energy-related GHG emissions are calculated by applying the following equation: Celectricity = EFke × Pe × Psk /Pstotal

(2)

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Table 1 Boundary definition and examples of energy-related GHG emissions. Emission type

Scope

Definition

Example

Direct Emissions

Scope 1

Direct GHG emissions that occur from sources that are owned or controlled by companies within the textile industry

Indirect Emissions

Scope 2

GHG emissions from the generation of purchased electricity, heat, steam, etc., consumed by these companies Emissions as a consequence of the activities of the textile industry but occurring from sources not owned or controlled by these companies

Emissions from combustion in owned or controlled boilers, furnaces, vehicles, etc.; emissions from chemical production in owned or controlled process equipment Emissions occur at the facility where purchased electricity, heat and steam are generated Emissions from the extraction and production of purchased materials; transportation of purchased fuels; and use of sold products and services

Scope 3

Sources: The Greenhouse Gas Protocol–A Corporate Accounting and Reporting Standard (Bhatia and Ranganathan, 2004)

Table 2 GHG emission factor of different energy sources.

Coal Coke Crude oil Gasoline Kerosene Diesel oil Fuel oil Natural gas

Oxidation rate (%)

EF (ton CO2 /ton)

EF (g CH4 /ton)

EF (g NO2 /ton)

GHG EF (ton CO2 e/ton)

100 100 100 100 100 100 100 100

2.01 3.04 3.07 2.99 3.10 3.16 3.24 21.8

209.8 284.35 125.45 129.21 129.21 127.96 125.45 389.3

31.36 42.65 25.09 25.84 25.84 25.59 25.09 38.9

2.02 3.06 3.08 3.00 3.11 3.17 3.25 21.86

Sources: GHG Protocol Tool for Energy Consumption in China (Song and Yang, 2011).

Where CElectricity represents the energy-related GHG emissions of electricity consumption and k represents the region that receives power supply from the state power grid. EFke represents the emission factor of GHG emissions from electricity generation, Pe denotes electricity power consumption in the textile industry, Psk denotes the yield of textile products in region k and Pstotal denotes the total yield of textile products in China. Currently, there are six state power grids in China: the Northern China Power Grid, the Northeast China Power Grid, the Northwest China Power Grid, the Central China Power Grid, the Eastern China Power Grid, and the Southern China Power Grid (Ma, 2012). The state grid electricity emission factors refer to the GHG protocol tool for energy consumption in China (Song et al., 2013) and are listed in Table 3. The textile industry’s electricity consumption can be collected from China Statistics Yearbook (NBS, 2015). The production quantity of textile products in the six power grid regions can be collected from Textile Industry Development Report (2000–2013) (NBS, 2014).

2.2. Driving force factor analysis To further identify the key factors that influence energy consumption-related GHG emissions in China’s textile industry and determine the relationship between the change in various factors and the corresponding changes in GHG emission, a factor decomposition analysis is employed (Guo, 2010; Wang et al., 2013). This paper established the factor decomposition formula for the energy consumption and related GHG emissions in China’s textile industry based on the Logarithmic mean division index method (LMDI). According to factor decomposition method theory, factors influencing energy-related GHG emissions include the following: production scale, energy structure, energy intensity, and energy GHG emissions (H.B. Sun, 2011; W.Q. Sun, 2011). Thus, the driv-

ing force factor decomposition of energy-related GHG emissions for China’s textile industry is provided as the following equation: C=



Ci =

i

 C i (

i

Ei

•

Ei E • • P) E P

(3)

where C represents total energy-related carbon emissions; Ci represents carbon emissions from fuel i; E represents total energy consumption; Ei represents the consumption of energy source i; P represents the consumption volume of textile products; Fi = Ci /Ei is the mean GHG emissions factor of energy source i, Si = Ei /E is the share of energy source i in total energy consumption; and B = E/P is the energy consumption per unit of textile products. Because the textile industry mainly adopts electricity and coal as energy sources in China, the driving force analysis involves carbon emissions from electricity and coal. According to the LMDI method, the result of factor decomposition can be expressed as follows: C = C r − C0 = C F + C S + C B + C P

CF

 





L Ci,0 − Ci,r ln

i

CS

 



L Ci,0 − Ci,r ln

i

CB

 



L Ci,0 − Ci,r ln

i

CP

 



L Ci,0 − Ci,r ln

i



Fi,r Fi,0

Si,r Si,0

 , emission factor effect



B  r

B0

P  r

P0

, energy structure effect

, energy intensity effect

, production scale effect

(4)

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Table 3 GHG electricity emission factors for different power grid regions. Region

CO2 EF (ton CO2 /kwh)

Northeast China Northwest China Central China Northern China Eastern China Southern China

EF (g/kwh)

EF (g/kwh)

EF (ton CO2 /kwh)

2007

2009

2011

2007

2009

2011

2007

2009

2011

2007

2009

2011

1.14 0.86 0.76 1.07 0.84 0.74

1.11 0.82 0.65 1.06 0.80 0.67

1.14 0.81 0.70 1.13 0.78 0.67

12.53 9.28 8.30 11.71 9.38 8.83

12.24 8.81 7.08 11.74 8.88 7.59

11.85 8.65 7.23 11.69 8.51 7.18

17.50 13.36 11.86 16.45 12.69 11.08

16.98 12.72 9.90 16.08 12.09 10.00

17.35 12.69 10.45 16.92 11.99 10.04

1.14 0.87 0.77 1.07 0.84 0.74

1.11 0.82 0.65 1.06 0.81 0.67

1.14 0.82 0.71 1.13 0.79 0.67

Sources: Getting every ton of emissions right: An analysis of emission factors for purchased electricity in China (Song et al., 2013).



L Ci,0 , Ci,r

⎧ Ci,0 − Ci,r  , Ci,0 =/ Ci,r ⎨  ⎩

ln Ci,0 /Ci,r

(5)

Ci,0 , Ci,0 = Ci,r

where i represents the energy consumption type (coal/electricity); r represents time of year, and 0 denotes the baseline year; and C represents carbon emission change. CF represents the emission factor effect, CS represents the energy structure effect, CB represents the energy intensity effect, and CP represents the production scale effect. Fi,r represents the emission factor of energy i in year r; Fi,0 represents the emission factor of energy i in the baseline year; Ci,0 represents the carbon emissions of energy i in the baseline year; Ci,r represents the carbon emissions of energy i in year r; Si,r represents the consumption percentage of energy I from the total energy consumption in year r; Si,0 represents the consumption percentage of energy i from the total energy consumption in the baseline year; Br represents the energy consumption per unit textile product in year r; B0 represents the energy consumption per unit textile product in the baseline year; Pr represents the consumption volume of textile products in year r; and P0 represents the consumption volume of textile products in the baseline year. 3. Results 3.1. GHG emissions 3.1.1. Total emissions Fig. 1 shows the textile production quantity and GHG emissions from China’s textile industry during the period from 2000 to 2011. As shown, the production amount continually increased from 2001 to 2011. However, yield stagnation appeared in 2008; this is likely due to the structural adjustment of the textile industry resulting from export restrictions from 2006 to 2007 (H.B. Sun, 2011; W.Q. Sun, 2011). In addition, textile production was influenced by the global financial crisis in 2008 (Zhang and Chen, 2011), and the growth rate rebounded in 2009. Regarding the energy consumption proportion, the results indicate that coal is the main source of GHG emissions in China’s textile industry, representing approximately 80% of total emissions. The proportion of electricity consumption was very low before 2006; however, the electricity consumption rate has increased rapidly since then. Regarding total GHG emissions, we find that GHG emissions continued increasing from 2001 to 2007 and have shown an obvious decreasing trend since 2008. This observation is partly because of the stagnation of textile production around 2008, and the increasing consumption of electricity. Furthermore, energy conservation in the textile industry has been promoted by the Chinese government since 2008 (Zhang, 2009; Zou, 2014). The production utilization rate has significantly improved due to energy saving technology applications such as waste heat recovery and energy saving control systems (Energy Conservation Advanced Application Technology Guide in Textile Industry, 2012) (UNIDO, 2012).

3.1.2. Indirect emissions The GHG emissions from electricity consumption show a large difference in the six power grid regions of China. Fig. 2 shows the indirect GHG emissions from electricity consumption in different power grid areas in 2007, 2009 and 2011. As shown, the GHG emissions from electricity consumption are primarily from the Eastern China Power Grid, the Central China Power Grid and the Northern China Power Grid. The GHG emissions from electricity consumption from the Eastern China Power Grid represent approximately 51% of the total GHG emissions. However, the GHG emissions in the northwest, the Northeast, and southern regions are very low and on a declining trend. The difference in GHG emissions in power grid regions is directly related to the textile industry distribution. Currently, the textile industry is mainly distributed in East China, Central China, and North China. Provinces such as Jiangsu, Zhejiang, Henan, Anhui and Shandong are highly centralized. In contrast, the textile industry is barely distributed in Northwest, Northeast and Southern China (Gao, 2010; M.H. Wang et al., 2015; Z.Z. Wang et al., 2015). To mitigate GHG emissions from electricity consumption in China’s textile industry, the GHG emission factors of each power grid is supposed to be reduced (Gao, 2010; Cao, 2012). Currently, the GHG emission factor in China is high because electricity depends largely on coal consumption. According to annual operation report from each power grids (Annual operation report from China Power Grid, 2014), more than 80% of the electricity in Northern China power grid, East China power grid and Northeast China power grid are from coal combustion, and the reliance rate of coal in the other three power grids is around 60%. Less than 10% of the electricity is from renewable energies (Fig. 3). If the means of electricity generation can be transferred from coal combustion to nuclear power and renewable energy, the GHG emission factor can be expected to be reduced. Another option is to transfer the textile industry to areas that depend on cleaner energy resources such as the Northwest Power Grid which has higher reliance rate of wind and nuclear power. A third option is to improve power consumption efficiency to ensure that the electricity consumption per textile yield can be reduced and consequently reduce the GHG emissions (ISO 14067, 2010).

3.1.3. GHG emission flows To clearly present the GHG emission flow of the textile industry, the flow chart method is applied in our study. he three main energy consumption processes are spinning, weaving and wetting (Hasanbeigi, 2010), and “Energy Conservation Advanced Application Technology Guide in the Textile Industry” revealed that the energy consumption rate of these three processes is 22%, 20% and 58%, respectively (Bai, 2012). Thus, the GHG emissions of the three processes can be estimated as approximately 1332 million tons, 1211 million tons and 3512 million tons, respectively. Furthermore, according to the division method in China’s National Bureau of Statistics (NSBC), the textile industry’s downstream industries are clothing, cotton textiles, wool textiles, ramie textiles, silk, knitwear,

7000

7000

4000

6000

6000

5000

5000

4000 3000

3000

2000

2000

1000

1000

0

0

Texle producon

8000 million tons

8000

GHG emission

9000

million ton

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73

Electricity Natural gas Fuel oil Diesel oil Kerosene Gasoline Coke Coal Textile production

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Fig. 1. Textile production and GHG emission (2000–2011).

Fig. 2. Indirect GHG emissions of textile industry in China.

textile exports and others (Lu, 2013). The GHG emissions of different textile products are estimated based on the proportion of their production quantity. Fig. 4 shows the final result of the GHG emission flow. The width of each line indicates the quantity of the GHG emissions. As shown in Fig. 4, the wetting process generates the most GHG emissions in the production process. Regarding the downstream products, clothing and cotton textiles carry the most GHG emissions in China’s textile industry. With the appearance of new textile products, such as new types of fibers and functional textile materials, China’s textile production is expected to maintain a growth

trend in the near future (Bai, 2012). Regarding the final step of the textile industry chain, recycling, our analysis finds that there is limited recycling activities in the textile industry in China. Luo et al. (2013) reveals that there are more than 2400 million tons of waste textiles each year in China, while the recycling rate is less than 30%. Among which the majority is “hidden” salvaged for use again, and very limited proportion of textile is really recycled through reproduction technologies. When looking at the countries with active textile recycling action such as USA, Germany and Japan, the textile recycling system is operating more mature. For instance, the Council for Textile Recycling in US is devoted to creating aware-

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100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%

Coal power Water power New clear power Renewable energy

East China power grid

Central North China Northeast Northwest Southern China power grid power grid power grid power grid power grid Fig. 3. Electricity source of China’s electricity.

Fig. 4. GHG emission flows of China’s textile industry in 2011.

ness about keeping clothing, footwear and textile out of landfills (Council for Textile Recycling, 2014) (http://weardonaterecycle. org/index.html).

According to the method introduced in section 2.2, the driving force factors of GHG emissions for China’s textile industry during the 2006–2011 period is analyzed based on data including the annual yield of China’s textile industry, the energy structure, the energy consumption quantity and GHG emission factors. The results are shown in Fig. 5: The result shows that the order of intensity for the driving forces is the production scale effect CP , the energy intensity effect CB , the energy structure effect CS , and the emission factor effect CF . The production scale effect CP has the most obvious positive effect on carbon emissions; the energy intensity effect CB has a negative influence effect. In comparison, the influence of the emission factor effect CF is very small because the GHG emission factors of different energy sources are fixed data. For instance, from 2010 to 2011, the production scale effect CP contributed 611.2% to GHG emissions, the contribution rate of energy intensity effect CB was −215.9%, and the energy structure effect CS and the emission factor effect CF contributed −214.3% and −80.7%, respectively. As shown in Fig. 5, the GHG emissions continued decreasing in most years, excluding 2009–2010. The main cause of the GHG emission

1000

CO2 million tons

3.2. Driving force factors

1500

500

∆C_P ∆C_S

0

∆C_F 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011

-500

∆C_B ∆C_

-1000 -1500 Fig. 5. Decomposition results of GHG emission factors of China’s textile industry (2006–2011).

increase may be caused by the change of energy structure. If we trace to Fig. 1, we can easily find that the energy consumption quantity of fuel oil and gasoline decreased from 2009 to 2010, while the consumption rate of coal is increased. Considering that textile production is expected to show an increasing trend in the near future, the production scale is unlikely to be contained. Instead, effective energy saving measures should be promoted for increasing the energy intensity. Meanwhile, appli-

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90000

70% 60%

GHG emission(104tCO2e)

Emission reduction potential percentage

75

50% 40% 30% 20% 10%

80000 70000 60000 50000 40000 30000 20000 10000 0

0% A1 A2 A3 A4 A5 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 Spinning

Weaving

Wetting

Fig. 6. Energy-saving of GHG reduction measures during the textile production process.

cation of cleaner energy need to be encouraged both for direct and indirect GHG emission energy sources in order to optimize the energy structure. 4. Emission reduction predictions 4.1. Energy saving measures The driving force factor analysis shows that reducing energy intensity is one of the essential means to reducing GHG emissions for the textile industry. Thus, it is necessary to find the energy saving potential in the process and improve energy efficiency (Ozturk, 2005; Kong et al., 2015). Based on research findings from Lawrence Berkeley National Laboratory (Hasanbeigi, 2010), the main energy saving measures and their payback period in the spinning, weaving, and wetting processes are summarized in The emission reduction potential of these energy saving measures is illustrated in Fig. 6. The fluctuation range is the extent of the general emission reduction potential, and the middle dot represents the average emission reduction potential for a specific energy saving measure. As shown, the energy saving measures with the highest potential are installing energy saving control systems in humidifying systems, replacing mercury lamps with high pressure sodium lamps and applying counter-current washing. The payback period for the primary energy saving measures is close to within 3 years. Currently, certain energy saving measures such as speed motor drives, heat recycling from flues and hot washing water are frequently practiced in China’s textile industry. However, most measures listed in Table 3 have not yet been widely applied (Li et al., 2011; Chou, 2014). 4.2. Scenario analysis To estimate the potential energy savings and GHG emissions for China’s textile industry, two scenarios are designed in our study. Due to data availability, the year 2012 was chosen as the baseline, whereas the year 2030 was chosen as the final year for scenario analysis because China’s economic development will reach a relatively steady phase by then (Jiang and Hu, 2007; Juntueng et al., 2014). A designed business-as-usual (BAU) scenario assumes that energy consumption per unit output value will maintain the 2012 level. The optimal technology application scenario (OTA) assumes that all of the technologies in Table 4 will be applied with the highest energy saving potential indicated in Fig. 5. Furthermore, the energy saving technologies will have a high popularization rate in the future. For the BAU scenario, (1) the average growth rate of China’s textile production from 2000 to 2012 is approximately 10%. If this growth rate is maintained, it can be predicted that the pro-

2015

2020

2025

2030

Business as usual scenario Optimal technology application scenario Fig. 7. Comparison of emission reduction between BAU scenario and OTA scenario.

duction quantity will be 9989.12 million tons, 16087.57 million tons, 25909.19 million tons and 41727.01 million tons in 2015, 2020, 2025 and 2030, respectively. The corresponding output values in 2015, 2020, 2025 and 2030 will be 7.3 × 108 million RMB, 1.2 × 109 million RMB,1.9 × 109 million RMB and 3.0 × 109 million RMB, respectively. (2) The energy consumption per unit of output value was 0.11 tce/million RMB in 2012, and we assume that the energy consumption per unit output value will maintain its current level through 2030. (3) Based on the previous two assumptions, the GHG emissions under the BAU scenario in 2015, 2020, 2025 and 2030 can be calculated (refer to Table 4). For the OTA scenario, (1) according to the National Key Energy-saving Technology Extension Directory (2013) and China National Textile And Apparel Council (2015), the extension rate of energy saving technologies in the textile industry in China is approximately 30% in 2015, assuming that the application rate will rise to 45%, 65%, and 90% in 2020, 2025, and 2030, respectively. (2) Referring to the Energy Saving Technology Guide for the Textile Industry energy consumption rate for the spinning, weaving and wetting process is 22%, 20%, and 58%, respectively. The original energy consumption for each technology is further established as equal in each process. Furthermore, assume that the energy saving potential of all of the technologies achieves the highest level as indicated in Fig. 5. (3) Based on the previous two assumptions, the energy savings for each technology can be calculated. The results of the GHG emissions for 2015, 2020, 2025 and 2030 under the OTA scenario are shown in Table 5. The comparison of GHG emissions under the two scenarios is shown in Fig. 7: Results show that GHG emissions are 82402.07 million tons in 2030 under the BAU scenario. GHG emissions under the OTA scenario are 54160.87 million tons, 34.3% less than the GHG emissions under the BAU scenario. From the perspective of GHG emissions per unit output value, the quantity in 2030 under the OTA scenario will be 0.18 t/million RMB, which nearly approaches the international advanced level of 0.14 t/million RMB (Chen and Fu, 2011). 5. Conclusions and recommendations The textile industry is one of China’s traditional pillar industries and has maintained rapid growth after China’s reform and opening. Consequently, a detailed analysis on Chinese textile industry is necessary so that the energy related GHG emission features can be uncovered. After the GHG emissions calculation, the driving force analysis and emission reduction scenario analysis, the main conclusions are provided as follows. Corresponding GHG emission reduction recommendations are also presented. (1) Coal consumption is the main source of GHG emissions in China’s textile industry, representing 80% of the total GHG from

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Table 4 Energy saving measures in the production process. Processes

Energy saving measures

Code

Emission reduction potential

Payback Period (Year)

Spinning

Energy-efficient Spindle Oil Energy-efficient Control System for Humidification System Speed Motor Drives Optimum Air Compression System Heat Recovery from Flues and Hot Washing

A1 A2 A3 A4 A5

3%–7% 25%–60% 7%–60% 1%–3% 2%–3%

– 2–3.5 <3 2.4 <1

Weaving

Strengthen Process Equipment Maintenance Speed Pump System Drives Multiple Pumps for Varying Loads Steam System Insulation Improvement Replace Mercury Lights with Metal Halide or High Pressure Sodium Lights

B1 B2 B3 B4 B5

2%–30% 20%–50% 10%–50% 6%–26% 50%–60%

<1 0.8–2.8 <1 0.3 0.08

Wetting

Counter-flow Current for Washing Mechanical De-watering or Contact Drying Before Stentering Use of Mixed Drying System Reduce Re-processing in Dyeing Speed Pump System Drives

C1 C4 C3 C2 C5

41%–62% 13%–50% 25%–40% 10%–12% 14%–49%

– 1.6 0.2–0.3 – <3

Sources: summarized from Energy-Efficiency Improvement Opportunities for the Textile Industry (Hasanbeigi, 2010).

Table 5 GHG emissions of BAU and OTA scenarios.

Output valuem (million RMB) GHG emissions under BAU scenario (million tons) Extension rate of energy saving technologies (%) OTA GHG reduction (million tons) SceGHG emissions under nBT scenario (million tons) nario

2015

2020

2025

2030

7.30 × 106 19726.40 30 5260.44 14465.96

1.20 × 107 31769.56 45 13283.22 18486.34

1.90 × 107 51165.19 65 12664.57 38500.62

3.00 × 107 82402.07 90 28241.20 54160.87

primary energy resources. The second largest GHG emission source is electricity consumption. The proportion of electricity consumption by China’s textile industry has obviously increased since 2006. The textile industry’s three largest electricity GHG emission sources are the Eastern China Power Grid, the Central China Power Grid and the Northern China Power Grid; of these, the Eastern China Power Grid has the highest proportion at 51%. Currently, the GHG emission factor in China’s Power Grids is high because electricity depends largely on coal consumption. If the source of electricity power can be transferred from coal to cleaner energy such as water power, nuclear power and renewable energy, the GHG emission factor can be expected to be reduced. Because the GHG emission calculation in our analysis covers relatively comprehensive energy sources, the CO2 emission is higher than that the results in other researches such as Ma et al. (2011), M.H. Wang et al. (2015) and Z.Z. Wang et al. (2015). (2) A GHG emission flow analysis reveals that there are three main energy consumption processes, namely spinning, weaving and wetting. Clothing, cotton textiles and textile exports are found to be the primary downstream items that are allocated high GHG emissions. It is expected that with the increasing demand for new types of textile products such as new fibers and functional textile materials, China’s textile production will maintain a high growth rate. We also found that textile recycling barely exists in China currently. Considering textile have high reused potential, recycling should be encouraged through legislation establishment and market supporting in order to promote GHG emission reduction. (3) The driving force factor analysis reveals that the order of intensity for the driving forces is the production scale, the energy intensity, the energy structure and the emission factors. Effective energy saving measures should be promoted for increasing the energy intensity. Meanwhile, application of cleaner energy need to be encouraged both for direct and indirect GHG emission energy sources in order to optimize the energy structure.

(4) Analysis of the emission predictions finds that there is high energy saving potential in the spinning, weaving and wetting processes, and the payback period of the primary energy saving measures is within nearly 3 years. Our scenario analysis reveals that if the extension rate of the energy saving measures in Table 3 can attain 90% in 2030, the GHG emissions will be 34.3% less than the GHG emissions under the baseline scenario. Furthermore, the GHG emissions per unit output value in 2030 under the optimal technology application scenario is 0.18 t/million RMB, which nearly approaches the international advanced level of 0.14 t/million RMB. While, the GHG emission in the future will depend on the real popularization condition of the energy saving technologies.

The analysis of the GHG emission characteristics and the discussion of the energy saving scenarios instruct blueprint for reducing GHG emission in China’s textile industry. Corresponding polices for the textile industry need to be considered based on the implications from this study. However, one limitation of this study is that the GHG emission resulting from the manufacturing technologies have not yet been considered. More comprehensive GHG emission for the textile industry need to be carried out when GHG emission factors during the complicated production process can be identified in the future.

Acknowledgements The research work of this paper was supported by a grant from the National Natural Science Foundation of China (Nos. 71403170, 71461137008; 71325006, 71402016), and Fudan Tyndall Centre of Fudan University (FTC98503B09a). The advice for revisions from Hao Han of Tsinghua University China Automotive Energy Research Center is highly valued. The authors are responsible for the remaining errors.

B. Huang et al. / Resources, Conservation and Recycling 119 (2017) 69–77

References Annual operation report from East/Central/North/Northwest/Southern China Power Grid power grids, 2014. BASF, 2009. Non-Iron Bed Line by Badische Anilin-und-Soda-Fabrik. Aktiengesellschaft, Germany. Bai, Y., 2012. International competitiveness of China’s textile export. Technol. Horiz. 30, 53–54. Bevilacqua, M., Ciarapica, R.E., Giacchetta, G., 2010. A carbon footprint analysis in the textile supply chain. Int. J. Eng. Sci. 4 (1), 24–36. Bhatia, P., Ranganathan, J., 2004. The Greenhouse Gas Protocol-A CorporateAccounting and Reporting Standard, World Business Council for Sustainable Development (WBCSD). Cao, F., 2012. An empirical analysis on the industrial international competitiveness and intra-industry trade–based on 2005–2010 China’s textile and clothing industry data. J. Shijiazhuang Univ. Econ. 35 (3), 60–66. Chen, Y., Fu, G.W., 2011. Textiles carbon analysis and calculation. China Text. Lead. 12, 12–15. China National Textile And Apparel Council (CNTAC), 2015. Guide for Advanced Emission Reduction Technologies of Textile Industry 2012–2015. Chemical Industry Press, Shanghai. Chou, B.H., 2014. Sustainable Development in Textile Industry and Carbon Footprint. Shanghai Jiao Tong University, Shanghai. Council for Textile Recycling, 2014 http://weardonaterecycle.org/index.html. Gao, H., 2010. Study on the Problems of China Textile Industry Cluster. Jilin University, Jilin. Guo, C.X., 2010. China’s carbon emission factors decomposition using the LMDI decomposition technology. China Popul. Resour. Environ. 20 (12), 4–9. Sun, H.B., 2011. The development prospects of China’s textile industry. China Cotton Ramie Circ. Econ. 8, 5–10. Hasanbeigi, A., 2010. Energy-Efficiency Improvement Opportunities for the Textile Industry, China Energy Group, Energy Analysis Department Environmental Energy Technologies Division. Lawrence Berkeley National Laboratory. Hong, G.B., Su, T.L., Lee, J.D., Hsu, T.C., Chen, H.W., 2010. Energy conservation potential in Taiwanese textile industy. Energy Policy 38, 7048–7053. IPCC National Greenhouse Gas List Guide, 2006. Intergovernmental Panel on Climate Change. Institute for Global Environmental Strategies, Japan. ISO 14067, 2010. Carbon Footprint of Products. International Organization for Standardization, Geneva. Jiang, K., Hu, X., 2007. Energy demand and emissions in 2030 in China: scenarios and policy options. Environ. Econ. Policy Stud. 7, 33–250. Juntueng, S., Towprayoon, S., Chiarakorn, S., 2014. Energy and carbon dioxide intensity of Thailand’s steel industry and greenhouse gas emission projection toward the year 2050. Resour. Conserv. Recycl. 84, 46–56. Kong, L.B., Hasanbeigic, A., Price, L., Liu, H.B., 2015. Energy conservation and CO2 mitigation potentials in the Chinese pulp and paper industry. Resour. Conserv. Recycl. 11, 1–11. Li, R., Wu, D.D., Jiang, H., 2011. Carbon footprint and its measurement in dyeing and finishing. Dyeing 37 (18), 40–43. Li, X., Wu, X.Y., Ding, X.M., 2013. Several problems of carbon footprint calculation in textile and clothing industry. Dyeing 12, 35–38. Lu, A., 2013. Evaluation of clothing product carbon footprint based on LCA. China Text. Lead. 2, 15–18. Luo, Y.H., Pu, Z.Y., Huang, Y.H., 2013. The present situation and development trend of recycling waste textiles. Dev. Rev. 3, 9–12. Wang, M.H., Li, R., Lin, L., 2015. Carbon emissions by energy consumption of China textile industry. Dyeing 50 (3), 50–53. Ma, Y.H., Lu, A., 2015. Estimation and factors analysis of carbon emission of textile and apparel industry. Wool Text. J. 43 (8), 62–65. Ma, X.L., 2012. China’s textile industry development trend analysis. China Fiber Bookmark 5, 38–40.

77

Mahler, D., Aurik, J., Hembert, E., 2012. A product life cycle approach to sustainability. Supply Chain Management Review 12, 50–51. National Bureau of Statistics (NBS). China Energy Statistic Yearbook 2000–2014. Beijing, China Statistics Press, 2001–2015. National Bureau of Statistics (NBS), 2014. China Textile Industry Development Report 2000–2013. Statistics Press, Beijing :China. National Bureau of Statistics (NBS), 2015. China Statistic Yearbook 2014. Statistic Press, Beijing :China. Norbert, J., 2009. The Carbon Footprint of a Cotton T-shirt. Christian Initiative Romero. CIR, London. Ozturk, H.K., 2005. Energy usage and cost in textile industry: a case study for Turkey. Energy 30 (13), 2424–2446. Reisinger, A., Meinshausen, M., Manning, M., 2011. Future changes in global warming potentials under representative concentration pathways. IOP Sci. 6, 1–8. Song, R.P., Yang, S., 2011. GHG Protocol Tool for Energy Consumption in China. World Resources Institute (WRI).Washington. Song, R.P., Zhu, J.J., Hou, P., 2013. Getting Every Ton of Emissions Right: An Analysis of Emission Factors for Purchased Electricity in China. World Resources Institute (WRI), Washington. Tian, Y.H., Zhu, Q.H., Geng, Y., 2013. An analysis of energy-related greenhouse gas emissions in the Chinese iron and steel industry. Energy Policy 56, 352–361. Tong, J.J., Gao, J., Huang, Q., 2012. Greenhouse gas inventories compilation methodology introduction on textile enterprises. China Stand. Rev. 8, 15–17. 2012. United Nations Industrial Development Organization (UNIDO).Energy Conservation Advanced Application Technology Guide in Textile Industry (Available at) http://www.eser-tech.com/upload/files/2013/8/21796623. Sun, W.Q., 2011. Change in carbon dioxide emissions from energy use in China’s iron and steel industry. J Iron Steel. Res. Int. 18 (6), 31–36. Wang, H., Xu, M.J., 2011. Summary of the textile industry chain carbon emission theoretical model. Shanghai Text. Technol. 39 (7), 1–4. Wang, L.L., Wang, L.H., Du, C., 2011. Industrial carbon footprint: a case study on knitting fabrics. Textile Bioengineering and Informatics Symposium Proceedings, Hong Kong, 22. Polytechnic University, Hong Kong, pp. 602–607. Wang, Y.L., Zhu, Q.H., Geng, Y., 2013. Trajectory and driving factors for GHG emissions in the Chinese cement industry. J. Clean. Prod. 53, 252–260. Xu, B., Jiang, Y.L., 2009. Foreign direct investment, processing trade, and the sophistication of China’s exports. China Econ. Rev. 20, 425–439. Yao, L., 2014. An evaluation of the carbon footprint and carbon reduction measures of textile raw material stage. Tianjin Polytech. Univ. J. 33 (1), 71–76. Yue, Q., Wang, H.M., Gao, C.K., Du, T., Liu, L.Y., Lu, Z.W., 2015. Resources saving and emissions reduction of the aluminum industry in China. Resour. Conserv. Recycl. 104, 68–75. Wang, Z.Z., Meng, H., et al., 2015. The agglomeration and transferring of Pan-Yangtze river delta’s textile industry. World Regional Studies 24 (2), 123–130. Zabaniotou, A., Andreou, K., 2010. Development of alternative energy sources for GHG emissions reduction in the textile industry by energy recovery from cotton ginning waste. J. Clean. Prod. 18, 784–790. Zhang, L., Chen, Y., 2011. Low carbon economy and ustainable development of textile. Print. Dyeing 37 (3), 38–41. Zhang, X.Y., 2009. Process equipment renovation is the key to the energy conservation and emission reduction of textile industry. China Electron. Rep. 3 (26), 2–4. Zhao, N.H., 2012. Carbon Footprint Assessment and Low Carbon Measures of Polyester Fabrics. Donghua University, Shanghai. Zhu, Z.B., Zhu, C.S., 2012. Present situation and development strategy of China textile industry foreign trade. Times Finance 32 (2), 71–72. Zou, J., 2014. Study on the International Competitiveness of China Textile Industry. Donghua University, Shanghai.