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dollar ratio
Energy Policy 39 (2011) 5980–5987
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CO2 emissions embodied in China–US trade: Input–output analysis based on the emergy/dollar ratio Huibin Du a,b, Jianghong Guo c, Guozhu Mao c,n, Alexander M. Smith d, Xuxu Wang e, Yuan Wang c a
Institute of Systems Engineering, School of Management, Tianjin University, Tianjin 300072, China H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA c School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China d School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332, USA e College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 January 2011 Accepted 27 June 2011 Available online 29 July 2011
To gain insight into changes in CO2 emissions embodied in China–US trade, an input–output analysis based on the emergy/dollar ratio (EDR) is used to estimate embodied CO2 emissions; a structural decomposition analysis (SDA) is employed to analyze the driving factors for changes in CO2 emissions embodied in China’s exports to the US during 2002–2007. The results of the input–output analysis show that net export of CO2 emissions increased quickly from 2002 to 2005 but decreased from 2005 to 2007. These trends are due to a reduction in total CO2 emission intensity, a decrease in the exchange rate, and small imports of embodied CO2 emissions. The results of the SDA demonstrate that total export volume was the largest driving factor for the increase in embodied CO2 emissions during 2002–2007, followed by intermediate input structure. Direct CO2 emissions intensity had a negative effect on changes in embodied CO2 emissions. The results suggest that China should establish a framework for allocating emission responsibilities, enhance energy efficiency, and improve intermediate input structure. & 2011 Elsevier Ltd. All rights reserved.
Keywords: Input–output analysis Emergy/dollar ratio Structural decomposition analysis
1. Introduction In 2010, China surpassed Japan and became the second-largest economy in the world; since 2006, however, it has become the world’s largest CO2 emitter (Gregg et al., 2008). As ‘‘the world’s factory’’, a large part of China’s economic growth is driven by foreign demand. Although China has an international trade surplus, particularly for trade with the US, environmental consequences of trade such as resource depletion and environmental destruction cannot be ignored. Many of China’s low-end and energy-intensive exports lead to a rise in CO2 emissions (Lin and Sun, 2010). Therefore, basic knowledge about CO2 embodied in international trade is essential. Embodied CO2 emissions can be defined as the total megatons of CO2 emitted during production. The most widely-used approach for studying CO2 embodied in international trade is input–output analysis. Machado et al. (2001) analyzed energy and carbon embodied in Brazilian international trade of 1995 and found that Brazil was a net exporter of energy and carbon embodied in nonenergy goods. Weber and Matthews (2007) quantified embodied environmental emissions in trade between the US and its seven
n
Corresponding author. Tel.: þ86 135 020 786 77; fax: þ 86 22 278 902 59. E-mail address: [email protected] (G. Mao).
0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.06.060
largest trading partners. Their results demonstrated that the net imports of embodied emissions for CO2, SO2, and NOx increased sharply from 1997 to 2004. Lin and Sun (2010) evaluated CO2 emissions embodied in China’s 2005 imports and exports and revealed that electricity generation contributed the most to embodied carbon emissions. CO2 embodied in international trade of ˜oz and Steininger, 2010), Italy countries, such as Australia (Mun (Mongelli et al., 2006), Spain (Sanchez Choliz and Duarte, 2004), Sweden (Kander and Lindmark, 2006), Denmark (Munksgaard et al., 2005), and Norway (Peters and Hertwich, 2006),;have also been studied. Additionally, research has been conducted to investigate CO2 emissions embodied in the bilateral trade between Japan and China (Liu et al., 2010a,b), China and the UK (Li and Hewitt, 2008), Japan and the US (Ackerman et al., 2007), and Korea and Japan (Rhee and Chung, 2006). The input–output analysis (IOA) is used to evaluate the influence of international trade on embodied CO2 emissions. Furthermore, factors analysis of embodied CO2 emissions could yield useful information for an in-depth understanding of the observed changes in embodied CO2 emissions. In recent years, structural decomposition analysis (SDA) built on the input–output model has been developed to analyze the key factors underlying the changes in CO2 emissions at the output level. Cheng and Lin (2001) examined key factors that affected CO2 emission changes in
H. Du et al. / Energy Policy 39 (2011) 5980–5987
Taiwan’s petrochemical industries during 1984–1994 and showed the relative contribution of each factor to industrial CO2 emission changes. Nobuko (2004) identified factors affecting on CO2 emissions in Japanese industries during 1985–1995, finding that changes in both environment and production technology contributed to a decrease in CO2 emissions. Llop (2007) analyzed the changes in Spanish emission multipliers during 1995–2000; she showed that Spain’s economic structure correlates positively with Spain’s emission multipliers, whereas Spain’s production pollution intensities correlate negatively with Spain’s emission multipliers. The US is China’s largest trading partner, and the total bilateral trade volume between these two countries reached 459 billion dollars in 2010 (Morrison, 2011). Moreover, China and the US dominate the world’s CO2 emissions. Hence, there is an obvious need for further studies on CO2 emissions embodied in China–US trade. Despite previous studies on the volume and impact of CO2 emissions in China–US trade (Guo et al., 2010; Shui and Harriss, 2006; Xu et al., 2009, 2010), the driving factors behind changes to these embodied emissions have received little attention. In this study, the emergy/dollar ratio (EDR) was used instead of purchasing power parity (PPP) to represent purchasing power, an input–output analysis was used to quantitatively investigate the direct and total CO2 emission intensities in China’s sectors and CO2 emissions embodied in China–US trade during 2002–2007. Furthermore, SDA was utilized to identify the driving factors behind changes in embodied CO2 emissions.
2. Methods and data 2.1. Input–output analysis The input–output analysis (IOA) developed by Leontief (1941) has been frequently applied to assess energy, CO2 emissions, and other pollutant emissions embodied in international trades (Chen and Chen, 2010; Ma¨enpa¨a¨ and Siikavirta, 2007; Serrano and Dietzenbacher, 2010). The standard input–output model is defined as follows (Leontief, 1970): X ¼ AXþ Y
ð1Þ
where X is the total output vector and Y is the final demand vector. A is a direct input coefficients matrix demonstrating the relationship among all sectors, in which Aij ¼Xij/Xj (i,j¼ 1,2,y,n) is the amount of input from sector i required directly to produce per unit output from sector j. AX represents the intermediate input vector. When solved for total output, Eq. (1) can be represented as X ¼ ðIAÞ1 Y ¼ CY
ð2Þ
where I denotes the identity matrix; C is the Leontief inverse matrix (I–A) 1, in which Cij represents the amount of output from sector i required directly and indirectly to produce per unit final demand from sector j. 2.2. CO2 emission intensity The direct CO2 emission intensity was estimated as ratio of direct CO2 emissions to total inputs:
yi ¼
m X
fk Rk Pik =Xi
ð3Þ
k¼1
where yi is the direct CO2 emission intensity of sector i and yi is the element of the direct CO2 emission intensity row vector h; fk is the CO2 emission factor of energy k (k¼ 1,2,y,m); Rk is the net calorific value of energy k; Pik is the energy k consumption of sector i in physical units, and Xi denotes the total input of sector i in monetary units.
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Total (direct and indirect) CO2 emission intensities are calculated by multiplying the direct CO2 emission intensity row vector, h, by the Leontief inverse matrix, C: M ¼ Ch
ð4Þ
where M is the total CO2 emission intensity row vector. Element Mi is the total CO2 emission intensity of sector i, showing the CO2 emissions for the production of one unit in sector i. 2.3. CO2 emissions embodied in international trade Based on the total CO2 emission intensities, the volume of exports from sector i, and the price index, the CO2 emissions embodied in exports of each sector can be calculated as (Shui and Harriss, 2006) ex ex ex Eex i ¼ Mi Xi ¼ Mi
XPIex i,2002 ex Xi,y XPIex i,y
ð5Þ
where Eex i is the total CO2 emissions embodied in the exports of sector i; Miex is the total CO2 emission intensity of sector i of the exporting country, which was calculated from an input–output table held at constant 2002 prices; XPIex i,y is the export price index (XPI) of sector i in the exporting country for the similar product category of the importing country in year y; XPIex i,2002 is the XPI of sector i in the ex exporting country in the year 2002; Xi,y is the total exports of sector i in year y in monetary units; and Xiex is the total exports of sector i calculated in monetary units at 2002 constant prices. The equation for CO2 emissions embodied in imports is referred to in Shui and Harriss (2006); however, the emergy/dollar ratio (EDR) was used instead of purchasing power parity (PPP) to value the purchasing power of money in a country. The concept of emergy was first presented by Odum in (1983) to fully integrate the values of energy, materials, and information into a common unit of the solar emjoule (abbreviated as sej). The solar emjoule evaluates the contribution of the natural environment to the human economic system through embodied energy synthesis. EDR is the total emergy from all sources in a country divided by its Gross National Product (GNP) and connects economic activities with their supporting energy flows. Many consumer products, such as food, clothing, and fuel, have emergy values. Thus, the division of the emergy by GNP of a country represents the purchasing power of the money in that country (Campbell et al., 2005; Jiang et al., 2008). PPP also represents the purchasing power of money; but it only considers the currency value of commodities, whereas EDR considers both the currency value and environmental value of the commodities. Therefore, the CO2 emissions embodied in imports of each sector can be expressed as follows: ex Eim i ¼ Mi
EDRim y EDRex y
MPIex i,2002 im Xi,y Fi,y MPIex i,y
ð6Þ
where Eim is the total CO2 emissions embodied in the imports of i ex sector i; EDRim y and EDR y are emergy/dollar ratios of the importing country and exporting country in year y, respectively; MPIex i,y is the import price index (MPI) of sector i of the exporting country; MPIex i,2002 is the MPI of sector i in the exporting country in the year im 2002; Xi,y is the total imports of sector i in year y in monetary units; and Fi,y is the ratio of carbon in the energy mix of sector i of the importing country to that of the exporting country’s sectors in year y, which can be written as follows: P im im k ðEnergyi,k,y CEk Þ ð7Þ Fi,y ¼ P ex ex k ðEnergyi,k,y CEk Þ where Energyex i,k,y is CO2 emissions proportion in sector i of the exporting country by energy k in year y; CEex k is the CO2 coefficient of energy k in the exporting country; Energyim i,k,y is the CO2
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emissions proportion in sector i of the importing country by energy k in year y; and CEim k is the CO2 coefficient of energy k in the importing country. For more details, see Shui and Harriss (2006).
Combined with the above analysis, decomposition of CO2 emissions embodied in exports can be expressed as ex ex ex 1 DEex ¼ 14 DhðC1 þ C2 ÞðXex 1 þ X2 Þ þ 4ðh1 þ h2 ÞDCðX1 þ X2 Þ ex ex ex ex 1 þ 14 ðM1 þ M2 ÞDXaex ðHex 1 þ H2 Þ þ 4ðM1 þ M2 ÞðXa1 þ Xa2 ÞDH
2.4. Structural decomposition analysis Many factors, such as trade volume and structure, contribute to shifts in CO2 emissions embodied in exports (Lee and Lin, 2001; Yabe, 2004; Yan and Yang, 2010). In this study, SDA was used to identify major factors leading to fluctuations in the export of CO2 emissions. The CO2 emissions embodied in exports between a base year (1) and a comparative year (2) can be calculated as follows (Liu et al., 2010a,b): ex ex ex DEex ¼ Eex 2 E1 ¼ M2 X2 M1 X1
ð13Þ The first term on the right side of Eq. (13) represents changes in direct CO2 emission intensities. The second term represents changes in the structure of intermediate inputs. The third term represents changes in the structure of exports, and the last term represents changes in total volume of exports. All four terms consider both direct and indirect influences on the CO2 emissions embodied in China’s exports to the US, because they are all multiplied by a Leontief inverse matrix.
ex ex ex ex ¼ ðM2 M1 ÞXex 2 þ M1 ðX2 X1 Þ ¼ DMX2 þ M1 DX ex ex ex ex ¼ ðM2 M1 ÞXex 1 þ M2 ðX2 X1 Þ ¼ DMX1 þ M2 DX
ð8Þ
This structural decomposition is non-unique, and the interaction terms are not included. Changes in one variable determined by n factors will have n! decomposition forms. To solve this problem, the variable DEex in Eq. (8) can be decomposed by two polar forms as follows (Dietzenbacher and Los, 1998): ex ex 1 DEex ¼ 12 DMðXex 1 þ X2 Þ þ 2ðM1 þM2 ÞDX
ð9Þ
Then, changes of total CO2 emission intensities (DM) can be decomposed as follows:
DM ¼ h2 C2 h1 C1 ¼ DhC2 þ h1 DC ¼ DhC1 þ h2 DC ¼ 12 DhðC1 þC2 Þ þ 12ðh1 þ h2 ÞDC
ð10Þ
where Dh represents the change in direct CO2 emission intensities and DC represents the change in the Leontief inverse. Exports are expressed as follows: P ex ex X X X Xex ¼ P i ex ¼ Xiex Hex ¼ Xaex Hex ð11Þ Xi P ex where Xaex ¼ Xi is the total volume of exports and ex ex P ex H ¼ X = Xi is a vector composed of ratios of each sector’s export to the total volume of exports; thus, Hex represents the export structure. Changes in exports can be further separated into the changes of exports’ volume and structure as shown in Eq. (12): ex ex ex ex ex ex ex ex ex ex DXex ¼ Xa2 H2 Xa1 H1 ¼ DXaex Hex 2 þ Xa1 DH ¼ DXa H1 þ Xa2 DH ex ex ex ex ex ¼ 12 DXaex ðH1 þ H2 Þ þ 12ðXa1 þXa2 ÞDH ð12Þ
2.5. Data sources Sources of data used in this study include: (1) Input–output tables were obtained from China’s National Bureau of Statistics for 42 sectors for the years 2002, 2005, and 2007. Because sector classifications in input–output tables are different from those in energy data, all input– output tables were aggregated into the 28 energy consumption sectors as shown in Table 1. Decomposition studies require economic data at constant prices, so 2005 and 2007 input–output tables were held at 2002 constant prices using the price index deflation method. These price indices included: the producer price index of industrial products and agricultural products, build-in project price index, retail price index, price index of fixed assets investment, and real estate price index. EPI and IPI were calculated based on the volume and value of main export and import commodities provided by the China Statistical Yearbook for 2003, 2006, and 2008, using the method recommended by the United Nations Statistics Division (Liu and Peng, 2010). (2) Energy consumption data of China’s 28 sectors were obtained from the energy balance tables provided by the China Energy Statistical Yearbook for 2003, 2006, and 2008. Energy consumption comprised nine types of energy including coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, natural gas, and electricity. The CO2 emission factor and net calorific value of each type of energy used for calculating CO2 emissions were obtained from the IPCC Guidelines for National
Table 1 Sectors and codes. Code Sector
Code Sector
1 2 3 4 5
Agriculture, hunting, forestry, and fishing Mining and washing of coal Extraction of petroleum and natural gas Mining and processing of metal ores Mining and processing of non-metal ores and other ores
15 16 17 18 19
6 7 8
Manufacture and processing of foods, beverages, and tobacco Manufacture of textile Manufacture of textile wearing apparel, footware, caps, leather, fur, and feather Processing of timber and manufacture of furniture Manufacture of paper, printing, articles for culture, education, and sport activity Processing of petroleum, coking, and nuclear fuel Chemical industry Manufacture of non-metallic mineral products Smelting and pressing of metals and manufacture of metal products
20 21 22
Manufacture of machinery Manufacture of transport equipment Manufacture of electrical machinery and equipment Manufacture of communication and other electronic equipment Manufacture of measuring instruments and machinery for cultural activity and office work Manufacture of artwork and other manufacturing Recycling and disposal of waste Production and distribution of electric power and heat power
23 24
Production and distribution of gas Production and distribution of water
25 26 27 28
Construction Transport, storage and post Wholesale, retail trade, and hotel, restaurants Other services
9 10 11 12 13 14
H. Du et al. / Energy Policy 39 (2011) 5980–5987
Greenhouse Gas Inventories (Eggleston et al., 2006; Guo et al., 2010). (3) Bilateral trade data between China and the US were available from the China Trade and External Economic Statistical Yearbook for 2003, 2006, and 2008. Since 1992, China has utilized the harmonized code system to code, classify, and conduct statistical analyses for China’s import and export commodities. The code system includes 22 sections and 98 chapters. Therefore, 98 chapters of trade data were aggregated into 28 sectors according to the National Bureau of Statistics of China’s Explanation and Code for Sector Classification of Input– Output Table of China in 2007. Commodities re-exported to the US are relatively small compared with total trade between China and the US, so they were not considered in this study. (4) CO2 emissions of the US sectors were obtained from Energy Information Administration (EIA, 2008a). CO2 coefficients of China and the US were provided by the IPCC Guidelines for National Greenhouse Gas Inventories and EIA (2006b), respectively. The EDR of China and the US in 2000 were 7.29 1012 sej/$ (Yang et al., 2011) and 1.07 1012 sej/$ (Campbell et al., 2005), respectively. Due to the lack of EDR data for 2002, 2005, and 2007, we assumed that the ratio of the US’s EDR to China’s EDR did not change from 2000 to 2007.
3. Results and discussion 3.1. Sector CO2 emission intensities in China The direct and total CO2 emission intensities for each sector in 2002, 2005, and 2007 were calculated and presented in Table 2 according to Eqs. (3) and (4). The average direct CO2 emission intensity of the 28 sectors decreased from 2002 to 2007; the average total CO2 emission intensity increased from 2002 to 2005, whereas it Table 2 Direct and total CO2 emission intensities of 28 sectors of China. Code
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Average
2002 (t CO2/103 Yuan)
2005 (t CO2/103 Yuan)
2007 (t CO2/103 Yuan)
Direct
Cumulative
Direct
Cumulative
Direct
Cumulative
0.0579 0.5838 0.5718 0.1472 0.1805 0.0750 0.0847 0.0219 0.0282 0.1137 1.3329 0.2577 0.5836 0.4043 0.0419 0.0453 0.0246 0.0139 0.0238 0.1360 0.0000 2.4526 0.9066 0.2296 0.0148 0.1716 0.0340 0.0968 0.2093
0.3203 1.0776 0.9144 0.9187 0.7218 0.4041 0.5876 0.4632 0.5399 0.5975 2.1498 1.0197 1.3040 1.2835 0.7181 0.6511 0.7441 0.5520 0.6181 0.7028 0.0000 2.9793 1.8389 1.0126 0.7189 0.7108 0.3442 0.4003 0.7213
0.0613 0.8056 0.3519 0.2099 0.1507 0.0480 0.0869 0.0182 0.0378 0.1168 1.6465 0.2147 0.3836 0.4935 0.0358 0.0281 0.0178 0.0106 0.0118 0.1115 0.0097 1.6369 0.6164 0.1998 0.0152 0.1554 0.0399 0.0852 0.2040
0.3681 1.7719 1.0038 1.6045 0.9701 0.4030 0.6541 0.4756 0.6209 0.7101 2.6485 1.1197 1.2371 1.6699 0.8207 0.6795 0.8224 0.5618 0.6483 0.7772 0.0097 2.4695 1.5647 1.0920 0.7849 0.7677 0.3110 0.4819 0.8330
0.0627 0.8158 0.2372 0.1822 0.1030 0.0363 0.0709 0.0159 0.0260 0.0957 1.4863 0.1704 0.3001 0.3973 0.0283 0.0184 0.0158 0.0095 0.0122 0.0726 0.0043 1.3770 0.5066 0.2166 0.0119 0.1588 0.0349 0.0798 0.1755
0.3434 1.6066 1.1529 1.5965 0.7407 0.3401 0.5632 0.4192 0.4964 0.5717 2.4934 0.9516 0.9898 1.4748 0.6404 0.5059 0.7415 0.3950 0.4780 0.6677 0.0993 2.6174 1.1264 1.0571 0.7514 0.6521 0.2834 0.3751 0.7457
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decreased from 2005 to 2007. The latter was almost four times higher than the former, indicating that CO2 emissions of intermediate inputs may be significant to the total CO2 emission intensities. Both direct and total CO2 emission intensities in sectors 11 and 22 were higher than those in other sectors, because processing of sector 11 (petroleum and coking) consumes a significant amount of oil. Furthermore, production of sector 22 (electric power and heat power) requires significant amounts of coal. In 2010, the highest CO2 emissions (40.1% of total) originated from the production and distribution of sector 22 (electric power and heat power). 15.7% of China’s total CO2 emissions resulted from the processing of sector 11 (petroleum, coking, and nuclear fuel) (China Climate Change Info-Net, 2010). Both sectors induced high energy consumption and high CO2 emissions. 3.2. Total and sector CO2 emissions embodied in China–US trade Compared to the relative PPP, the EDR reflects the purchasing power of real wealth from the perspective of emergy (Yang et al., 2010). The CO2 emission intensities of the US sectors were estimated by modifying those of China’s sectors through multiplication with the CO2 emissions ratios (Fi,y) based on the energy used in sectors of China and the US (Shui and Harriss, 2006). The CO2 emissions embodied in imports were calculated in Table 3 with the EDR and CO2 emission intensities of the US sectors. Rapid growth of bilateral trade between China and the US has resulted in an increase of the export-embodied CO2 emissions from 408.49 Mt CO2 in 2002 to 812.01 Mt CO2 in 2007. These export emissions accounted for 11.04% and 12.08% of China’s total CO2 emissions, respectively. Surprisingly, CO2 emissions embodied in imports from the US were much smaller than those embodied in exports to the US. Net exports of embodied CO2 emissions also increased quickly with the growth in CO2 emissions embodied in exports. Therefore, more attention should be paid to controlling CO2 emissions embodied in China’s exports. CO2 emissions embodied in exports grew rapidly from 2002 to 2005, whereas they decreased from 2005 to 2007. Two reasons account for this phenomenon: first, the exchange rate of the RMB against the US dollar declined from 2005 to 2007, which increased the cost of export products and decreased the growth rate of export volume; second, total CO2 emission intensities in all sectors were reduced from 2005 to 2007. Bilateral trade between China and the US is concentrated in 16 sectors. The main sectors of CO2 emissions embodied in exports from 2002, 2005, and 2007 were: sector 8 (manufacture of textile wearing apparel, footware, caps, leather, fur, and feather), accounting for 7.13–11.24% of the total CO2 emissions; sector 12 (the chemical industry), accounting for 5.53–11.42% of the total CO2 emissions; sector 14 (smelting and pressing of metals and the manufacture of metal products), accounting for 9.82–11.46% of the total CO2 emissions; sector 15 (manufacture of machinery), accounting for15.98–21.3% of the total CO2 emissions; and sector 17 (the manufacture of electrical machinery), accounting for 19.97–21.45% of the total CO2 emissions. These embodied emissions are shown in Figs. 1–3. Although the trade volumes of sector 12 and sector 14 were relatively small, high total CO2 emission Table 3 Total CO2 emissions embodied in trade between China and the US. Year
Embodied CO2 emissions (Mt CO2) CO2 emissions exports
2002 408.49 2005 887.93 2007 812.01
CO2 emissions imports
Net exports of CO2 emissions
21.63 24.20 19.33
386.86 863.73 792.68
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Fig. 1. CO2 emissions embodied in China–US trade in 2002.
Fig. 2. CO2 emissions embodied in China–US trade in 2005.
Fig. 3. CO2 emissions embodied in China–US trade in 2007.
intensities resulted in large quantities of embodied CO2 emissions for these two sectors. Thus, CO2 emission intensities of these sectors should be reduced through advances in technology. From 2002 to 2005, embodied CO2 emissions in all sectors’ exports experienced varying degrees of growth. The CO2 emissions embodied in sector 15 (the manufacture of machinery) increased by 198% (highest growth rate). The CO2 emissions embodied in sector 6 (the manufacture and processing of foods, beverages, and tobacco) increased by 36% (the lowest growth rate). Some of the sectors’ embodied CO2 emissions increased from 2005 to 2007, such as sector 6 (the manufacture and processing of foods, beverages, and tobacco) with 26%. In contrast, sector 16 (manufacture of transport equipment) and sector 19 (manufacture of measuring instruments and machinery for cultural activity and office work) decreased notably by 30% and 35%, respectively.
3.3. SDA results The CO2 emissions embodied in China’s exports to the US increased by 479.44 Mt CO2 from 2002 to 2005 and decreased by 75.92 Mt CO2 from 2005 to 2007. The results of the SDA for China’s exports to the US are shown in Fig. 4. During 2002–2007, growth in total volume of exports played the largest role in the increase of CO2 emissions embodied in exports, accounting for 109.17%; changes in intermediate input structure followed behind, accounting for 35.34%. On the other hand, the direct CO2 emission intensity accounted for –43.28% of the changes in embodied CO2 emissions. Shifts in the export structure had little effect on changes in embodied CO2 emissions. From 2002 to 2005, the increased volume of exports was still the largest driving factor behind the increase of embodied CO2
H. Du et al. / Energy Policy 39 (2011) 5980–5987
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Fig. 4. SDA results of changes in CO2 emissions embodied in China’s exports to US.
Table 4 SDA results of changes in embodied CO2 emissions of secondary industry in China–US trade. Change of CO2 emissions (Mt CO2)
Percent
2002–2007
2002–2007
2002–2005
2002–2005
2005–2007
2005–2007
Energy Industry Change of CO2 emissions Direct CO2 emissions intensity Structure of intermediate inputs Structure of exports Total volume of exports
0.70 1.58 1.68 2.57 3.16
2.49 0.78 2.18 2.21 3.29
1.79 0.99 0.40 0.84 0.44
100 227.14 241.89 369.09 454.34
100 31.17 87.71 88.69 132.15
100 55.43 22.28 46.83 24.54
Light Industry Change of CO2 emissions Direct CO2 emissions intensity Structure of intermediate inputs Structure of exports Total volume of exports
98.04 48.81 36.20 5.39 116.05
93.44 19.08 34.35 23.43 101.61
4.60 34.72 2.50 24.29 17.53
100 49.79 36.93 5.50 118.36
100 20.42 36.76 25.08 108.74
100 754.53 54.37 527.87 381.03
Heavy Industry Change of CO2 emissions Direct CO2 emissions intensity Structure of intermediate inputs Structure of exports Total volume of exports
301.49 121.38 101.96 6.97 313.93
378.04 40.95 98.74 25.02 295.22
76.54 109.34 0.94 21.11 52.97
100 40.26 33.82 2.31 104.13
100 10.83 26.12 6.62 78.09
100 142.85 1.23 27.58 69.20
Other Industry Change of CO2 emissions Direct CO2 emissions intensity Structure of intermediate inputs Structure of exports Total volume of exports
1.33 1.56 1.33 1.96 3.53
2.37 0.61 1.12 1.51 3.38
1.04 1.14 0.27 0.66 0.49
100 117.80 100.11 147.89 265.58
100 25.94 47.32 63.90 142.53
100 109.79 26.28 63.14 46.66
emissions, accounting for 84.93%. The negative effect from direct CO2 emission intensity only accounted for 12.95% of the changes in embodied CO2 emissions. During 2005–2007, shifts in intermediate input structure exerted a negative effect on changes in embodied CO2 emissions. The positive effect from the total volume of exports and the negative effect from direct CO2 emission intensity increased in absolute value compared with 2002–2005. CO2 emissions embodied in the primary industries of sector 1 (agriculture, hunting, forestry, and fishing) and the tertiary industries of sector 28 (other services) occupied a small proportion of CO2 emissions embodied in China–US trade, most of which were from secondary industries. These embodied emissions are shown in Figs. 1–3. The secondary industries were separated into four types: Energy Industry (sector 5), Light Industry (sectors 6, 7, 8, 9, and 10), Heavy Industry (sectors 12, 13, 14, 15, 16, 17, and 19), and
Other Industry (sector 20). The SDA results for the four types of industry are shown in Table 4. Heavy Industry had the largest embodied CO2 emissions of the secondary industries, followed by Light Industry. The CO2 emissions embodied in Heavy Industry increased by 301.49 Mt CO2 from 2002 to 2007, promoted by the intermediate input structure (33.82%), the structure of exports (2.31%), and the total volume of exports (104.13%) but hampered by the direct CO2 emissions intensity ( 40.26%). Due to the decrease of direct CO2 emissions intensity and the improvement of export structure between 2005 and 2007, the CO2 emissions embodied in Heavy Industry declined during this time. Outside of the 2005–2007 period, the largest driving factor for the increase of embodied CO2 emissions in Light Industry was the total volume of exports. Shifts in the intermediate input structure increased embodied CO2 emissions by 34.35% from 2002 to 2005 and decreased embodied CO2 emissions by 2.5% from 2005 to 2007.
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The direct CO2 emissions intensity negatively affected embodied CO2 emissions during all periods. The export structure negatively affected embodied CO2 emissions between 2002 and 2005, whereas it positively affected embodied CO2 emissions between 2005 and 2007. The factor effects on changes in embodied CO2 emissions of the Energy Industry during 2002–2005 and 2002–2007 were similar to those of Light Industry during the same period. The CO2 emissions embodied in Energy Industry decreased by 1.79 Mt CO2 from 2005 to 2007. Other than the export volume factor, all factors exerted negative effects on these emissions. The factor effects on changes in embodied CO2 emissions of Other Industry during 2002–2005 and 2002–2007 were the same as those of Light Industry. The embodied CO2 emissions decreased by 1.04 Mt CO2 between 2005 and 2007, promoted by the direct CO2 emissions intensity and the export structure and hampered by the intermediate input structure and the total export volume. Based on the above analyses, the intermediate input structure of the four types of industry should be improved to control CO2 emissions embodied in secondary industries.
4. Uncertainties Although this study has revealed significant net exports of CO2 emissions embodied in China–US trade and the driving factors for changes in CO2 emissions embodied in China’s exports to the US, many uncertainties still exist for estimating embodied CO2 emissions and conducting a decomposition analysis. The different classifications in the input–output tables and the Energy Statistical Yearbook had to be aggregated into 28 sectors, which may have produced some uncertainties (Weber, 2009). To estimate CO2 emissions embodied in imports from the US, the total CO2 emission intensities of the US sectors were calculated by modifying those of China’s sectors through multiplication with the CO2 emissions ratios (Fi,y). The ratio of the US’s EDR to China’s EDR was assumed to be constant due to lack of data, which was also an uncertainty in this study. Although all economic data were held at constant prices for the decomposition analysis, price indices for certain sectors were unavailable and had to be replaced by other price indices. Despite many uncertainties associated with both the data and methodology, bilateral trade data between China and the US were deflated by XPI and MPI, and CO2 emissions embodied in imports were amended by Fi,y and EDR to reduce uncertainties; these trade data and embodied CO2 emissions were expected to be approximate (Dong et al., 2010).
5. Conclusions Bilateral trade between China and the US has increased swiftly with the rapid development of China’s economy. From 2002 to 2007, total trade between the two countries grew from 97.18 to 302.07 billion dollars; during this period, net exports rose to 162.99 billion dollars (National Bureau of Statistics of China, 2008). Although this large trade surplus has resulted in growth of China’s economy, it has also resulted in high energy consumption and high CO2 emissions. In this study, an input–output analysis based on EDR was utilized to calculate direct and total CO2 emission intensities of 28 sectors in China and to estimate CO2 emissions embodied in China–US trade. Furthermore, driving factors for the changes in CO2 emissions embodied in China’s exports to the US were investigated with SDA. The results can be summarized as follows: (1) Direct CO2 emission intensities of most sectors in China decreased gradually from 2002 to 2007 due to advances in
technology. Total CO2 emission intensities of the sectors increased from 2002 to 2005 but decreased from 2005 to 2007 due to changes in CO2 emissions of intermediate inputs. Because of the large consumption of oil and coal, both direct and total CO2 emission intensities of processing petroleum, coking, and nuclear fuel (sector 11) and the production and distribution of electric power and heat power (sector 22) were higher than those of other sectors. (2) CO2 emissions embodied in imports from the US were much smaller than those embodied in exports to the US; that is, China is a net exporter of embodied CO2 emissions in China–US trade. The CO2 emissions embodied in exports grew rapidly from 2002 to 2005, whereas they declined from 2005 to 2007 due to changes in the exchange rate and a reduction in total CO2 emission intensities during 2005–2007. In 2002, 2005, and 2007, the CO2 emissions embodied in exports were concentrated in the following sectors: sector 8 (the manufacture of textile wearing apparel, footware, caps, leather, fur, and feather), sector 12 (the chemical industry), sector 14 (smelting and pressing of metals and manufacture of metal products), sector 15 (manufacture of machinery), and sector 17 (manufacture of electrical machinery and equipment). (3) The total volume of exports was the largest driving factor for the increase of CO2 emissions embodied in China’s exports to the US, followed by the intermediate input structure. Shifts in export structure had little effect on changes in embodied CO2 emissions, whereas the direct CO2 emission intensity had a significant negative effect. (4) Most CO2 emissions embodied in China–US trade originated from secondary industries, particularly Heavy Industry. The CO2 emissions embodied in Heavy Industry were promoted by intermediate input structure (33.82%), export structure (2.31%), and total export volume (104.13%), but were hampered by direct CO2 emissions intensity (40.26%). In all periods, the largest driving factor for the increase of embodied CO2 emissions in Light Industry, Energy Industry, and Other Industry was total export volume; intermediate input structure had a positive effect, whereas direct CO2 emissions intensity and export structure had a negative effect. Policy implications can be derived from these results. A reasonable framework should be established for allocating CO2 emission responsibilities. Much of China’s CO2 emissions were embodied in China’s international trades, and this fact may be neglected by existing climate agreements. Countries that consume products made in China, particularly the US, should share the responsibilities for CO2 emissions and for other greenhouse gas emissions. Sectors with high direct CO2 emissions intensities should enhance their energy efficiency. Moreover, the intermediate input structure also needs improvement to decrease China’s CO2 emissions.
Acknowledgements This research has been supported by the National Natural Sciences Foundation of China (Grant no. 70801042), National Program on Key Basic Research Project (973 Program) (Grant no. 2010CB955502-02), National Major Science and Technology Program of China–Water Body Pollution Control and Remediation (Grant no. 2008ZX07102-001), and Tianjin Science and Technology Key Supporting Fund (Grant no. 09ZCGYSF02200). Thanks to Dr. Marilyn Brown and Xiaojing Sun of the Georgia Institute of Technology’s School of Public Policy for research guidance, and to my host Dr. Shijie Deng of the Georgia Institute of Technology’s H.
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Milton Stewart School of Industrial and Systems Engineering for research support. References Ackerman, F., Ishikawa, M., Suga, M., 2007. The carbon content of Japan–US trade. Energy Policy 35, 4455–4462. Campbell, D.E., Brandt-Williams, S.L., Meisch, M.E.A., 2005. Environmental Accounting Using Emergy: Evaluation of the State of West Virginia. United States Environmental Protection Agency. Chen, G.Q., Chen, Z.M., 2010. Carbon emissions and resources use by Chinese economy 2007: a 135-sector inventory and input–output embodiment. Communications in Nonlinear Science and Numerical Simulation 15, 3647–3732. Cheng, Y.F., Lin, S.J., 2001. Structural decomposition of CO2 emissions from Taiwan’s petrochemical industries. Energy Policy 29 (3), 237–244. China Climate Change Info-Net, 2010-2-23. CO2 Emissions Estimated by Chinese Academy of Sciences According to Industry. /http://www.ccchina.gov.cn/cn/ index.aspS. Dietzenbacher, E., Los, B., 1998. Structural decomposition techniques: sense and sensitivity. Economic Systems Research 10, 307–323. Dong, Y.L., Ishikawa, M., Liu, X.B., Wang, C., 2010. An analysis of the driving forces of CO2 emissions embodied in Japan–China trade. Energy Policy 38, 6784–6792. Eggleston, S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change. Energy Information Administration (EIA), 2008a. Carbon Dioxide Emissions by Sector and Source: 1990 to 2008. /http://www.eia.gov/totalenergy/data/annualS. Energy Information Administration (EIA), 2006b. Documentation for Emissions of Greenhouse Gases in the United States 2006. /http://www.eia.doe.gov/oiaf/ 1605/ggrpt/documentation/pdf/0638(2006).pdfS. Gregg, J., Andres, R., Marland, G., 2008. China: emissions pattern of the world leader in CO2 emissions from fossil fuel consumption and cement production. Geophysical Research Letters 35. Guo, J., Zou, L.L., Wei, Y.M., 2010. Impact of inter-sectoral trade on national and global CO2 emissions: an empirical analysis of China and US. Energy Policy 38, 1389–1397. Jiang, M.M., Zhou, J.B., Chen, B., Chen, G.Q., 2008. Emergy-based ecological account for the Chinese economy in 2004. Communications in Nonlinear Science and Numerical Simulation 13, 2337–2356. Kander, A., Lindmark, M., 2006. Foreign trade and declining pollution in Sweden: a decomposition analysis of long-term structural and technological effects. Energy Policy 34 (13), 1590–1599. Lee, C.F., Lin, S.J., 2001. Structural decomposition of CO2 emissions from Taiwan’s petrochemical industries. Energy Policy 29, 237–244. Leontief, W.W., 1941. The Structure of American Economy, 1919–1929: An Empirical Application of Equilibrium Analysis. Harvard University Press, Cambridge. Leontief, W.W., 1970. Environmental repercussions and the economic structure: an input–output approach. Review of Economic Statistics 52, 262–277. Li, Y., Hewitt, C.N., 2008. The effect of trade between China and the UK on national and global carbon dioxide emissions. Energy Policy 36, 1907–1914. Lin, B.Q., Sun, C.W., 2010. Evaluating carbon dioxide emissions in international trade of China. Energy Policy 38, 613–621. Liu, H.T., Xi, Y.M., Guo, J.E., Li, X., 2010a. Energy embodied in the international trade of China: an energy input–output analysis. Energy Policy 38, 3957–3964.
5987
Liu, Q.Y., Peng, Z.L., 2010. Analysis and Comparable Input–Output Tables of China During 1992–2005. China Statistics Press, Beijing. Liu, X., Ishikawab, M., Wang, C., Dong, Y., Liu, W., 2010b. Analyses of CO2 emissions embodied in Japan–China trade. Energy Policy 38 (3), 1510–1518. Llop, M., 2007. Economic structure and pollution intensity within the environmental input–output framework. Energy Policy 35 (6), 3410–3417. Machado, G., Schaeffer, R., Worrell, E., 2001. Energy and carbon embodied in the international trade of Brazil: an input–output approach. Ecological Economics 39, 409–424. Ma¨enpa¨a¨, I., Siikavirta, H., 2007. Greenhouse gases embodied in the international trade and final consumption of Finland: an input–output analysis. Energy Policy 35, 128–143. Mongelli, I., Tassielli, G., Notarnicola, B., 2006. Global warming agreements, international trade and energy/carbon embodiments: an input–output approach to the Italian case. Energy Policy 34, 88–100. Morrison, W.M., 2011. China–U.S. Trade Issues. Congressional Research Service Report for Congress. Updated January 7. Munksgaard, J., Pade, L.L., Minx, J., Lenzen, M., 2005. Influence of trade on national CO2 emissions. International Journal of Global Energy 23 (4), 324–336. ˜ oz, P., Steininger, K.W., 2010. Austria’s CO2 responsibility and the carbon Mun content of its international trade. Ecological Economics 69, 2003–2019. National Bureau of Statistics of China, 2008. China Trade and External Economic Statistical Yearbook 2008. China Statistics Press. Nobuko, Y., 2004. An analysis of CO2 emissions of Japanese industries during the period between 1985 and 1995. Energy Policy 32 (5), 595–610. Odum, H.T., 1983. System Ecology: An Introduction. John Wiley and Sons, New York. Peters, G.P., Hertwich, E.G., 2006. Pollution embodied in trade: the Norwegian case. Global Environmental Change 16 (4), 379–387. Rhee, H.C., Chung, H.S., 2006. Change in CO2 emission and its transmissions between Korea and Japan Using international input–output analysis. Ecological Economics 58, 788–800. Sa´nchez Cho´liz, J., Duarte, R., 2004. CO2 emissions embodied in international trade: evidence for Spain. Energy Policy 32, 1999–2005. Serrano, M., Dietzenbacher, E., 2010. Responsibility and trade emission balances: an evaluation of approaches. Ecological Economics 69 (11), 2224–2232. Shui, B., Harriss, R.C., 2006. The role of CO2 embodiment in US–China trade. Ecological Economics 34, 4063–4068. Weber, C.L., 2009. Measuring structural change and energy use: decomposition of the US economy from 1997 to 2002. Energy Policy 37, 1561–1570. Weber, C.L., Matthews, H.S., 2007. Embodied environmental emissions in US international trade, 1997–2004. Environmental Science and Technology 41, 4875–4881. Xu, M., Allenby, B., Chen, W.Q., 2009. Energy and air emissions embodied in China– US trade: eastbound assessment using adjusted bilateral trade data. Environmental Science and Technology 43, 3378–3384. Xu, M., Williams, E., Allenby, B., 2010. Assessing environmental impacts embodied in manufacturing and labor input for the China–US trade. Environmental Science and Technology 44, 567–573. Yabe, N., 2004. An analysis of CO2 emissions of Japanese industries during the period between 1985 and 1995. Energy Policy 32, 595–610. Yan, Y.F., Yang, L.K., 2010. China’s foreign trade and climate change: a case study of CO2 emissions. Energy Policy 38, 350–356. Yang, H., Chen, L., Yan, Z.C., Wang, H.L., 2011. Emergy analysis of cassava-based fuel ethanol in China. Biomass and Bioenergy 35, 581–589. Yang, Z.F., Jiang, M.M., Chen, B., Zhou, J.B., Chen, G.Q., Li, S.C., 2010. Solar emergy evaluation for Chinese economy. Energy Policy 38, 875–886.
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Energy Policy journal homepage: www.elsevier.com/locate/enpol
CO2 emissions embodied in China–US trade: Input–output analysis based on the emergy/dollar ratio Huibin Du a,b, Jianghong Guo c, Guozhu Mao c,n, Alexander M. Smith d, Xuxu Wang e, Yuan Wang c a
Institute of Systems Engineering, School of Management, Tianjin University, Tianjin 300072, China H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA c School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China d School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332, USA e College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 January 2011 Accepted 27 June 2011 Available online 29 July 2011
To gain insight into changes in CO2 emissions embodied in China–US trade, an input–output analysis based on the emergy/dollar ratio (EDR) is used to estimate embodied CO2 emissions; a structural decomposition analysis (SDA) is employed to analyze the driving factors for changes in CO2 emissions embodied in China’s exports to the US during 2002–2007. The results of the input–output analysis show that net export of CO2 emissions increased quickly from 2002 to 2005 but decreased from 2005 to 2007. These trends are due to a reduction in total CO2 emission intensity, a decrease in the exchange rate, and small imports of embodied CO2 emissions. The results of the SDA demonstrate that total export volume was the largest driving factor for the increase in embodied CO2 emissions during 2002–2007, followed by intermediate input structure. Direct CO2 emissions intensity had a negative effect on changes in embodied CO2 emissions. The results suggest that China should establish a framework for allocating emission responsibilities, enhance energy efficiency, and improve intermediate input structure. & 2011 Elsevier Ltd. All rights reserved.
Keywords: Input–output analysis Emergy/dollar ratio Structural decomposition analysis
1. Introduction In 2010, China surpassed Japan and became the second-largest economy in the world; since 2006, however, it has become the world’s largest CO2 emitter (Gregg et al., 2008). As ‘‘the world’s factory’’, a large part of China’s economic growth is driven by foreign demand. Although China has an international trade surplus, particularly for trade with the US, environmental consequences of trade such as resource depletion and environmental destruction cannot be ignored. Many of China’s low-end and energy-intensive exports lead to a rise in CO2 emissions (Lin and Sun, 2010). Therefore, basic knowledge about CO2 embodied in international trade is essential. Embodied CO2 emissions can be defined as the total megatons of CO2 emitted during production. The most widely-used approach for studying CO2 embodied in international trade is input–output analysis. Machado et al. (2001) analyzed energy and carbon embodied in Brazilian international trade of 1995 and found that Brazil was a net exporter of energy and carbon embodied in nonenergy goods. Weber and Matthews (2007) quantified embodied environmental emissions in trade between the US and its seven
n
Corresponding author. Tel.: þ86 135 020 786 77; fax: þ 86 22 278 902 59. E-mail address: [email protected] (G. Mao).
0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.06.060
largest trading partners. Their results demonstrated that the net imports of embodied emissions for CO2, SO2, and NOx increased sharply from 1997 to 2004. Lin and Sun (2010) evaluated CO2 emissions embodied in China’s 2005 imports and exports and revealed that electricity generation contributed the most to embodied carbon emissions. CO2 embodied in international trade of ˜oz and Steininger, 2010), Italy countries, such as Australia (Mun (Mongelli et al., 2006), Spain (Sanchez Choliz and Duarte, 2004), Sweden (Kander and Lindmark, 2006), Denmark (Munksgaard et al., 2005), and Norway (Peters and Hertwich, 2006),;have also been studied. Additionally, research has been conducted to investigate CO2 emissions embodied in the bilateral trade between Japan and China (Liu et al., 2010a,b), China and the UK (Li and Hewitt, 2008), Japan and the US (Ackerman et al., 2007), and Korea and Japan (Rhee and Chung, 2006). The input–output analysis (IOA) is used to evaluate the influence of international trade on embodied CO2 emissions. Furthermore, factors analysis of embodied CO2 emissions could yield useful information for an in-depth understanding of the observed changes in embodied CO2 emissions. In recent years, structural decomposition analysis (SDA) built on the input–output model has been developed to analyze the key factors underlying the changes in CO2 emissions at the output level. Cheng and Lin (2001) examined key factors that affected CO2 emission changes in
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Taiwan’s petrochemical industries during 1984–1994 and showed the relative contribution of each factor to industrial CO2 emission changes. Nobuko (2004) identified factors affecting on CO2 emissions in Japanese industries during 1985–1995, finding that changes in both environment and production technology contributed to a decrease in CO2 emissions. Llop (2007) analyzed the changes in Spanish emission multipliers during 1995–2000; she showed that Spain’s economic structure correlates positively with Spain’s emission multipliers, whereas Spain’s production pollution intensities correlate negatively with Spain’s emission multipliers. The US is China’s largest trading partner, and the total bilateral trade volume between these two countries reached 459 billion dollars in 2010 (Morrison, 2011). Moreover, China and the US dominate the world’s CO2 emissions. Hence, there is an obvious need for further studies on CO2 emissions embodied in China–US trade. Despite previous studies on the volume and impact of CO2 emissions in China–US trade (Guo et al., 2010; Shui and Harriss, 2006; Xu et al., 2009, 2010), the driving factors behind changes to these embodied emissions have received little attention. In this study, the emergy/dollar ratio (EDR) was used instead of purchasing power parity (PPP) to represent purchasing power, an input–output analysis was used to quantitatively investigate the direct and total CO2 emission intensities in China’s sectors and CO2 emissions embodied in China–US trade during 2002–2007. Furthermore, SDA was utilized to identify the driving factors behind changes in embodied CO2 emissions.
2. Methods and data 2.1. Input–output analysis The input–output analysis (IOA) developed by Leontief (1941) has been frequently applied to assess energy, CO2 emissions, and other pollutant emissions embodied in international trades (Chen and Chen, 2010; Ma¨enpa¨a¨ and Siikavirta, 2007; Serrano and Dietzenbacher, 2010). The standard input–output model is defined as follows (Leontief, 1970): X ¼ AXþ Y
ð1Þ
where X is the total output vector and Y is the final demand vector. A is a direct input coefficients matrix demonstrating the relationship among all sectors, in which Aij ¼Xij/Xj (i,j¼ 1,2,y,n) is the amount of input from sector i required directly to produce per unit output from sector j. AX represents the intermediate input vector. When solved for total output, Eq. (1) can be represented as X ¼ ðIAÞ1 Y ¼ CY
ð2Þ
where I denotes the identity matrix; C is the Leontief inverse matrix (I–A) 1, in which Cij represents the amount of output from sector i required directly and indirectly to produce per unit final demand from sector j. 2.2. CO2 emission intensity The direct CO2 emission intensity was estimated as ratio of direct CO2 emissions to total inputs:
yi ¼
m X
fk Rk Pik =Xi
ð3Þ
k¼1
where yi is the direct CO2 emission intensity of sector i and yi is the element of the direct CO2 emission intensity row vector h; fk is the CO2 emission factor of energy k (k¼ 1,2,y,m); Rk is the net calorific value of energy k; Pik is the energy k consumption of sector i in physical units, and Xi denotes the total input of sector i in monetary units.
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Total (direct and indirect) CO2 emission intensities are calculated by multiplying the direct CO2 emission intensity row vector, h, by the Leontief inverse matrix, C: M ¼ Ch
ð4Þ
where M is the total CO2 emission intensity row vector. Element Mi is the total CO2 emission intensity of sector i, showing the CO2 emissions for the production of one unit in sector i. 2.3. CO2 emissions embodied in international trade Based on the total CO2 emission intensities, the volume of exports from sector i, and the price index, the CO2 emissions embodied in exports of each sector can be calculated as (Shui and Harriss, 2006) ex ex ex Eex i ¼ Mi Xi ¼ Mi
XPIex i,2002 ex Xi,y XPIex i,y
ð5Þ
where Eex i is the total CO2 emissions embodied in the exports of sector i; Miex is the total CO2 emission intensity of sector i of the exporting country, which was calculated from an input–output table held at constant 2002 prices; XPIex i,y is the export price index (XPI) of sector i in the exporting country for the similar product category of the importing country in year y; XPIex i,2002 is the XPI of sector i in the ex exporting country in the year 2002; Xi,y is the total exports of sector i in year y in monetary units; and Xiex is the total exports of sector i calculated in monetary units at 2002 constant prices. The equation for CO2 emissions embodied in imports is referred to in Shui and Harriss (2006); however, the emergy/dollar ratio (EDR) was used instead of purchasing power parity (PPP) to value the purchasing power of money in a country. The concept of emergy was first presented by Odum in (1983) to fully integrate the values of energy, materials, and information into a common unit of the solar emjoule (abbreviated as sej). The solar emjoule evaluates the contribution of the natural environment to the human economic system through embodied energy synthesis. EDR is the total emergy from all sources in a country divided by its Gross National Product (GNP) and connects economic activities with their supporting energy flows. Many consumer products, such as food, clothing, and fuel, have emergy values. Thus, the division of the emergy by GNP of a country represents the purchasing power of the money in that country (Campbell et al., 2005; Jiang et al., 2008). PPP also represents the purchasing power of money; but it only considers the currency value of commodities, whereas EDR considers both the currency value and environmental value of the commodities. Therefore, the CO2 emissions embodied in imports of each sector can be expressed as follows: ex Eim i ¼ Mi
EDRim y EDRex y
MPIex i,2002 im Xi,y Fi,y MPIex i,y
ð6Þ
where Eim is the total CO2 emissions embodied in the imports of i ex sector i; EDRim y and EDR y are emergy/dollar ratios of the importing country and exporting country in year y, respectively; MPIex i,y is the import price index (MPI) of sector i of the exporting country; MPIex i,2002 is the MPI of sector i in the exporting country in the year im 2002; Xi,y is the total imports of sector i in year y in monetary units; and Fi,y is the ratio of carbon in the energy mix of sector i of the importing country to that of the exporting country’s sectors in year y, which can be written as follows: P im im k ðEnergyi,k,y CEk Þ ð7Þ Fi,y ¼ P ex ex k ðEnergyi,k,y CEk Þ where Energyex i,k,y is CO2 emissions proportion in sector i of the exporting country by energy k in year y; CEex k is the CO2 coefficient of energy k in the exporting country; Energyim i,k,y is the CO2
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emissions proportion in sector i of the importing country by energy k in year y; and CEim k is the CO2 coefficient of energy k in the importing country. For more details, see Shui and Harriss (2006).
Combined with the above analysis, decomposition of CO2 emissions embodied in exports can be expressed as ex ex ex 1 DEex ¼ 14 DhðC1 þ C2 ÞðXex 1 þ X2 Þ þ 4ðh1 þ h2 ÞDCðX1 þ X2 Þ ex ex ex ex 1 þ 14 ðM1 þ M2 ÞDXaex ðHex 1 þ H2 Þ þ 4ðM1 þ M2 ÞðXa1 þ Xa2 ÞDH
2.4. Structural decomposition analysis Many factors, such as trade volume and structure, contribute to shifts in CO2 emissions embodied in exports (Lee and Lin, 2001; Yabe, 2004; Yan and Yang, 2010). In this study, SDA was used to identify major factors leading to fluctuations in the export of CO2 emissions. The CO2 emissions embodied in exports between a base year (1) and a comparative year (2) can be calculated as follows (Liu et al., 2010a,b): ex ex ex DEex ¼ Eex 2 E1 ¼ M2 X2 M1 X1
ð13Þ The first term on the right side of Eq. (13) represents changes in direct CO2 emission intensities. The second term represents changes in the structure of intermediate inputs. The third term represents changes in the structure of exports, and the last term represents changes in total volume of exports. All four terms consider both direct and indirect influences on the CO2 emissions embodied in China’s exports to the US, because they are all multiplied by a Leontief inverse matrix.
ex ex ex ex ¼ ðM2 M1 ÞXex 2 þ M1 ðX2 X1 Þ ¼ DMX2 þ M1 DX ex ex ex ex ¼ ðM2 M1 ÞXex 1 þ M2 ðX2 X1 Þ ¼ DMX1 þ M2 DX
ð8Þ
This structural decomposition is non-unique, and the interaction terms are not included. Changes in one variable determined by n factors will have n! decomposition forms. To solve this problem, the variable DEex in Eq. (8) can be decomposed by two polar forms as follows (Dietzenbacher and Los, 1998): ex ex 1 DEex ¼ 12 DMðXex 1 þ X2 Þ þ 2ðM1 þM2 ÞDX
ð9Þ
Then, changes of total CO2 emission intensities (DM) can be decomposed as follows:
DM ¼ h2 C2 h1 C1 ¼ DhC2 þ h1 DC ¼ DhC1 þ h2 DC ¼ 12 DhðC1 þC2 Þ þ 12ðh1 þ h2 ÞDC
ð10Þ
where Dh represents the change in direct CO2 emission intensities and DC represents the change in the Leontief inverse. Exports are expressed as follows: P ex ex X X X Xex ¼ P i ex ¼ Xiex Hex ¼ Xaex Hex ð11Þ Xi P ex where Xaex ¼ Xi is the total volume of exports and ex ex P ex H ¼ X = Xi is a vector composed of ratios of each sector’s export to the total volume of exports; thus, Hex represents the export structure. Changes in exports can be further separated into the changes of exports’ volume and structure as shown in Eq. (12): ex ex ex ex ex ex ex ex ex ex DXex ¼ Xa2 H2 Xa1 H1 ¼ DXaex Hex 2 þ Xa1 DH ¼ DXa H1 þ Xa2 DH ex ex ex ex ex ¼ 12 DXaex ðH1 þ H2 Þ þ 12ðXa1 þXa2 ÞDH ð12Þ
2.5. Data sources Sources of data used in this study include: (1) Input–output tables were obtained from China’s National Bureau of Statistics for 42 sectors for the years 2002, 2005, and 2007. Because sector classifications in input–output tables are different from those in energy data, all input– output tables were aggregated into the 28 energy consumption sectors as shown in Table 1. Decomposition studies require economic data at constant prices, so 2005 and 2007 input–output tables were held at 2002 constant prices using the price index deflation method. These price indices included: the producer price index of industrial products and agricultural products, build-in project price index, retail price index, price index of fixed assets investment, and real estate price index. EPI and IPI were calculated based on the volume and value of main export and import commodities provided by the China Statistical Yearbook for 2003, 2006, and 2008, using the method recommended by the United Nations Statistics Division (Liu and Peng, 2010). (2) Energy consumption data of China’s 28 sectors were obtained from the energy balance tables provided by the China Energy Statistical Yearbook for 2003, 2006, and 2008. Energy consumption comprised nine types of energy including coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, natural gas, and electricity. The CO2 emission factor and net calorific value of each type of energy used for calculating CO2 emissions were obtained from the IPCC Guidelines for National
Table 1 Sectors and codes. Code Sector
Code Sector
1 2 3 4 5
Agriculture, hunting, forestry, and fishing Mining and washing of coal Extraction of petroleum and natural gas Mining and processing of metal ores Mining and processing of non-metal ores and other ores
15 16 17 18 19
6 7 8
Manufacture and processing of foods, beverages, and tobacco Manufacture of textile Manufacture of textile wearing apparel, footware, caps, leather, fur, and feather Processing of timber and manufacture of furniture Manufacture of paper, printing, articles for culture, education, and sport activity Processing of petroleum, coking, and nuclear fuel Chemical industry Manufacture of non-metallic mineral products Smelting and pressing of metals and manufacture of metal products
20 21 22
Manufacture of machinery Manufacture of transport equipment Manufacture of electrical machinery and equipment Manufacture of communication and other electronic equipment Manufacture of measuring instruments and machinery for cultural activity and office work Manufacture of artwork and other manufacturing Recycling and disposal of waste Production and distribution of electric power and heat power
23 24
Production and distribution of gas Production and distribution of water
25 26 27 28
Construction Transport, storage and post Wholesale, retail trade, and hotel, restaurants Other services
9 10 11 12 13 14
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Greenhouse Gas Inventories (Eggleston et al., 2006; Guo et al., 2010). (3) Bilateral trade data between China and the US were available from the China Trade and External Economic Statistical Yearbook for 2003, 2006, and 2008. Since 1992, China has utilized the harmonized code system to code, classify, and conduct statistical analyses for China’s import and export commodities. The code system includes 22 sections and 98 chapters. Therefore, 98 chapters of trade data were aggregated into 28 sectors according to the National Bureau of Statistics of China’s Explanation and Code for Sector Classification of Input– Output Table of China in 2007. Commodities re-exported to the US are relatively small compared with total trade between China and the US, so they were not considered in this study. (4) CO2 emissions of the US sectors were obtained from Energy Information Administration (EIA, 2008a). CO2 coefficients of China and the US were provided by the IPCC Guidelines for National Greenhouse Gas Inventories and EIA (2006b), respectively. The EDR of China and the US in 2000 were 7.29 1012 sej/$ (Yang et al., 2011) and 1.07 1012 sej/$ (Campbell et al., 2005), respectively. Due to the lack of EDR data for 2002, 2005, and 2007, we assumed that the ratio of the US’s EDR to China’s EDR did not change from 2000 to 2007.
3. Results and discussion 3.1. Sector CO2 emission intensities in China The direct and total CO2 emission intensities for each sector in 2002, 2005, and 2007 were calculated and presented in Table 2 according to Eqs. (3) and (4). The average direct CO2 emission intensity of the 28 sectors decreased from 2002 to 2007; the average total CO2 emission intensity increased from 2002 to 2005, whereas it Table 2 Direct and total CO2 emission intensities of 28 sectors of China. Code
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Average
2002 (t CO2/103 Yuan)
2005 (t CO2/103 Yuan)
2007 (t CO2/103 Yuan)
Direct
Cumulative
Direct
Cumulative
Direct
Cumulative
0.0579 0.5838 0.5718 0.1472 0.1805 0.0750 0.0847 0.0219 0.0282 0.1137 1.3329 0.2577 0.5836 0.4043 0.0419 0.0453 0.0246 0.0139 0.0238 0.1360 0.0000 2.4526 0.9066 0.2296 0.0148 0.1716 0.0340 0.0968 0.2093
0.3203 1.0776 0.9144 0.9187 0.7218 0.4041 0.5876 0.4632 0.5399 0.5975 2.1498 1.0197 1.3040 1.2835 0.7181 0.6511 0.7441 0.5520 0.6181 0.7028 0.0000 2.9793 1.8389 1.0126 0.7189 0.7108 0.3442 0.4003 0.7213
0.0613 0.8056 0.3519 0.2099 0.1507 0.0480 0.0869 0.0182 0.0378 0.1168 1.6465 0.2147 0.3836 0.4935 0.0358 0.0281 0.0178 0.0106 0.0118 0.1115 0.0097 1.6369 0.6164 0.1998 0.0152 0.1554 0.0399 0.0852 0.2040
0.3681 1.7719 1.0038 1.6045 0.9701 0.4030 0.6541 0.4756 0.6209 0.7101 2.6485 1.1197 1.2371 1.6699 0.8207 0.6795 0.8224 0.5618 0.6483 0.7772 0.0097 2.4695 1.5647 1.0920 0.7849 0.7677 0.3110 0.4819 0.8330
0.0627 0.8158 0.2372 0.1822 0.1030 0.0363 0.0709 0.0159 0.0260 0.0957 1.4863 0.1704 0.3001 0.3973 0.0283 0.0184 0.0158 0.0095 0.0122 0.0726 0.0043 1.3770 0.5066 0.2166 0.0119 0.1588 0.0349 0.0798 0.1755
0.3434 1.6066 1.1529 1.5965 0.7407 0.3401 0.5632 0.4192 0.4964 0.5717 2.4934 0.9516 0.9898 1.4748 0.6404 0.5059 0.7415 0.3950 0.4780 0.6677 0.0993 2.6174 1.1264 1.0571 0.7514 0.6521 0.2834 0.3751 0.7457
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decreased from 2005 to 2007. The latter was almost four times higher than the former, indicating that CO2 emissions of intermediate inputs may be significant to the total CO2 emission intensities. Both direct and total CO2 emission intensities in sectors 11 and 22 were higher than those in other sectors, because processing of sector 11 (petroleum and coking) consumes a significant amount of oil. Furthermore, production of sector 22 (electric power and heat power) requires significant amounts of coal. In 2010, the highest CO2 emissions (40.1% of total) originated from the production and distribution of sector 22 (electric power and heat power). 15.7% of China’s total CO2 emissions resulted from the processing of sector 11 (petroleum, coking, and nuclear fuel) (China Climate Change Info-Net, 2010). Both sectors induced high energy consumption and high CO2 emissions. 3.2. Total and sector CO2 emissions embodied in China–US trade Compared to the relative PPP, the EDR reflects the purchasing power of real wealth from the perspective of emergy (Yang et al., 2010). The CO2 emission intensities of the US sectors were estimated by modifying those of China’s sectors through multiplication with the CO2 emissions ratios (Fi,y) based on the energy used in sectors of China and the US (Shui and Harriss, 2006). The CO2 emissions embodied in imports were calculated in Table 3 with the EDR and CO2 emission intensities of the US sectors. Rapid growth of bilateral trade between China and the US has resulted in an increase of the export-embodied CO2 emissions from 408.49 Mt CO2 in 2002 to 812.01 Mt CO2 in 2007. These export emissions accounted for 11.04% and 12.08% of China’s total CO2 emissions, respectively. Surprisingly, CO2 emissions embodied in imports from the US were much smaller than those embodied in exports to the US. Net exports of embodied CO2 emissions also increased quickly with the growth in CO2 emissions embodied in exports. Therefore, more attention should be paid to controlling CO2 emissions embodied in China’s exports. CO2 emissions embodied in exports grew rapidly from 2002 to 2005, whereas they decreased from 2005 to 2007. Two reasons account for this phenomenon: first, the exchange rate of the RMB against the US dollar declined from 2005 to 2007, which increased the cost of export products and decreased the growth rate of export volume; second, total CO2 emission intensities in all sectors were reduced from 2005 to 2007. Bilateral trade between China and the US is concentrated in 16 sectors. The main sectors of CO2 emissions embodied in exports from 2002, 2005, and 2007 were: sector 8 (manufacture of textile wearing apparel, footware, caps, leather, fur, and feather), accounting for 7.13–11.24% of the total CO2 emissions; sector 12 (the chemical industry), accounting for 5.53–11.42% of the total CO2 emissions; sector 14 (smelting and pressing of metals and the manufacture of metal products), accounting for 9.82–11.46% of the total CO2 emissions; sector 15 (manufacture of machinery), accounting for15.98–21.3% of the total CO2 emissions; and sector 17 (the manufacture of electrical machinery), accounting for 19.97–21.45% of the total CO2 emissions. These embodied emissions are shown in Figs. 1–3. Although the trade volumes of sector 12 and sector 14 were relatively small, high total CO2 emission Table 3 Total CO2 emissions embodied in trade between China and the US. Year
Embodied CO2 emissions (Mt CO2) CO2 emissions exports
2002 408.49 2005 887.93 2007 812.01
CO2 emissions imports
Net exports of CO2 emissions
21.63 24.20 19.33
386.86 863.73 792.68
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Fig. 1. CO2 emissions embodied in China–US trade in 2002.
Fig. 2. CO2 emissions embodied in China–US trade in 2005.
Fig. 3. CO2 emissions embodied in China–US trade in 2007.
intensities resulted in large quantities of embodied CO2 emissions for these two sectors. Thus, CO2 emission intensities of these sectors should be reduced through advances in technology. From 2002 to 2005, embodied CO2 emissions in all sectors’ exports experienced varying degrees of growth. The CO2 emissions embodied in sector 15 (the manufacture of machinery) increased by 198% (highest growth rate). The CO2 emissions embodied in sector 6 (the manufacture and processing of foods, beverages, and tobacco) increased by 36% (the lowest growth rate). Some of the sectors’ embodied CO2 emissions increased from 2005 to 2007, such as sector 6 (the manufacture and processing of foods, beverages, and tobacco) with 26%. In contrast, sector 16 (manufacture of transport equipment) and sector 19 (manufacture of measuring instruments and machinery for cultural activity and office work) decreased notably by 30% and 35%, respectively.
3.3. SDA results The CO2 emissions embodied in China’s exports to the US increased by 479.44 Mt CO2 from 2002 to 2005 and decreased by 75.92 Mt CO2 from 2005 to 2007. The results of the SDA for China’s exports to the US are shown in Fig. 4. During 2002–2007, growth in total volume of exports played the largest role in the increase of CO2 emissions embodied in exports, accounting for 109.17%; changes in intermediate input structure followed behind, accounting for 35.34%. On the other hand, the direct CO2 emission intensity accounted for –43.28% of the changes in embodied CO2 emissions. Shifts in the export structure had little effect on changes in embodied CO2 emissions. From 2002 to 2005, the increased volume of exports was still the largest driving factor behind the increase of embodied CO2
H. Du et al. / Energy Policy 39 (2011) 5980–5987
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Fig. 4. SDA results of changes in CO2 emissions embodied in China’s exports to US.
Table 4 SDA results of changes in embodied CO2 emissions of secondary industry in China–US trade. Change of CO2 emissions (Mt CO2)
Percent
2002–2007
2002–2007
2002–2005
2002–2005
2005–2007
2005–2007
Energy Industry Change of CO2 emissions Direct CO2 emissions intensity Structure of intermediate inputs Structure of exports Total volume of exports
0.70 1.58 1.68 2.57 3.16
2.49 0.78 2.18 2.21 3.29
1.79 0.99 0.40 0.84 0.44
100 227.14 241.89 369.09 454.34
100 31.17 87.71 88.69 132.15
100 55.43 22.28 46.83 24.54
Light Industry Change of CO2 emissions Direct CO2 emissions intensity Structure of intermediate inputs Structure of exports Total volume of exports
98.04 48.81 36.20 5.39 116.05
93.44 19.08 34.35 23.43 101.61
4.60 34.72 2.50 24.29 17.53
100 49.79 36.93 5.50 118.36
100 20.42 36.76 25.08 108.74
100 754.53 54.37 527.87 381.03
Heavy Industry Change of CO2 emissions Direct CO2 emissions intensity Structure of intermediate inputs Structure of exports Total volume of exports
301.49 121.38 101.96 6.97 313.93
378.04 40.95 98.74 25.02 295.22
76.54 109.34 0.94 21.11 52.97
100 40.26 33.82 2.31 104.13
100 10.83 26.12 6.62 78.09
100 142.85 1.23 27.58 69.20
Other Industry Change of CO2 emissions Direct CO2 emissions intensity Structure of intermediate inputs Structure of exports Total volume of exports
1.33 1.56 1.33 1.96 3.53
2.37 0.61 1.12 1.51 3.38
1.04 1.14 0.27 0.66 0.49
100 117.80 100.11 147.89 265.58
100 25.94 47.32 63.90 142.53
100 109.79 26.28 63.14 46.66
emissions, accounting for 84.93%. The negative effect from direct CO2 emission intensity only accounted for 12.95% of the changes in embodied CO2 emissions. During 2005–2007, shifts in intermediate input structure exerted a negative effect on changes in embodied CO2 emissions. The positive effect from the total volume of exports and the negative effect from direct CO2 emission intensity increased in absolute value compared with 2002–2005. CO2 emissions embodied in the primary industries of sector 1 (agriculture, hunting, forestry, and fishing) and the tertiary industries of sector 28 (other services) occupied a small proportion of CO2 emissions embodied in China–US trade, most of which were from secondary industries. These embodied emissions are shown in Figs. 1–3. The secondary industries were separated into four types: Energy Industry (sector 5), Light Industry (sectors 6, 7, 8, 9, and 10), Heavy Industry (sectors 12, 13, 14, 15, 16, 17, and 19), and
Other Industry (sector 20). The SDA results for the four types of industry are shown in Table 4. Heavy Industry had the largest embodied CO2 emissions of the secondary industries, followed by Light Industry. The CO2 emissions embodied in Heavy Industry increased by 301.49 Mt CO2 from 2002 to 2007, promoted by the intermediate input structure (33.82%), the structure of exports (2.31%), and the total volume of exports (104.13%) but hampered by the direct CO2 emissions intensity ( 40.26%). Due to the decrease of direct CO2 emissions intensity and the improvement of export structure between 2005 and 2007, the CO2 emissions embodied in Heavy Industry declined during this time. Outside of the 2005–2007 period, the largest driving factor for the increase of embodied CO2 emissions in Light Industry was the total volume of exports. Shifts in the intermediate input structure increased embodied CO2 emissions by 34.35% from 2002 to 2005 and decreased embodied CO2 emissions by 2.5% from 2005 to 2007.
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The direct CO2 emissions intensity negatively affected embodied CO2 emissions during all periods. The export structure negatively affected embodied CO2 emissions between 2002 and 2005, whereas it positively affected embodied CO2 emissions between 2005 and 2007. The factor effects on changes in embodied CO2 emissions of the Energy Industry during 2002–2005 and 2002–2007 were similar to those of Light Industry during the same period. The CO2 emissions embodied in Energy Industry decreased by 1.79 Mt CO2 from 2005 to 2007. Other than the export volume factor, all factors exerted negative effects on these emissions. The factor effects on changes in embodied CO2 emissions of Other Industry during 2002–2005 and 2002–2007 were the same as those of Light Industry. The embodied CO2 emissions decreased by 1.04 Mt CO2 between 2005 and 2007, promoted by the direct CO2 emissions intensity and the export structure and hampered by the intermediate input structure and the total export volume. Based on the above analyses, the intermediate input structure of the four types of industry should be improved to control CO2 emissions embodied in secondary industries.
4. Uncertainties Although this study has revealed significant net exports of CO2 emissions embodied in China–US trade and the driving factors for changes in CO2 emissions embodied in China’s exports to the US, many uncertainties still exist for estimating embodied CO2 emissions and conducting a decomposition analysis. The different classifications in the input–output tables and the Energy Statistical Yearbook had to be aggregated into 28 sectors, which may have produced some uncertainties (Weber, 2009). To estimate CO2 emissions embodied in imports from the US, the total CO2 emission intensities of the US sectors were calculated by modifying those of China’s sectors through multiplication with the CO2 emissions ratios (Fi,y). The ratio of the US’s EDR to China’s EDR was assumed to be constant due to lack of data, which was also an uncertainty in this study. Although all economic data were held at constant prices for the decomposition analysis, price indices for certain sectors were unavailable and had to be replaced by other price indices. Despite many uncertainties associated with both the data and methodology, bilateral trade data between China and the US were deflated by XPI and MPI, and CO2 emissions embodied in imports were amended by Fi,y and EDR to reduce uncertainties; these trade data and embodied CO2 emissions were expected to be approximate (Dong et al., 2010).
5. Conclusions Bilateral trade between China and the US has increased swiftly with the rapid development of China’s economy. From 2002 to 2007, total trade between the two countries grew from 97.18 to 302.07 billion dollars; during this period, net exports rose to 162.99 billion dollars (National Bureau of Statistics of China, 2008). Although this large trade surplus has resulted in growth of China’s economy, it has also resulted in high energy consumption and high CO2 emissions. In this study, an input–output analysis based on EDR was utilized to calculate direct and total CO2 emission intensities of 28 sectors in China and to estimate CO2 emissions embodied in China–US trade. Furthermore, driving factors for the changes in CO2 emissions embodied in China’s exports to the US were investigated with SDA. The results can be summarized as follows: (1) Direct CO2 emission intensities of most sectors in China decreased gradually from 2002 to 2007 due to advances in
technology. Total CO2 emission intensities of the sectors increased from 2002 to 2005 but decreased from 2005 to 2007 due to changes in CO2 emissions of intermediate inputs. Because of the large consumption of oil and coal, both direct and total CO2 emission intensities of processing petroleum, coking, and nuclear fuel (sector 11) and the production and distribution of electric power and heat power (sector 22) were higher than those of other sectors. (2) CO2 emissions embodied in imports from the US were much smaller than those embodied in exports to the US; that is, China is a net exporter of embodied CO2 emissions in China–US trade. The CO2 emissions embodied in exports grew rapidly from 2002 to 2005, whereas they declined from 2005 to 2007 due to changes in the exchange rate and a reduction in total CO2 emission intensities during 2005–2007. In 2002, 2005, and 2007, the CO2 emissions embodied in exports were concentrated in the following sectors: sector 8 (the manufacture of textile wearing apparel, footware, caps, leather, fur, and feather), sector 12 (the chemical industry), sector 14 (smelting and pressing of metals and manufacture of metal products), sector 15 (manufacture of machinery), and sector 17 (manufacture of electrical machinery and equipment). (3) The total volume of exports was the largest driving factor for the increase of CO2 emissions embodied in China’s exports to the US, followed by the intermediate input structure. Shifts in export structure had little effect on changes in embodied CO2 emissions, whereas the direct CO2 emission intensity had a significant negative effect. (4) Most CO2 emissions embodied in China–US trade originated from secondary industries, particularly Heavy Industry. The CO2 emissions embodied in Heavy Industry were promoted by intermediate input structure (33.82%), export structure (2.31%), and total export volume (104.13%), but were hampered by direct CO2 emissions intensity (40.26%). In all periods, the largest driving factor for the increase of embodied CO2 emissions in Light Industry, Energy Industry, and Other Industry was total export volume; intermediate input structure had a positive effect, whereas direct CO2 emissions intensity and export structure had a negative effect. Policy implications can be derived from these results. A reasonable framework should be established for allocating CO2 emission responsibilities. Much of China’s CO2 emissions were embodied in China’s international trades, and this fact may be neglected by existing climate agreements. Countries that consume products made in China, particularly the US, should share the responsibilities for CO2 emissions and for other greenhouse gas emissions. Sectors with high direct CO2 emissions intensities should enhance their energy efficiency. Moreover, the intermediate input structure also needs improvement to decrease China’s CO2 emissions.
Acknowledgements This research has been supported by the National Natural Sciences Foundation of China (Grant no. 70801042), National Program on Key Basic Research Project (973 Program) (Grant no. 2010CB955502-02), National Major Science and Technology Program of China–Water Body Pollution Control and Remediation (Grant no. 2008ZX07102-001), and Tianjin Science and Technology Key Supporting Fund (Grant no. 09ZCGYSF02200). Thanks to Dr. Marilyn Brown and Xiaojing Sun of the Georgia Institute of Technology’s School of Public Policy for research guidance, and to my host Dr. Shijie Deng of the Georgia Institute of Technology’s H.
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Milton Stewart School of Industrial and Systems Engineering for research support. References Ackerman, F., Ishikawa, M., Suga, M., 2007. The carbon content of Japan–US trade. Energy Policy 35, 4455–4462. Campbell, D.E., Brandt-Williams, S.L., Meisch, M.E.A., 2005. Environmental Accounting Using Emergy: Evaluation of the State of West Virginia. United States Environmental Protection Agency. Chen, G.Q., Chen, Z.M., 2010. Carbon emissions and resources use by Chinese economy 2007: a 135-sector inventory and input–output embodiment. Communications in Nonlinear Science and Numerical Simulation 15, 3647–3732. Cheng, Y.F., Lin, S.J., 2001. Structural decomposition of CO2 emissions from Taiwan’s petrochemical industries. Energy Policy 29 (3), 237–244. China Climate Change Info-Net, 2010-2-23. CO2 Emissions Estimated by Chinese Academy of Sciences According to Industry. /http://www.ccchina.gov.cn/cn/ index.aspS. Dietzenbacher, E., Los, B., 1998. Structural decomposition techniques: sense and sensitivity. Economic Systems Research 10, 307–323. Dong, Y.L., Ishikawa, M., Liu, X.B., Wang, C., 2010. An analysis of the driving forces of CO2 emissions embodied in Japan–China trade. Energy Policy 38, 6784–6792. Eggleston, S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change. Energy Information Administration (EIA), 2008a. Carbon Dioxide Emissions by Sector and Source: 1990 to 2008. /http://www.eia.gov/totalenergy/data/annualS. Energy Information Administration (EIA), 2006b. Documentation for Emissions of Greenhouse Gases in the United States 2006. /http://www.eia.doe.gov/oiaf/ 1605/ggrpt/documentation/pdf/0638(2006).pdfS. Gregg, J., Andres, R., Marland, G., 2008. China: emissions pattern of the world leader in CO2 emissions from fossil fuel consumption and cement production. Geophysical Research Letters 35. Guo, J., Zou, L.L., Wei, Y.M., 2010. Impact of inter-sectoral trade on national and global CO2 emissions: an empirical analysis of China and US. Energy Policy 38, 1389–1397. Jiang, M.M., Zhou, J.B., Chen, B., Chen, G.Q., 2008. Emergy-based ecological account for the Chinese economy in 2004. Communications in Nonlinear Science and Numerical Simulation 13, 2337–2356. Kander, A., Lindmark, M., 2006. Foreign trade and declining pollution in Sweden: a decomposition analysis of long-term structural and technological effects. Energy Policy 34 (13), 1590–1599. Lee, C.F., Lin, S.J., 2001. Structural decomposition of CO2 emissions from Taiwan’s petrochemical industries. Energy Policy 29, 237–244. Leontief, W.W., 1941. The Structure of American Economy, 1919–1929: An Empirical Application of Equilibrium Analysis. Harvard University Press, Cambridge. Leontief, W.W., 1970. Environmental repercussions and the economic structure: an input–output approach. Review of Economic Statistics 52, 262–277. Li, Y., Hewitt, C.N., 2008. The effect of trade between China and the UK on national and global carbon dioxide emissions. Energy Policy 36, 1907–1914. Lin, B.Q., Sun, C.W., 2010. Evaluating carbon dioxide emissions in international trade of China. Energy Policy 38, 613–621. Liu, H.T., Xi, Y.M., Guo, J.E., Li, X., 2010a. Energy embodied in the international trade of China: an energy input–output analysis. Energy Policy 38, 3957–3964.
5987
Liu, Q.Y., Peng, Z.L., 2010. Analysis and Comparable Input–Output Tables of China During 1992–2005. China Statistics Press, Beijing. Liu, X., Ishikawab, M., Wang, C., Dong, Y., Liu, W., 2010b. Analyses of CO2 emissions embodied in Japan–China trade. Energy Policy 38 (3), 1510–1518. Llop, M., 2007. Economic structure and pollution intensity within the environmental input–output framework. Energy Policy 35 (6), 3410–3417. Machado, G., Schaeffer, R., Worrell, E., 2001. Energy and carbon embodied in the international trade of Brazil: an input–output approach. Ecological Economics 39, 409–424. Ma¨enpa¨a¨, I., Siikavirta, H., 2007. Greenhouse gases embodied in the international trade and final consumption of Finland: an input–output analysis. Energy Policy 35, 128–143. Mongelli, I., Tassielli, G., Notarnicola, B., 2006. Global warming agreements, international trade and energy/carbon embodiments: an input–output approach to the Italian case. Energy Policy 34, 88–100. Morrison, W.M., 2011. China–U.S. Trade Issues. Congressional Research Service Report for Congress. Updated January 7. Munksgaard, J., Pade, L.L., Minx, J., Lenzen, M., 2005. Influence of trade on national CO2 emissions. International Journal of Global Energy 23 (4), 324–336. ˜ oz, P., Steininger, K.W., 2010. Austria’s CO2 responsibility and the carbon Mun content of its international trade. Ecological Economics 69, 2003–2019. National Bureau of Statistics of China, 2008. China Trade and External Economic Statistical Yearbook 2008. China Statistics Press. Nobuko, Y., 2004. An analysis of CO2 emissions of Japanese industries during the period between 1985 and 1995. Energy Policy 32 (5), 595–610. Odum, H.T., 1983. System Ecology: An Introduction. John Wiley and Sons, New York. Peters, G.P., Hertwich, E.G., 2006. Pollution embodied in trade: the Norwegian case. Global Environmental Change 16 (4), 379–387. Rhee, H.C., Chung, H.S., 2006. Change in CO2 emission and its transmissions between Korea and Japan Using international input–output analysis. Ecological Economics 58, 788–800. Sa´nchez Cho´liz, J., Duarte, R., 2004. CO2 emissions embodied in international trade: evidence for Spain. Energy Policy 32, 1999–2005. Serrano, M., Dietzenbacher, E., 2010. Responsibility and trade emission balances: an evaluation of approaches. Ecological Economics 69 (11), 2224–2232. Shui, B., Harriss, R.C., 2006. The role of CO2 embodiment in US–China trade. Ecological Economics 34, 4063–4068. Weber, C.L., 2009. Measuring structural change and energy use: decomposition of the US economy from 1997 to 2002. Energy Policy 37, 1561–1570. Weber, C.L., Matthews, H.S., 2007. Embodied environmental emissions in US international trade, 1997–2004. Environmental Science and Technology 41, 4875–4881. Xu, M., Allenby, B., Chen, W.Q., 2009. Energy and air emissions embodied in China– US trade: eastbound assessment using adjusted bilateral trade data. Environmental Science and Technology 43, 3378–3384. Xu, M., Williams, E., Allenby, B., 2010. Assessing environmental impacts embodied in manufacturing and labor input for the China–US trade. Environmental Science and Technology 44, 567–573. Yabe, N., 2004. An analysis of CO2 emissions of Japanese industries during the period between 1985 and 1995. Energy Policy 32, 595–610. Yan, Y.F., Yang, L.K., 2010. China’s foreign trade and climate change: a case study of CO2 emissions. Energy Policy 38, 350–356. Yang, H., Chen, L., Yan, Z.C., Wang, H.L., 2011. Emergy analysis of cassava-based fuel ethanol in China. Biomass and Bioenergy 35, 581–589. Yang, Z.F., Jiang, M.M., Chen, B., Zhou, J.B., Chen, G.Q., Li, S.C., 2010. Solar emergy evaluation for Chinese economy. Energy Policy 38, 875–886.