Imbalance of carbon embodied in South-South trade: Evidence from China-India trade

Science of the Total Environment 707 (2020) 134473

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Imbalance of carbon embodied in South-South trade: Evidence from China-India trade Qiang Wang a,b,⇑, Xue Yang a,b a b

School of Economics and Management, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China Institute for Energy Economics and Policy, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China

g r a p h i c a l a b s t r a c t

a r t i c l e

i n f o

Article history: Received 7 August 2019 Received in revised form 13 September 2019 Accepted 14 September 2019 Available online 10 December 2019 Editor: Huu Hao Ngo Keywords: South-South trade China-India trade Embodied carbon emissions Multi-regional input–output model Structural decomposition analysis

a b s t r a c t China and India are the countries with the largest increase in carbon emissions and one of the fastest growing economies in the world. A better understanding of the carbon emissions embodied in ChinaIndia trade can service to curb carbon emission in both countries. In this work, we investigated temporal change and driving forces of the carbon emissions embodied in China-India trade from 2000 to 2015 using the Multi-Regional Input-Output model and Structural Decomposition Analysis. The results showed China was a net exporter of embodied carbon and a net exporter of trade in China-India trade, which indicated that China increased its environmental costs while gaining economic benefits. And the imbalance in China’s embodied carbon trade was far greater than the trade imbalance. The industrial structure of China’s export of embodied carbon and India’s export of embodied carbon were difference, although electricity and heavy manufacturing industries dominated the embodied carbon exports of China and India. The decomposition results showed the leading contributor to increase in the embodied carbon emissions of China and India was the increase in final demand, in which the effect of per capita demand was the main driving factor affecting the change of embodied carbon emissions. The carbon intensity coefficient effect was the driving factor in suppressing the increase in embodied carbon emissions in China and India. This research could enrich the study of carbon emission embodied in South-South trade. Ó 2019 Elsevier B.V. All rights reserved.

1. Introduction ⇑ Corresponding author at: School of Economics and Management, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China. E-mail address: [email protected] (Q. Wang). https://doi.org/10.1016/j.scitotenv.2019.134473 0048-9697/Ó 2019 Elsevier B.V. All rights reserved.

The rapid development of international trade has made it one of the main drivers of carbon transfer (Peters et al., 2011; Zhong et al.,

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Q. Wang, X. Yang / Science of the Total Environment 707 (2020) 134473

2018) and carbon emissions growth (Aichele and Felbermayr, 2012; Andrew et al., 2013; Peters et al., 2012), contributing about 30% of global carbon emissions (Davis et al., 2011; Meng et al., 2018a). Therefore, the role of international trade in environmental issues has received increasing attention from scholars (FernándezAmador et al., 2016; Herrmann and Hauschild, 2009; Jiborn et al., 2018; Sakai and Barrett, 2016; Yang et al., 2016). Studies have shown that international trade flows have caused carbon emissions to flow and transfer continuously on a global scale, bringing enormous environmental pressures to relevant countries, especially developing countries (Ertugrul et al., 2016; Ren et al., 2014; Zhang and Zhang, 2018). Most of the previous research focused on carbon emissions in trade between developed and developing countries. The results showed that most developing countries undertake a large amount of carbon emissions for developed countries in the trade process (Davis et al., 2011; Steen-Olsen et al., 2012), so trade between developed and developing countries (also known as North-South trade) has also become a way for developed countries to avoid carbon emissions (Carvalho et al., 2013; Duan et al., 2018; Su and Ang, 2014b; Zhang et al., 2017b). However, it is worth noting that since 2008, the scale of trade between developing countries (also known as South-South trade) has expanded, accounting for about 60% of the total exports of all developing countries (Meng et al., 2018b). Thus, the carbon emissions embodied in the huge trade volume of developing countries have a huge impact on global carbon emissions (Kim et al., 2019; Mi et al., 2017; Peng et al., 2016), but there is relatively little research on the impact of trade between developing countries on carbon emissions. As the main developing country in the world, coal is the main energy in China and India. China and India contributed about 35% of global carbon emissions, which had a significant impact on global carbon emissions (Govindaraju and Tang, 2013), and were expected to have an increasing impact in the future (Garg and Shukla, 2009; Parikh and Parikh, 2011; Rout et al., 2011). At present, China has become India’s largest trading partner, and the economic and trade cooperation between the two countries has reached a new height. Meanwhile, the trade volume between China and India has increased rapidly in recent years, from 40.73 billion dollars in 2008 to 90.27 billion dollars in 2018. According to World Bank data, the growth rate of China-India trade volume far exceeds the growth rate of trade between China and other trading countries. And because China and India had strong economic complementarities, China-India trade will continue to maintain a momentum of rapid development (Yuan, 2016). Economic growth has brought about an increase in carbon emissions to some extent. Therefore, the rapid development of China-India trade has accelerated the redistribution of carbon emissions, which has brought enormous pressure on emission reductions between the two countries. How to reduce carbon emissions from China and India in China-India trade has become a problem that the two countries need to solve. Therefore, this paper choosed carbon emissions in China-India trade as the research object, and deeply analysed the impact of carbon emissions embodied in bilateral trade between China and India on carbon emissions in China, India and even the world. This is conducive to the coordinated development of the two countries and the realization of emission reduction. Under the above background, the main purposes of this paper are: (1) calculating and analyzing the characteristics of carbon emissions embodied in China-India trade, analyzing the gains and losses of China-India trade from various perspectives; (2) analyzing the transfer of carbon emissions in China-India trade from a sectoral perspective, and comparing the export industrial structure of China and India in China-India trade; (3) exploring the factors behind the changes in carbon emissions in China-India trade, so as to provide

reference value for the establishment of emission reduction policies in China and India. The rest of the arrangements in this study are as follows. Section 2 is a brief review and research progress on carbon emissions research. Section 3 describes the calculation methods and data sources; Section 4 describes and analyzes the carbon emissions in China-India trade, and further discusses and analyzes the impact of drivers on carbon emissions in China-India trade. Finally, Section 5 summarizes the findings of this study.

2. Literature review Carbon emissions have become a common concern worldwide. Because of the promotion of trade, most of the research on carbon emissions was focused on carbon emissions in trade activities (Minx et al., 2009; Prell and Feng, 2016; Shahbaz et al., 2017). Carbon emissions embodied in trade was proposed to quantify direct or indirect carbon emissions in the trade process of goods and services, which was an effective means to assess carbon transfer (Ackerman et al., 2007; Peters, 2010; Wiedmann, 2016). Scholars used the concept of carbon emissions embodied in trade to study the carbon transfer between different trading partners, and put forward corresponding suggestions on the division of responsibility for carbon emission reduction. Carbon leakage caused by trade between developed and developing countries has always been one of the focuses of research on carbon emissions embodied in trade (Liu et al., 2017; Peters and Hertwich, 2006; Peters and Hertwich, 2008). Davis and Caldeira (Davis and Caldeira, 2010) pointed out that 23% of carbon emissions from developing countries and other emerging economies were to meet demand from developed countries. Chen and Chen (Chen and Chen, 2011) analyzed the carbon trade imbalance with G7 and BRICS, which further confirmed the phenomenon of carbon emissions transfer. Ertugrul et al. (Ertugrul et al., 2016) proved that trade liberalization increased carbon emissions in Turkey, India, China and Indonesia, and turned them into pollution-havens in developed countries. The research results illustrated the transfer of carbon emissions from developed countries to developing countries through international trade. In order to further explain the carbon transfer between different countries in international trade, many scholars analyzed the carbon emissions embodied in a country’s foreign trade (Liu and Ma, 2011; Machado et al., 2001) and in bilateral trade (Ding et al., 2018; Long et al., 2018). Especially for China, the world’s largest carbon emitter, nearly one-third of carbon emissions were related to foreign trade (Liu et al., 2016b; Zhang et al., 2017a). Therefore, the embodied carbon emissions in China’s export trade attracted widespread attention from scholars (Feng et al., 2013; Liu et al., 2016a; Yuan and Zhao, 2016). Weber et al. (Weber et al., 2008) pointed out that exports were the main reason for China’s rapid increase in carbon emissions, and the consumption in developed countries may be promoting this trend (Minx et al., 2011). Lin and Sun (Lin and Sun, 2010) pointed out that China had carbon leakage in current international trade rules, and carbon emissions in China’s export trade were constantly increasing. Andersson (Andersson, 2018) also confirmed this. Arce et al. (Arce et al., 2016) pointed out that China’s higher carbon emission coefficient than the major trading countries made China have a huge carbon trade surplus. Changes in carbon emissions in China’s bilateral trade have also attracted much attention, but for trading partners in bilateral trade, the targets were concentrated in the United States, Japan, Australia, South Korea, the EU and other developed countries (more details in Table A1 of Appendix A). Zhao et al. (Zhao et al., 2016b) pointed out that the trade structure was the

Q. Wang, X. Yang / Science of the Total Environment 707 (2020) 134473

reason for the increase of carbon emissions from China’s exports to the United States in China-US trade. Liu et al. (Liu et al., 2010) revealed that the embodied carbon emissions from China’s exports to Japan increased significantly during the study period. Yang et al. (Yang et al., 2011) showed that there was a huge carbon trade imbalance in China-EU trade. Wang et al. (Wang et al., 2019b) analyzed the carbon emissions embodied in China-Australian bilateral trade from 2000 to 2014, and conducted a scenario analysis. China was in a position as a net exporter of embodied carbon emissions in bilateral trade with these developed countries, which have transferred large amounts of carbon emissions to China through trade (Cai et al., 2018; Qi et al., 2014). For India, with the gradual transfer of low-value-added products to developing countries such as India, India’s emissions continued to grow rapidly in recent years (Garg et al., 2017; Wang and Song, 2019). However, compared with China, there was insufficient attention to carbon emissions in India’s foreign trade (Zhu et al., 2018). Mukhopadhyay (Mukhopadhyay, 2004) used the input–output table to reveal that India was a net carbon importer in 1993–1994. Mukhhopadhyay et al. (Mukhopadhyay et al., 2005) analyzed whether India was a pollution refuge and indicated that India achieved considerable gains in the trade process. Nguyen et al. (Nguyen et al., 2017) showed that trade openness did not make a significant contribution to India’s carbon emissions and energy growth. But with the increase in export trade, research showed that the impact of trade on India’s carbon emissions is becoming more and more obvious. Tiwari et al. (Tiwari et al., 2013) revealed that trade liberalization contributes to India’s carbon emissions, as evidenced by Yang and Zhao’s findings(Yang and Zhao, 2014). Nakano et al.(Nakano et al., 2009) pointed out that India became one of the major carbon trade surplus countries since 2000, and the increase in trade intensity increased the carbon emissions embodied in trade. As international trade externalizes environmental burdens, Bruckner et al. (Bruckner et al., 2010) accounted for the extent of carbon leakage between OECD countries and India between 1995 and 2005. Embodied carbon in India’s trade was mainly exported to developed countries, but the main source of imported carbon emissions was developing countries (mainly from China) (Wang et al., 2018). Wiebe et al. (Wiebe et al., 2012) analyzed the carbon emissions embodied in India’s foreign trade in 2005, pointing out that net carbon emissions were increasing. Deng and Xu (Deng and Xu, 2017) showed that carbon emissions in India’s foreign trade grew faster in 1995–2009, and final demand was the main driver. In particular, the research on India focuses on foreign trade, but there is no in-depth study on the carbon emission flow in India’s bilateral trade. In the study of embodied carbon in trade, whether in China or India, most of them were related to developed countries, while few were related to developing countries (Wang et al., 2020; Wang and Zhou, 2019). But with China’s Belt and Road development and the support of the Asian infrastructure investment bank to developing countries, the trade scale between developing countries will continue to grow strongly in the future (Hannam et al., 2015; Meng et al., 2018b; Zhai, 2018). However, there is a lack of research on the embodied carbon characteristics of trade between developing countries, especially China-India trade. Therefore, this paper focuses on carbon emissions embodied in China-India trade. To some extent, this is representative in exploring carbon transfer between developing countries. Input-output method is the most commonly used method to quantify carbon emissions (Brizga et al., 2017; Pablo-Romero and Sánchez-Braza, 2017). Among them, multi-regional input–output (MRIO) model tracks carbon emissions transfer and flows through multi-national trade links, taking into account the technical factors of different countries, and is an effective method to quantify carbon emissions in national trade (Usubiaga and Acosta-Fernández,

3

2015; Zhu et al., 2018). For example, Andrew and Peters (Andrew and Peters, 2013) build a multi-region input–output table to estimate carbon footprint. Su and Ang (Su and Ang, 2014a) explained the impact of inter-regional trade and international trade on China’s domestic carbon emissions based on MRIO. Duarte et al. used the MRIO method to study the trajectory of carbon emissions in trades in countries around the world for 15 years. Structural decomposition analysis (SDA) is based on the input–output model and considers the inter-regional feedback effect (Su and Ang, 2012). It is the preferred method to identify the drivers of carbon emissions embodied in trade (Su and Ang, 2014a; Su et al., 2013; Zhao et al., 2016b). Using SDA method to analyze changes in embodied carbon emissions was mainly related to three driving factor, namely carbon emissions intensity, production structure and final demand (Bruckner et al., 2010; Deng and Xu, 2017; Wang and Yang, 2019). Many scholars further subdivided the driving factors of the changes in embodied carbon emissions. Lim et al. (Lim et al., 2009) analyzed the effects of eight factors, including carbon emission factors and demand structural changes, on the carbon emissions of the Korean industrial sector in 1990–2003. Cansino et al. (Cansino et al., 2016) analyzed the causes of the changes in Spain’s carbon emissions at the sectoral level, and further broke down the influencing factors into six. Among them, Duarte et al. (Duarte et al., 2018) pointed out that final demand was the key to explaining carbon emissions in trade, and the impact of final users was usually explained by changes in per capita and population. Meng et al. (Meng et al., 2018c) also showed that the changes can be decomposed into basic factors such as technological change, affluence, per capita final aggregate demand and population growth through SDA, and analyzed whether these driving factors played an accelerating or delaying role. Therefore, Plank et al. (Plank et al., 2018) analyzed the impact of seven driving factors on the increase of raw materials, including intermediate demand import structure, final demand import structure, final per capita demand and population. Wang and Zhou (Wang and Zhou, 2019)introduced population as the driving force, revealing that per capita final consumption was the main reason for the increase of carbon emissions in German-American trade. Arto et al. (Arto and Dietzenbacher, 2014), Hoekstra et al. (Hoekstra et al., 2016) and Malik and Lan (Malik and Lan, 2016), analyzed the changes in greenhouse gas emissions through final demand composition, final demand destinations, affluence and population drivers. For the study participants, some scholars pointed out that the current size and expected growth of the population of China and India means that they will play an important role in carbon emissions (Das and Paul, 2014; Johansson et al., 2015; Ohlan, 2015), and the per capita carbon emissions also showed an increasing trend (Jayanthakumaran et al., 2012). Therefore, the impact of population factors on carbon emissions in China and India cannot be ignored. Therefore, the research combined MRIO and SDA, and also took the population as one of the driving forces, further decomposing the final demand into three factors: population factor, per capita demand and demand structure. It also deeply explored the impact of the final demand factor on carbon emissions in China-Indian trade, which supplemented the research results of assessing the driving forces of carbon emissions. Compared with the existing research, this paper puts China and India in the global value chain, constructs the MRIO model, and systematically analyzes the carbon emissions embodied in SinoIndia trade, supplementing the research field of carbon trade among developing countries. Secondly, trade gains and losses can be judged by weighing economic benefits and environmental transaction costs, which is helpful to understand the trade between the two countries in an all-round way (Liu et al., 2018; Ye and Meng, 2015; Zhao et al., 2017). Therefore, this paper not only cares about the gains and losses of environmental interests

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in China-India trade, but also analyzes the trade interests of China and India in combination with economic benefits. Finally, in addition to conventional factors, this paper considers population factors in the decomposition analysis of final demand. In the process of structural decomposition analysis, the key driving factors of carbon embodied in China-India trade are discussed, and suggestions are provided for the two countries to achieve carbon emission reduction. 3. Methodology and data

2

3.1. Carbon emissions embodied in the trade between China and India Multi-regional input–output (MRIO) models are widely used to calculate carbon emissions and energy embodied in trade, which comprehensively portrays the complete production chain between various departments of different countries and distinguishes the different flows of imported and exported products(Wang and Jiang, 2019). Based on the model of multi-regional input–output framework, this paper analyzes carbon emissions embodied in trade between China and India under the global production chain. The basic formula for the multi-regional input–output model is as follows:

0

x1

1

0

Bx C B B 2C B B . C¼B B . C B @ . A @ xn

A11 A21 .. .

A12 A22 .. .

   A1n    A2n .. .. . .

An1

An2

   Ann

10

x1

1

0

y11 þ

P

Pj–1

y1j

1

CB x C B y22 þ j–2 y2j C C CB 2 C B C CB . C þ B C . CB . C B C .. A@ . A B A @ P xn ynn þ j–n ynj

1

ð2Þ Pn

0

An1

xn

A12 I  A22 .. .

   A1n    A2n .. .. . .

An2

   I  Ann

11 0 y þ P y 1 11 1j Pj–1 C C B y þ y B 22 2j C j–2 C B C C B C .. C B C A @ . A P ynn þ j–n ynj

ð3Þ 0 B B ¼B B @

L11 L21 .. . Ln1

LRC

LCI LII

32 3 Y CC þ Y CI þ Y CR LCR 76 7 LIR 54 Y IC þ Y II þ Y IR 5

LRI

LRR

ð4Þ

Y RC þ Y RI þ Y RR

The exports in country N’s trade is caused by the final demand of other countries. So when considering the exports in trade between the two countries, Y N ¼ 0. Similarly, when considering the input coefficient matrix of intermediate products(L), L except LN are 0. The calculation of China’s exports (EX C Þand India’s exports(EX I ) are shown in formula (5) and(6):

2

LCC 6 EX C ¼ 4 0 0

LCI

32

3 0 þ Y CI þ Y CR 76 7 0 54 0 þ Y II þ Y IR 5 0 0 þ Y RI þ Y RR

LCR

0 0

ð5Þ

0 6 EX I ¼ 4 LIC 0

0 LII 0

32

3 Y CC þ 0 þ Y CR 76 7 LIR 54 Y IC þ 0 þ Y IR 5 0 Y RC þ 0 þ Y RR 0

ð6Þ

¼ LIC ðY CC þ Y CR Þ þ LII ðY IC þ Y IR Þ þ LCR ðY RC þ Y RR Þ

X ij Xi

I  A11 B x C B A21 B 2C B B . C¼B . B . C B . @ . A @ . x1

XR

2

In the formula: where X i ¼ 1 X in represents the output of country i, sum of the output of intermediate products and final products. The matrix expression X in formula (1) is derived. The first part represents the output of each country’s intermediate products. The variable A represents the matrix of consumption coefficients between different countries’ production departments, and depicts economic links in trade activities. The variable Aij is the input coefficient matrix representing country j’s intermediate use of goods produced in country i;Aii represents the direct consumption coefficient matrix of domestic departments in country i. Among them, the calculation method for the elements of the matrix Aij is: the ratio of the output of the country i which meeting meet country j’s demand ðX ij Þ to the total input of the country i (X i ). P The second partðY i = n1 Y in Þrepresents the output of the final products of each country. Y ij represents final product consumption of country i to meet the demands of country j. After transformation, formula (1) can be expressed as:

0

3 2 LCC XC 6 7 6 4 X I 5 ¼ 4 LIC

¼ LCC ðY CI þ Y CR Þ þ LCI ðY II þ Y IR Þ þ LCR ðY RI þ Y RR Þ

ð1Þ Aij ¼

L = ðI  AÞ1 is the famous Leontief inverse matrix, reflecting technological linkages among departments.Lij represents the country i’s demand for products when meeting one unit of the final product of country j. This paper focuses on the carbon emissions embodied in trade between China and India, so countries and regions outside China and India can be considered as one country, namely ‘‘rest of the countries (ROC)”, thus treating three countries and regions as a global value chain system. This article uses the letters C, I, and R to represent China, India, and ‘‘rest of the countries (ROC)”. Then formula (4) is written as follow:

L12 L22 .. . Ln2

10 y þ P y 1 11    L1n j–1 1j By þ P y C B    L2n C 22 j–2 2j C CB C C .. .. C . CB C .. . . AB A @ P    Lnn ynn þ j–n ynj

According to formulas (5) and (6), China and India are placed in the global value chain system to obtain formulas for bilateral trade between China and India. China’s exports to India(EX CI ) and India’s exports to China(EX IC ) are expressed as:

EX CI ¼ LCC Y CI þ LCI Y II þ LCR Y RI

ð7Þ

EX IC ¼ LCC Y CI þ LCI Y II þ LCR Y RI

ð8Þ

Taking the formula (7) as an example, the first part is the final product of China’s direct export to India; the second part is the intermediate products of China’s indirect export to India; The third part is the intermediate products that China exports to India, and after being manufactured, again exported to India from other countries. Mark the carbon intensity coefficient as F ¼ ðf 1 ; f 2   Þ, which can be expressed as f i ¼ OC ii and represents the carbon emissions when country i produce per-unit output. The carbon intensity coefficient is combined with the formulas (7) and (8) to calculate embodied carbon (EC) in the trade between China and India. Taking Carbon emissions embodied in China’s exports to India (EC CI ) as an example, Fig. 1 shows the process of calculation. Carbon emissions embodied in China’s exports to India can be written as follows:

2

EC CI ¼ ½ f C

LCC

6 0 0 4 LIC LRC

LCI LII LRI

LCR

32

0 þ Y CI þ 0

3

7 76 LIR 54 0 þ Y II þ 0 5 0 þ Y RI þ 0 LRR

ð9Þ

¼ f C LCC Y CI þ f C LCI Y II þ f C LCR Y RI Corresponding, carbon emissions embodied in India’s exports to China (EC IC ) can be written as follows:

Q. Wang, X. Yang / Science of the Total Environment 707 (2020) 134473

5

Fig. 1. The calculation process of carbon emissions embodied in China’s exports to India.

2 EC IC ¼ ½ 0 f I

LCC

6 0 4 LIC LRC

LCI LII LRI

LCR

32

Y CC þ 0 þ 0

3

7 76 LIR 54 Y IC þ 0 þ 0 5 Y RC þ 0 þ 0 LRR

ð10Þ

¼ f I LCC Y CI þ f I LCI Y II þ f I LCR Y RI Formula (9) consists of three parts. The first part is the carbon emissions embodied in final products exported from China to India .The second part is the carbon emissions embodied in the intermediate products exported from China to India for further processing into final products. The third part is the carbon emissions embodied in the intermediate products exported from China to rest of the countries for further processing into meeting India’ demands of final products. Similarly, formula (10) is also divided into three parts. With carbon emissions embodied in exports, net embodied carbon emissions exports(NC) and net exports(NX) between China and India is obtained:

NCCI ¼ EC CI  EC IC

ð11Þ

NXCI ¼ EX CI  EX IC

ð12Þ

Net exports and net embodied carbon exports represent the corresponding environmental and economic benefits that trade brings to China and India, respectively. In addition, in order to further evaluate the embodied carbon trade and commodity trade relations between the two countries, four scenarios can be obtained according to formulas (11) and (12): (1) NXCI > 0 and NCCI > 0. In this situation, China, as a trade surplus country, gains economic benefits in the trade between the two countries, but it has caused increased pressure on carbon emission reduction. (2) NXCI > 0and NCCI < 0. This means that China not only gains economic benefits through bilateral trade, but also reduces its carbon emissions. (3) NXCI < 0and NCCI > 0. This situation is at a disadvantage for China. China has not only failed to gain economic benefits in C-India trade, but also increased its carbon emissions. (4) NXCI < 0 and NCCI > 0. This situation represents that although China has not gained economic benefits through China-India trade, it has reduced carbon emissions.

The situation in India is the opposite of China. In addition, the level of production and trade between China and India in bilateral trade is more easily understood by formula (13) and (14).

CTTChina ¼

EC CI EC IC = EX CI EX IC

ð13Þ

CTTIndia ¼

EC IC EC CI = EX IC EX CI

ð13Þ

In 1996, Antweiler proposed the Pollution Terms of Trade (PPT), an index used to measure the environmental benefits and losses of a country’s participation in international trade. Therefore, this paper introduces ‘‘Carbon Terms of Trade” (CTT) to measure the relative scale of carbon emissions caused by a country’s participation in international trade. The formula represents the ratio of carbon intensity coefficients of exports and imports. For example, when CTTChina > 1,it means that the carbon emissions caused by China’s export unit products are higher than those of India’s export unit products, and vice versa. 3.2. Structural decomposition analysis of carbon emissions embodied in the trade between China and India Structural decomposition analysis is a decomposition analysis method based on input–output tables. Its main purpose is to judge the contribution of different driving factors to the change of target variables by decomposing a certain target variable. According to the calculation method of embodied carbon (EC ¼ FLY), the changes of embodied carbon emissions (DEC) can be allocated into the variation effects of three factors. The calculation formula of the change of embodied carbon can be written as:

DEC ¼ ECt  ECt1 ¼ F t Lt Yt  F t1 Lt1 Yt1

ð14Þ

Where D represents the amount of change, t and t-1 represent different time periods. In order to further explore the changes in embodied carbon, the formula EC ¼ FLY is further decomposed as shown in Fig. 2 and formula (15):

EC ¼ FLY ¼

c e Y YT L P ¼ CELY s Y v Y p e o YT P

ð15Þ

Where c, e, o and P represent carbon emissions, energy consumption, economic output and population respectively. Y T is a scalar and represents total final demand. As shown in formula (15), the embodied carbon emissions is related to six factors:

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Q. Wang, X. Yang / Science of the Total Environment 707 (2020) 134473

Fig. 2. Further decomposition of embodied carbon emissions.

carbon intensity coefficient of energy(carbon emissions caused by per unit of energy consumption, C), energy intensity (energy consumption caused by per unit of economic output, E) , production structure (L), demand structure (Y s ) and per capita demand (Y v ), population (scalar, Y p ). Thus, according to formula (14) and Equation (15), the specific formula for the further decomposition result of DEC is expressed as:

DEC ¼ ECt  ECt1

ð16Þ

¼ C t Et Lt Y s t Y v t Y p t  C t1 Et1 Lt1 Y s t1 Y v t1 Y p t1 ¼ DCEt Lt Y s t Y v t Y p t þ C t1 DELt Y s t Y v t Y p t þ C t1 Et1 DLY s t Y v t Y p t þ C t1 Et1 Lt1 DY s Y v t Y p t þ C t1 Et1 Lt1 Y s t1 DY v Y p t

DCðCÞ |fflfflffl{zfflfflffl}

þ

The effect of carbon emission coefficient of energy

In order to accurately obtain the influence of the driving factor on the change of carbon emissions embodied in the export, this paper uses the two-polar decomposition method to obtain the average value of different decomposition methods. This method has been widely used in SDA (Chang and Lahr, 2016; Muñoz and Hubacek, 2008; Nie et al., 2016). Formula (17) is another decomposition result of DEC and is written as follows:

ð17Þ

¼ C t Et Lt Y s t Y v t Y p t  C t1 Et1 Lt1 Y s t1 Y v t1 Y p t1 ¼ DCEt1 Lt1 Y s t1 Y v t1 Y p t1 þ C t DELt1 Y s t1 Y v t1 Y p t1 þ C t Et DLY s t1 Y v t1 Y p t1 þ C t Et Lt DY s Y v t1 Y p t1 þ C t Et Lt Y s t DY v Y p t1 þ C t Et Lt Y s t Y v t DY p

þ

DCðLÞ |fflffl{zfflffl} The effect of production structure

þ

  DC Y p |fflfflfflffl{zfflfflfflffl}

þ

DCðEÞ |fflffl{zfflffl} Energy Intensity Effect

DCðY s Þ |fflfflffl{zfflfflffl} The effect of demand structure

The effect of population

The sum of DCðCÞ and DCðEÞ represents the carbon intensity coefficient effect, which is denoted as DCðFÞ. The sum of   DCðY s Þ、DCðY v Þand DC Y p represents the effect of final demand, which is denoted as DCðY Þ.Specifically, take DCðC Þ as an example. If DCðC Þ < 0, it means that energy ’s carbon intensity coefficient has a restraining effect on the increase of carbon emissions, and vice versa. Referring to formula (14)- (18) ,we can decompose the change of embodied carbon from China’s exports to India (DEC CI ) and embodied carbon from India’s exports to China (DEC IC ). 3.3. Data sources

 0 0 0 0 0 0 ¼ DCðCÞ þ DCðEÞ þ DCðLÞ þ DCðY s Þ þ DCðY v Þ þ DC Y p According to formulas (16) and (17), the average of two decomposition results are given. The final decomposition result of DEC can be expressed as:

DEC ¼ ECt  ECt1

DCEt Lt Y s t Y v t Y p t þ DCEt1 Lt1 Y s t1 Y v t1 Y p t1 2 C t1 DELt Y s t Y v t Y p t þ C t DELt1 Y s t1 Y v t1 Y p t1 þ 2 C t1 Et1 DLY s t Y v t Y p t þ C t Et DLY s t1 Y v t1 Y p t1 þ 2 C t1 Et1 Lt1 DY s Y v t Y p t þ C t Et Lt DY s Y v t1 Y p t1 þ 2 C t1 Et1 Lt1 Y s t1 Y v t1 DY p þ C t Et Lt Y s t Y v t DY p þ 2 C t1 Et1 Lt1 Y s t1 Y v t1 DY p þ C t Et Lt Y s t Y v t DY p þ 2

¼

þ C t1 Et1 Lt1 Y s t1 Y v t1 DY p

DDEC ¼ ECt  ECt1

¼

ð18Þ

This paper uses the world input–output tables and carbon emissions data in the Eora database to measure carbon emissions embodied in C-India trade. Eora is a database based on national input–output tables, which contains data from 189 countries, so that it can understand the global production and trade patterns and trade links between countries. Not only that, but as one of the frequently used official input–output databases, Eora’s database

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contains data from 1990 to 2015, the latest update year for all databases. Therefore, this paper select this database to analyze the carbon emission trend under time series. Due to the wide time span, the study period is from 2000 to 2015.The Eora database mainly involves 26 departmental data. This paper combines these departmental data into seven categories. The details are in Table A2 in Appendix A. The demographic data used in the structural decomposition analysis comes from the World Bank. In addition, in order to eliminate the price impact, this paper converts the corresponding data into the constant price of 2010 according to the World Bank’s GDP deflator.

4. Results 4.1. Embodied carbon changes in China-India trade Fig. 3 showed the embodied carbon emissions and their changes in the China-India trade from 2000 to 2015. However, it is difficult to judge the impact of China-India trade by the single index of carbon emissions embodied in imports and exports, and it is necessary to make comprehensive judgments by means of other indicators. Therefore, in order to fully understand the carbon emissions embodied in China-India trade, Fig. 4 depicted the changing

7

trend of export volume in China-India trade and showed the economic effects in China-India trade. The carbon emissions embodied in China’s exports to India grown significantly and increased by 40.51 MtCO2 in 15 years, with a growth rate of 499.74%. China’s exports to India increased by only 41.93% during this period. Thus, there was a serious imbalance between the embodied carbon emissions and trade volume in China-India trade. The carbon emissions embodied in China’s exports to India increased rapidly since China joined the WTO in 2001, reaching 46.41 M tCO2 (an average annual growth rate of 33.59%) in 2008, an increase of 479.41%. During this period, China’s exports to India, as shown in Fig. 4, also increased by 14036.15 million dollars, an increase of 20.58%. After the 2008 economic crisis, the carbon emissions embodied in trade fell by 4.20 MtCO2 in 2009; as the economy recovered, carbon emissions also increased, and in 2011 carbon emissions peaked at 59.72 MtCO2, followed by a downward trend. The embodied carbon emissions decreased by 11.11 MtCO2 between 2011 and 2015, as China continued to adjust its industrial structure during this period, and India’s industrial strength continued to increase and import tariffs increased, causing the corresponding import scale to decline. Despite this, China’s export volumes increased by 7891.219 million dollars during this period. But overall, the growth rate of export volumes (only increased by 41.93%) was much smaller than the growth rate of embodied carbon emission.

Fig. 3. Carbon emissions embodied in China-India trade.

Fig. 4. The exports in China-India trade in 2000–2015.

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Fig. 5. The net exports(NX) and net embodied carbon exports(NC) of China in China-India trade (2000–2015).

The carbon emissions embodied in India’s exports to China showed similar characteristics: the overall trend of growth between 2000 and 2015 was obvious,but the growth trend of carbon emissions embodied in trade was more rapid. The carbon emissions embodied in India’s exports to China increased by 34.86 MtCO2 in 15 years, with a growth rate of 572.82%. In particular, the carbon emissions embodied in India’s exports to China generally stabilized before 2003, maintaining at 6–7 MtCO2. In 2003–2011, the embodied carbon emissions showed a gradual increase, with an increase of 33.41 MtCO2. Among them, the average annual growth rate in 2003–2009 was 19.91%. The economic crisis had not a significant impact on the carbon emissions embodied in trade. And in 2009–2011, the embodied carbon emissions increased sharply, an increase of 58.27%. At the same time, India’s export volume increased by 78.89% during 2003–2011, which partly reflected the fact that the expansion of export volume had a certain impact on carbon emissions embodied in C-India trade. Since 2011, due to the reorganization of the international supply chain, carbon emissions fluctuated within a small range of 40–45 MtCO2, but the overall declined. Overall, the proportion of the carbon emissions embodied in India’s exports to China to India’s carbon emissions embodied in exports was increasing (by 4.86%), which was larger than that of China. Trade between China and India has become became closer in the past 15 years. As shown in Fig. 3 and Fig. 4, in the C-India trade, China was not only a net carbon exporter but also a net exporter of trade. This indicated that while increasing exports, China also increased its carbon emissions, which can be regarded as sacrificing environmental benefits for economic benefits during 2000–2015. For India, although it suffered economic losses in China-India trade, it avoided domestic carbon emissions. China has gradually become a ‘‘pollution sanctuary” in India. Fig. 5 showed the relationship between China’s net embodied carbon exports and net trade exports (relative India’s net trade imports and net embodied carbon imports) in China-India trade. China’s net embodied carbon exports surged from 6.81MtCO2 to 23.01 MtCO2 in 2003–2007. However, during this period, the net trade exports increased by a small margin (increased by 3507.08 million dollars, with an average annual rate of change of 2.41%), revealing that the imbalance of China’s embodied carbon in China-India trade was far greater than the imbalance of export volume. During this period, China vigorously developed its economy, and the degree of carbon emissions increased in exchange for a trade surplus gradually deep-

ened, which led to a gradual increase in net embodied carbon exports. However, after 2010, China’s net embodied carbon exports showed a general downward trend, stabilizing at 5–7 MtCO2 during 2012–2015.The main reason was that carbon emissions embodied in China’s exports declined due to technological progress, and carbon emissions embodied in India’s exports rose as India’s industrialization accelerates. During this period, India’s trade exports to China grew faster than China’s, resulting in a 12.51% decline in China’s net exports. This result showed that the degree of China’s carbon emissions increase in exchange for a trade surplus is gradually decreasing, so China’s economic benefits in China-India trade were gradually less and the benefits of carbon emission reduction were gradually expanding. With the decrease of China’s net embodied carbon exports, we can predict that China’s economic interests will decline at a lower rate of change each year, but at the same time China will gradually become a net embodied carbon importer in China-India trade, that is, to avoid carbon emissions and obtain economic benefits through trade, which has created a ‘‘win-win” situation for China. 4.2. Embodied carbon features in sectoral trade As shown in Fig. 6 and Fig. 7, this paper analyzed the embodied carbon emissions and the exports in China-India trade from an industry perspective. The results showed that the vast majority of carbon emissions embodied in trade came from heavy manufacturing and the electricity sector. About 80% of the carbon emissions embodied in China’s exports to India came from these two sectors, which showed that China’s export structure to India was single, but it also had an absolute advantage. Among them, the embodied carbon exports from the electricity sector increased by 21.19 MtCO2 in 15 years, accounting for a proportion of embodied carbon exports from 34.73% to 49.4% (see Appendix B, Figure B1 for details), which was mainly related to coal as the main raw material for China’s power supply system. While the embodied carbon exports from heavy manufacturing increased from 3.61 MtCO2 to 17.54 MtCO2.However, the share of heavy manufacturing in the embodied carbon exports declined during this period(from 44.5% to 36.1%). Among them, about 80% of the embodied carbon exports in heavy manufacturing came from the ‘‘Petroleum, Chemical and Non-Metallic Mineral Products” and ‘‘Metal Products” sectors. Because these sectors not only had a higher direct carbon emission coefficient, but other industries’ exports had also indirectly caused

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Fig.6. Sectoral structure of embodied carbon exports in China-India trade during 2000–2015.

Fig.7. Sectoral structure of the exports in China-India trade during 2000–2015.

large amounts of carbon emissions in the industry. In addition, the transport industry was also the main embodied carbon exports sector, accounting for about 8% of the embodied carbon exports. As for China’s exports to India, they mainly came from heavy manufacturing, service industries and light manufacturing. The proportion of these sectors’ exports to total exports has been stable for 15 years. These three sectors accounted for about 60%, 11% and 8% of trade exports respectively (see Appendix B, Figure B2 for details). However, for the electricity sector with large embodied carbon exports, the export volume only accounted for about 4%. This was mainly because the electricity sector in the upstream of the industrial chain needs to invest in the production, manufacturing, and service processes of various industries and had a large consumption coefficient, which caused the sector to generate a large amount of carbon emissions but causes less economic output. For heavy manufacturing, the proportion of heavy manufacturing in the embodied carbon exports was about 40%, but heavy manufacturing’s export volume accounted for more than 60% of the total export volume. This situation also existed in Indian export trade. For India, the proportion of heavy manufacturing in the embodied carbon exports fell from 27.6% to 22.1%, but the proportion of the heavy manufacturing’s export volume to total export volume

increased from 32% to 42%. This showed that the technological progress of heavy manufacturing in China and India increased export volume while relatively adjusting the industrial structure of embodied carbon exports. In China-India bilateral trade, there were similarities in the industrial structure of embodied carbon exports in China and India. For example, embodied carbon exports mainly came from electricity, heavy manufacturing and transport industries, and there were imbalances between embodied carbon emissions and trade exports in different sectors. But the industrial structure of the embodied carbon exports in the two countries also had many differences. For the carbon emissions embodied in India’s exports to China, carbon exports embodied in the electricity increased from 1.96 MtCO2 to 21.25 MtCO2, and the share increased from 39.2% to 51.9%.This was related to the fact that India’s coal accounts for more than 50% of its total energy consumption, and its economy was dominated by coal-fired power generation. The carbon exports embodied in heavy manufacturing only increased by 7.33 MtCO2, accounting for less than 30% of the embodied carbon exports. Secondly, the proportion of carbon exports embodied in mining industry and light manufacturing (mainly textiles) averaged around 12% and 7%, because China has outsourced sector resource-intensive and

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Table 1 The sectoral flows of NCCI andNXCI in China-India trade. 2000

Agriculture Mining Light manufacturing Heavy manufacturing Electricity Service Transport Total

2005

2010

2015

NCCI (Mt CO2)

NXCI ($1M)

NCCI (Mt CO2)

NXCI ($1M)

NCCI (Mt CO2)

NXCI ($1M)

NCCI (Mt CO2)

NXCI ($1M)

0.44 0.61 0.23 1.88 0.86 0.12 0.44 2.019

403.49 908.35 3429.69 34114.08 2438.39 4800.77 2120.63 47408.425

0.67 1.25 0.18 7.77 8.04 0.40 1.71 15.832

681.59 474.24 3328.56 35184.09 2487.97 4725.73 2075.58 47594.581

1.39 2.85 0.26 10.97 8.21 0.30 2.56 17.537

1540.00 941.81 2839.52 36876.29 2523.95 4251.24 1828.42 45837.606

1.74 3.41 0.64 8.48 2.75 0.11 2.11 7.661

2800.02 3123.31 1882.34 37181.32 2418.40 3226.22 1319.34 40104.299

labor-intensive products to India in recent years. The different sectoral structures of embodied carbon exports between China and India showed India’s obvious dependence on China’s imports. For trade exports, trade exports from heavy manufacturing, mining and light manufacturing accounted for more than 70% of total exports. It is worth noting that the proportion of carbon exports embodied in services and light manufacturing was less than that in trade exports. Especially in the service industry, the carbon emissions embodied in the service industry accounted for a small proportion and grow slowly, but the economic output generated accounted for about 13% of total trade exports, which was related to the growing share of computer programming and information services in India’s export products. On the sectoral basis, in order to further explore the relationship between the embodied carbon emissions and exports of the two countries, Table 1 listed the net embodied carbon exports and net exports of seven sectors in China-India trade during the four periods of 2000, 2005, 2010 and 2015 (because the carbon embodied in China’s exports was greater than that in India’s exports, it was represented by NCCI and NXCI ). Generally speaking, in China-India trade, most sectors of China were in the position of net embodied carbon exports and trade surplus, and mainly concentrated in the carbon-intensive manufacturing sector. From the data in the table, the net import and net embodied carbon imports of heavy manufacturing were the largest, which means that China obtained a large amount of economic benefits and carbon emissions through manufacturing trade. And the proportion of net exports of heavy manufacturing to total net exports was increasing, exceeding 90% in 2015.The net embodied carbon exports of transport industry showed an increasing trend, but the net export volume was declining, showing that China was gradually at a disadvantage in this sector. The net export of the electricity sector was relatively stable, but the net embodied carbon exports between 2010 and 2015 were declining, mainly due to the increase in Indian embodied carbon exports of electricity sector. In 2005– 2015, the net embodied carbon exports in the electricity sector was nearly 20 times that of the service industry. However, the added value of net embodied carbon exports in these two industries was not much different. For the two sectors of electricity and transportation, China undertaken a large amount of carbon emissions transfer but got less economic benefits. For the mining industry and light manufacturing, China was in the state of net embodied carbon imports but net exports of trade (just the opposite for India), and this situation was a ‘‘win-win” situation for China. But for India, it not only increased its carbon emissions, but also lost economic benefits, and this unfavorable situation was gradually increasing in India’s mining industry. While net embodied carbon exports in India’s light manufacturing were increasing, economic losses were gradually declining. Corresponding to India’s agricultural sector exports, India exchanged economic benefits at the expense of increased carbon emissions, and this degree was deepening.

4.3. Estimating China’s and India’s status in bilateral trade EC CI Fig. 8 showed the carbon intensity of China’s export (EX ) and CI EC CI India’s export (EX ) in China-India trade, as well as carbon terms CI

of trade (CTT). Carbon terms of trade described the relative value of carbon coefficient efficiency of two countries’ exports. Roughly speaking, China and India’s export carbon coefficient declined in 15 years, which indicated that the energy efficiency of the two countries was improving, and China’s and India’s carbon emission reduction policies achieved some results. However, India’s carbon export coefficient was always greater than China’s, which caused India’s embodied carbon exports was growing at a much faster rate than trade exports. Among them, India’s export carbon coefficient decreased by more than China, and fell from 0.0024 MtCO2 per $1M to 0.008 Mt CO2 per $1M in 15 years. CTT China was less than 1 during the study period, indicating that the carbon emissions embodied in unit products exported in China were always lower than that in India’ export (i.e. imported by China).This showed that carbon terms of trade in China-India trade was generally conducive to China’s emission reductions. However, CTT China showed an upward trend after 2002 and was close to 1 in 2007. During this period, China focused on economic development and neglected carbon emissions, making the trade environment increasingly unfavorable to China. It should also be noted that CTT China was at its peak after 2007, and then the decline tends to be stable. This showed that with the increasing introduction, development and utilization of advanced industrial technology in China in recent years, carbon terms of trade has been greatly improved to a certain extent. Because CTT India was greater than 1, India was the opposite of China. This showed that the carbon emissions of India’s export unit products was always greater than that of import unit products, which indicated that China-Indian trade was generally not conducive to India’s carbon emissions. And since 2006, CTT India was continuing to increase, which was more and more evident to the detriment of India’s carbon emissions. Fig. 9 showed the export carbon intensity and CTT China of different sectors between 2000 and 2015(CTT China and CTT India were opposite to each other. Because CTT India was too large to be represented in the graph, only CTT China was displayed.). If CTT < 1, it means that the products of this department in China’s export was ‘‘cleaner” than the products of this department in India’s export. Except for the transport industry, theCTT China of other departments was basically less than 1. This was because transportation consumed a lot of oil and caused a huge amount of carbon emissions. China’s motor vehicle pollution problem led to its high carbon intensity, and CTT China > 1, that is, China’s carbon terms of trade in China-India trade was unfavorable. And the carbon terms of trade in the transport industry was greater than the average carbon terms of trade of China’s export trade to India, which was thus clear that the transport industry was the ‘‘culprit” of deterioration of China’s trade environment. On average, the

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Fig.8. carbon intensity of export and CTT(the ratio of carbon intensity of export of China and India) in India-China trade.

Fig.9. Carbon intensity of export and CTT China in sectoral trade.

export carbon intensity of the electricity industry was the highest in all sectors, especially in India, where coal-fired power plants generated a large amount of carbon emissions, and thus the sector had the greatest pressure to reduce emissions. Fig. 9 showed that China’s electricity sector was in an obvious advantage in ChinaIndia trade, which was consistent with the gradual decrease in China’s net carbon imports embodied in electricity. If the trade volume between China and India is in a balanced state, electricity industry in the China-India trade should greatly reduce China’s carbon emissions. Heavy manufacturing industries in China and India both had high export carbon intensity, but both declined rapidly during the study period, reducing by more than 70%. Compared with the above-mentioned departments, the export carbon intensity of the light manufacturing, agriculture and service sectors was relatively small, and the magnitude of change was relatively small. TheCTT China of these three industries was stable at around 0.4 and 0.2 and 0.6 respectively, which indicated that the carbon terms of trade of these three sectors were more favorable to the improvement of China’s trade environment than other industries. But for India, CTT India was relatively large, and these industries in India did not have an advantage in China-Indian trade. In order to study the driving factors of the dynamic change of China’s and India’s embodied carbon emissions in China-India trade from 2000 to 2015, the change of embodied carbon emissions

in exports was divided into six parts: energy intensity, carbon emission coefficient of energy, production structure, demand structure, per capita demand and population. The decomposition results were shown in Fig. 10. 4.4. Structural decomposition of CO2 emissions embodied in ChinaIndian trade Overall, from 2000 to 2015, the driving factors had similar effects on carbon emissions, mainly as follows: the carbon intensity coefficient always inhibited the increase of carbon emissions; the production structure, demand structure and per capita demand mainly had a promoting effect. Energy intensity did not have a significant impact on embodied carbon exports during the period 2000–2015, which was related to the energy structure in China and India. On the contrary, the carbon emission coefficient of energy was the most important factor to restrain the growth of embodied carbon emissions. The carbon emission coefficient of energy inhibited 63.44 MtCO2 in China, which was higher than that of India (35.94 MtCO2). Whether in India or China, due to the energy structure transformation in 2010–2015, the carbon emission coefficient of energy had a significant inhibitory effect on the embodied carbon export, but it did not offset the promotion of carbon emissions by the scale of exports. The final demand effect

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Fig.10. Structural Decomposition of CO2 embodied ((a1, a2) China’s embodied carbon exports, (b1,b2)India’s embodied carbon exports.

(DCðY Þ) was the strongest driving force behind the increase in the embodied carbon emissions. Specifically, India’s final demand led to an increase of 49.72 MtCO2 in EC CI . Compared with China’s population effect and demand structure effect(19.33% and 5.37% respectively), India’s population effect (14.21%) and demand structure effect (46.4%) had greater impact on the increase in carbon emissions. This was mainly due to the surge in the population of India during this period. And China’s export structure was mostly based on heavy manufacturing and electricity industry, which was also reflected in the production structure. The production structure promoted EC CI by 129.35%, which was greater than the promotion of EC IC . This paper further analyzed the contribution of different drivers to EC CI between 2000 and 2005, 2005–2010, and 2010–2015. During 2000–2005, the inhibition effect of carbon intensity coefficientðDCðFÞÞ on EC CI was little (-17.83%), and was completely offset by the promotion effect of production structure effect (49.53%) and per capita demand effect (36.84%) on the growth of EC CI . Finally, EC CI increased by 21.03 MtCO2 during this period. In 2005–2010, the inhibition of carbon intensity coefficient on EC CI was as high as 36.45 MtCO2.However, as the population of India increased by 90 million during this period, India’s final demand for China expanded, and the effect of per capita demand became the most important factor in promoting the growth of EC CI . In the phase of 2010–2015, the carbon intensity coefficient became the most critical factor affecting DEC CI . It made EC CI drop by 16.08 MtCO2 during this period, accounting for 30.4% of EC CI in 2010. Among them, the carbon coefficient effect and energy intensity effect of energy accounted for 117.36% and 262.30% of EC CI , respectively. It is worth mentioning that the production structure effect increased the EC CI by 1.26 MtCO2. This showed that China’s industrial restructuring achieved initial results. During this period, EC CI overall decreased by 4.23 MtCO2. The carbon intensity coefficient had a key factor in inhibiting the increase of EC IC . The carbon intensity coefficient in the three periods inhibited the increase of carbon emissions of 4.68 MtCO2, 12.5 MtCO2 and 3.33 MtCO2, respectively. However, the inhibition of the carbon intensity coefficient on EC IC was much less than that of EC CI , which indirectly reflected that China’s production technology was higher than India. On the contrary, China’s final demand was the most critical factor in promoting the increase of EC IC .

The final demand increased carbon emissions by 23.23 MtCO2 between 2005 and 2010, which was nearly four times that of carbon emissions during 2000–2005. Although the promotion effect of final demand declined in 2010–2015, it was 3.27 times that of DEC IC , which was the dominant position in promoting the increase of EC IC . Among them, the per capita demand effect was the biggest driving factor to promote the increase of carbon emissions, accounting for 75.72%, 94.99% and 299.42% of DEC IC , respectively. Compared with the effect of demand structure on EC CI , the effect of demand structure on the increase of EC IC was almost negligible. Similarly, the production structure effect was the second most driving factor to promote the increase of EC IC . However, the promotion effect of the production structure on the increase of EC IC was much less than that of EC CI . This was mainly related to the industrial structure in the export trade between China and India. 5. Conclusions In this work, we investigated temporal change and driving forces of the carbon emissions embodied in China-India trade from 2000 to 2015 using the Multi-Regional Input-Output model and Structural Decomposition Analysis. The main findings are as follows: (i) At the national level, China was not only a net exporter of embodied carbon but also a net exporter of trade in ChinaIndia trade, which indicated that China increased its environmental costs while gaining economic benefits. But the imbalance in China’s embodied carbon trade was far greater than the trade imbalance. However, over time, China’s net embodied carbon exports generally showed a downward trend, while net exports were also slowly decreasing. The degree of China’s carbon emissions in exchange for a trade surplus gradually declined. Carbon terms of trade between China and India showed that China’s exports to India are ‘‘cleaner’’ than India’s exports to China. If China and India are in a state of bilateral trade balance, China should be in an ‘‘embodied carbon deficit” position in China-India trade. (ii) From an industry perspective, the industrial structure of China’s export of embodied carbon and India’s export of embodied carbon had certain complementarities. The export

Q. Wang, X. Yang / Science of the Total Environment 707 (2020) 134473

carbon intensity of electricity, heavy manufacturing and transportation industries was relatively high, but in recent years it showed a certain downward trend, which will directly affect the reduction of carbon emissions. Among them, electricity and heavy manufacturing industries dominated the embodied carbon exports of China and India. (iii) The results of structural decomposition indicated that the main reason for the increase in the embodied carbon emissions of China and India was the increase in final demand, in which the effect of per capita demand was the main driving factor affecting the change of embodied carbon emissions. The effect of carbon intensity coefficient was the driving factor in suppressing the increase in embodied carbon emissions in China and India. The results revealed that the main reason why China’s net embodied carbon export to India was positive (pollution surplus) was that China’s sustained trade surplus with India. However, blindly controlling China’s export to India in order to reduce the embodied carbon emissions in China-India trade will inevitably bring huge economic losses. In order to achieve emission reduction targets, more efforts were required in two areas. Above all, China and India need to optimize the trade structure and transform the industrial structure into technology- intensive and capital-intensive industries with high added value. At the same time increase investment in these sectors to seek technological advances and improve energy efficiency in the production process of these upstream sectors. The imbalance between embodied carbon trade and export trade was also largely reflected in different industries, such as electricity industry and service industry. For the electricity sector, which had high carbon emissions and low economic output, China and

India should reduce the proportion of coal in the energy structure to reduce carbon emissions, such as replacing coal-fired generating units with nuclear power plants, developing renewable energy resources to adjust the energy structure and so on. In addition, China and India should seek trade cooperation and should import products with high carbon emission coefficient as much as possible to replace domestic production. At the same time, exporting products with lower carbon emission factors in the country to alleviate the increase in carbon emissions that cannot be avoided due to the increase in export scale. What’s more, accelerate the development of low-carbon production technology to reduce the carbon intensity coefficient of China and India. Under this trade model, China and India can achieve the realization of carbon emission reduction and achieve a win–win situation as much as possible without losing the trade surplus or reducing the scale of foreign trade. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Acknowledgements This work is supported by National Natural Science Foundation of China (Grant No. 71874203), Humanities and Social Science Fund of Ministry of Education of China (Grant No.18YJA790081), and Natural Science Foundation of Shandong Province, China (Grant No. ZR2018MG016) Appendix A

Table A1 Literature about the carbon emissions embodied in trade between China and other countries Research object

Relevant literature

Research results

China and US

Shui and Harriss (2006)

Studies indicated that 7%-14% of China’s carbon emissions in 1997-2003 were due to meet the final demands of the United States. In 2002-2007, with the increase of Sino-US trade volume, the carbon emissions embodied in trade also increased, accounting for about 8-12% of China’s carbon emissions. The results show that Sino-US trade in 2005 caused the United States to avoid 19.01Mt CO2, but it had increased global carbon emissions by 51.53 Mt CO2. The exports were the biggest driver of the increase in carbon emissions embodied in Sino-US trade between 2002 and 2007. It mainly analyzed the carbon transfer embodied in Sino-US trade in 1997 and 2002. In Sino-US trade, China made certain contributions to the reduction of carbon emissions in the United States. The increase in Sino-Japan trade volume between 1990 and 2000 greatly contributed to the increase in carbon emissions in bilateral trade. The study pointed out that the scale effect had greatly promoted the increase of carbon emissions in Sino-Japan trade. Between 2000 and 2009, China was a net carbon exporter of in Sino-Japan trade. Trade volume was the main driver for increasing carbon emissions. The carbon emissions embodied in Sino-Australian trade between 2001 and 2010 were assessed, and the scale effect was the main factor driving the increase in carbon emissions. China was a net carbon exporter in bilateral trade, but trade between the two countries has contributed to a reduction in global carbon emissions. The study found that the UK avoided about 11% of its carbon emissions in 2004 through Sino-UK trade. And Sino-UK trade contributed to an increase in global carbon emissions. This work used the input-output approach to investigate the net carbon emissions of trade between China and Germany. The results showed that since 2000, the net carbon emissions in Sino-German trade increased significantly with the sharp increase in net trade.

Xu et al. (2009) Guo et al. (2010)

China and Japan

Du et al. (2011) Huichao and Limao (2010) Dong et al. (2010) Zhao et al. (2016a) Wu et al. (2016)

China and Australia

China and UK China and Germany

Tan et al. (2013) Jayanthakumaran and Liu (2016) Li and Hewitt (2008) Wang et al. (2019a)

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Sectors

Abbreviations

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

Agriculture Fishing Mining and Quarrying Food & Beverages Textiles and Wearing Apparel Wood and Paper Petroleum, Chemical and Non-Metallic Mineral Products Metal Products Electrical and Machinery Transport Equipment Other Manufacturing Recycling Electricity, Gas and Water Construction Maintenance and Repair Wholesale Trade Retail Trade Hotels and Restaurants Transport Post and Telecommunications Financial Intermediation and Business Activities Public Administration Education, Health and Other Services Private Households Others Re-export & Re-import

Agriculture Agriculture Mining Light manufacturing Light manufacturing Light manufacturing Heavy manufacturing Heavy manufacturing Heavy manufacturing Heavy manufacturing Heavy manufacturing Light manufacturing Electricity Heavy manufacturing Service Service Service Service Transport Service Service Service Service Service Service Service

Appendix B

Fig. B1. The proportion of each sector in China’s embodied carbon exports (a) and in India’s embodied carbon exports (b)

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Fig. B2. The proportion of each sector in China’s exports (a), and in India’s exports (b)

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