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Research in International Business and Finance 51 (2020) 101101

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Can China reduce the carbon emissions of its manufacturing exports by moving up the global value chain?

T

Huizheng Liua, Zhe Zonga, , Kate Hynesb, Karolien De Bruynec ⁎

a b c

College of Economics and Management, Beijing University of Technology, China Department of Economics, Dublin City University, Business School, Ireland KU Leuven, Faculty of Economics and Business, Belgium

ARTICLE INFO

ABSTRACT

JEL classification: F6 Q5

This study analyses whether embedding in the global value chain has an impact on the carbon emissions of China’s exports. We develop a carbon decomposition model and use panel data for 14 manufacturing industries in China from 1995 to 2009 to empirically analyse the impact of China’s exports on carbon emissions. Our results show that the GVC effect on China’s carbon emissions embodied in manufacturing exports outweighs the scale, composition and technique effects.

Keywords: Carbon emissions effects of export trade GVC effect Scale effect Composition effect Technique effect

1. Introduction One of the most interesting developments in international trade since the 1990s has been the emergence of trade liberalization as an environmental issue. Moreover, with the rise of the global value chain (GVC) in the past two decades, much debate about the environmental consequences of embedding in the GVC has emerged. Given the decrease in transportation costs and improvements in information and communication technology, production processes can now be “sliced” into several segments, each corresponding to a particular task, such as design, parts procurement, assembly, and distribution. By being part of the GVC, firms, especially in developing economies, can use their comparative advantage to concentrate on a specific production process or task, which enables them to integrate into the global economy more rapidly than was possible in the previous industrialization period (Kowalski et al., 2015). The high-tech and high value-added segments, such as design, brands operation and sales are typically located upstream the GVC. They demand relatively fewer resources and less energy and are predominantly produced by developed countries. On the other hand, some segments, especially production and manufacturing, which have low value-added, are located downstream in the GVC. These segments require high energy consumption and high emission levels and are generally carried out in developing countries. After joining the WTO in 2001, China has become increasingly embedded in the GVC. Focused on the production and assembling processes, China was embedded in the GVC through the export-oriented processing trade and became the “world factory”. The value of exports has increased rapidly from $0.27 trillion in 2001 to $2.48 trillion in 20181, which made China the largest exporter of goods and the second largest economy in the world. The rapid growth of exports in China is at the expense of pollution (Weber et al., 2008; Corresponding author. E-mail address: [email protected] (Z. Zong). 1 Source: General Administration of Customs of the People’s Republic of China. ⁎

https://doi.org/10.1016/j.ribaf.2019.101101 Received 14 September 2018; Received in revised form 29 August 2019; Accepted 1 September 2019 Available online 07 September 2019 0275-5319/ © 2019 Published by Elsevier B.V.

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Peters et al., 2011; Su and Thomson, 2016). It has been estimated that the pollution increases by 0.25%–0.50% for every 1% increase in economic scale (Copeland and Taylor, 1994). According to the authors’ calculations, CO2 emissions embodied in China’s exports increased from 0.6 gigatons in 2001 to 1.51 gigatons in 2009. The manufacturing industry’s CO2 emissions increased from 0.47 gigatons to 1.31 gigatons, accounting for more than 80% of total emissions. At the end of 2014, the Central Economic Working Conference stated that China’s environmental carrying capacity has reached or is close to reaching its limit, which implies the need for China to adopt a path of green, low carbon and cyclic development. This paper focuses on one way for China to initiate the lowcarbon transformation. Indeed, the GVC also provides an opportunity for technology transfers or spillovers from developed countries to developing countries through local learning (Pietrobelli and Rabellotti, 2011; Kawakami and Sturgeon, 2011). Moreover, some developing economies are increasingly participating in the GVC through exports and imports of intermediate manufactured goods, and some are upgrading along the GVC. According to the comparative advantage theory, China for example will specialize in labor-intensive industries, import intermediate products, engage in simple processing and assembly operations, and export these products. The rapid expansion of the processing trade model has greatly expanded the scale of exports at low processing levels, resulting in a large consumption of energy and resources, which in turn has led to an increase in carbon emissions. In contrast, participating in the GVC also allows countries to produce more (high-tech) intermediate goods locally (i.e. upstream) or rather import these intermediate goods and opt for final assembly (i.e. produce downstream). Therefore, upgrading along the GVC would help reduce carbon emissions embodied in exports. China now tends to export more intermediate goods to other low-income downstream countries to support final goods exports to the global market (Zhang and Zhai, 2018). In the “Thirteenth Five-year” Development Program, China set the target of reducing its carbon intensity, i.e. carbon dioxide emissions per unit of GDP, by 18% from the 2015 level by 2020. Non-fossil fuels will increase to approximately 20% of primary energy consumption by 2030.2 This paper discusses whether embedding in the GVC has an impact on the carbon emissions of China’s export trade. Carbon emissions embodied in international trade is a controversial topic in the study of trade and environmental relations. Grossman and Krueger (1991) built a general equilibrium model of environment and trade and decomposed it into scale, composition and technique effects. Copeland and Taylor (1994) found that free trade has a negative environmental effect on the rich North, and unilateral transfers from North to South reduce worldwide pollution. Zhuang et al. (2009) followed Grossman and Kruger’s methodology and found that China’s liberalization of trade can help to improve the environment because of scale, technique and composition effects. Several studies have shown that the relationship between carbon emissions and trade is stronger when intra-industry specialization is considered. Dai (2010) found that intra-industry specialization is the main determinant in reducing China’s export pollution intensity; other factors he tested for are FDI, terms of trade and trade barriers. Tian (2012) found that intra-industry specialization in China can potentially reduce the export pollution level of pollution-intensive industries, enhance the environmental and technological effects, and thus decrease the export environmental costs. Li and Lin (2013) focused on the heterogeneity of carbon emissions across different industries. Their study found that the scale expansion of most industries leads to an increase in carbon emissions, whereas structural improvements lead to an decrease in carbon emissions. Bai and Zhao (2015) used the factor decomposition method to analyse the impact of exports on carbon emissions across 14 industries in China. Their study found that the scale and composition effects of exports increased carbon emissions in 2004–2008 but decreased them in 2008–2011; the technique effect decreased carbon emissions in both periods. Gao et al. (2015) investigated the scale, composition and technique effects on carbon emissions across 30 provinces in China. They found that these three factors exert significant influence on carbon emissions when investigated separately, while the scale and technique effects are more important in reducing carbon emissions when investigated jointly. Sun et al. (2015) decomposed the carbon emission effects into scale, composition and technique effects using a modified GML index method, and found that trade openness increases China’s carbon emissions, mainly due to a positive technique and scale effect; the composition effect turns out negative but not significant. Ma et al. (2016a) found that China’s export scale and intermediate input structure significantly drive the increase in imbalance in carbon emissions embodied in bilateral trade, while China’s carbon intensity and import scale play the opposite role. There are several studies that have analysed the carbon emission effects of foreign trade, considering scale, composition and technique effects. Few studies, however, focus on the GVC perspective. Li and Peng (2011) examined the connection between foreign trade and carbon emissions of China and showed that every 1% increase in the position of the GVC reduce carbon emissions by 0.56%, which provided an important reference for our study. Hu (2016) analysed the impact of the GVC on environmental quality through scale, composition, technique, vertical FDI and chain transition effects. Yang et al. (2017) studied the impact of the GVC on pollution through technological progress using data from Chinese industries. The results showed that technological progress increases pollution emissions if embedding in the GVC is less than the threshold value. However, there are some shortcomings in the existing studies. Firstly, it is incomplete to analyse the proportion of processing trade in general trade (backward linkages) as an alternative to the GVC position, neglecting China’s intermediate exports to foreign countries (forward linkages). Secondly, it is not (fully) correct to use China’s total carbon emissions as an approximation of the carbon emissions of exports (as Li and Peng (2011) among others did). We calculate the carbon emissions of exports directly. Thirdly, when considering the total factor productivity of technology, only the relationship between input and output of production factors such as 2

This has been reported by the China US joint statement on climate change, November 2014. 2

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capital and labor have been considered and the technical efficiency in energy conservation and emissions reductions have not been highlighted. Fourthly, the GVC effect has not explicitly been considered or estimated in most of the existing papers. To address these problems, (i) this paper uses a domestic GVC_Position index, capturing one country’s supply of intermediates used in other countries’ exports to the use of imported intermediates in its own production to gauge the relative position in the GVC; (ii) this paper uses non-competitive input-output and total domestic demand coefficients to calculate carbon emissions from China’s manufacturing export trade; (iii) environmental total factor productivity is calculated based on non-parametric data envelopment analysis (DEA), in which energy consumption and carbon emissions are taken as input and undesirable output respectively; (iv) a new export trade carbon emission decomposition model is established, introducing a perspective of the GVC to empirically analyse the impact of export trade on carbon emissions using data of 14 manufacturing industries in China; (v) this paper further examines the effect of participation in the GVC and the indirect effect of the GVC position on the carbon emissions. The remainder of the paper is organized as follows. Section 2 develops our theoretical framework and analyses the carbon emissions effects. Section 3 describes the variables used in the regression equation. Section 4 discusses the empirical results while Section 5 concludes. 2. Theoretical framework We assume an economy without tariff barriers where only two goods, X and Y, are produced. X is a capital-intensive polluting good, producing carbon in the process, while Y is a labor-intensive and clean good. Both goods can be produced by capital and labor with return r (for capital) and w (for labor); the supply of each factor of production is assumed perfectly inelastic. The price of Y is normalized to 1 (PY = 1), and the relative price of X is PX. Moreover, we assume constant returns to scale and perfect competition. Finally, note that we only consider a carbon emission effect while other environmental issues are neglected. Given the international division of labor, the traditional trade in goods produced by a single country is transformed into task trade3, since imported intermediates are used in the manufacturing process. The production capacity of one single country is therefore automatically over-estimated if all the final output is attributed to this country. We will thus ultimately consider the effective production function of a particular good. The gross production function for polluting goodX is: (1)

Xg = Q (LX , KX )

where LX and KX represent the input of labor and capital used to produce good X and Q(LX,KX) is the potential output of X. Producing within a global value chain, countries use imported intermediates. We therefore rewrite the production function for polluting good X – excluding imported intermediates – as follows: (2)

X = Q (LX , KX ) V

where V is the position where the country is embedded in the GVC: the higher up the GVC (and the higher V), the more effective the production process4 . V can take any value between 0 and 1 and represents the high-tech intermediates that are produced by the country. If V equals 0, the country imports all (high-tech, less-polluting) intermediates and produces none of good X . The country therefore only focuses on the polluting final assembly production part – i.e. downstream production. If on the other hand V equals 1, the country does not import any high-tech intermediates and produces them on her own – in that case the production excluding hightech imported intermediates equals the gross production. The country will in that case not perform any polluting assembly activities and hence produce less-polluting upstream. The government wants to reduce the carbon emissions C as much as possible. We introduce a regulatory policy on carbon emission reduction such that a fraction θ (0≤θ≤1) of the potential output will be devoted to reductions in carbon emissions5 . The effective production function for polluting good X – excluding imported intermediates – is then:

x = (1

(3)

) Q (LX , KX ) V

A higher production of the polluting good X implies higher carbon emissions. We express carbon emissions therefore as:

C=

(4)

( ) Q (LX , KX ) V

where φ(θ) is the carbon emissions per unit of output. It is a function of environmental technology T, in which a higher technological 1 )1/ , 0 < α < 1. As is obvious from level implies lower carbon emissions. We assume the following functional form: ( ) = T (1 Eq. (4), the impact of the position embedded in the GVC (indicated by V) on carbon emissions must be considered as well. CV < 0 because the high end of the GVC mainly involves the R&D, branding sales and operations with low carbon emissions. Substituting Eq. (3) into Eq. (4), we obtain the following expression for the effective production of good X: 3

See Grossman and Rossi-Hansberg (2008) More high-tech segments (higher up the value chain) require fewer resources to obtain the same output (or with the same resources one could obtain a higher output). 5 Stated otherwise, the firm uses θ (0 < θ < 1) input factors in carbon reduction: θ=0 implies no factors of production are invested in carbon reduction, while θ =1 implies all factors of production are invested in carbon reduction (and hence in that case effective production is by definition 0) 4

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(5)

x = (T C ) (Q V )1

where (T·C) represents the carbon emissions after the technology treatment and (Q·V) is the output embedded in the GVC. It is obvious from Eq. (5) that in an open economy, the output of X is a function of the production technology, the carbon emissions, the potential output and the position in the GVC. Firms are motivated to produce X at as low a cost as possible. Their cost factors are threefold: they need to pay their factors of production (labor and capital) and they must pay a tax on their carbon emissions. Given that the per unit output cost under international division of labor is cf(w,r), firms need to choose the optimal level of carbon emissions and output embedded in the GVC to minimize the cost of producing X for a given tax level and prices of capital and labor:

min{ T C + c f (w, r ) Q (LX ,KX ) V , s. t . (T C ) (Q V )1

(6)

= 1}

Using the Lagrange method and dividing the partial derivatives with respect to carbon emissions after technology treatment and output embedded in the GVC, we obtain the following expression:

cf

=

(1

)T C QV

(7)

As the market is perfectly competitive, the net profit of firms is zero, i.e.

c fQ V

= PX x

(8)

TC=0

Combing with Eq. (7) we obtain:

x=

c fQ V + T C TC = PX PX

(9)

The carbon emissions per product embedded in the GVC, φ(θ) are therefore:

( )

C = X

PX T

(10)

After some substitution and rewriting of the initial expression for carbon emissions (4) we obtain the following expression (see Appendix A):

C=SJ

T

V

(11)

where S = PXXg+PYYg is the scale of economy and J =

P X Xg PX Xg + P Y Yg

is the commodity composition, i.e. the share of the value of the

production of the polluting product in the total value of production. Eq. (11) clearly reflects that the level of carbon emissions is determined by scale, composition, technique and position in the GVC. Taking the logarithm of both sides of Eq. (11), we finally obtain:

ln C = ln S + ln J

(12)

ln T + ln V + ln

where ln = ln is constant. With Eq. (12), we have now decomposed the carbon emission effect into a scale effect, a composition effect, a technique effect and a GVC effect. The scale effect states that increasing exports will rise carbon emissions. Everything else being equal, an increase in the scale of economic activity – and hence energy use – will lead to higher levels of carbon emissions. The composition effect refers to the way that trade liberalization changes the mix of a country’s production towards those products where it has a comparative advantage. This re-allocation of resources within a country is how trade improves economic efficiency. The effect on carbon emissions will depend on the sectors in which a country has a comparative advantage. Generally, capital-intensive products release more carbon dioxide in the whole manufacturing process compared to labor-intensive products.6 Since China has a comparative advantage in the production of labor-intensive products, the composition effect is therefore likely to reduce carbon emissions. The technique effect refers to a country’s capability to introduce advanced technology in order to reduce carbon emissions. This might include a learning effect, a spillover effect and a forced effect. A learning effect refers to the idea that accumulation of experience in the production of a particular good can boost energy efficiency. A spillover effect refers to a situation when foreign advanced emission reduction technology is introduced. A forced effect refers to the positive effect of exports on economic growth, the subsequent increase in R&D investment and its positive impact on environmental governance through improving productivity, which consequently reduces carbon emissions. The GVC effect refers to changes in the level of carbon emissions caused by changes in the position in the GVC. Generally speaking, developed countries are usually at the high end of the GVC, which mainly involves R&D, branding sales and operations and other high-tech, high value-added activities with a low-level demand of resource consumption and low usage of energy. In contrast, less developed countries are usually at the low end of the GVC and undertake low value-added and high energy consumption processes, such as processing, assembling, manufacturing thus producing large amounts of carbon emissions. Hence, moving up the 6 Among others Li and Peng (2011); Li and Lin (2013) both indicated that capital-intensive products, such as metallurgical, petroleum, mechanical industries, release more carbon dioxide.

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Fig. 1. China’s Manufacturing embodied carbon emissions in export and domestic value added in exports. Source: authors’ calculation based on World Input-Output Database.

GVC would decrease carbon emissions in developing countries. 3. Data Given our derivation in the previous Section, in Section 4, we ultimately estimate Eq. (13) for 14 Chinese manufacturing industries from 1995 to 2009 in logarithmic form:

ln C =

0

+

1 ln S

+

2

ln J +

3 ln T

+

4

(13)

ln V + µ

where ln S is the scale effect, ln J the composition effect, ln T the technique effect, lnV the GVC effect, μ a stochastic error, and β0…β4 the parameters to be estimated. All data are derived from the World Input-Output Database (WIOD). We start by describing the construction of the dependent variable, i.e. the carbon emission variable (Eq. 14). We then move on to the scale effect (Eq. (15) and GVC variable (Eq. 16), followed by the composition effect variable and the technique effect variable (Eq. 20). 3.1. Carbon emissions of export trade and scale effect As suggested by Su and Ang (2013), the non-competitive imports assumption is generally used in embodied emission studies. To develop calculations for carbon emissions embodied in exports, this paper quotes Ma et al.’s (2016b) approach, using an input-output model. The embodied carbon emissions in the final exports of products and services are calculated as follows:

Cr = ErD (I where ErD =

(14)

ArD ) 1Er

{ } denotes the direct coefficient matrix of carbon emissions per unit of output, F FrD

D r

XrD XrD is

is the direct carbon emissions of

the total output of industry r, is the domestic direct consumption coefficient matrix, ErD (I ArD ) 1 is the industry r, complete coefficient matrix which indicates the direct and indirect carbon emissions to meet the final demand of output per unit, and Er is gross exports. Furthermore, as different stages of production are nowadays regularly performed in different countries, intermediate inputs cross borders multiple times. As a result, traditional statistics, which attributed total trade volume to final exports, are inadequate. Hence, domestic value added (DVA) has become a better way to capture the real gains of countries in the GVC. The DVA in a country’s exports hence indicates the true scale at which goods are produced without overestimating it by double-counting. In this paper, we refer to Wang et al.’s (2015) method to evaluate the DVA above and beyond (re-)imported intermediates and double-counting to accurately reflect the DVA in a country’s exports:

ArD

DVA = (V sB ss ) #Y sr + (V sLss ) #(Asr Brr Y rr ) + (V sLss ) #(Asr Brt Y tt ) (1)

(2)

(3)

+ (V sLss ) #(Asr Brr Y rt ) + (V sLss ) #(Asr Brt Y tr ) (4)

(15)

(5)

sr

ss

Where Y is demand in country r for final goods produced in country s; V denotes the direct domestic value-added coefficient vector for country s; Asr is a coefficient matrix, reflecting the intermediate use in r of goods produced in s; Brt denotes the Leontief inverse matrix, which is the total requirement matrix that gives the amount of gross output in producing country r required for a one-unit increase in final demand in country t; Lss = (I Ass ) 1 denotes the Leontief inverse matrix of country s; # denotes product in matrix form. Fig. 1 shows China’s embodied carbon emissions in exports and the DVA in exports between 1995 and 2011. China’s manufacturing embodied carbon emissions moved smoothly within a small range before 2001, at around 0.5 gigatons. However, since joining the WTO, exported carbon emissions escalated7 as well as the DVA in exports. This demonstrates that carbon emissions 7

They increased by over 1.73 times over the last decade 5

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Fig. 2. China’s Manufacturing embodied carbon emissions per unit of domestic value added in exports. Source: authors’ calculation based on World Input-Output Database.

increased in China after its integration into the global trading system. By participation in the GVC, the production of several energyintensive and high-pollution products were moved to China from developed countries with strict(er) emission regulations. It can be noted, however, that manufacturing embodied carbon emissions generated per unit of DVA falls in exports as shown in Fig. 2. China’s manufacturing embodied carbon emissions per unit of DVA in exports fell from 4.47 kg/dollar in 1995 to 1.53 kg/ dollar in 2009, indicating that with economic growth, firms are benefiting from spillovers and new technologies. The decrease happened in all manufacturing industries but was noticeably larger for the medium-low-tech manufacturing sectors (decreasing from 7.60 kg/USD in 1995 to 2.67 kg/USD in 2009). Both medium-high-tech and high-tech manufacturing already had relatively low carbon emissions per unit of DVA: every one dollar of DVA produced less than 2 kg carbon emissions in 2009. Finally, because of their industrial structure, the low-tech manufacturing sectors, which include food, textiles, leather, wood products, printing and recycling have the lowest carbon emissions per unit of DVA. 3.2. Participation and pattern in global value chain Since Gereffi et al. (2001) introduced the concept of the GVC, Smith et al. (2002) analysed GVCs as activities that create value in the entire life cycle, ranging from initial conception to final sales, consisting of design, production, marketing, delivery and final use and service, etc. A country can participate in the GVC by producing high value-added segments, such as product research and development, brands operation and sales, or low value-added segments, such as production and assembling processes. As suggested by Koopman et al. (2010), the GVC_Participation index can be used to summarize the importance of the global supply chain.

GVC _Participation =

IVir FVir + Eir Eir

(16)

Where IVir denotes indirect value-added exports, the value of inputs produced domestically that are used in other countries’ exports, FVir denotes foreign value added in gross exports, Eir is gross exports, subscripts i and r denote a country and sector respectively. This index gives a sense of how integrated a country is in the GVC. On this basis, Ahmed et al. (2015) defined forward linkages as the domestic content of a country-sector in third-country exports as a share of the gross exports, and backward linkages as the foreign content in a country-sector’s gross exports. Formally, the forward and backward linkages indexes are given by

GVC _Forward Linkagesir =

IVir Eir

GVC _Backward Linkagesir =

(17)

FVir Eir

(18)

Applying the method proposed by Wang et al. (2015) and using the World Input-Output Tables, we calculate China’s manufacturing level and pattern of GVC participation from 1995 to 20098 . We divided the 14 manufacturing industries into four types according to the OECD classification method based on R&D intensity, including low-tech, medium-low-tech, medium-high-tech and high-tech manufacturing9, the results of which are shown in Table 1. The GVC participation and backward linkages of high-tech manufacturing are much higher than those of the other three sectors. The foreign value added of high-tech manufacturing is 8 The World Input-Output Tables were available for 1995-2011 and the Air Emission Accounts were available for 1995-2009 only. We hence set the study period of this paper from 1995-2009. 9 Low-tech manufacturing includes (i) Food, Beverages and Tobacco, (ii) Textiles and Textile Products, (iii) Leather, Leather and Footwear, (iv) Wood and Products of Wood and Cork, (v) Pulp, Paper, Paper, Printing and Publishing, (vi) Manufacturing, Nec., Recycling; Medium-low-tech manufacturing includes (i) Coke, Refined Petroleum and Nuclear Fuel, (ii) Rubber and Plastics, (iii) Other Non-Metallic Mineral, (iv) Basic Metals and Fabricated Metal; Medium-high-tech manufacturing includes (i) Chemicals and Chemical Products, (ii) Machinery, Nec., (iii) Transport Equipment; High-tech manufacturing includes Electrical and Optical Equipment.

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Table 1 The GVC participation and pattern of China’s manufacturing industry. Source: authors’ calculation based on World Input-Output Database. manufacturing par 0.24 0.23 0.23 0.23 0.25 0.28 0.27 0.28 0.30 0.33 0.33 0.33 0.32 0.32 0.28

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

FL 0.09 0.10 0.10 0.11 0.11 0.13 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.13 0.12

low-tech BL 0.15 0.13 0.13 0.12 0.13 0.15 0.15 0.16 0.19 0.21 0.22 0.21 0.20 0.19 0.16

par 0.21 0.19 0.18 0.18 0.19 0.22 0.21 0.21 0.22 0.24 0.24 0.23 0.22 0.22 0.20

Medium-low-tech FL 0.06 0.06 0.06 0.07 0.07 0.08 0.08 0.07 0.08 0.08 0.09 0.09 0.08 0.09 0.09

BL 0.14 0.12 0.12 0.11 0.12 0.14 0.13 0.13 0.14 0.16 0.15 0.14 0.14 0.13 0.11

par 0.25 0.25 0.25 0.25 0.26 0.30 0.29 0.30 0.32 0.35 0.34 0.35 0.36 0.36 0.31

FL 0.12 0.13 0.13 0.15 0.15 0.17 0.17 0.17 0.17 0.18 0.16 0.18 0.19 0.19 0.16

Medium-high-tech BL 0.13 0.12 0.12 0.10 0.11 0.13 0.12 0.13 0.15 0.17 0.18 0.17 0.17 0.17 0.15

par 0.23 0.23 0.25 0.24 0.25 0.28 0.27 0.27 0.29 0.32 0.32 0.32 0.32 0.31 0.28

FL 0.10 0.12 0.13 0.14 0.14 0.15 0.14 0.14 0.13 0.12 0.13 0.13 0.13 0.15 0.13

High-tech BL 0.13 0.12 0.12 0.10 0.11 0.13 0.13 0.14 0.16 0.20 0.19 0.19 0.19 0.17 0.15

par 0.29 0.29 0.29 0.28 0.30 0.34 0.34 0.35 0.37 0.40 0.39 0.38 0.38 0.36 0.33

FL 0.11 0.12 0.12 0.12 0.13 0.15 0.15 0.14 0.12 0.12 0.12 0.12 0.12 0.12 0.12

BL 0.18 0.16 0.16 0.15 0.17 0.19 0.19 0.21 0.25 0.27 0.28 0.26 0.26 0.23 0.21

Note: “par” stands for participation, “FL” and “BL” stand for forward linkages and backward linkages respectively.

estimated at USD 99.6 billion, while the indirect value-added exports is only USD 58.7 billion. This implies that China’s high-tech manufacturing industry, which is dominated by foreign value-added in final goods, mainly engages in final assembling activities, although its participation is deepening. In contrast, the forward linkages index of medium-low-tech manufacturing is significantly higher than that of other industries, as its intermediate export share reached 56.4% in 2009, indicating a high level of participation and an upstream position in the GVC. In addition, low-tech manufacturing has the lowest forward and backward linkages, indicating a low and passive position in the GVC with production and processing segments. Table 2 summarises the descriptive statistics of the dependent and explanatory variables. 3.3. Position in global value chain In order to analyse China’s manufacturing position in the GVC, we use the GVC_Position index defined by Koopman et al. (2010):

GVC _Positionir = ln(1 +

IVir ) Eir

ln (1 +

FVir ) Eir

(19)

This index compares one country’s supply of intermediates used in other countries’ exports to the use of imported intermediates in its own production to gauge whether a country is likely to be in the upstream or downstream part of the GVC. A higher value for the index indicates that the country is in the upstream part of the GVC. Fig. 3 illustrates China’s manufacturing position in the GVC from 1995 to 2009. In the early years after joining the WTO, China successfully integrated into the global production system, but primarily in the processing trade, such as food processing, resulting in a sharp decline in the GVC position of China’s manufacturing industry as processing trade exports rose from $147 billion in 2001 to $417 billion in 2005. It illustrates the weak ability of China’s manufacturing value-added with its high dependency on foreign input in its exports, which makes manufacturing exports lack competitiveness and restricts the development of domestic enterprises in the long run. As a result, the Central Economic Working Conference in 2004 viewed development of quality and quantity as an important road to “convert means of foreign trade growth”, aiming at exports of low value-added, high energy consumption and pollution. As a result, the GVC_Position index began to recover steadily after 2005. For all manufacturing types, the value of the position index is Table 2 Descriptive Statistics. Variables

Observation

Mean

Std. Dev.

Max

Min

Carbon Emissions (C) Scale (S) Composition (J) Technique (T) GVC (V) Forward Linkage (FL) Backward Linkage (BL)

210 210 210 210 210 210 210

60.26 28.20 0.65 0.23 −0.04 0.10 0.14

85.98 57.53 0.21 0.11 0.05 0.06 0.04

534.73 436.25 1.18 0.47 0.09 0.22 0.30

3.73 0.78 0.27 0.00 −0.15 0.13 0.06

Note: All currency-related data are deflated (1995 = 100).

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Fig. 3. China’s Manufacturing position in GVC between 1995 and 2009 Source: authors’ calculation based on World Input-Output Database.

below 0.04, which implies that they are all located at the downstream part of the GVC10 . Among them, medium-low-tech manufacturing has the highest position index in the GVC, while the high-tech manufacturing’s GVC position is relatively low, with an approximately flat pattern in low-tech and medium-high-tech after 2004. Compared to developed countries, China’s high-tech manufacturing industry remains less competitive in the international market. 3.4. Composition effect To measure the commodity composition, this paper uses the rate of return of labor divided by the rate of return of capital provided by the WIOD Socio Economic Accounts. A larger ratio indicates a lower relative price of capital and more capital input. Since the more capital-intensive products are more carbon-intensive, a higher relative capital input implies higher carbon emissions. 3.5. Technique effect Regarding technology, we adopt Sun’s (2015) environmental factor productivity to measure technique efficiency (TE):

TEit =

1

D (xit , yit , bit )

1 + D (xit , yit , bit )

(20)

D(·) denotes the directional distance function and x、y、b denote inputs, desirable outputs and undesirable outputs respectively. We obtained the data on labor force, capital stock, energy consumption, real GDP and CO2 emissions for 14 manufacturing industries over the period 1995–2009. The first three variables are inputs, while real GDP is chosen as a proxy for the desirable output, and carbon emissions as the proxy for the undesirable output. All data are collected from the Socio Economic Accounts, the Gross Energy Accounts and the Air Emission Accounts in WIOD. The MALMQUIST-DEA method (Sun et al (2015); Oh (2010)) is used to solve the directional distance function. Finally, the higher the TE index, the higher the environmental total factor productivity and therefore the higher the technical level. 4. Empirical results We first discuss our baseline results after which we undertake a sensitivity analysis. We end the empirical section with some robustness checks. 4.1. Baseline results Based on the results of the F test, LM test and Hausman test, we estimated a fixed effects panel model for the whole manufacturing sector and the four manufacturing sub-sectors separately. Table 3 illustrates the estimation results. From the first column of Table 3 we can see that: (i) There is a positive relationship between carbon emissions and the scale of China’s manufacturing exports. An increase in the scale of exports by 1% increases carbon emissions by 0.7%. This indicates that the expansion of China’s manufacturing exports is one of the reasons for the growth in carbon emissions. (ii) There is a relationship between carbon emissions and the composition of China’s export trade. A 1% increase in capital/labor input in exports releases 0.2% more carbon emissions. This is as expected as the most capital-intensive products are the most carbon-intensive products (i.e., more capital input implies more carbon emissions). (iii) There is a negative relationship between carbon emissions and technology: carbon emissions decline by 0.3% for each percentage-point increase in the level of technology. This finding suggests that more advanced technologies help to reduce the level of emissions. (iv) There is a negative relationship between carbon emissions and the position in the GVC. If the position in the GVC increases by 1%, carbon emissions will be reduced by 3.1%, which shows a significant influence of 10 There is no clear-cut boundary between upstream and downstream. 0.04 is the maximum value for the whole of manufacturing in this graph. Compared to ln2 (maximum value of the position), it implies a relative downstream value.

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Table 3 Baseline Results.

Scale (G) Composition (J) Technique (T) GVC (V) Constant Adj-R2 F-statistic Observations

Manufacturing industry

Low-tech

Medium-low-tech

Medium-high-tech

High-tech

0.702 *** (31.42) 0.155 ** (2.28) −0.340 *** (−6.54) −3.122 *** (−7.73) 3.224 *** (13.27) 0.989 1050.679 210

0.768 ** (14.92) 0.146 (1.32) −0.595 *** (−5.27) −3.344 *** (−4.03) 1.814 *** (3.05) 0.983 568.183 90

0.663 *** (8.72) 0.129 (0.75) −0.286 *** (−3.55) −4.316 *** (−4.62) 4.294 *** (6.28) 0.991 618.812 60

0.625 *** (27.34) −0.607 *** (-4.12) −0.098 (−1.37) −2.321 *** (−4.90) 4.214 *** (15.51) 0.990 746.260 45

1.128 *** (7.75) 4.075 *** (4.79) −0.094 (−0.33) −4.499 *** (−5.66) 0.962 (0.58) 0.992 420.295 15

Note: the values in parentheses are t-Statistics, and ***, **, * denote significance level of 1%, 5%, 10% respectively.

embedding into the high ends of the GVC. Note that the GVC effect in size outweighs the first three effects. For all four types of manufacturing industry, the GVC effect, compared with the scale, composition and technique effects, has the highest impact on the embodied carbon emissions in exports. For every 1% increase in the GVC position of low-tech, medium-lowtech, medium-high-tech and high-tech manufacturing, the carbon emissions reduce by 3.3%, 4.3%, 2.3% and 4.5% respectively. From the comparison between the various types of manufacturing industry, the scale, composition and GVC position effects of high-tech manufacturing industry are obviously higher than the other three, which is due to its highest participation, lowest position and backward linkages. For every 1% increase in the technological level of low-tech manufacturing, carbon emissions would be reduced by 0.6%, which is the highest effect of all four industries. 4.2. Sensitivity analysis 4.2.1. Pattern of GVC participation In order to further investigate the effect of the participation in the GVC on carbon emissions, we subdivide the GVC effect into the forward linkages (FL) and backward linkages (BL) effects. Table 4 shows the results. For every 1% increase in forward linkages, carbon emissions would be reduced by 0.2%, while for every 1% increase in backward linkages, carbon emissions would increase by 0.3%. This is because a country could reduce its carbon emissions by exporting intermediates and transferring processes with low value added and high carbon emissions to other countries. On the other hand, overdependence on imported intermediate goods and participation in the low-end of the GVC would lead to an increase in carbon emissions. The forward linkages have a higher (negative) impact for the high-tech manufacturing industry than for the other industries. This indicates not only that the higher the proportion of forward trade in intermediate goods, the higher the reduction of carbon emissions would be, but also that this effect will be larger in high-tech sectors.

Table 4 Pattern of GVC Participation.

Scale (S) Composition (J) Technique (T) Forward Linkage (FL) Backward Linkage (BL) Constant Adj-R2 F-statistic Observations

Manufacturing industry

Low-tech

Medium-low-tech

Medium-high-tech

High-tech

0.716*** (28.15) 0.173** (2.39) −0.339*** (−6.40) −0.238*** (-4.15) 0.327*** (4.50) 3.265*** (7.67) 0.988 930.466 210

0.778*** (12.82) 0.175 (1.54) −0.604***(-5.09)

0.667*** (8.27) 0.181 (0.93) −0.251***(−3.03)

0.950*** (19.94) −0.650*** (−4.24) −0.123*(−1.70)

−0.178**(-2.23)

−0.582**(-2.74)

−0.368**(-2.52)

0.820*** (3.28) 2.236 (1.47) 0.156 (0.49) −1.333**(−2.92)

0.281** (2.62) 1.966** (2.31) 0.982 480.997 90

0.587*** (2.95) 4.341*** (3.98) 0.990 506.265 60

0.218* (1.98) 3.608*** (4.76) 0.9990 624.444 45

0.586** (2.86) 2.798 (1.50) 0.992 348.955 15

Note: the values in parentheses are t-Statistics, and ***, **, * denote significance at 1%, 5%, 10% levels respectively. 9

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Table 5 Indirect Effects of Global Value Chain.

Scale (S) Composition (J) Technique (T) GVC (V) Scale*GVC (S*V) Composition*GVC (J*V) Technique*GVC (T*V) Constant Adj-R2 F-statistic Observations

Manufacturing industry

Low-tech

Medium-low-tech

Medium-high-tech

High-tech

0.881*** (39.95) −0.028 (−0.61) −0.026 (0.485) −4.734** (-2.36) 0.416** (2.48) −1.469** (-2.44) 0.736 (1.45) 2.338*** (11.45) 0.839 437.75 210

0.823*** (18.83) 0.169*** (3.27) −0.182*(−1.89)

0.732*** (11.22) −0.596***(−3.00)

−5.700*(−1.71)

1.001*** (19.14) 0.225*** (3.40) −0.034 (−0.99) −7.936***(−3.93)

0.613** (2.04) 1.157 (1.32) −1.219 (−1.46) 2.604*** (5.53) 0.980 147.89 90

0.693** (2.68) −0.999 (-1.52) −0.384 (−0.67) 1.995*** (4.48) 0.909 190.76 60

0.968** (2.06) −2.169 (-1.06) −2.025 (−1.66) 3.819*** (6.05) 0.848 556.26 45

1.017*** (4.79) 2.202 (1.82) −1.301*** (−4.53) −14.092 (-0.48) −1.032 (-0.32) −11.563 (-0.54) −11.921***(−3.22)

0.140 (1.70) −12.92***(-2.41)

−0.392 (-0.19) 0.998 1169.043 15

Note: the values in parentheses are t-Statistics, and ***, **, * denote significance at 1%, 5%, 10% levels respectively.

4.2.2. Indirect effects of global value chain The position in the GVC may indirectly affect carbon emissions through the scale, composition and technology of exports; this is the indirect GVC effect. We test for these indirect effects of China’s participation in the GVC by introducing the cross terms of GVC and scale, composition and technology (S*V, J*V and T*V). Table 5 reports the regression results. As per the first column in the table, the overall GVC position of the manufacturing industry still has a significant direct impact on carbon emissions embodied in exports. With a 1% increase in GVC position, carbon emissions decrease by 4.7%. However, the significant positive coefficient of S*V indicates that the scale effect has slowed down the contribution of GVC to carbon reduction. The reason is that the Chinese domestic companies are playing more of a “participant” role in the GVC of manufacturing industries. They are mainly engaged in activities such as processing and assembly, where the pollution intensity is relatively high compared to activities related to non-productive links such as design and research and development. Therefore, the expansion of exports has weakened the negative impact of the GVC position on export carbon emissions. However, the composition effect has a stronger effect on the negative impact of the GVC, as China will mainly focus on labour-intensive less-polluting production. Finally, the technique effect has no significant effect on the negative impact of the GVC. From columns (2)–(5), it can be seen that the scale effect is an important indirect factor through which the position in the GVC affects carbon emission in the low-tech, medium-low-tech and medium-high-tech manufacturing sectors, while the technical level is an important indirect factor through which the position in the GVC affects carbon emission in the high-tech manufacturing sectors. This is consistent with China’s reality of having a low position in the GVC. According to the comparative advantage theory, China will specialize in labor-intensive industries, import intermediate products, engage in simple processing and assembly operations, and export these products. The rapid expansion of the processing trade model has greatly expanded the scale of exports at low processing levels, resulting in a large consumption of energy and resources, which in turn has led to an increase in carbon emissions. In contrast, the high-tech manufacturing’s clean production technology and input-output efficiency helps to reduce resource consumption and environmental pollution. Therefore, embedding high-tech manufacturing links in the GVC would help reduce carbon emissions embodied in exports. 4.3. Robustness checks 4.3.1. Endogeneity One of the challenges of investigating the relationship between embodied carbon emissions and the GVC position is the potential of reverse causality. We hence proceed to a two-stage least squares estimation where the instrumental variable is the lagged GVC position. Table 6 reports the estimation results. Neither the sign nor the significance of the key variables changes significantly. Every 1% rise in the manufacturing sector’s lagged GVC position reduces embodied carbon emissions by 1.1%. These estimations confirm the previous results that a higher position in the GVC would indeed decrease carbon emissions. 4.3.2. Alternative measure We carry out an additional test to verify the robustness of our baseline specification by using an alternative measure of the GVC position. As discussed by Wang et al. (2017a,b), a production chain starts from the sector’s primary inputs or value added such as labor and capital, not its gross output. When the direct exports of an industry are very low, the traditional measure may result in a large value of forward and backward linkages, which overestimates the GVC position of an industry. Hence, the GDP by industry is 10

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Table 6 Instrumental Variable Estimations.

Scale (S) Composition (J) Technique (T) GVC (Vt-1) Constant Adj-R2 Observations

Manufacturing industry

Low-tech

Medium-low-tech

Medium-high-tech

High-tech

0.817*** (35.70) −0.035 (−0.61) −0.101*** (−3.04) −1.099*** (−2.74) 1.531*** (6.27) 0.997 210

0.744*** (13.15) 0.135 (1.06) −0.466***(−4.37)

0.610*** (4.20) 0.322 (0.80) −0.234*(−1.84)

0.584*** (16.75) −0.537***(−3.57)

−3.353***(−4.47)

−12.257***(−3.00)

−0.080 (−0.82) −4.968***(−5.93)

1.980*** (3.20) 0.987 90

4.462*** (4.52) 0.974 60

5.401*** (12.28) 0.988 45

1.048*** (8.91) 4.329*** (4.92) 0.214 (0.64) −5.801*** (-7.79) 2.338 (1.61) 0.992 15

Note: the values in parentheses are t-Statistics, and ***, **, * denote significance at 1%, 5%, 10% levels respectively. Table 7 Alternative measure of the GVC Position.

Scale (S) Composition (J) Technique (T) GVC (V) Constant 2

Adj-R F-statistic Observations

Manufacturing industry

Low-tech

Medium-low-tech

Medium-high-tech

High-tech

0.713*** (29.94) 0.181** (2.49) −0.402*** (−7.35) −10.441*** (−5.64) 3.090 *** (11.99) 0.988 920.328 210

0.750*** (15.59) 0.206** (1.97) −0.627***(−5.79)

0.621*** (7.98) −0.024 (−0.14) −0.367***(−4.34)

0.628*** (23.98) −0.598***(−3.59)

−19.776***(−5.09)

−53.639***(−4.40)

−21.151***(−3.29)

1.034*** (9.99) 3.178*** (5.55) −0.333 (−1.75) −11.008***(−8.75)

1.994*** (3.63) 0.984 626.381 90

4.438*** (6.35) 0.991 593.356 60

4.143*** (13.53) 0.988 586.498 45

1.337 (1.16) 0.996 867.441 15

0.140*(−1.73)

Note: the values in parentheses are t-Statistics, and ***, **, * denote significance at 1%, 5%, 10% levels respectively.

used as a denominator of the GVC_Position index, and Table 7 reports the new regression results. These results also show that the GVC position is an important determinant of the embodied carbon emissions in exports. 5. Concluding remarks With the increasing economic integration, the Global Value Chain (GVC) has become the new normal of economic globalization and international division of labor. However, the nature of China’s low position in the GVC determines the high carbonization of its manufacturing exports. Our paper establishes a model of carbon emission effects that explicitly allows for the importance of the position in the GVC. We then empirically analyse China’s manufacturing carbon emission effect of exports by using a panel data of China’s manufacturing industry from 1995 to 2009. First and foremost, our data show that both the carbon emissions and domestic value added in exports of China’s manufacturing industry increase continuously over the years, while the embodied carbon emissions per unit of domestic value added in exports decrease year by year. Carbon emissions per unit of domestic value added of medium-low-tech manufacturing have the biggest reduction and low-tech manufacturing has the lowest carbon emissions. Secondly, there are differences between the different types of manufacturing industry with regard to GVC participation and position. High-tech manufacturing has the highest participation but lowest position in the GVC, medium-low-tech manufacturing has a high degree of participation, and embeds upstream through forward linkages, while low-tech manufacturing has a low degree of participation and a low position. Thirdly, the trade scale, commodity composition, technique and the position in GVC can effectively explain the changes in China’s embodied carbon emissions in foreign trade. An increase in the volume of foreign trade and the proportion of capital-intensive production in exports would induce a higher level of carbon emissions. In contrast, upgrades in technology and GVC would decrease carbon emissions. In addition, the scale effect is an important indirect factor through which the position in the GVC affects carbon emission in the low-tech, medium-low-tech and medium-high-tech manufacturing sectors, while the technical level is an important indirect factor through which the position in the GVC affects carbon emission in the high-tech manufacturing sectors. Fourthly, as far as the impact size is concerned, the GVC has the greatest impact on carbon emissions from China’s manufacturing exports. The higher the proportion of exports in intermediate goods (the more upstream the production), the more conductive to reducing carbon emissions. However, the amount of carbon emissions would increase in the processing and assembly segments, 11

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which is particularly evident in high-tech manufacturing. Increasing exports have stimulated the exceptional growth of the Chinese economy, which inevitably induces an increase in carbon emissions as a side effect. It is unsustainable however to remain mainly dependent on labor, resources and capital to support economic growth and expansion. At the end of 2014, the Central Economic Working Conference put forward that China’s environmental carrying capacity has reached or is close to reaching its limit, which implies China needs to adopt a path of green, low carbon and cyclic development. The above conclusions became the theoretical foundation for China to initiate the low-carbon transformation. To implement the 2030 Agenda for Sustainable Development, we suggest transforming the growth pattern of foreign trade, including the following recommendations: (i) Embed in the higher ends of the GVC: the high ends of the GVC not only obtain more generous profits, but also help to reduce carbon emissions. Therefore, apart from deepening involvement in the international division of labor, China should participate in activities with high value-added through R&D and marketing to boost international competitiveness. (ii) Adjust and optimize exported commodity composition. Labor-intensive industries make numerous contributions to China’s economic development and carbon emission reduction. Therefore, China should uphold this comparative advantage and strive to develop, and export labor-intensive industries. Meanwhile, China should attempt to make those labor-intensive industries more high-tech intensive as this improved technology would again imply a reduction in carbon emissions. (iii) Accelerate the process of technology research and development. Innovation holds the key to fundamentally unleashing the green growth potential. The government and enterprises should first introduce foreign advanced energy conservation and emission reduction technologies and facilities to improve energy and resource utilization rather than relying on technology to improve production efficiency. Second, the government should increase the combination intensity of production, study and research, increase the cooperation with colleges at home or abroad, cultivate a number of advanced technical personnel and continuously improve China’s independent research and innovation ability on low carbon technology. Finally, although we explore the impact of GVC embeddedness on embodied carbon emissions from both a theoretical and an empirical point of view, several extensions and possible generalizations merit special consideration. One of them is to update the data to examine whether the results conform to the recent characteristics of China’s foreign trade. Another possible extension is to consider how policy shocks like free trade and environmental protection policies affect the GVC embeddedness and embodied carbon emissions in exports. Appendix A Derivative Eq. (11) Start from Eq. (4):

C=

( ) Q (LX , KX ) V

Substitute Eq. (8) and divide and multiply by the same term to obtain the following expression:

C=

PX Q (LX , KX ) V T

= (PX Xg + PY Yg )· =SJ

T

PX Xg

PX Xg + PY Yg

·

T

·V

V

where S = PXX + PYY is the scale of economy and J = production in the total production.

PX X PX X + P Y Y

is the commodity composition, i.e. the share of the polluting good

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