Slowing down of globalization and global CO2 emissions – A causal or casual association?

Energy Economics xxx (xxxx) xxx

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Slowing down of globalization and global CO2 emissions – A causal or casual association? Kunfu Zhu a, Xuemei Jiang b,⁎ a b

Research Institution for Global Value Chains, University of International Business and Economics, Beijing 100029, China School of Economics, Capital University of Economics and Business, Beijing 100070, China

a r t i c l e

i n f o

Article history: Received 23 April 2019 Received in revised form 23 July 2019 Accepted 7 August 2019 Available online xxxx JEL classification: C67 F18 Q43 Q56 Keywords: Globalization Global CO2 emissions Multi-regional input-output tables

a b s t r a c t In recent years we have witnessed slowdowns of both globalization and global CO2 emissions. Not only have Organization for Economic Co-operation and Development (OECD) economies, such as the US and the UK, tried to bring manufacturing back to their home countries but non-OECD economies, such as China and India, have also increased their shares of domestic products at both intermediate and final goods. In this paper, we employ the annual global multi-regional input–output tables compiled by the Asian Development Bank to explore the linkage of the recent slowdown in globalization and global CO2 emissions for the period 2012–2016. Our results suggest that there are indeed some clues indicating a slowdown of globalization in several leading OECD and nonOECD economies. However, the changes of consumption in non-OECD economies are much larger than are those in OECD economies. At the aggregate level, the effects of globalization on emissions have been dominated by non-OECD economies (in particular China and India), showing a negative linkage. More specifically, the changing pattern of globalization has contributed a net increase of 202 Mt. in global CO2 emissions. The recent slowdown of global CO2 emissions cannot, in general, be attributed to the slowdown of globalization. © 2019 Elsevier B.V. All rights reserved.

1. Introduction For most of the past several decades, globalization was seen as an unstoppable positive force, lifting millions of people in developing countries out of poverty and promoting the welfare of people in developed countries through reducing their consumer prices. However, globalization increasingly seems more vulnerable than inexorable. At the global level, one of the most notable changes in recent years has been a reversal of the trend whereby the annual growth rate of global merchandise trade began to be continuously lower than that of global real GDP, especially after 2012 (Fig. 1). At the regional level, the evidence is even clearer. The earlier failure of the Doha round negotiation initiated by the World Trade Organization (WTO), the UK's withdrawal from the European Union, the basket of policies to bring manufacturing jobs back to the US, led by the Trump administration, and the recent US– China “trade war” all seem to suggest that globalization is slowing down and that trade protectionism and nationalism are taking over. Source: The Global GDP growth rate is taken from the World Bank, and the merchandise export value growth rate is taken from the WTO. Meanwhile, the global energy-related CO2 emissions have plateaued. From 1995 to 2008, the global CO2 emissions increased ⁎ Corresponding author at: School of Economics, Capital University of Economics and, Business, No.121, Zhang Jia Lu Kou, Fengtai District, Beijing 100070, China. E-mail address: [email protected] (X. Jiang).

https://doi.org/10.1016/j.eneco.2019.104483 0140-9883/© 2019 Elsevier B.V. All rights reserved.

from 2.2 billion tonnes to 3.0 billion tonnes, with an annual growth rate of 2.68%. They then experienced a fall during the international financial crisis and a recovery in 2010–2011. Since 2012, they have plateaued temporarily at approximately 3.3 billion tonnes, with an annual growth rate of 0.09% for the period 2012–2017 (Fig. 2). This is thought to be linked with growing renewable power generation, switches from coal to natural gas, improvements in energy efficiency, and structural changes in non-Organization for Economic Cooperation and Development (OECD) economies, such as China (IEA, 2018 report). In addition, a slowdown of emissions embodied in the exports from developing countries (especially China) to developed countries is also observed for the same period (Pan et al., 2017). To what extent has the recent slowdown of globalization contributed to the plateauing of global CO2 emissions? Many papers have been trying to study the relationship between international trade and global CO2 emissions. One of the main streams of research is empirical testing of the so-called pollution haven hypothesis (PHH) effect, yielding contradictory results. Some of the studies have provided evidence for the pollution haven effect; that is, international trade has brought an increase in global CO2 emissions through a displacement of “dirty” industries from the developed regions to developing regions with lax environmental standards (see, e.g., Copeland and Taylor, 2004; Cole, 2004; Silva and Zhu, 2009; Fell and Maniloff, 2018). In contrast, other studies have found that international trade generates a significant saving of emissions, as the environmentally efficiency of resource-

2

K. Zhu, X. Jiang / Energy Economics xxx (xxxx) xxx

Fig. 1. The growth rate of merchandise trade volume and real global GDP, 1995–2017 (%).

intensive countries supplying developing countries (especially China) is capable of offsetting the increase in emissions associated with the outsourcing from developed to developing countries (see, e.g., Strømman et al., 2009; Lopez et al., 2013; Zhang et al., 2017). Another stream of the literature has employed global multi-regional input–output tables (GMRIO) to investigate the role of international trade in the net growth of global CO2 emissions (see, e.g. Arto and Dietzenbacher, 2014; Hoekstra et al., 2016; Malik and Lan, 2016; Jiang and Green, 2017; Jiang et al., 2018). One of the advantages of the GMRIO-based model is that it can trace the emissions generated along entire global production chains at a detailed national and sectoral level (Wiedmann, 2009; Jiang et al., 2016). By focusing on the changing pattern of trade structure, the studies based on GMRIO tables have generally found a positive impact of international trade on global CO2 emissions. For example, Hoekstra et al. (2016) decomposed the effects of changes in the structure of international trade between different income groups of economies on the growth of their CO2 emissions, and they found that the net global effects derived from the change of international sourcing pattern amounted to 18% of the total global CO2 emissions' growth (i.e., 1.1 Gt) over the period 1995–2007. In a similar vein, Jiang and Green (2017) found that changing trade geography toward China alone contributed, on average (2001–2008), a positive 919 Mt. CO2 equivalent to global GHG emissions annually. They attributed such a net positive effect to the lower carbon intensities in developing countries (especially China) compared to developed countries. However, the above studies have mainly paid attention to the period before 2012, when the international trade was still expanding under rapid globalization. As mentioned above, the recent slowdown of globalization has been accompanied by significant changes of consumption

and trade patterns. Not only have the US and the UK tried to bring manufacturing back to their home countries, but the major developing countries, China in particular, have also increased their shares of domestic products at both intermediate and final goods (Jiang et al., 2018). Given the gap of lower emission intensity of developed countries over developing countries, developed countries bringing their manufacturing back home may lead to a net decrease of global CO2 emissions, whereas developing countries increasing their use of domestic products may lead to a net increase of global CO2 emissions. Thus, the total impact of a slowdown in globalization in global CO2 emissions is uncertain. In this paper, we will employ the annual GMRIO tables compiled by the Asian Development Bank to explore the link between the recent slowdown in globalization and global CO2 emissions for the period 2012–2016. Moreover, many new technology trends are reshaping manufacturing into local production; these trends include robotics, artificial intelligence, 3D printing, and new energy production, such as solar, wind, and hydro power. This may lessen or even reverse globalization over the next decades (see, e.g., Hammes, 2016; Klaus, 2016; World Economic Forum, 2016; Rahman et al., 2017). In this context, exploration of the link between recent slowdowns in both globalization and CO2 emissions is not only important for understanding the recent plateauing of global CO2 emissions but also provides insight for future climate change mitigation. 2. Methodology The slowdown of globalization may influence the global CO2 emissions through several channels. First, the slowdown of globalization

Fig. 2. The global CO2 emissions and their growth rate, 1995–2017.

K. Zhu, X. Jiang / Energy Economics xxx (xxxx) xxx

may lead to an increasing proportion of consumption of domestic final products, and, consequently, a reduction of volume in foreign trade in final products. That is, country A would consume more domestic final goods/services, rather than import them from either country B or country C. However, whether this leads to either a net growth or a net reduction would depend on the emission intensity of country A compared to other countries. If country A has much lower emission intensity than do countries B and C, then increasing consumption of domestic final products in country A would lead to a net growth of global CO2 emissions, and vice versa. Second, the slowdown of globalization would influence the fragmentation of production. One direct consequence is that country A would increasingly use domestic intermediates, rather than slicing up the production activities into the different countries B and C. Again, if country A has much lower emission intensity than do countries B and C in terms of the productions of intermediates, then an increasing usage of domestic intermediates by country A would also lead to a net growth of global CO2 emissions, and vice versa. Last but not least, the slowdown of globalization would influence the entire foreign trade pattern of each country, in terms of both intermediate and final products. We refer to them as another two major channels. For example, country A may shift some of its consumption (of either intermediate or final products) from country B to country C, mainly because country C has either a lower trade cost or a lower labor cost compared with country B or because country B has lower product quality compared with country C. The impacts of such a shift in trade patterns have been recognized as the role of international trade in the net growth of global CO2 emissions (see, e.g., Arto and Dietzenbacher, 2014; Hoekstra et al., 2016; Malik and Lan, 2016; Jiang and Green, 2017; Jiang et al., 2018).1 In the following, we will introduce how we quantify these effects based on environmental multi-regional input–output (E-MRIO) tables.

2.1. Environmental multi-regional input–output (E-MRIO) framework and data source In this paper, we employ E-MRIO tables to trace the change of performance of each country in respect of globalization, and we evaluate the impacts of these changes on global CO2 emissions through the above-mentioned three major channels. In Table 1, we first present the E-MRIO framework. The diagonal matrices of intermediate use give the intra-regional intermediate deliveries, where Zrr = {zrr ij }, and zrr ij represent the intermediate deliveries from industry i in region r to industry j in region r, with i, j = 1,…,m, where m is the number of industries, and r = 1,…,n, where n is the number of regions. The non-diagonal matrices give inter-regional intermediate deliveries, where Zrs = {zrs ij }, and zrs ij represent the intermediate deliveries of products from industry i (=1,…,m) in region r (=1,…,n) that are used to produce products in industry j (=1,…,m) in region s (=1,…,n; ≠ r). The matrices Yrs = rs {yrs i } give the final demand, with yi being the final demand (including consumption and fixed capital formation) of region s (=1,…,n) for output of industry i (=1,…,m) that is fulfilled by region r (=1,…,n). Xr (r = 1,…,n) represents the total output in region r (=1,…,n) and Er (r = 1,…,n) represents the total CO2 emissions in region r (=1,…,n) that are generated from combustion of fossil fuels in industrial processes.

1 Please note that, in this paper, we only consider the channels whereby slowdown of globalization influences trade and consumption structure and the subsequent influence on global CO2 emissions. There is another channel by which globalization may influence global emissions: the scale effect. That is, the slowdown of globalization would have a negative impact on economic development and, subsequently, may reduce the global final demand as well as global emissions. However, this is outside the focus of this paper.

3

According to Table 1, we have row equilibrium in matrix notation as follows: h

11

Z

1n

n1

þ ⋯ þ Z ; ⋮⋱⋮; Z 2

3 X1 4 ¼ ⋮ 5 Xn

2 11 3 i Y þ ⋯ þ Y1n 5 þ⋯þZ uþ4 ⋯ Yn1 þ ⋯ þ Ynn nn

ð1Þ

The direct input coefficients can then be obtained by normalizing the columns in the IO table; that is:  −1 cs Ars ¼ Zrs X

ð2Þ

where “^” represents diagonalization of vector. The Leontief inverse is 2 11 3 ⋯ B1n B −1 thus defined as B¼4 ⋮ ⋱ ⋮ 5 ¼ ðI−AÞ ¼ n1 nn B ⋯ B 2 3−1 I−A11 ⋯ −A1n −1 r r cr 4 ⋮ ⋱ ⋮ 5 . Using CA ¼ E ðX Þ to denote the matrix n1 nn −A ⋯ I−A of carbon emissions intensity per unit of output by sector in region r, the CO2 emissions generated along global production chains can be traced as: 2

E11 4 ⋮ En1

32 3 2 1 c ⋯ E1n B11 CA 0 0 6 74 5 ⋱ ⋮ 5¼4 0 ⋮ ⋯ 0 cn Bn1 ⋯ Enn 0 0 CA 2 11 3 1n ^ ^ ⋯ Y Y 6 7 4 ⋮ ⋱ ⋮ 5 n1 nn ^ ^ ⋯ Y Y

⋯ ⋱ ⋯

3 B1n ⋮ 5 Bnn ð3Þ

rs where the elements Ers ij of matrix E indicate the production-based emissions of industry i (=1,…,m) in region r (=1,…,n) led by the final demand in industry j (=1,…,m) of region s (=1,…,n). The summation of Ers, ∑sErs and ∑rErs will give the production-based emissions of region r and consumption-based emissions of region s, respectively. As mentioned, our GMRIO database is taken from the Asia Development Bank (ADB). This is a symmetric industry-by-industry IO database that covers the years 2000, 2005–2008 and 2010–2017. For the years 2000 and 2005–2008, it covers 46 regions (45 economies and 1 rest of the world) and 35 industries. For the period 2010–2017, it is extended to distinguish 63 regions (62 economies and 1 rest of the world) and 35 industries. As our focus is the recent slowdown of globalization, we rely mainly on the recent tables for 2011–2017 that distinguish 63 regions.2 Please refer to the Appendix Tables A and B for their classifications of economies and industries. For simplification of our study, we have followed the classification of the OECD and refer to OECD/nonOECD economies as developed/developing economies.3 Regarding CO2 emissions, we rely mainly on IEA's statistics on CO2 emissions from fuel combustion and reconcile them into the classification of ADB MRIO table (IEA, 2018).4 Note that the IEA CO2 emissions' data mainly distinguish manufacturing and utilities. We adopted the method of Jiang and Chris (2017) to use intermediate energy in an IO table to proportionally decompose the CO2 emissions released by the IEA into a consistent classification with ADB MRIO tables. Finally, all the CO2 2 When we conduct comparisons for the entire period 2000–2016 in Section 3.1, we aggregate the tables after 2011 into a consistent classification with 46 economies. 3 Please refer to the OECD website (http://www.oecd.org/about/membersandpartners/ list-oecd-member-countries.htm) for the list of OECD member countries. 4 That means that, in this paper, we focus only on the CO2 emissions generated in the production of goods and services. The CO2 emissions from land use, forestry, and household activities through combustion of fossil fuels (e.g., either driving cars or cooking) are excluded.

4

K. Zhu, X. Jiang / Energy Economics xxx (xxxx) xxx

Table 1 The environmental multi-regional input–output framework.

emissions at industry level were calibrated to ensure that a reaggregation would result in an official release by the IEA. 2.2. The effects of changing globalization on CO2 emissions We introduce our method of evaluating the impacts of globalization on global CO2 emissions through the above channels. In general, we follow the literatures that study the role of international trade on the net growth of global CO2 emissions, such as Arto and Dietzenbacher (2014), Hoekstra et al. (2016), Malik and Lan (2016), Jiang and Green (2017),and Jiang et al. (2018), and we further isolate the impact of a changing share of consumption on domestic final and intermediate products. Our first step is to isolate the share of each economy in the international trade of intermediates and final demand. In this paper, we follow the line with Arto and Dietzenbacher (2014), Hoekstra et al. (2016) and Jiang et al. (2018), and decompose the A-matrix into technical coefficients and pattern of international trade. That is, we define the total technical input coefficients of industry j (=1,…,35) in region s (=1, X …,63) from industry i (i-input, i = 1,…,35) as as ars ij . In matrix ij ¼ r

form, the technical input coefficients of region s would be As 2

3 as ⋯ as 11 1m ⋱ ⋮ 5 as a 35 × 35 matrix. Then, if we horizontally stack ¼4 ⋮ as ⋯ zs mm m1 the A ∗s matrices and further vertically stack the result 63 times, we 2 1 3 ⋯ An A  ∗ would have A ¼ 4 ⋮ ⋱ ⋮ 5 as a 2205 × 2205 matrix. A repreA1 ⋯ An sents the technical intermediate input coefficient, irrespective of the sourcing region. rs ∗s Let crs ij = aij /aij indicate the share sourced from region r (=1,…,63) ∗s in the input aij in region s (=1,…,63); then, in the matrix form, we 2 rs 3 c11 ⋯ crs 1m rs 4 ⋮ ⋱ ⋮ 5 as a 35*35 matrix (where ∑rcrs would have C ¼ ij rs ⋯ c crs mm 2 11 m1 3 C ⋯ C1n = 1), and C ¼ 4 ⋮ ⋱ ⋮ 5 as a 2205 × 2205 matrix, to reflect the Cn1 ⋯ Cnn

pattern of international sourcing. Then, the A-matrix can be decomposed as A ¼ C⊗A

ð4Þ

where ⊗ stands for the Hadamard product. Moreover, we can split the C-matrix into sub-matrices for each re0 0 C1s ⋯ ⋯ ⋮ ⋮ ⋮ ⋱ ⋱ S ⋯ ⋯ gion s (=1,…,65). By letting C ¼ ½ 0 0  , we Css ⋱ ⋱ ⋮ ⋮ ⋮ ⋯ ⋯ 0 0 C63;s X S would have C ¼ C . The Leontief inverse can be rewritten as s

   −1 B ¼ I− ∑ CS ⊗A s

ð5Þ

In a similar fashion, the final demand can be decomposed into the determinants of total final demand and the pattern of sourcing. Let ys i ¼ X yrs i indicate the total final demand in region s for output of industry i r

rs ∗s from all source regions, frs i = yi /yi indicate the share sourced from region r (=1,…,63) in the final demand of region s (=1,…,63) for output in industry i (=1,…,35), and define the matrices correspondingly, and the final demand can be decomposed as

^¼ Y

" X

# ^FS ⨂Y ^

ð6Þ

s

Then, let subscripts t0 and t1 denote the years t0 and t1, and the actual emissions in year t1 can be traced as: 2

3 ⋯ E1n E11 t1 t1 4 ⋮ ⋱ ⋮ 5 nn En1 t12 ⋯ Et1 3 1 !    −1 X c ⋯ 0 CA t1 s 6 7  s ^ ^ Ft1 ⊗Y ∙ ¼4 ⋮ ⋱ ⋮ 5∙ I− ∑ Ct1 ⊗At1 t1 n s s c 0 ⋯ CA t1

ð7Þ

K. Zhu, X. Jiang / Energy Economics xxx (xxxx) xxx rs rs where the sum of Ers t1, ∑sEt1, and ∑rEt1 gives the production-based emissions of region r and consumption-based emissions of region s, respectively. For region l, the slowdown of globalization may lead to an increasing proportion of consumption of domestic final products. Assume that, for region l, the share of domestic final products in year t1 remained the same as that in year t0, and the shares of foreign final products in year rl g t1 changed proportionally, so that we still have ∑r f i ðt1Þ ¼ 1, that is:

r¼1;…;63 rl

∑ f i ðt0Þ rl g rl , if r ≠ l (Strømman et al., 2009) f i ðt1Þ ¼ f i ðt1Þ  r≠l r¼1;…;63 rl ∑r≠l f i ðt1Þ rl g rl f i ðt1Þ ¼ f i ðt0Þ, if r = l. Then, the global emissions in year t1 without the change in the proportion of domestic final products in region l (Scenario I) would become: 2

1n E11 t1 I ⋯ Et1 4 ⋮ ⋱ ⋮ nn En1 t12I ⋯ Et1 c1 6 CAt1 ⋯ ¼4 ⋮ ⋱ 0 ⋯

I

remained the same as that in year t0, that is: g rlg rl f i ðt1Þ ¼ f i ðt0Þ

ð11Þ

Then, global emissions in year t1, without the change in the trade pattern of final demand in region l (Scenario II), would become: 2

3 1n E11 t1 II ⋯ Et1 II 4 ⋮ ⋱ ⋮ 5 nn En1 II ⋯ E t12 t1 II 3 !  −1 c1 ⋯ 0 7  CA X s f  f 6 t1  s l ^ ^ C ∙ F þ ⊗A ∙ I− ∑ F ¼4 ⋮ ⋱ ⋮ 5 t1 ⊗Y t1 t1 t1 t1 n s s≠l c 0 ⋯ CA t1 ð12Þ Similarly, the effect on global emissions due to the change in the trade pattern of final demand in region l for the period t0-t1 would be:

3 5

I

3 !  −1 0 7  X s  f  s l ^ ^ ∙ I− ∑ C ∙ F þ ⊗A F ⋮ 5 t1 ⊗Y t1 t1 t1 t1 n s s≠l c CA t1 ð9Þ

As the final demand of region s (=1, …, n; ≠l) remains unchanged, rs we would have Ers t1_I = Et1(r, s = 1, …, n; s ≠ l). Meanwhile, the effect on global emissions due to the change in the proportion of domestic final products in region l for the period t0-t1 would be: EGF lt1 I ¼ ∑r Erlt1 −∑r Erlt1 I

5

ð10Þ

Among these, the diagonal elements Ellt1 − Ellt1_I give the domestic emission effects in region l, where Ellt1 − Ellt1_I N 0 suggests that region l purchased more domestic final products. The sum of non-diagonal elerl ments along the column ∑r≠lErl t1 − ∑r≠lEt1_I gives the effects on consumption-based emissions of region l due to the change in the proportion of domestic final products of region l, that is, how global emissions changed due to the change in the proportion of domestic (and correspondingly foreign) final products of region l. If we summarize the entire column, EGFlt1_I = ∑rErlt1 − ∑rErlt1_I N 0 suggests that either region l, with higher carbon intensities, purchased more domestic final products or region l, with lower carbon intensities, purchased fewer domestic final products. For example, China has relied highly on coal for its primary energy input, and, as a result, China's CO2 emission intensity per US$ GDP in constant price has been approximately 1.8–2.0 times that of the world average (IEA, 2018). When China starts to consume more domestic final products rather than import them from other countries with lower emission intensities, there would be a net increase both in China's domestic CO2 emissions and in global CO2 emissions, indicated as Ellt1 − Ellt1_I N 0 and EGFlt1_I = ∑rErlt1 − ∑rErlt1_I N 0. In contrast, as the US (or OECD economies) has generally lower emission intensity than the world average, there would be a net decrease in both the US's domestic CO2 emissions and global CO2 emissions when the US (or OECD economy) starts to consume more domestic final products, rather than importing them. Thus, the summation ∑lEGFlt1_I N 0 implies that more economies with higher emission intensities (mostly non-OECD economies), rather than economies with lower emission intensities (mostly OECD economies), are inclined to purchase more domestic final products. Among these, the sum of non-diagonal elements ls along the row ∑s≠lEls t1 − ∑s≠lEt1_I gives the overall effects of production-based emissions of region l due to the change in the proportion of domestic final products of other regions s (≠l). Now, we turn to measurement of the effects of the changing trade pattern. Assume that, for region l, the share of final products in year t1

EGF lt1 II ¼ ∑r Erlt1 I−∑r Erlt1 II

ð13Þ

where EGFlt1_II N 0 suggests that region l is inclined to purchase more foreign final products from economies with higher emission intensities (mostly non-OECD economies). Such an effect is irrelevant in respect of whether region l purchased either more or fewer domestic products, and it is relevant only in respect of the changing pattern of foreign trade in region l. The summation ∑lEGFlt1_II N 0 implies that the world is, in general, inclined to import more final products from economies with higher emission intensities (mostly non-OECD economies). Among these, the sum of non-diagonal elements along the row ∑s≠lEt1 ls _I − ∑s≠lEls t1_II gives the overall effects of production-based emissions of region l due to the change in the foreign trade pattern of the final products of all the other regions s (≠l). In a similar vein, assume that, for region l, the share of domestic intermediates in year t1 remained as same as that in year t0, and the shares on foreign intermediates in year t1 changed proportionally, so that we still have ∑ crl g ðt1Þ ¼ 1, that is: r ij

crlij g ðt1Þ ¼ crlij ðt1Þ 

r¼1;…63 rl cij ðt0Þ , r¼1;…;63 rl cij ðt1Þ ∑r≠l

∑r≠l

if r ≠ l (Arto and Dietzenbacher,

2014) crl g ðt1Þ ¼ crl ðt0Þ, if r = l ij

ij

Then, the global emissions in year t1, without the change in the proportion of domestic intermediates in region l (Scenario III), would become: 2 4

E11 t1 III ⋯ ⋮ ⋱ n1 Et12III ⋯ c1 6 CAt1 ¼4 ⋮ 0

3 E1n t1 III ⋮ 5 Enn t1 III 3

!  −1 X ⋯ 0 7  f s   l s ^ ^ Ft1 ⊗Y ∙ ⋱ ⋮ 5∙ I− Ct1 þ ∑ Ct1 ⊗At1 t1 n s≠l s c ⋯ CA t1 ð15Þ

The effect on global emissions due to the change in the proportion of domestic intermediates in region l for the period t0-t1 would be: EGIlt1 I ¼ ∑r Erlt1 −∑r Erlt1 III

ð16Þ

where the diagonal elements Ellt1 − Ellt1_III give the domestic emission effects in region l, and the sums of non-diagonal elements along the colrl umn ∑r≠lErl t1 − ∑r≠lEt1_III give the effects on consumption-based emissions of region l, i.e., the change of production-based emissions in other regions s (≠l) driven by the change in the proportion of domestic final products of region l. Summing along the column, EGIlt1_I = ∑rErlt1

6

K. Zhu, X. Jiang / Energy Economics xxx (xxxx) xxx

− ∑rErlt1_III N 0 suggests that region l is inclined to purchase more intermediates from economies with higher emission intensities (mostly non-OECD economies). Again, we use China as the example. China has a much higher CO2 emission intensity in terms of its supply of intermediates. When China slows down international production sharing activities and starts to consume more domestic intermediates rather than imports, there would be a net increase in both China's domestic CO2 emissions and global CO2 emissions, indicated as Ellt1 − Ellt1_III N 0 and EGIlt1_I = ∑rErlt1 − ∑rErlt1_III N 0. In contrast, when the US slows down international production sharing activities and starts to use more domestic intermediates, there would be a net decrease in both the US's CO2 emissions and global CO2 emissions. Again, the sums of nonls diagonal elements along the row ∑s≠lEls t1 − ∑s≠lEt1_III give the overall effects of production-based emissions of region l due to the change in the proportion of domestic intermediates of other regions s (≠l). Now, we turn to measurement of the effects of the changing trade pattern in intermediates. Assume that, for region l, the shares of intermediates in year t1 remained the same as that in year t0, that is: g ðt1Þ ¼ crlij ðt0Þ crlijg

ð17Þ

Then, global emissions in year t1, without the change in trade pattern of final demand in region l (Scenario IV), would become: 2

E11 t1 IV ⋯ 4 ⋮ ⋱ En1 t12IV ⋯ c1 6 CAt1 ¼4 ⋮ 0

3 E1n t1 IV ⋮ 5 Enn t1 IV 3

" ! #−1 ! ⋯ 0 7 X s f  f  l s ^F ⊗Y ^ ∙ ⋱ ⋮ 5∙ I− Ct1 þ ∑ Ct1 ⊗At1 t1 t1 s≠l s cn ⋯ CA t1 ð18Þ

The effect on global emissions due to the change in the trade pattern of intermediates in region l for the period t0-t1 would be: EGIlt1 II ¼ ∑r Erlt1 III−∑r Erlt1 IV

ð19Þ

where EGIlt1_II N 0 suggests that region l is inclined to import more foreign intermediates from economies with higher emission intensities (mostly non-OECD economies). Compared either with studies using the Structural Decomposition Analysis (SDA) method (Arto and Dietzenbacher, 2014; Hoekstra et al., 2016) or with studies focused on the PHH (Lopez et al., 2013; Zhang et al., 2017), our method is concerned mainly with the spatial difference of emission intensity in year t1 and the changing consumption pattern in both final and intermediate products from t0 to t1.5 In addition, against the background that both OECD and non-OECD economies slowed down the pace of globalization, our method isolates the effect on emissions when one economy increases their proportion of consumption of domestic intermediate and final products. More specifically, the above effects EGFlt1_I and EGIlt1_I isolate such effects driven by the changes in consumption of domestic final and intermediate products, respectively, and the effects EGFlt1_II and EGIlt1_II quantify the effects driven by the changes in the foreign trade pattern for final and intermediate products. 3. Empirical results 3.1. The aggregate effects of changing globalization on global CO2 emissions In Fig. 3, we firstly summarize the four kinds of effects on global CO2 emissions driven by the changing globalization across all economies, including the effect driven by the changes in consumption of domestic 5

Please refer to Jiang et al. (2018) for more detail on the comparisons of methods.

intermediates across all economies (∑lEGIlt1_I), the effect driven by the changes in consumption of domestic final products across all economies (∑lEGFlt1_I), the effect driven by the changes in the foreign trade pattern of intermediates across all economies (∑lEGIlt1_II), and the effect driven by the changes in the foreign trade pattern of final products across all economies (∑lEGFlt1_II). For comparison, we also include the results of the other three sub-periods that are covered by the ADB MRIO tables, including 2000–2005 (the period with rapid globalization), 2005–2008 (pre-crisis era), and 2008–2012 (post-crisis era), and compare those results with that of the sub-period 2012–2016 (the period with a slowdown of globalization). The four sub-periods are characterized with different features in terms of globalization and, thus, with different effects on global CO2 emissions. As for the effect of changes in the foreign trade pattern of final products (∑lEGFlt1_II), it remained significantly negative for all the sub-periods before 2012 and became negligibly negative for the sub-period 2012–2016. Further investigations of the share of final demands show that both OECD and non-OECD economies increasingly imported final products from (other) OECD economies with lower emission intensities before 2008 (Fig. 4c and d) and that such changes together reduced net CO2 emissions by 871 Mt. In contrast, the effect of changes in the foreign trade pattern of intermediate products (∑lEGIlt1_II) remained positive before 2012. Further investigations of the share of intermediate inputs show that both OECD and non-OECD economies increasingly imported intermediates from other non-OECD economies with higher emission intensities (Fig. 4a and b). Accumulatively, these changes increased net CO2 emissions by 1023 Mt. This is in line with the trend of international fragmentation that led a shift of global production chains from developed economies to developing economies (see also Jiang et al., 2018). Note that the effect of changes in the foreign trade pattern of intermediate products (∑lEGIlt1_II) turned to negative for the sub-period 2012–2016. This is accompanied by the fact that both OECD and non-OECD economies purchased fewer intermediates from non-OECD economies with higher emission intensities after 2012 (Fig. 4a and b). However, this is not very surprising, as some major OECD economies, such as the US and the UK, as well as some non-OECD economies, such as China, have been trying to bring manufacturing back after the international crisis. As for the effect of changes in consumptions on domestic final products (∑lEGFlt1_I), it was positive from 2000 to 2008. Further investigations of the share of final demand show that both OECD and nonOECD economies consumed fewer domestic final products for the period 2000–2008 (Fig. 4c and d). However, this effect turned to negative for the period 2008–2012, yielding a reduction of 210 Mt. CO2 emissions. During the international crisis, both OECD and non-OECD economies started to purchase more domestic final products (Fig. 4c and d). It is clear that the negative effects driven by OECD economies consuming more domestic final products outperformed the positive effects driven by non-OECD economies consuming more domestic final products. As for the effect of changes in consumption of domestic intermediates (∑lEGIlt1_I), it varies considerably during the four sub-periods. For the sub-periods 2000–2005 and 2008–2012, it remained negative, but by relatively small amounts (18 Mt. and 66 Mt., respectively). For the sub-periods 2005–2008 and 2012–2016, the effect of changes in consumption of domestic intermediates became significantly positive, at 328 Mt. and 263 Mt., respectively. This might be because, for different sub-periods, OECD and non-OECD economies show different trends of change. For example, OECD economies kept reducing domestic intermediate inputs from 2000 to 2008, whereas non-OECD economies first reduced their domestic intermediate inputs from 2000 to 2005 and consumed more and more domestic intermediate inputs from 2005 to 2016 (Fig. 4a and b). The sub-period 2012–2016 deserves particular note, as the global CO2 emissions in the industrial process increased only by 429 Mt. from 2012 to 2016 — the effect of more non-OECD economies purchasing more domestic intermediates alone contributed 263 Mt. This seems to suggest a negative link between the slowdown of

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Fig. 3. The effects of changing globalization on global CO2 emissions, 2000–2016.

globalization and the slowdown of global CO2 emissions. As mentioned already, after the international financial crisis, both OECD economies (such as the US and the UK) and non-OECD economies (such as China) started to consume more domestic intermediates and final products. This negative effect seems to imply that the effect driven by nonOECD economies outperformed the effect driven by OECD economies for the recent period in which there was a slowdown of globalization. Thus, further exploration at a more disaggregated national level for the period 2012–2016 is necessary.

3.2. The effects of the slowdown of globalization during 2012–2016: a consumption-based perspective 3.2.1. Two benchmark economies: China vs. the US Now, we turn to the recent sub-period that experienced a slowdown of globalization, i.e., 2012–2016. We first isolate the two largest OECD and non-OECD economies, the US and China, to explore the changes in their consumption patterns and their influences on global consumption-based emissions. That is, we explore how the changes in the consumption patterns of intermediate and final products by the US and China influence the production-based emissions of their own and other economies through the above-mentioned four types of channels. More specifically, we aggregate the four types of effects into three

Fig. 4. The consumption patterns of intermediate inputs and final demands, by OECD and non-OECD economies, 2000–2016.

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major economies: domestic economy, OECD economies, and non-OECD economies. Taking the example of the effect of changes in consumption of domestic final products for China (EGFlt1_I = ∑rErlt1 − ∑rErlt1_I, l = 8 representing China), we first have Ellt1 − Ellt1_I (l = 8) to measure how China's change in consumption of domestic products influences its own domestic emissions and then sum along the column ∑r≠l, r∈OECDE-t1 rl − ∑rErlt1_I(l = 8) to measure how such a change in China's final demand influences the production-based emissions in OECD economies; the sum ∑r≠l, r∈non−OECDErlt1 − ∑rErlt1_I (l = 8) can measure how such a change influences the production-based emissions in other nonOECD economies. It should be noted that, for the effect of EGIlt1_I and EGFlt1_I (i.e., effects driven by the changes in consumption of domestic intermediate and final products), as we assume identical amounts of final demand, an increase of consumption of domestic final products would be accompanied always by decreases of consumption of final products in both OECD and non-OECD economies, and vice versa. As for the effect of EGIlt1_II and EGFlt1_II (i.e., effects driven by the changes in the foreign trade patterns of intermediate and final products), an increase of consumption of products of (other) OECD economies would be accompanied always by a decrease of consumption of products of (other) non-OECD economies, and vice versa.

In Fig. 5a, we summarize the effects of China's changing consumptions patterns during 2012–2016 on both the country's domestic CO2 emissions and global CO2 emissions. China alone would lead a net increase of global CO2 emissions by 261 Mt., with China's increased consumption of domestic intermediate and final products contributing +205 Mt. and +50 Mt., respectively. Further investigations of the MRIO tables show that, from 2012 to 2016, China's consumption of domestic intermediates and final products in current prices increased by US$457 billion and US$107 billion, respectively. As mentioned already, China's emission intensities are generally higher than is the world average. Thus, these changes in consumption have led to net increases of China's domestic production-based emissions by 383 Mt. and 74 Mt., respectively. As a result, for intermediates, China's imports from OECD and non-OECD economies decreased by US$178 billion and US$279 billion in current prices under Scenario III and reduced the production-based emissions of OECD and non-OECD economies by 60 Mt. and 119 Mt., respectively (EGI_I effect). Such change might be attributed largely to the decreasing proportion of China's processing exports. From 2000 to 2010, the proportion of processing exports in China's total exports decreased from 41.1% to 29.9%, and it then decreased further to 25.0% in 2016. As the production of processing exports involves mainly either

Fig. 5. The effects of changes in the consumption patterns of intermediates and final products in the US and China on global CO2 emissions, 2012–2016.

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assembly or package of imported intermediates and domestic inputs are hardly involved, it is not surprising that the decreasing proportion of processing exports (i.e., the increasing proportion of normal productions) requires more domestic intermediates. As for final products, China's imports from OECD and non-OECD economies decreased by US$70 and US$37 billion in current prices under Scenario I, reducing the production-based emissions of OECD and non-OECD economies by 15 Mt. and 10 Mt., respectively (EGF_I effect). Note that the net increase of global CO2 emissions in industrial processes for the same period was only 429 Mt. It seems that the slowdown of globalization in China alone (i.e., China consumed more domestic products) contributed considerably to the global emissions' growth after 2012. After the financial crisis, China seemed to purchase more products from OECD economies than from non-OECD economies. In Fig. 5b, we present the results for the US. Against the background of “bring manufacturing back” in the US, the nation's consumption of domestic final products in constant prices increased by US$82 billion from 2012 to 2016, and its consumption of final products of other OECD and non-OECD economies decreased by US$40 billion and US $42 billion, respectively. As the emission intensity of the US is generally lower than is the world average, at the aggregate level, this change led to a net reduction of global CO2 emissions by 33 Mt. However, as for intermediates, the US decreased its consumption of domestic intermediates by US$17 billion, and increased its imports from non-OECD economies by US$17 billion, leading to a net increase of global CO2 emissions by 20 Mt. Another notable finding is that, regarding the trade pattern, the US also expanded its imports of intermediates and final products from OECD economies (by US$37 billion and US$13 billion separately), and shrank its corresponding imports from non-OECD economies, suggesting a growing trend of North–North trade rather than North–South trade. As for the emissions, as OECD economies generally have lower emission intensities than do non-OECD economies, the change in the foreign trade pattern of the US had a negative effect on emissions, at 13 Mt. for the period 2012–2016. In brief, both China and the US show clear signs of a slowdown in globalization from 2012 to 2016. China has considerably increased its consumption of domestic intermediate and final products and expanded its imports of intermediates and final products from OECD economies under Scenarios I and III. All of these changes have led to a significant increase of both China's domestic CO2 emissions and global CO2 emissions. In total, China's change in its consumption pattern alone led to a net increase of global CO2 emissions by +261 Mt. for the period 2012–2016. In contrast, the increase in consumption of domestic final products of the US has been smaller and has led only to a subtle increase of the nation's domestic CO2 emissions. In addition, since the US started to consume more intermediate and final products from OECD economies, its aggregate effect on global CO2 emissions has been negative, at −26 Mt. for the period 2012–2016. 3.2.2. Other major economies of OECD and non-OECD A further interesting question then arises as to whether other OECD and non-OECD economies also followed the pattern of either China or the US regarding the change of globalization. In Fig. 6a–d we list the four types of effects by economy. For simplification of our analysis, we list only the economies for which the absolute effects were larger than 5 Mt. for the period 2012–2016.6 We also aggregate the four types of effects into three major economies: domestic economy, OECD economies, and non-OECD economies. Taking the example of the first bar of India in Fig. 6a (EGI_I), it shows how the changes in India's consumption of domestic intermediates influences the production-based CO2 emissions in India itself (Domestic), OECD economies, and other non-OECD economies. 6 The results for all 63 economies are available upon request. We also omitted the results for rest of the World (RoW) because the RoW contains a lot of economies, and its results only reflect an aggregate effect and lack specific policy implications.

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In general, only several economies out of the 63 economies experienced a significant change in globalization (larger than 5 Mt). Among them, India is a typical non-OECD economy following China's pattern, increasing its domestic consumption of intermediates and final products in current prices by US$ 45 billion and US$ 46 billion from 2012 to 2016, leading to net increases of global emissions by 35 Mt. (EGI_I effect, as shown in Fig. 6a) and 6 Mt. (EGF_I effect, as shown in Fig. 6c), respectively. As regards foreign trade, India has also expanded imports of final products from OECD economies, leading to a net decrease of emissions by 24 Mt. (EGF_II effect, as shown in Fig. 6d). However, there are also non-OECD economies that show effects opposite to those seen in the case of China; among them, one typical example is Russia. Russia decreased its domestic consumption of intermediates by US$11 billion in current prices for the period 2012–2016, leading to a net decrease of global emissions by 12 Mt. As for final products, Russia expanded its imports from non-OECD economies rather than from OECD economies, leading to a net decrease of global emissions by 13 Mt. The OECD economies also show somehow opposite effects, with the US, Switzerland, and France, for example, decreasing their consumption of domestic intermediates, leading to a net increase of global emissions (EGI_I, as shown in Fig. 6a). Conversely, Japan and the UK increased their consumption of domestic intermediates, leading to a net decrease of global emissions (EGI_I, as shown in Fig. 6a). As regards the trade of intermediates, Belgium expanded its imports from non-OECD economies, whereas the Netherlands and Germany expanded their imports from OECD economies. Consequently, a net positive effect is observed for Belgium, and net negative effects are observed for the Netherlands and Germany (EGI_II, as shown in Fig. 6b). As for final products, Mexico and Czech expanded their imports from non-OECD economies, leading to a positive effect on emissions. In brief, there is no clear sign that OECD/non-OECD economies have similar changes in globalization — all economies show quite different patterns in terms of their change of consumption of domestic and imported products. 3.3. The effects of the slowdown of globalization during 2012–2016: A production-based perspective 3.3.1. Two benchmark economies: China vs. the US Now, we turn our attention to the production-based perspective. That is, we explore how the changes of the pattern of world final demand influence the production-based emissions in one specific economy. We have still selected China and the US as the benchmark economies, and we aggregate the four types of effects into three major economies : domestic economy, OECD economies, and non − OECD economies. We still take the example of the effect of changes in consumption of rl domestic final products in China (EGFlt1_I = ∑rErl t1 − ∑rEt1_I, l = 8 representing China). Though the domestic effect under the productionbased perspective remains the same as that under the consumptionbased perspective (Ellt1 − Ellt1_I, l = 8), we sum along the row, ∑s≠l, ls ls s∈OECDEt1 − ∑rEt1_I(l = 8), to measure how the changes of OECD economies on domestic products influence China's production-based emisls sions, and the sum, ∑s≠l, s∈non−OECDEls t1 − ∑rEt1_I(l = 8), is to measure how the changes of non-OECD economies influence China's production-based emissions. As for the effects EGI_II and EGF_II (i.e., the effects driven by the change in the foreign trade pattern), they reflect how the increasing/decreasing share of OECD/other nonOECD economies in their imports of intermediate and final products from China influences China's production-based emissions. In Fig. 7a, we summarize the effects of changing globalization worldwide during 2012–2016 on China's production-based CO2 emissions. While China increased its consumption of both domestic intermediates and final products, non-OECD economies also increased their consumption of domestic products, leading to a total net decrease of emissions in China by 52 Mt. OECD economies, in general, shrank their consumption of domestic intermediates but expanded their consumptions of domestic final products, leading to a total net increase of emissions in China by

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Fig. 6. The four type of effects of changing globalization on global CO2 emissions, by sourcing economy, 2012–2016.

5 Mt. As regards the trade pattern, both OECD and non-OECD economies expanded their share of imports from China. More specifically, OECD and non-OECD economies increased their imports of intermediates from China by US$22 billion and US$34 billion, respectively, and increased their imports of final products from China by US$24 billion and US$12 billion, respectively. These changes led to a total net increase of emissions in China by 107 Mt. This implies that China still expanded its proportions in the world trade market during 2012–2016, and this also led to a net increase of China's production-based emissions. The US shows different changes in both globalization and net effects on emissions. From 2012 to 2016, while the US's change of consumption of domestic intermediates and final products also led to a net increase of emissions in the US, the change of consumption of domestic intermediates and final products by non-OECD economies significantly reduced the US's emissions, and, at aggregate level, these changes led to a net decrease of the US's emissions by 17 Mt. As regards the foreign trade pattern, while OECD economies expanded their imports of both intermediates and final products from the US by US$11 billion and US $9 billion, the non-OECD economies in general shrunk their imports of intermediates and final products from the US by US$22 billion and US $6 billion. As a result, at the aggregate level these changes led to net decrease of US's emissions by 11 Mt. This suggested the US as a whole failed to expand its proportions in world trade market during 2012–2016, and this also led to a net decrease of US's productionbased emissions.

7 The results for all 63 economies are available upon request. We have also omitted the results for the RoW because the RoW contains a lot of economies, and its results only reflect an aggregate effect and lack specific policy implications.

3.3.2. Other major economies of OECD and non-OECD In Fig. 8a–d we list the four types of effects by economy for which the absolute effects were larger than 5 Mt. for the period 2012–2016.7 We also aggregate the four types of effects into three major economies: domestic economy, OECD economies, and non-OECD economies. The economy with the highest positive EGI_I effect (i.e., the effect driven by changes of consumption of domestic intermediates) is India, and it is followed by Indonesia and Korea. Coincidentally, the economy with the highest positive EGF_I effect (i.e., the effect driven by changes of consumption of domestic final products) is also India. Ever since 2014, the government of India has launched a “Make in India” initiative to attract foreign direct investment (FDI) and create more jobs and outputs in the manufacturing sectors, with the objective of “transform(ing) India into a global design and manufacturing hub”. This initiative may indeed improve the competitiveness of India's manufacturing. According to the commerce and industry ministry, FDI inflows to India were $55.6 billion for the year 2015–2016, thereby reaching a record, and were even higher in 2016–2017, at $60.08 billion. Given the fact that India has relatively high emission intensity, these changes would lead to a net total of increases of India's domestic CO2 emissions by 115 Mt. Indonesia and Korea also increased their consumption of domestic intermediates by US$ 46 billion and US$ 52 billion, respectively, during 2012–2016. As for the economies with negative signs regarding the EGI_I effect (i.e., the effect driven by changes in consumption of domestic intermediates), they are mostly OECD economies, such as Australia, Germany, Italy, France, and Belgium. Further investigations of MRIO tables show that these economies generally reduced their domestic intermediate inputs, suggesting economic recession. After the international crisis,

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Fig. 7. The effects of changing globalization worldwide on CO2 emissions of China and the US, by sourcing economy, 2012–2016.

European countries experienced a long-lasting recession, and this may have influenced both their manufacturing production and their production-based emissions. There are also two non-OECD economies, Russia and Vietnam, that experienced decreases of domestic intermediates inputs. From 2012 to 2016, the total intermediate inputs of Russia and Vietnam in constant prices decreased by US$ 20 billion and US$ 24 billion, respectively, leading to corresponding net reductions of production-based emissions by 27 Mt. in Russia and 10 Mt. in Vietnam.. As for the EGF_I effect (i.e., the effect driven by changes in consumption of domestic final products), as mentioned earlier, India was the only economy with a positive sign, driven by its considerable increase in consumption of domestic products. The economies with negative signs include both OECD economies (such as Germany, Slovak, Japan, and Mexico) and non-OECD economies (such as Malaysia and Thailand). They significantly reduced inputs of domestic intermediates, with the result that the corresponding production-based emissions also decreased. Now, we turn our attention to the EGI_II effect (i.e., the effect driven by changes in the foreign trade pattern of intermediates). Only three economies, Vietnam, Poland, and Korea, show significant positive EGI_II effects (larger than 5 Mt). Further investigations of the MRIO

tables show that, during the period 2012–2016, the world increased imports of intermediates from these economies — US$ 37 billion from Vietnam, US$ 19 billion from Poland, and US$ 32 billion from Korea. Of these, Korea also increased domestic intermediates inputs for the same period (see also Fig. 8a), suggesting a very rapid recovery from the crisis in terms of its economy. In contrast, Vietnam and Poland decreased their domestic intermediate inputs by approximately US$ 12 billion for the same period, suggesting a shift from domestic intermediates to foreign intermediates. If we summarize the EGI_I and EGI_II effects (i.e., the effects driven by changes in the consumption pattern of intermediates), both Poland and Vietnam experienced positive influences on their production-based emissions, as the negative effect driven by reducing domestic intermediates is smaller than is the positive effect driven by increasing foreign intermediates. Other economies also show significant negative EGI_II effects; these include both OECD economies (UK, the Netherlands, Turkey, and Canada) and non-OECD economies (Malaysia, Brazil, Kazakhstan, and Russia). Among them, Kazakhstan and Russia experience the largest influences. From 2012 to 2016, the world (including OECD and non-OECD economies) reduced imports of intermediates from Kazakhstan by US $42 billion and from Russia by US$106 billion, and these changes

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Fig. 8. The four type of effects of changing globalization on production-based CO2 emissions, by economy, 2012–2016.

reduced the emissions of Kazakhstan and Russia by 46 Mt. and 137 Mt., respectively. The world also significantly decreased the imports of intermediates from the OECD economies, such as the UK (by US$34 billion), the Netherlands (by US$26 billion), and Japan (by US$32 billion). However, as OECD economies generally have lower emission intensities, these changes reduced their production-based emissions to a smaller extent. As for the EGF_II effect (i.e., the effect driven by changes in the foreign trade pattern of final products), the economies with positive effects larger than 5 Mt. include Vietnam, Thailand, and Mexico. From 2012 to 2016, they expanded considerably their share of final products in the world trade market, leading to corresponding net increases of their production-based emissions. The economies with negative effects are mostly OECD economies, such as the Netherlands, Canada, and the UK. Russia shows the highest negative EGF_II effect, at 19 Mt., as the world decreased the imports of final products from Russia by US$18 billion from 2012 to 2016. 4. Conclusions In this paper, we have employed the annual multi-regional inputoutput tables compiled by the Asian Development Bank to explore the link between the recent slowdown in globalization and the global CO2 emissions for the period 2012–2016. More specifically, we have isolated four kind of effects, including the effect on emissions when one economy increased/decreased their proportion of consumption of domestic intermediate (EGI_I effect) and final products (EGF_I effect), and the effect on emissions when one economy shifted its pattern of imports of

intermediate (EGI_II effect) and final products (EGF_II effect). We also classify the economies into Organization for Economic Co-operation and Development (OECD) and non-OECD economies, trying to clarify whether OECD/non-OECD economies show similar changes of globalization for the period 2012–2016. Our results suggest that, for the period 2012–2016, China and India, out of all the non-OECD economies, clearly slowed down the pace of globalization by increasing consumption of domestic intermediate and final products, leading to a considerable net increase of not only their domestic CO2 emissions but also as global CO2 emissions. Meanwhile, they also significantly expanded their shares in the global trade market of both intermediate and final products. From a consumption-based perspective, the changes of China and India's patterns of globalization for the period 2012–2016 led to a net increase of global CO2 emissions by +262 Mt. and + 26 Mt., respectively. From a production-based perspective, the changes of globalization across all economies for the period 2012–2016 together led to net increases of CO2 emissions in China by +516 Mt., and in India by +124 Mt. However, this is not always the case for other non-OECD economies. For the period 2012–2016, there are also non-OECD economies (such as Russia, Vietnam, and Thailand) that reduced their consumption of domestic intermediate and final products, leading to a net decrease of global emissions, and non-OECD economies (such as Russia, Kazakhstan, and Malaysia) that failed to expand their share in world trade market, leading to a net decrease of both domestic and global emissions. As for OECD economies, the US and Korea are two typical examples of economies that expanded their consumption of domestic intermediate and final products. They also expanded their shares in the world

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trade market of intermediate and final products in OECD economies. From a consumption-based perspective, the changes of the US's and Korea's patterns of globalization for the period 2012–2016 led to net decreases of global CO2 emissions by −26 Mt. and − 2 Mt., respectively. However, this is, again, not always the case for all OECD economies. Some European economies, including Germany, Italy, France, and Belgium, generally reduced their consumption of domestic intermediate inputs and final products for the period 2012–2016, whereas the UK and Austria increased their consumption of domestic intermediate inputs and final products. As for the foreign trade market, Germany successfully expanded its share of intermediates and final products in both OECD and non-OECD economies, whereas the UK and France failed to do so. Consequently, the induced effects of the changes of the consumption patterns of OECD economies on global emissions generally offset each other. In addition, the emission intensities of OECD economies are much smaller than are those of non-OECD economies (especially China), and their changes of consumption patterns are generally much smaller than that are those in non-OECD economies (especially China). As a result, their induced net effects on domestic as well as global CO2 emissions are much smaller, often below 10 Mt. In summary, we found some clues showing a slowdown of globalization in several leading economies, as suggested either by increasing consumption of domestic intermediate and final products (such as in China, India, the US, and the UK) or a shrinking share in the global trade market (such as in Russia, the UK, and France). However, the changes of consumption in non-OECD economies (particularly China and India) are much larger than are those in OECD economies. Against the background of the much higher emission intensities of non-OECD economies, at the aggregate level, the effects of globalization on global CO2 emissions have been dominated by non-OECD economies, particularly China and India. As mentioned earlier, from 2012 to 2016, the global CO2 emissions of

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the industrial processes increased by only 429 Mt. Among these, the changes of China and India's patterns of globalization for the period 2012–2016 together led to a net increase of global CO2 emissions by 288 Mt. Thus, the recent slowdown of global CO2 emissions cannot be attributed to the slowdown of globalization. On the contrary, the slowdown of globalization led by non-OECD economies actually brings a net increase, rather than a net decrease, in the global CO2 emissions. Furthermore, we would like to address that such a negative link between the slowdown of globalization and global CO2 emissions is partly due to the emission intensity gap between non-OECD economies and OECD economies. The recent reshaping of manufacturing into local production is characterized not only by new IT technologies, such as robotics, artificial intelligence, and 3D printing, but also by scalable new energy production such as solar, wind, and hydro power. The scalable deployment of renewable energies may significantly decrease the emission intensities of all economies, especially non-OECD economies. In addition, the slowdown of globalization may also significantly reduce long-distance (marine) transportation and the consequent CO2 emissions. In this context, the future change of globalization may become beneficial for global climate change mitigation, but only if the emission intensities, especially those in non-OECD economies, can be reduced through the scalable deployment of renewable energies. Serious attention still needs to be paid to breakthrough low-carbon technologies that are especially applicable to the non-OECD world. Acknowledgments The authors acknowledge the funding from the National Natural Science Foundation of China (71873091 and 71473245), Natural Science Foundation of Beijing (9172006) and the Fundamental Research Funds for the Central Universities in UIBE (CXTD7-06).

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.eneco.2019.104483. Appendix B Table A The classification of economies. No.

Code

Country

No.

Code

Country

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

AUS AUT BEL BGR BRA CAN CHE CHN CYP CZE DEU DNK ESP EST FIN FRA GBR GRC HRV HUN IDN IND IRL ITA JPN KOR LTU LUX

Australia Austria Belgium Bulgaria Brazil Canada Switzerland China Cyprus Czech Republic Germany Denmark Spain Estonia Finland France United Kingdom Greece Croatia Hungary Indonesia India Ireland Italy Japan Korea Lithuania Luxembourg

33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

NOR POL PRT ROM RUS SVK SVN SWE TUR TAP USA BAN MAL PHI THA VIE KAZ MON SRI PAK FIJ LAO BRN BTN KGZ CAM MDV NPL

Norway Poland Portugal Romania Russia Slovak Republic Slovenia Sweden Turkey Taiwan United States Bangladesh Malaysia Philippines Thailand Viet Nam Kazakhstan Mongolia Sri Lanka Pakistan Fiji Lao People's Democratic Republic Brunei Darussalam Bhutan Kyrgyz Republic Cambodia Maldives Nepal (continued on next page)

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Table A (continued) No.

Code

Country

No.

Code

Country

29 30 31 32

LVA MEX MLT NLD

Latvia Mexico Malta Netherlands

61 62 63

SIN HKG RoW

Singapore Hong Kong Rest of the World

Table B The classification of industries. No.

Industry

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Agriculture, hunting, forestry, and fishing Mining and quarrying Food, beverages, and tobacco Textiles and textile products Leather, leather and footwear Wood and products of wood and cork Pulp, paper, paper, printing, and publishing Coke, refined petroleum, and nuclear fuel Chemicals and chemical products Rubber and plastics Other non-metallic mineral Basic metals and fabricated metal Machinery, Nec Electrical and optical equipment Transport equipment Manufacturing, Nec; recycling Electricity, gas, and water supply Construction Sale, maintenance and repair of motor vehicles and motorcycles; retail sale of fuel Wholesale trade and commission trade, except of motor vehicles and motorcycles Retail trade, except of motor vehicles and motorcycles; repair of household goods Hotels and restaurants Inland transport Water transport Air transport Other supporting and auxiliary transport activities; activities of travel agencies Post and telecommunications Financial intermediation Real estate activities Renting of M&Eq and other business activities Public admin and defense; compulsory social security Education Health and social work Other community, social, and personal services Private households with employed persons

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