Carbon emissions embodied in international trade: The post-China era

Arce, Guadalupea; López, Luis Antonioa and Guan, Dabob

Guadalupe.Arce@uclm.es; Luis.LSantiago@uclm.es; Dabo.Guan@uea.ac.uka Faculty of Economics and Business, University of Castilla-La Mancha

Plaza de la Universidad, 1, 02071, Albacete, Spain

b Tyndall Centre for Climate Change Research, School of International Development, University of East Anglia, Norwich, NR4 7TJ, United Kingdom

Abstract

The so-called post-China countries (PC-16´s), distinguished by low wages and high economic growth, will

replace China as the “world’s factory”. The aim of this paper is to assess the effect of these changes on

global CO2 emissions pathways. To achieve this, a counterfactual is proposed wherein China's trade with

the rest of the world is replaced by the PC-16’s trade in a global multiregional input-output context. The

emissions savings realized by trade replacement are significant in those scenarios where the current

pattern of trade is maintained (-13% on emissions traded and -3.5% on global emissions) and in

scenarios where enterprises relocate their production directly or indirectly to the most environmentally

efficient countries (ranging from -15.2% to -18.2% on emissions embodied in trade). Nevertheless, the

potential savings drop considerably (ranging from -1.5% to -7.1%) if companies and host countries take

advantage of cheaper, but more polluting means of production and do not internalize the externalities.

Through changes in international trade, there is a possibility of reducing emissions, which have to be

included in international, multilateral and bilateral agreements to mitigate climate change if we do not

want to lose the opportunities these changes present.

Keywords: Emissions embodied in trade, China, post-China countries, input-output analysis.

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Highlights:

China's trade with the rest of the world is replaced by the post-China countries. Under the best scenario, the CO2 emissions in trade savings reach a peak of 18.2%. Non-traded goods and indirect emissions are driving the global emissions reduction. Trade diversion to less efficient countries could reduce the emissions only a 1.5%. If environmental variables are ignored, a chance to mitigate could be overlooked.

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1. Introduction

The composition of world trade is changing. China is currently the world’s largest exporter—the

“world’s factory”—with its exports accounting for 10.4% of world trade in 2010[1]. However, China is far

from being specialized only in the trade of low-tech and labor-intensive goods. In 2005, most Chinese

exports were in high-tech manufacturing[2], and in 2009, China became the largest exporter of tech-

intensive goods, parts and components, ahead of the United States, Germany and Japan[3]. Although

offshoring or processing exports play an important role in these sectors, China’s processing exports as a

percentage of its total exports decreased from the 1997-2006 period to 2012, falling from over 55% to

below 45%[4]. Furthermore, China is not only the largest recipient of foreign investment, it is also

among the world’s top investors[5].

The economic strategy of China has changed. In recent years Chinese economy has moved from a

system of growth based on exports, high investment strategies and energy-intensive manufacturing to a

new growth model based on heavy-industrial investment and toward domestic consumption,

particularly of services. These are important structural changes can achieve still-strong but lower

economic growth, of a much better quality in terms of its social distribution and impact on the natural

environment[6]. The improvement in the quality of life in China is changing the lifestyle of its

population. Consumption patterns are shifting, becoming more similar to those of developed countries,

and public spending on health and the development of a pension system is increasing[7]. After the 2007

crisis, China’s growth cannot be explained by increasing exports, given that its trade surplus has shrunk

from $261.8 billion in 2007 to $155.1 billion in 2011. Instead it can be explained by heavy investment in

capital accumulation[8]. The end of cheap Chinese labor and China’s development into a middle-wage

country[9] is likely to change its role in the international sphere: it will cease to be the largest world

export producer to instead become the largest global consumer of its own production and importer of

final goods and services.

About half of cumulative anthropogenic CO2 emissions between 1750 and 2010 have occurred in the

past 40 years. Since 2001, coinciding with the entry of China into the World Trade Organization (WTO),

total anthropogenic GHG emissions have increased on average 1 GtCO2eq per year[10]. In a

globalization context, CO2 emissions embodied in the production of internationally traded goods and

services represent a large share of global CO2 emissions (26% in 2008). This virtual carbon, moreover,

and others factors embodied in international trade such as water and materials use, are growing faster

than other economic indicators such as the GDP or population[11]. To date, China has played a major

role in this emissions increase; in fact, it has emerged as the largest emitter of carbon dioxide in the

world. According to Energy Information Administration, Chinese emissions increased by 170% between

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2000 and 2009[12]. This dramatic increase in CO2 emissions can be explained by China’s coal-based

energy mix and the very high emissions intensity[13]. In 2009, approximately 82% of electricity

generation in China was produced with conventional technologies, mainly coal[14]. China’s increasing

urbanization means that the construction industry, followed by electricity generation, represent the

largest share of emissions associated with household consumption[15].

The affluence of rich countries is causing the international displacement of GHG emissions[16-18] land

use[19], biodiversity[20], nitrogen[21], water[22] and energy[23]. This virtual trade is not only a

question of extracting natural resources and exporting pollution from developing countries[24], the

offshoring phenomenon has an important impact on the environment, also explained by emissions

embodied by international transport[25] and processing exports[26]. As far as emissions are concerned,

borders are meaningless. Their negative effects spread throughout the world, no matter where they

occur, as evidenced in the case of China-US trade, where emissions avoided by offshoring to China were

shown to be returning to the US by air[27].

The challenge of a transition from the current economic system towards a low-carbon economy to

mitigate the climate change, has prompted the proliferation of several research on these topics. Some

of these studies focus on assessing the improvement in the emissions intensity and the penetration of

new technologies in the different countries of the world economy by the concept of ‘stabilization

wedges’[28]. Meanwhile, other studies highlight the importance of the reduction in energy intensity in

different regions of China in the global context of reducing CO2 emissions[29]. Other options proposed

in the literature is the implementation of Pigouvanian tax to reduce the environmental externality of

coal use in China[30] or [31] that propose 4 steps to China’s carbon peak (set regional emissions targets,

transparent emissions monitoring, expand carbon trading nationwide and advance green technology).

However, the future implications in carbon emissions of the deviations of trade have been less analyzed

due to the difficult to assume the direction (origin and destination) of these deviations.

Recent international experience where carbon leakage from developed to emerging countries has been

very significant[11, 32] and where there is a real risk that unless major economies recognize their

imported carbon footprint even strong regulation on domestic emissions in major economies may not

be effective in reducing total global emissions[33], it makes it interesting to study the potential trade

deviations that could reduce the benefits of mitigation policies and the necessary penetration of cleaner

energy. This trade diversion (or replacement) also could take a new direction, for some emerging

countries (China, India, Mexico, etc.), which have substantially improved the living standards of their

population, to other developing countries. This emissions leakage could reach a significant amount,

since from 2005 emissions from developing countries are higher than the emissions of developed

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countries[34], because emerging countries have emissions intensities linked to the use of cheaper but

more polluting[13]. Although they are incorporating in recent years mitigation policies to face of the

demand that their citizens made to reduce local pollution and by international commitments adopted

by these countries, such as Paris Agreement[35].

Replacing China as the world’s factory is not simple, given its large size. It requires a set of countries

with a large labor force and low wages. The PC-16´s, with a population of one billion, fulfills these

criteria and will likely be assuming the role of being the world's factory in the next decades[36]. These

countries are Mexico, the Dominican Republic, Nicaragua, Peru, Uganda, Ethiopia, Kenya, Tanzania, Sri

Lanka, Bangladesh, Laos, Myanmar, Vietnam, Cambodia, the Philippines and Indonesia. Given this

change in the “status quo” of international trade, this paper attempts to analyze the effects of these

changes on the environment, and specifically on CO2 emissions. Previous literature investigating the

pollution haven hypothesis or the avoided emissions balance assesses the impact on GHG emissions of

the substitution of international trade between countries that trade with each other, using the

technological assumption to build a balance of avoided emissions[13, 37-43]. Liu et al.[13] found if the

emissions intensity of China's international exports were equal to the intensity of its imports, it would

avoided emissions by 86% in 2007. However, these studies do not evaluate the impact of changing an

international supplier due to possible environmental constraints or in pursuit of lower costs of

production. In the context of criteria of responsibility, Kander et al.[44] proposed an improvement to

consumption-based carbon accounting considered the different in technology by country´s exports and

the average carbon intensity for the relevant sector on the world market. They conduct an analysis

making the assumption of the exports of a country rather than are produced with domestic technology

are produced with the medium technology around the world. Other example where only accurate in

trade can be seen in López et al[45], but only for the case of the possible diversion of US imports from

China to Mexico.

This paper proposes different scenarios wherein China's share of world trade is replaced by the PC-16´s

trade. To illustrate this problem, a multiregional input-output (MRIO) model is developed to calculate

the CO2 emissions embodied in international trade in all scenarios proposed. Specifically, these

scenarios are put forward that propose the replacement of China’s trade based on PC-16´s trade

patterns, their environmental efficiency (direct and total) and a combination of both assumptions. The

significance of this work lies in measuring the impact and possible consequences of the changes

anticipated in the global economic order and their implications on economic and environmental policy

issues. The analysis draws on data from the Global Trade Analysis Project (GTAP) database (version 8.0)

and is performed for 129 regions with a sectoral disaggregation of 57 industries.

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This paper is structured as follows: section 2 describes the methodology; section 3 explains the main

results; and section 4 presents the conclusions and policy implications.

2. Materials and methods

To analyze the effects that economic activities and the increase in international trade have on the

environment, we use an environmentally extended input–output model[46]. Specifically, we define a

MRIO model. A more detailed description of this model is available in Miller and Blair [47] and in the

supporting information of this paper.

Extending the model for environmental impacts (e.g., CO2 emissions), the total emissions from

production processes are expressed in a MRIO framework by the following equation:

f =e ¿ [1]

where e refers to the direct emissions coefficients or the diagonalized vector of emissions per unit of

output. They are calculated by er=f r ( xr)−1, being f r total emissions by country r and xr the output of

the region indicated in the superscript. A refers to the matrix of technical coefficients, integrated by Arr

, in the main diagonal, that is the domestic matrix of coefficients of production (intraregional matrix),

and Arsin the off-diagonal positions that shows the trade between industries from region r to region s

(intermediate exports of region r or intermediate imports of region s). e ¿ is the emissions multiplier (or

total emissions intensity), which multiplied by the matrix of final demand y, yields the total emissions

(direct and indirect) (f ¿ to meet the final demand. The final demand matrix, y, is compound by yrr,

diagonalized vectors of domestic final demand and yrs, diagonalized vectors that show the trade

between industries in region r to final consumers in region s (final exports of region r o final imports of

region s).

2.1. Counterfactual approach: China replaced by PC-16’s

The baseline scenario provided in this paper assumes that China's exports, in terms of both

intermediate and final goods, are produced by the PC-16´s. Exports are allocated to each PC-16 country

proportionally based on each individual country’s trade specialization. Namely, in proportion to the

weight that each PC-16 have over they total trade (information about this trade can be found in the

supporting information). In terms of imports, we assume that China maintains the same level of imports

of final goods, which implies that China maintains the income that supports its demand level, even

though Chinese export levels have changed. Imports of intermediate goods change to correspond to the

level needed to satisfy final demand and are obtained from expression 2, keeping Chinese technology

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constant (with the same technical coefficients). The diversion of trade from China to the PC-16`s is done

instantly, with no time delay. The technology used by the PC-16's is the same as in the reference year. In

addition, we disregard investment growth in the PC-16's to allow the supply in these countries to satisfy

their new demand. Finally, we assume no change in China and the PC-16’s incomes, thereby keeping

constant the demand for final goods both economies.

For the calculation of emissions embodied in international trade will be define a MRIO model to 4

regions, for simplifying. First, the counterfactual on trade in intermediate goods (eq. 6) may be applied,

considering that the country 3 refers to China and country 4 is the PC-16’s.

Z¿=( z11 z12 z13 z14

z21 z22 z23 z24

0 0 z33 0z31+z41 z32+z 42 z 43 z34+z44) [2]

Where Z¿ refers to the intermediate consumption matrix and each z ij term represent the interindustry

sales by sector i to all sectors j. The last column shows the new amount of exports that includes

including exports actually do plus the exports that would have to do if this group of countries replaces

China as a supplier1.

Therefore, a new matrix of technical coefficients will be obtained as a result of the ratio of each element

z ij and the total output of the sector j (x j), which it be defined as A¿:

A¿=(a¿11 a¿ 12 a¿ 13 a¿14

a¿ 21 a¿ 22 a¿ 23 a¿24

0 a¿ 32 0 0a¿ 41 a¿ 42 a¿ 43 a¿ 44) [3]

The counterfactual on trade in final goods (eq. 4) could be expressed by the following, where the

exports of final goods that China performs (region 3) now are sold and produced by PC-16’s

(represented here by the region 4). However, is considered that the Chinese imports of final goods do

not change2:

1 In practice, this change don’t imply a change the production technology of any of the countries considered, just implies a change of supplier. With the counterfactual approach PC-16's supply with its technology the intermediate and final demand that was previously supplied by China.2 The assumption is that China maintains its demand level through internal growth that generates a value added which allows to China to maintain the same level of imports of final goods and implies to assess the change in global value chains, but not the induced changes as a result of an income change and, thereby, changes in final demand.

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2021

y¿=( y11 y12 y13 y14

y21 y22 y23 y24

0 0 y33 0y31+ y41 y32+ y42 y43 y34+ y 44) [4]

To obtain the effects on CO2 emissions, new terms are added to equation 1, yielding expression 5 as

follows:

f ¿= e¿ [5]

In matrix form, to m regions, assuming that country 3 refers to China and country 4 is one of the PC-

16’s, expression 2 becomes:

(f ¿1

f ¿2

f ¿3

f ¿4

⋮f ¿m

)=¿ (e1 0 0 0 ⋯ 00 e2 0 0 ⋯ 00 0 e3 0 ⋯ 00 0 0 e4 ⋯ 0⋮ ⋮ ⋮ ⋮ ⋱ ⋮0 0 0 0 ⋯ em

)(L¿ 11 L¿12 L¿13 L¿14 ⋯ L¿1 m

L¿ 21 L¿22 L¿23 L¿24 ⋯ L¿2 m

L¿ 31 L¿32 L¿33 L¿34 ⋯ L¿3 m

L¿ 41 L¿42 L¿ 43 L¿44 ⋯ L¿4 m

⋮ ⋮ ⋮ ⋮ ⋱ ⋮L¿m1 L¿m2 L¿ m3 L¿m 4 ⋯ L¿ mm

)(y11 y12 y13 y14 ⋯ y1 m

y21 y22 y23 y13 ⋯ y2 m

0 0 y33 0 ⋯ 0y31+ y41 y32+ y43 y43 y34+ y44 ⋯ y3 m+ y 4 m

⋮ ⋮ ⋮ ⋮ ⋱ ⋮ym1 ym2 ym 3 y m4 ⋯ ymm

)[6]

With the generalization to m regions, we assign China’s trade (in both final and intermediate goods) to

the PC-16´s proportionally based on each individual country’s trade specialization. See supporting

information for a more detailed description of the methodology.

For the analysis we use input-output, international trade and CO2 emissions data extracted from the

GTAP database (version 8.0)[48-50] for the year 2007. The analysis is performed for 129 regions and

with a sectoral disaggregation of 57 industries.

2.2. Alternative scenarios

2.2.1. Chinese trade replaced by the PC-16’s in terms of environmental efficiency

In the following scenarios we introduce an environmental variable as a criterion for the allocation of

Chinese exports. This variable is measured in terms of the pollution intensity, direct and total, of the PC-

16’s for every industry. In Scenario 2, environmental efficiency is measured by the direct emissions

coefficients or emissions per unit of output (e). In Scenario 3, the total emissions multipliers (P) are

used with the same aim. However, with this last measure, we examine the linkages effects, as well as

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direct and indirect emissions; we thus consider it a more comprehensive indicator. This allocation

method involves identifying within the PC-16's the countries that have the lowest direct and total

emissions multipliers and assuming that Chinese exports (of final and intermediate goods) will be

produced by these countries. The criterion chosen to classify the PC-16's according to their direct

emissions coefficients is the median of direct emissions coefficients by sector (me) in Scenario 2, and

total emissions multipliers (mε) in Scenario 3. Assuming the PC-16’s to be a statistical population, the

median is a way of summarizing the typical values associated with the members of this group more

efficiently in this case than, for example, the mean, because among the values analyzed, there are

extreme values or outliers that may distort the mean.

Thus, Chinese exports are allocated as follows:

If e iPC−16 ' s>mei

PC−16 ' s (in Scenario 2) or PiPC−16 ' s>mεi

PC−16 ' s (in Scenario 3), then the country

does not assume Chinese exports (intermediate and final exports diverted will be zero).

If e iPC−16 ' s≤ me i

PC−16 ' s (in Scenario 2) orPiPC−16 ' s ≤mε i

PC−16 ' s(in Scenario 3), then the country

concerned will assume part of the exports previously produced by China.

In both scenarios, Chinese exports of intermediate and final goods are distributed equally among the

countries that meet the environmental efficiency criterion previously defined and following the same

trading pattern as China. China’s share is replaced by the PC-16’s, but they flow to the same

destinations. The application of this export allocation method does not take into account the PC-16’s

total production capacity for exports. We only consider the amount involved in the change in

international trade patterns (from China to the PC-16’s), rather than the overall effects with regard to

domestic structural changes.

To illustrate the opposite extreme, two alternative scenarios are proposed. In Scenario 4, Chinese

exports of intermediate and final goods are distributed equally among the countries that have the

highest direct emissions coefficients (e iPC−16 ' s≥ me i

PC−16 ' s ¿, and, in Scenario 5, Chinese exports are

distributed equally among the countries that have the highest total emissions multipliers (

PiPC−16 ' s ≥mε i

PC−16 ' s). The supporting information discusses the classification of the industries in the PC-

16's based on environmental efficiency.

2.2.2. Combining scenarios: Chinese trade replaced by the PC-16’s, taking into account patterns

of trade and environmental efficiency

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In Scenarios 6 and 7, PC-16´s countries take over production of Chinese exports based on their

environmental efficiency, following the approach described in section 2.3.1 (the median of direct

emissions coefficients by sector, me, and total emissions multipliers, mε). However, the allocation of

exports that satisfy this criterion is based on the weight of exports by industry of total exports in that

sector produced by the countries selected.

Analogously to Scenarios 4 and 5, in Scenarios 8 and 9 the countries that replace Chinese exports are

chosen according to the highest emissions intensity in direct (e iPC−16 ' s≥ me i

PC−16 ' s ¿ and total terms,

respectively (PiPC−16 ' s ≥mε i

PC−16 ' s).

3. Main results

3.1. Emissions embodied in international trade in 2007: An overview.

In 2007, CO2 emissions embodied in international trade account for 26.4% of global emissions (6,013.14

Mt). China is the main emissions exporter to the rest of the world, responsible for 23.3% of the total

emissions embodied in exports (EEX), followed by the US (8.8%), Russia (5.5%), Germany (3.7%) and

India (3.2%). These 5 countries (out of 129 considered in the analysis) make up 44.5% of the EEX. The

emissions exported by China are heavily concentrated depending on the size and the per capita income

of destination regions, going mainly to the US (24.3%), Japan (9.2%), Germany (5.3%), the UK (4.1%),

France (2.9%), Russia (2.9%), India (2.6%) and Spain (2.2%). In terms of the emissions embodied in

imports (EEM), the EU-25 and the US are the regions that account for the majority of the EEM,

representing 32% and 17.2%, respectively, along with Japan (5.6%). Within the EU-25, we highlight the

shares of Germany (6.1%), the UK (5.2%), France (3.9%), Italy (3.5%) and Spain (2.6%).

In terms of the balance of emissions, we observe that emissions deficits are concentrated in developed

countries, while emerging countries are net exporters of CO2 emissions[33, 34]. These emissions

balances are determined mostly by 2 factors: the sign of the trade balance of the regions involved and

the pollution intensities by industry of these countries or regions. The size of the Chinese trade surplus

in conjunction with China’s high pollution intensity —higher than that of its trading partners— explain

the large surplus of 1,094.6 MtCO2, which covers 52.9% of the global emissions deficit in 2007. With

respect to the PC-16’s, it is observed that almost all countries present an emissions deficit, particularly

in the case of Mexico, Bangladesh and the Philippines. The exception is Indonesia, which shows a

substantial emissions surplus, leading it to become the only country that in 2007 made large transfers of

emissions to developed countries, especially in the chemicals, mineral products, electronics, paper

products and textiles industries. A more detailed description of emissions embodied in trade and of

emissions and trade balances in 2007 is provided in the supporting information of this paper.

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Figure 1 shows the average emission coefficients of China and the PC-16´s countries and, in brackets,

the emissions coefficients of the electricity sector in each country. With the exception of Cambodia, the

PC-16´s countries replacing China as suppliers to the world have a lower average pollution intensity than

China. For the rest of the world economies, countries with higher pollution intensity (emissions

coefficients) are concentrated in the “rest of East Asia” region, which includes countries or regions like

Korea, Macao, the “rest of the former Soviet Union” (Tajikistan, Turkmenistan and Uzbekistan),

Cambodia, Oman, Qatar, Kuwait, Saudi Arabia and China. In Africa we highlight the emissions

coefficients of South Africa, Zimbabwe and Nigeria. In Latin America, the emissions intensities of Chile,

Venezuela, Argentina and Bolivia are noteworthy.

The electricity emissions coefficient is key in determining the evolution of the post-China world's

emissions and provides information about how the energy will be supplied to the “new world” (Figure

1). In this regard, the electricity industry in all PC-16´s countries has a lower emissions coefficients than

the electricity industry of China, highlighting in particular the cases of Ethiopia (94.3% lower) and Kenya

(83.2% lower). In contrast, Indonesia, which has a heavily polluting electricity sector, has a situation

worsened by regulatory uncertainty and inadequate investment. Petroleum represents the largest share

in Indonesia's energy mix at 36% in 2012. In the past decade, coal consumption nearly tripled and

surpassed natural gas as the second most consumed fuel[51]. In spite of this, Indonesia's electricity

factor is 13% less than China’s. Therefore, we can expect a reduction in global emissions embodied in

trade as a result of trade diversion to the PC-16’s, although in different amounts, depending on the

trade distribution criterion and the assumptions established for the analysis.

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Figure 1. CO2 emissions intensities of the PC-16’s and China in 2007 (KtCO2 per million US$).Note: The Figure shows, out of brackets, average emissions coefficients in each country and, in brackets, the corresponding emissions coefficient of the electricity sector.

3.2. Emissions embodied in international trade under the main counterfactual

The first scenario (baseline) shows emissions that would have occurred in 2007 in the extreme case in

which all of China’s exports had been replaced by PC-16´s exports (Figure 2, part corresponding to

scenario 1). In this case, emissions embodied in international trade would have been lower by 13%,

decreasing from 6,013.13 MtCO2 to 5,231.6 MtCO2. The higher environmental efficiency of the PC-16

economies compared to China explains the reduction of emissions due to the diversion of international

trade. In relation to total production, the emissions savings represents a reduction of 3.5% of total

emissions in 2007. The importance of trade diversion with regard to emissions is evident when our

results are compared with the results of Cristea et al.[52], which uses a general equilibrium model to

show the potential effect of the elimination of tariffs in 2004 in the world economy, obtaining a

reduction in emissions associated with production by 0.41% in 2020 (and also finds that exports grow by

6% and transport emissions between 7.5% and 8.6%, depending on the proposed scenario).

The emissions savings are also consistent with studies that evaluate whether the pollution haven

hypothesis applies to China, by assessing the impact of the substitution of imports by domestic

production between the two regions or countries under consideration. Different papers focusing on

China have found that countries that assume the production of Chinese exports are able to reduce

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emissions (as in our case for the PC-16’s), although they are not able to compensate for the emissions

increase if China had had to produce their imports, leading to an increase in global emissions 21-23. The

only exception to this trend is found in Tan et al. 24, which evaluates the emissions embodied in trade

between Australia and China for the period 2002-2010. The study finds that, for the years 2009-2010,

trade between these two countries causes a reduction in emissions explained by natural resources

exports from Australia to China and the related production savings, which are enough to compensate

for the emissions increase that would be generated in China by the extraction of these natural

resources.

Figure 2. Emissions embodied in trade in 2007 and PC-16´s trade under scenarios 1, 3, 7 and 9 (KtCO2)NOTE: The core circle shows the flows of emissions embodied in international trade from China or PC-16´s (indicated by their flags) and located on the left side of the figure, to the rest of the world, grouped in large regions, as shown in the legend, and located on the right side of the figure. Therefore, the directions of the flows for the interpretation of the figure would be from left to right, if we talk about EEX from the PC-16’s to other countries. On the contrary, from the perspective of EEM made by different countries to PC-16’s, the direction would be from the right to the left. In brackets are shown the percentages of reduction of emissions embodied in trade in each of the selected scenarios. We have used the software Circos[53] for performing this Figure.

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When China is eliminated as leading exporter to the PC-16’s, the US, Russia, Germany, India and Japan

become the most important emissions exporters. When considering only the countries in the PC-16’s

group, Indonesia, Mexico and Vietnam are the world's largest CO2 emitters. The countries or regions

receiving the EEX would be the EU-25, the US, East Asia and China. It is worth noting the importance of

emission flows from Mexico to the US, Mexico’s largest trading partner. Hence, the emissions

associated with international freight transport are significantly reduced, which is meaningful because

they amounted to 34% of direct emissions of its imports in 2004[52].

The trade balance and the balance of emissions of the PC-16’s are affected after application of the

counterfactual. The Chinese surplus now affects almost all the other regions under consideration by

increasing the surplus in the balance of emissions, as in Indonesia, for instance, where it rose 339%. We

also find countries with an emissions deficit in 2007 where, after trade diversion, the deficit decreases,

as in Sri Lanka or Nicaragua. Likewise we observe countries with an emissions deficit that becomes a

surplus following trade diversion, as in the cases of Mexico, Vietnam, Peru, Bangladesh and Kenya. In

Figure 3, we observe the variation in PC-16´s emissions caused by trade diversion. The increase in

exports in each of these countries causes an increase in emissions, which is much more significant in

indirect terms. It should be emphasized that Indonesia is the only country that does not avoid direct

emissions from trade replacement, although indirect savings lead to a reduction. It is important to note

that the only country where direct emissions are greater than those in China is Vietnam. Therefore, it is

the only one that shows a negative value for direct emissions (red bar).

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Figure 3. Variations in emissions (direct and indirect) produced by trade diversion, 2007 (MtCO2)NOTE: To calculate the difference between direct emissions produced by trade diversion, the following expression is used: ∆ Ed=( eC h ina−ePC 16 ) D, where e refers to the direct emission coefficients, and the matrix D refers to the trade volume diverted from China to the PC-16’s. In a similar way, the expression that allows us to evaluate the differences between the total, direct and indirect emissions from trade could be ∆ ET=( PC hina−PPC16 ) D. The difference between these two expressions allows us to isolate the indirect emissions.

The results show that most of the PC-16’s economies are more efficient than China’s indirectly through

a lower emission coefficient for electricity (see Figure S4 in the supporting information). Indirect

emissions are driving the reduction of global emissions due to trade diversion and because of the

linkage’s effect on the electricity sector, which generally exhibits lower pollution intensity in the PC-16’s

than in China. Particularly significant is the case of Mexico. The small direct differences observed when

China’s trade is diverted to the PC-16's are consistent with the results of Cristea et al.[52], which

investigates what would happen to emissions levels if production of a certain good were relocated so

that a pair of trading partners both produced that good (so each country produces that import). The

authors find that in 2004 international trade increases emissions because the difference in direct

emissions between both countries is not high enough to offset the increase in international traffic (in

particular for agricultural products). Therefore, the transition to a more sustainable global economy

requires planning and support of the incorporation of renewable energies in these countries to reduce

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the emissions associated with the electricity sector, which transfers the pollution intensity to other

sectors[54-56].

3.3. Proposed assumptions: emissions embodied in international trade under the remaining scenarios

As described in the methodology section, different scenarios are proposed to compare the impact of

trade replacement on CO2 emissions. From the baseline scenario, where the substitution of Chinese

exports is performed based on the pattern of trade in the PC-16's, we develop 8 additional scenarios,

described in Table 1.

Table 1. Description of the proposed scenarios

Trade pattern Environmental efficiency Environmental inefficiency Scenario 1 (baseline) X Scenario 2 direct emissions coefficients Scenario 3 total emissions multipliers Scenario 4 direct emissions coefficients Scenario 5 total emissions multipliers Scenario 6 X direct emissions coefficients Scenario 7 X total emissions multipliers Scenario 8 X direct emissions coefficients Scenario 9 X total emissions multipliers

A key finding is that, regardless of the criteria chosen, trade diversion generates emissions savings over

the level of emissions embodied in trade in 2007. This underscores how high emissions intensity is in

China compared to the countries that replace it in our study. However, emissions savings differ greatly

depending on the assumptions underlying exports allocation, ranging from 1.5% to 18.16% (from 0.091

GtCO2 to 1.092 GtCO2; Table 2). Furthermore, when these data are compared to the baseline scenario

results, we observe how the change in CO2 emissions is significantly higher in inefficient scenarios than

in scenarios that take into account the environmental efficiency of the trade replacement. In the most

efficient scenario (Scenario 7), the emissions reduction compared to the baseline scenario is -310.6

MtCO2 and in the least efficient scenario (Scenario 8), the emissions increase 691.2 MtCO2 (Table 2).

Table 2. Emissions under different scenarios (MtCO2)

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Emissions in trade

Total Emissions

% Emissions in trade

% Reduction in emissions traded

% Reduction in total emissions

Emissions 2007 6,013.1 22,800.3 26.4% - - Scenario 1 5,231.6 21,999.5 23.8% -13.0% -3.5% Scenario 2 5,099.5 21,859.1 23.3% -15.2% -4.1% Scenario 3 5,048.8 21,807.7 23.2% -16.0% -4.4% Scenario 4 5,836.2 22,597.0 25.8% -2.9% -0.9% Scenario 5 5,583.8 22,344.6 25.0% -7.1% -2.0% Scenario 6 4,969.9 21,738.0 22.9% -17.3% -4.7% Scenario 7 4,921.0 21,687.7 22.7% -18.2% -4.9% Scenario 8 5,922.8 22,692.3 26.1% -1.5% -0.5% Scenario 9 5,811.7 22,580.5 25.7% -3.3% -1.0%

Focusing on the composition of the exporting countries after trade diversion, we observe that three

countries assume a large part of exports production regardless of the scenario: Mexico, Indonesia and

Vietnam. By contrast, other countries are absent in all of the proposed scenarios (Peru, Nicaragua,

Ethiopia, Kenya, Tanzania and Uganda). Nevertheless, the weight of each country varies significantly

depending on the scenario. Considering only the environmental efficiency criteria (Scenarios 2 and 3),

the countries that assume most of the production of Chinese exports are Mexico (19.9% of the PC-16’s

EEX, in terms of direct emissions, and 20.1% in terms of total emissions), Indonesia (17.9% and 13.8%),

Vietnam (13.3% and 14.9%) and Cambodia (7.1% and 7.3%).

In general, the differences in terms of emissions when the direct and total coefficients are considered

are very small (approximately 1%), which can be explained by the high correlation between direct and

total emissions coefficients (whether or not trade patterns are taken into consideration). However, a

more detailed analysis by country allows us to see how the linkage effects are more significant in some

countries such as Bangladesh, where, taking into account direct efficiency, they are responsible for 8.7%

of the EEX of PC-16's, while they represent only 1.2% when taking into account total multipliers. This is

also the case of Ethiopia (7.5% and 2.8%, respectively). In contrast Sri Lanka increases from 3.5% when

we consider direct emissions factors to 7.1% when total multipliers are considered. Kenya (3.5% and

6.1%), Tanzania (3.3% and 5.3%) and Uganda (1.6% and 5.5%) also follow this trend.

When trade is diverted to the most inefficient countries (Scenarios 4 and 5), Myanmar, Nicaragua, Laos

and Bangladesh are included among the countries replacing Chinese exports. The inclusion of these

countries occurs mainly at the expense of Mexico, which has gone from representing 19.9% and 20.1%

of total PC-16 EEX, to representing 7.1% and 9.7%, respectively.

After adding trade patterns to the environmental variable (Scenarios 6 and 7), the countries to which

Chinese exports are diverted remain nearly the same as in the previous two scenarios. Mexico, in

particular, is significant, as it takes on 55% and 51% of Chinese trade (using direct factors and total

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multipliers, respectively). Mexico is thus confirmed as the country most apt to replace China based on

trade patterns (due to their trade specialization), while it is also more efficient in terms of CO 2

emissions. In Scenarios 8 and 9, where trade is diverted to less efficient countries while taking into

account trade specialization, the two main countries replacing China are Indonesia and Vietnam, which

represent 79% and 81% of the PC-16 EEX, respectively, followed by Mexico, now producing only 7.7%

and 10% of the EEX.

Figure 4 shows, by countries (PC-16’s), carbon emissions embodied in trade under the different

scenarios proposed. The orange bar shows the scenario 1 (baseline), which only takes into account the

trade pattern, where it can observe that Indonesia, Mexico, Vietnam and Philippines become the main

emissions exporters because they currently hold an important role in international trade. From there,

the bubbles represent the rest of proposed scenarios, where it can be seen the relevance of some

countries on the rest, again Indonesia and Vietnam in scenarios 8 and 9, because they maintain their

weight in international trade and are more inefficient than other countries and, on the other hand,

Mexico in scenarios 6 and 7, due to its relevance in terms of trade and its higher environmental

efficiency compared to others post-China countries. It is also important to note that the importance of

some countries as Myanmar in the most inefficient scenarios (4 and 5). Finally, Figure 4 shows how

some countries as Kenya, Tanzania, Uganda or Laos do not change their participation in the EEX

whatever the scenario proposed. They are quite small countries and they could not reach the total

amount of trade required and, on the other hand, low efficiency by own resources are probably not

reproducible on a wider scale.

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Figure 4. Emissions embodied in trade of PC-16’s under the different scenarios by countries (MtCO2).

Despite having 9 scenarios, in Figure 2 we have chosen 4 Scenarios: 1, 3, 7 and 9 (in addition to the

emissions data for 2007). We have opted for these scenarios because, apart from Scenario 1 (baseline),

Scenarios 3, 7 and 9 refer to the total emissions (direct and indirect), which is one of the singularities

that allows us to calculate the input-output model. In addition, scenarios 3 and 7 are the most efficient

and allows us to compare with scenario 9, one of the most inefficient.

In the 4 scenarios (Figure 2) can be seen as China, the (red) most significant part in the EEX in 2007,

disappears as a leading exporter, due to the assumptions made. In scenario 1 (baseline) Indonesia (blue

part), Mexico (green) and Vietnam (purple) are the main focus, leaving to the rest of countries a residual

role. On the contrary, when we ignore the pattern of trade but take into account environmental

efficiency in total terms (scenario 3), trade is more distributed among all the PC-16's (which indicates

that the environmental efficiency of the sector is not directly related to the weight that the country has

in international trade. In the most efficient scenario (number 7), is Mexico (green part) the main

exporting country, as discussed in the next section. However, in the most inefficient scenario in total

terms (scenario 9), are Indonesia (blue part) and Vietnam (purple part) those who assume most of the

trade diverted from China, what it represents a real risk because these countries already have a

significant share of trade making it easier for these countries to accede to trade diversion than other

economies.

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3.4. Looking ahead: the most efficient option

In this section, we focus on the option that generates the highest emissions savings in global terms. This

is Scenario 7, which combines environmental efficiency information (total emissions) and trade patterns

(Figure 2). Under these assumptions, the PC-16´s countries that become the main exporters are Mexico,

responsible for 51.1% of PC-16´s EEX, followed by Indonesia (14.9%), the Philippines (9.4%) and Vietnam

(6.4%). As with the baseline scenario, the main emissions importing countries and regions are the US

(30.1%), the EU-25 (22.5%), East Asia (14.4%) and the RoW (8.6%). Compared to the main

counterfactual, Mexico increases its weight in terms of EEX at the expense of Indonesia and the

Philippines.

Focusing on analysis by country, it can be noted that Mexico exports mainly to the US (representing

38.5% of its EEX), and the EU-25 (20.5%), mainly to Germany (4.4% of Mexican EEX), the UK (2.6%),

France (2.6%), Italy (2.1%) and Spain (1.9%). The Indonesian EEX are distributed to the EU-25 (20.7%)

and East Asia (18.5%), which acquires more weight in Indonesia’s total EEX due to geographical

proximity, highlighting the significance of Japan (11.8%), China (8.5%) and Korea (4.1%). The US is also

significant for Indonesia, receiving 17.1% of Indonesian EEX. Regarding emissions balances, they

maintain the same trends as in Scenario 1, although in general terms the emissions deficits decrease

and the surpluses increase.

Analyzing the emissions savings that would occur in each of the proposed scenarios, we suggest that it is

essential to consider the environmental efficiency variable, as well as the linkage effects. Even though

they do not have a significant impact in global terms, they are important at the country and sector level.

Therefore, combining environmental information and trade specialization yields more significant

emissions savings.

4. Conclusions and discussion

China has grown on average 9% annually over the last 30 years, contributing more than any other

country to the growth of world consumption, especially in the last 5 years. These high levels of

economic and population growth are the most important drivers of the increase in global CO 2 emissions.

China's development and its position as a major consumer will give way to a greater role of the so-called

PC-16’s economies in world exports. This paper assesses the impact on CO2 emissions of this changing

role.

The application of Scenario 1 (baseline) produces CO2 emissions embodied in international trade that

are 13% lower in 2007, mainly due to the lower pollution intensity of PC-16’s in comparison with China.

The application of the rest of the scenarios (9 in total) introduces other variables in order to assess the

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importance of factors such as trade patterns and environmental efficiency (in direct and total terms).

We observe that the scenario where trade is diverted toward the most efficient countries in total terms

combined with information about trade patterns (Scenario 7) generates the most emissions savings

(18.2%). Based on this, we suggest that, if environmental variables are ignored, an important

opportunity to reduce global emissions could be overlooked. Other changes with less impact in terms of

volume are not evaluated in this paper, such as the effect on international freight transport of changes

in the origin or destination of the trade or the mode of transport used[25, 52].

In a dynamic context, the net effect of trade diversion in terms of emissions requires the assessment of

emissions related to the investment needed to relocate production to the PC-16’s or of the emissions

payback that have to be compensated for before starting production in these countries. The main

emissions flows, which move largely from developing countries to developed countries (which are the

large primary emitters accounting for most of the emissions shifting), are concentrated in the coal and

oil production, electricity generation and transportation sectors[33], i.e., through linkage effects and not

directly by the industries that are mainly internationally traded[57]. Therefore, the transition to

prosperity with or without growth, to paraphrase Tim Jackson[58], requires that investments in the PC-

16’s be carried out with environmentally friendly technology and, in the electricity industry in particular,

through a mix focused on renewable energy. The threat posed by trade diversion to inefficient countries

can be viewed as an opportunity for these countries to grow while gradually improving their

environmental efficiency.

The scenarios proposed in this paper show extreme results, generating carbon emissions savings

ranging from 18.2% to 1.5%, as it represents a modeling exercise, ceteris paribus whose consequences

are difficult to account for economy-wide. From this point, many improvements could be undertaken in

order to meet some limitations and reduce the level of uncertainty associated with the results. Some of

these improvements could be the consideration of natural endowments cannot be transferred or

perhaps the PC-16 are not be able to increase their production volumes enough to take over all Chinese

exports in the short term, and it would be necessary to implement these changes in the scenario

gradually. Another potential improvement could be consider that some emissions intensities appear to

be extraordinarily smaller than the Chinese due to local circumstances and cannot be guaranteed that

this could be maintained in the event of such a massive increase in their production levels.

China is increasingly concerned about reducing its environmental impact, leading policy makers to set

goals for reducing pollution intensity (CO2 per unit of GDP) to 55-60% of 2005 levels by 2020[8],

reducing rebates to energy-intensive exports and having established taxes on its exports since 2004[59].

It also announced, in November 2014, an agreement with the US to reduce GHG emissions or the sign of

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the Paris Agreement in 2015. However, these positive steps will not yield the desired results because

changes in the Chinese production structure toward domestic-based production driven by investment

imply a stagnation in the improvement of carbon intensity in China[60]. In this regard, our results

suggest that trade diversion to the PC-16’s which is expected in the following years, will be

accompanied by emission reductions to offset such stagnation.

If developed countries fail to design effective cross-border carbon adjusting mechanisms or to

implement policies enabling them to internalize part of the emissions embodied in their imports[61,

62], the diversion of international trade to the PC-16’s will perpetuate the emissions leakage that occurs

from developed countries to developing countries under unilateral climate policies because firms’

offshoring decisions are guided mainly by cost advantages and not by environmental benefits. This

diversion would reduce the importance of the bilateral or multilateral agreements reached to date, such

as the agreement by the US and China in 2013 to reduce hydrofluorocarbons (HFC) and would force

more players/countries to take an increasing role in international agreements. However, it also provides

an opportunity for new regions to reach agreements with developed countries to anticipate future

negative environmental effects and to promote the implementation of energy production systems

based on renewable energy to reduce emissions associated with the electricity sector[54, 56].

Developed regions should commit to using clean development mechanisms to offset imported virtual

carbon and to enable the sustainable development of local communities in the countries with which

they trade and sometimes avoiding their import, creating corridors of protected areas[63] under the

protection of Article XX of the General Agreement on Tariffs and Trade (1994).

ACKNOWLEDGMENTS

This research was funded by the Spanish Ministry of Education, Culture and Sports through the National

FPU Program (grant ref. FPU 13/05869). This work has been developed within the framework of the PhD

grants for research stays in foreign institutions (2014), funded by the UCLM-Enresa Chair.

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