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Economic Systems Research

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The role of outsourcing in driving global carbonemissions

Arunima Malik & Jun Lan

To cite this article: Arunima Malik & Jun Lan (2016) The role of outsourcing in driving globalcarbon emissions, Economic Systems Research, 28:2, 168-182

To link to this article: https://doi.org/10.1080/09535314.2016.1172475

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ECONOMIC SYSTEMS RESEARCH, 2016VOL. 28, NO. 2, 168–182http://dx.doi.org/10.1080/09535314.2016.1172475

The role of outsourcing in driving global carbon emissions

Arunima Malik and Jun Lan

ISA, School of Physics A28, The University of Sydney, Sydney, NSW, Australia

ABSTRACTGlobalisation has narrowed the gap between producers and con-sumers of goods and services. The linkages between internationaltrade and carbon dioxide (CO2) emissions have started to be recog-nised, yet the extent of outsourcing of emissions across nations isunknown. Filling this gap in knowledge is critical for designing effec-tive policy mechanisms for assigning responsibility for reductions inemissions. Here we present a structural decomposition analysis ofglobal trends in outsourcing of emissions from 1990 to 2010 for 186individual countries. To this end, we disaggregate total CO2 emis-sions for each country into contributions from the domestic econ-omy and international trade. This allows us to unveil outsourcingtrends for all nations confirming a world-wide shifting of emissions-intensive production across borders. We categorise nations into“outsourcers” – countries that outsource carbon-intensive produc-tion to so-called contractor nations. Our detailed assessment of thecommodity content of global outsourcing flows reveals interestinginsights about the trade of carbon-intensive commodities.

ARTICLE HISTORYReceived 3 October 2015In final form 27 March 2016

KEYWORDSStructural decompositionanalysis; outsourcing; CO2emissions; input–outputanalysis; international trade

1. Introduction

In November 2015, world leaders gathered in Paris to negotiate an agreement on reduc-ing climate change caused by greenhouse gas emissions (UNCCC, 2015). Whilst there isa global consensus that climate change is real and happening, a lot needs to be done tolimit global emissions to nomore than 1.5°C above pre-industrial levels. In order to designand implement strategies for effectively mitigating climate change, climate policy needs toaddress the root causes of global warming, which in turn requires a thorough understand-ing of the trends and outsourcing of CO2 emissions (Ekins, 2004). In essence, outsourcingof emissions refers to the decoupling of the location of production of goods from that ofconsumption. Hoekstra et al. (2016) use the term outsourcing to refer to the purchase ofintermediate and final goods from abroad, whilst we refer to outsourcing as imports of car-bon emissions embodied commodities. Many developing nations such as China and Indiamanufacture carbon-intensive goods that are eventually imported and consumed by devel-oped nations such as the USA andmany European countries, leading to significant carbonflows around the globe (Peters et al., 2011).

CONTACT Arunima Malik arunima.malik@sydney.edu.au

Supplemental data for this article can be accessed here. http://dx.doi.org/10.1080/09535314.2016.1172475

© 2016 The International Input–Output Association

ECONOMIC SYSTEMS RESEARCH 169

Undoubtedly, globalisation and international trade have narrowed the gap between pro-ducers and consumers. This is evident in the amazing levels of growth in the gross domesticproduct of many nations (Dreher, 2006). The role of international trade in increasing car-bon dioxide (CO2) emissions has been widely recognised (Peters et al., 2012); however, theextent of outsourcing of emissions across borders is unknown. In particular, there is insuffi-cient detail on the outsourcing trends of emissions for all world countries, more specificallythe kinds of CO2-embodied commodities that are traded across borders (referred hereonas the commodity content of outsourcing). The changes and trends in global carbon emis-sions can be studied using a well-established technique called structural decompositionanalysis (SDA).

SDA is based onmacroeconomic input–output theory (Leontief, 1936). SDAcanbe usedto break down changes in CO2 emissions over time, into contributions from key phys-ical and economic determinants such as fuel efficiency, technological change, affluenceand population growth. These determinants can act as either accelerators or retardantsof emissions. SDA has been widely used for identifying drivers of change for a range ofenvironmental indicators, such as energy use in the USA (Weber, 2009); CO2 emissions inNorway (Yamakawa and Peters, 2011); air pollution in the Netherlands (De Haan, 2001);material flow in Australia (Wood et al., 2009); nitrogen emissions in Denmark (Wier andHasler, 1999); and water use in China (Zhang et al., 2012), to name a few.

Most SDA-related studies to date have utilised a single-region input–output database,and hence lack insight about international trade, which in turn is explicitly documentedin multi-regional input–output (MRIO) tables. Global MRIO databases include detaileddata on international trade between industry sectors of countries, and are thereforeacknowledged as a powerful cutting-edge tool for underpinning CO2 accounting from aconsumption perspective, widely known as carbon footprinting (Wiedmann, 2009; Peters,2010). More importantly, these databases have been used to inform real-world policy-makingwith the aimof reducing greenhouse gas emissions (Barrett et al., 2013; Kokoni andSkea, 2014). However, the compilation of MRIO data is a labour- and resource-intensivetask (Tukker and Dietzenbacher, 2013), and as a consequence, only the recent advent ofadvanced computation has enabled IO practitioners to develop detailed MRIO databasesthat in turn can be used for undertaking MRIO-based structural decomposition analy-ses of carbon emissions (Baiocchi and Minx, 2010; Arto and Dietzenbacher, 2014; Owenet al., 2014; Xu and Dietzenbacher, 2014; see SI S1 for a detailed review of SDA-relatedliterature). Owing to the detail presented in MRIO databases, it is now well-understoodthat international trade plays a major role in increasing global carbon emissions (Peterset al., 2011). Even though it is known that rich developed countries outsource carbon-intensive production to developing countries (Peters et al., 2011), there is insufficientevidence about the change in outsourcing trends over time, in particular the commoditycontent of outsourcing, for all world countries.

Here we present a comprehensive analysis of the outsourcing trends for 186 world coun-tries using anMRIO-based SDA. Our study goes beyond prior work mentioned above (seealso SI S1), and advances the existing body of knowledge in four ways: (a) we present adetailed assessment of global outsourcing trends for 186 world countries; (b) we anal-yse changes in outsourcing trends over a 20 years period, and are hence able to capturedevelopments within the former Eastern Bloc and the financial crisis of 2007–2008; (c) wesplit the carbon footprint for each country into contributions from the domestic economy

170 A. MALIK AND J. LAN

and from global imports, which allows us to unveil and quantify outsourcing of carbon-intensive production and carbon leakage trends for every individual country; and (d) wecategorise the countries into “outsourcers” – those that outsource carbon-intensive pro-duction to so-called contractor world nations, and determine the commodity content ofglobal outsourcing flows.

Our paper is organised as follows. In Section 2, we provide a brief introduction of SDAfollowed by the mathematical formulation of the technique. In Section 3, we present theresults and provide a detailed discussion of the trends. We conclude in Section 4.

2. Methods

We undertake a SDA to break down the total change in CO2 emissions during the periodfrom 1990 to 2010 into contributions from six key determinants – carbon efficiency,production recipe, final demand composition, final demand destination, affluence andpopulation. First, we construct a time series of constant-price MRIO tables, which is akey requirement for carrying out SDA. To this end, we use a high-resolution global MRIOdatabase containing domestic and international monetary transactions data for 186 worldcountries, over a continuous time series from1990 to 2010 (Lenzen et al., 2012).We converttime series data into constant prices by following the “convert-first then deflate” procedure(Fremdling et al., 2007), which involves converting themonetary data for each country intoa common currency (typically US dollars), and then deflating the resulting current-priceIO tables to remove the effects of inflation over time (seeAppendixA in Lan et al. (2016) fora detailed explanation).We then follow Leontief ’s generalisation (Leontief and Ford, 1970)in linking these constant-priceMRIO tables with data on CO2 emissions, obtained by con-verting energy consumption data for each country (IEA, 2012), using carbon content andCO2 emission factors (IPCC, 2015). Next, we select Dietzenbacher and Los’ (1998) SDAmethod, out of the three predominant approaches (Su andAng, 2012) because this methodhas been shown to be exact, zero-robust and non-parametric (Lenzen, 2006). We alsodecompose the change in CO2 emissions geographically to highlight the phenomenon ofcarbon leakage and outsourcing. To this end, we split the total carbon footprint (TCFP) foreach of the 186 countries into contributions from domestic production and internationaltrade. This allows us to quantify the domestic carbon footprint (DCFP) and the Rest-of-World carbon footprint (RoWCFP) for every nation. We then compare these footprints toexamine global outsourcing trends. These steps are explained in detail below.

2.1. Introduction to SDA

SDA relies on the input–output demand-pull model, formulated by the Nobel PrizeLaureate Wassily Leontief. In essence, the total output of an economy x can berepresented as

x = LY, (1)

where L is a n× n Leontief inverse matrix capturing the interactions between differentsectors in an economy, and Y is a n×m matrix of final demand. L is derived from then× nmatrix of technical coefficients, commonly called A, as

L = (I − A)−1. (2)

ECONOMIC SYSTEMS RESEARCH 171

To enumerate the drivers of a change in CO2 emissions over time, the aforementionedinput–output system needs to be generalised as

Q = qLY, (3)

whereQ represents the total CO2 emissions, and q is a 1× n vector of direct CO2 emissionsintensity representing the CO2 emissions of each production sector per unit of the outputof the corresponding sector (e.g. Gg/$).

To examine the drivers of a change in emissions, Equation 3 can be further decomposedinto a number of elements:

(1) final demand composition u = (uid)n×m = F(g−1), where F is the n×m flow matrix

of final demand and g = ∑ni=1 Fid is the 1×m matrix of total final demand by

category,(2) final demand destination v = (vd)m×1 = (g(Y−1))′,(3) final demand/capita y (1× 1) and(4) population P (1× 1).

Note that F is a complete final demand matrix, Y is total final demand (scalar) and y isfinal demand/capita (scalar). Also, final demand composition refers to the changes in theconsumption baskets of households, whilst final demand destination refers to the changesin the consumption-vs.-investment balance.

Mathematically, the decomposition can be realised as follows:

Q∗ = qLuvyP. (4)

Suppose the total CO2 emissions from production at time 0 and t are Q0 and Qt , respec-tively; the change in CO2 emissions �Q∗ can then be decomposed into the following sixeffects:

�Q∗ = �qLuvyP︸ ︷︷ ︸Efficiency

+ q�LuvyP︸ ︷︷ ︸Prod recipe

+ qL�uvyP︸ ︷︷ ︸Dem compos

+ qLu�vyP︸ ︷︷ ︸Demdestin

+ qLuv�yP︸ ︷︷ ︸Affluence

+ qLuvy�P︸ ︷︷ ︸Population

, (5)

where,

�q is carbon efficiency of production,�L is production recipe,�u is final demand composition,�v is final demand destination,�y is affluence and�P is population.

2.2. Mathematical formulation of SDA

We use the SDA method described by Dietzenbacher and Los (1998), because it is exact(i.e. leaves no residual) and non-parametrical (Lenzen, 2006), as well as zero-robust (Woodand Lenzen, 2004).

172 A. MALIK AND J. LAN

When applying the D&L method in our study, it is obvious that the core of this methodis the existence of 6! = 720 equivalent exact decomposition forms based on the concept ofreordering the sequence of six drivers (q, L, u, v, Y and P) in the product terms containedwithin individual exact decompositions. Assuming no preference of any particular formthe full D&Lmethod, more popular in recent studies after 2005 than the approximate D&Ltechniques (Su and Ang, 2012), takes all the 6! = 720 exact possibilities to address thenon-arbitrary issues with respect to the factor-sequence chosen as follows:

�QDLek =

6∑k=1

⎛⎝

k−1∏j=1

ek0S(j)6∏

j=k+1

ekTS(j)�ekS(k)

⎞⎠ , (6)

where S(j) is any sequence of the numbers 1–6, prescribing the ordering of the deter-minants (that is, the sequence of the integral path) in the sum in Equation 6. Detailedexplanation of the D&L method is provided elsewhere (Dietzenbacher and Los, 1998;Lenzen, 2006).

Figure 1. MRIO-based geographical decomposition.Notes: Schematic of MRIO-based geographical decomposition models for a three-country economy,showing components of the carbon footprint of the United Kingdom of Great Britain and Northern Ire-land (GBR) sourced from China (CHN) and France (FRA). TCFP – Total Carbon Footprint, DCFP – DomesticCarbon Footprint, RoWCFP – Rest-of-World Carbon Footprint, T – intermediate transactions (MRIO), Y –final demand (MRIO), Q – carbon footprint (satellite). ”GBR”, “CHN” and “FRA” are ISO ALPHA-3 countrycodes issued by the United Nations (UN, 2014).

ECONOMIC SYSTEMS RESEARCH 173

2.3. MRIO-based geographical decomposition

We decompose the change in CO2 emissions not only structurally but also geographicallyto highlight the effect of international outsourcing of CO2-intensive production processes.To this end, we split the MRIO framework into a domestic component (referred hereon asthe DCFP) and an international trade component (referred hereon as the RoWCFP) forevery country we appraise. In essence, adding DCFP and RoWCFP results in the TCFP.

Taking the case of the Great Britain, the TCFP can be calculated as

QTCFP = qWorldLWorldyGBR, (7)

where qWorld is the direct CO2 emissions intensity for the entire world, LWorld is the Leon-tief ’s inverse for the world and yGBR is the final demand of the Great Britain (Figure 1).Likewise, the DCFP of the Great Britain takes the Leontief ’s inverse LWorld for the wholeworld and Great Britain’s final demand yGBR. However, it considers the direct CO2emissions intensity for only the Great Britain qGBR. The equation can be written as follows:

QDCFP = qGBRLWorldyGBR. (8)

The RoWCFP of the Great Britain takes into account all regions except the Great Britain.Mathematically, the RoWCFP can be expressed as

QRoWCFP = qRoWLWorldyGBR. (9)

3. Results and discussion

3.1. Carbon leakage and outsourcing

We decompose the change in global CO2 emissions over a 20-year period into six mutuallyexclusive causal determinants:

(1) carbon efficiency (e.g. technological change leading to changes in emissions per unitof output),

(2) production recipe (e.g. changes in the inputs of industries),(3) final demand composition (e.g. changes in consumption baskets of households),(4) final demand destination (e.g. changes in the consumption-vs-investment balance),(5) affluence (e.g. changes in per-capita consumption) and(6) population.

The effect of these determinants is shown as respective coloured-coded bars for selectedworld countries (Figure 2). In the following paragraphs, we first provide a basic explanationof Figure 2, and then delve deeper into the outsourcing trends depicted in the figure. Thecountries shown in Figure 2 are typical of four economy types (starting from left to right):(i) low-income developing nations, (ii) low-to-medium-income resource-rich emergingeconomies, (iii) high-income resource-rich nations, and (iv) high-income resource-poornations. The countries are classified into groups according to their economic situationbetween 1990 and 2010.

Out of the six colour-coded bars, two stand out in particular – carbon efficiency andaffluence. Carbon efficiency has resulted in a decrease in emissions for almost all world

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Figure 2. Structural decomposition of Total Carbon Footprints (TCFPs) (Colour online).Notes: DCFP (top panel) and RoWCFP (bottompanel) in units of teragram (Tg)/Megaton (Mt) for 11 selected countries, based on different economy types: low-incomedevelopingnations (India, China and SouthAfrica); low-to-medium-income resource-rich emerging economies (Brazil andRussia); high-income resource-rich nations(Australia and USA); and high-income resource-poor nations (Japan, Germany, France and Great Britain).

ECONOMIC SYSTEMS RESEARCH 175

countries, whilst affluence has contributed to an increase in emissions (Figure 2). The greenbars show that carbon efficiency of all economy types has improved over time (Figure 2),mostly due to key sectors becomingmore energy-efficient. On the whole, improved energyefficiency in vehicles, appliances and industrial processes has been driving an ongoingdecrease in emissions, facilitated by a range of policy strategies such as providing finan-cial incentives to industries and/or organisations for accelerating the adoption of energyefficiency measures (Geller and Attali, 2005). Affluence, in contrast, has predominantlydriven the staggering rise in CO2 emissions for almost all economy types (see red bars,Figure 2). This finding is consistent with other SDA studies (Baiocchi and Minx, 2010).

Figure 2 shows two specific rows – top row for the DCFP, whilst the bottom row forthe RoWCFP for selected economies. In addition to structurally decomposing global car-bon footprints, we split the total CO2 emissions of each country into contributions fromdomestic production and global imports, and determine the drivers thereof. This split isnecessary to highlight the trends in outsourcing of emissions across borders. Note thecolumn diagram for Russia, where the effects of the Soviet Union break up are clearly vis-ible as disintegrating production structures and tumbling family incomes. We find similartrends for other former Soviet Republics (see SI S4). The 2007–2008 financial crisis is alsodiscernible as the columns labelled “05–10” are lower than preceding columns for manydeveloped nations that were primarily affected by the turmoil (Pincock, 2010; Hou et al.,2012), but emissions growth is rather unaffected for strong exporters such as China, Brazil,South Africa and India (see SI S2, S5 and S6).

Figure 2 also shows the outsourcing trends for different economy types over a 20-yearperiod, which has been divided into five-year intervals. Our trend analysis shows thechanges in outsourcing of emissions over time. Examining a sequence starting with low-income developing nations proceedingwith low-to-medium-income resource-rich emerg-ing economies and high-income resource-rich nations, and ending with high-incomeresource-poor nations, we find an interesting transition in the relationship between DCFPand the RoWCFP. Over the 20-year period, low-income developing nations have experi-enced a strong growth in their DCFP, whilst their RoWCFP has not grown significantly.As we move to the right of Figure 2, the RoWCFP increases in comparison to the domesticfootprint. Changes in the rest-of-world footprint of high-income resource-poor nations aremuch higher than changes in their domestic footprint. In particular, the United Kingdom’s(UK) and Germany’s DCFPs have continuously decreased at the cost of carbon embodiedin their imports. Moreover, the increase in the rest-of-the world footprint of Germany andespecially the UK is much larger than the decrease in their DCFP. This finding is consis-tent with previous work (Baiocchi and Minx, 2010; Lenzen et al., 2010; Wiedmann et al.,2010), but here, we offer this kind of evidence using SDA over a 20-year period for theentire world.

Our further investigation of individual international supply chains shows that the casesof Germany and the UK constitute classic examples of outsourcing, where rich countriesshift emissions-intensive processes to pollution havens in developing countries that do nothave strict environmental legislation (Peters et al., 2011; Barrett et al., 2013) (see SI S3 formore information on carbon leakage). A closer analysis of the drivers of both the domes-tic and the rest-of-the world footprints reveals interesting insights. For example, changesin the UK’s own production recipes have had a retarding effect on its domestic carbonemissions. However, the opposite is true for the rest-of-the world carbon footprint: the

176 A. MALIK AND J. LAN

UK’s carbon-intensive imports are sourced from countries where the change in productionrecipe is driving up emissions (see the light blue column segments, Figure 2). This findingestablishes clear evidence for carbon leakage to be associated with input substitution. Thiseffect is predominantly occurring with trading partners such as China and India, whereinput restructuring has led to an increase in emissions (see SI S2 for detailed results). Inaddition, these “sinks” of carbon leakage also happen to be countries where consumer bas-kets have becomemore carbon-intensive as these economies develop (see the pink columnsegments, Figure 2).

3.2. Leaks and sinks of emissions

Some countries leaking carbon require others to absorb carbon, allowing the division ofthe world into leaks and sinks. In this article, we refer to countries with RoWCFP grow-ing stronger than their DCFP (see red countries in Figure 3) as carbon leaking nations, andcountries withDCFP growing stronger than their RoWCP (see green countries in Figure 3)as carbon sinks. Unsurprisingly, China stands out as a sink – the country’s DCFP hasgrown stronger than the RoWCFP, due to the rapid expansion of China’s domestic econ-omy caused by rising population and domestic affluence, but as we show in SI S6 caused

Figure 3. Leaks and sinks (Colour online).Notes: Outsourcing trends are indicated by comparing changes in the domestic and RoWCFPs, accord-ing toλ = (�RoWCFP − �DCFP)/TCFPwith TCFP = (TCFP1990 + TCFP2010)/2being the average TCFP.Red and green colour coding represents positive and negative values of λ, indicating disproportionatelyhigh growth of the rest-of-world or domestic footprints, respectively. China stands out as a sink of carbonemissions, whereas the majority of European countries are marked red indicating that they outsourcecarbon-intensive production to other nations.

ECONOMIC SYSTEMS RESEARCH 177

mainly by the increasing production of exports for countries such as the United Kingdom,Germany and France. Similarly, CO2 emissions in countries such as India, Brazil, Russia,and certain Middle Eastern and African nations’ have grown domestically, but predomi-nantly because of a growth in exports of resources such as minerals, oil and agriculturalcommodities.

An escalation of the rest-of-the world carbon footprint in comparison to the domesticfootprint for countries coloured red (Figure 3) is primarily due to any of the following threereasons:

(a) carbon leakage: outsourcing of carbon-intensive production to countries that havelaxer environmental legislation: for example, we have shown that high-incomeresource-poor nations such as the United Kingdom, Germany and France (see alsoFigure 2) outsource carbon-intensive production to China.

(b) shrinking of the domestic economy: for example Romania and Ukraine were hit heav-ily by recession in the late 2000s. In particular, Ukraine’s economy shrunk by almost15% in 2009 leading to a decline in the country’s DCFP; and

(c) accumulation of considerable debt due to ongoing trade deficit: the reasons of thiscould either be continued war, such as in Congo; natural calamity such as the droughtin Mauritania; or a reduction in resource production such as in Zimbabwe andMongolia (see SI S6 for individual country information).

3.3. Commodity content of outsourcing

International trade has grown rapidly over the past few decades. The value of export ofgoods and services is about 300 times larger today than it was in 1950 (Wiedmann, 2015).Interestingly, there are significant embodied flows of anthropogenic CO2 emissions ininternational trade (Peters and Hertwich, 2008a). In this section, we assess the commoditycontent of outsourcing flows for all world nations (Figure 4). Out of all nations, China andUSA stand out in particular as major trading partners.

China is the world’s leading exporter, with the country producing roughly US$ 2.3 tril-lion worth of goods for export in 2014 (CIA, 2014). China’s main export partners are USA,Hong Kong, Japan and South Korea. China’s export of motor vehicle parts, bicycles, trail-ers, cars and other transport equipment to the USA (ATLAS, 2014) resulted in an increaseof roughly 7000 Gg of CO2 emissions from 1990 to 2010 (Figure 4). A similar increase canbe seen for the export of electrical machinery from China to the USA, in particular auto-matic data processing machines, telephones, printers and other energy-intensive electricalequipment. China also exports electrical machinery to Brazil, Germany, South Korea andJapan (Figure 4). China’s textile industry is a major emitter of greenhouse gases (Wei et al.,2011). More specifically, China exports leather products to Hong Kong (ATLAS, 2014).Even though China has employed measures for increasing energy and carbon efficiency,rapid economic development, growth of manufacturing sectors and a rise in exports haveoutpaced any efforts to reduce emissions (Guan et al., 2012). Being the top exporter, it isunsurprising that China is also the world’s leading carbon emitter, with majority of theemissions caused by the production of exports (Minx et al., 2011; Wang andWang, 2015).

In addition to importing equipment from China, USA is also a significant exporter(CIA, 2014). USA exports transport equipment to Japan (mainly aircraft, spacecraft and

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Figure 4. Commodity content of outsourcing (Colour online).Notes: The arrows signify the emissions resulting from trade betweendifferent countries. The commodity content of the trade is colour-coded according to respectivesectors listed in the key, for example, CO2 emitted in the metal products sector of South Africa for supplying products consumed by the electrical and machinerysector of the USA resulted in an increase of 579 Gg of emissions from 1990 to 2010. Colour online.

0 7,670 Gg

Germany

Japan

Russia

Norway

UK

USA

Poland

Italy

Viet Nam

Canada

South Korea

Mexico

Australia

Singapore

Spain

BrazilIndonesia

France

Thailand

IndiaHong Kong

South Africa

Kazakhstan

Saudi Arabia

Colombia

Venezuela

China USA (7670)China USA (7195)India USA (1040)

China Japan (7340)China Japan (5760)USA Japan (1884)

China Hong Kong (6058)China Hong Kong (3907)

China South Korea (3156)

Electricity, gas and water sector

Key:

Transport equipment

Electrical and machineryConstruction

Petroleum, chemical and non-metallic mineral productsTextiles and wearing apparel

China Germany (2531)China Germany (1680)

China India (1655)

Metal products

Russia India (910)

Agriculture

China Italy (958)

USA South Korea (672)

USA Canada (670)

USA Germany (662)China Russia (490)

China Brazil (1220)Australia China (536)

South Africa USA (579)

China France (1260)

ECONOMIC SYSTEMS RESEARCH 179

launch vehicles), Germany (mainly cars), Canada (mainly cars and motor vehicle parts)and South Korea (mainly aircraft, spacecraft and launch vehicles), to name a few (ATLAS,2014). For constructing transport equipment, the USA imports metal products, morespecifically iron and steel from India and ferroalloys from South Africa. Production of ironand steel is energy and carbon-intensive (Sarker et al., 2013), mainly because the process ofconverting iron to steel involves the use of carbon-intensive fuels and reductants (Kundaket al., 2009). In the same vein, ferroalloy production has a high emission factor in coun-tries such as South Africa and India, which still rely on carbon-intensive fuels for carryingout the industrial processes (Gasik, 2013). International trade of petroleum products andcoal is also a major contributor to an increase in global carbon emissions (see SI S7 for anexhaustive list of the commodity content of outsourcing flows).

4. Outlook

Climate change, caused by anthropogenic CO2 emissions, is considered as one of thegreatest threats to ecosystem’s life-support systems. CO2 has a long life span and has thepotential to affect the climate for thousands of years. Therefore, policy-makers recognisethe importance of devising effective strategies for mitigating the harmful effects of climatechange. Efforts to reduce CO2 emissions thus far have largely been directed towards spe-cific countries leading to fragmentation and inefficient outcomes (Rosen, 2015). The Kyotoprotocol, in particular, has received significant criticism, primarily due to the exclusion ofdeveloping countries such as China and India. Furthermore, it has been suggested that alack of global agreement has facilitated the phenomenon of carbon leakage where emis-sion reductions by developed countries are achieved by outsourcing emissions-intensiveproduction to developing nations (Jakob et al., 2014). A prominent example is the case ofthe United Kingdom (UK) that is included in Annex B of the Kyoto Protocol and thus wasbound by an emissions reduction target. TheUK’s recent emission reductions and itsmeet-ing of targets (Baiocchi andMinx, 2010; Kanemoto et al., 2014) have been achievedmainlyby outsourcing carbon-intensive production to developing countries as is evident from theresults presented in this study. More recently, negotiations at 2015 United Nations ClimateChange Conference have paved the way for a global agreement to develop a more univer-sal framework that requires all countries to take action against climate change (UNCCC,2015). Regardless of these agreements, there is still the question of responsibility that needsto be addressed – who is responsible for greenhouse gas emissions? The use of production-or consumption-based accounting for emission allocation is a highly debated topic.1 Jakobet al. (2014) suggest the need to focus on certain highly traded emission intensive productswhilst devising abatement strategies; and the identification of these products (commoditycontent of outsourcing) is themain contribution of this study. The results of this study haveimplications for any international agreement aimed at imposing emission reduction targetson countries. Our results emphasise the need to report on the rest-of-the world carbonemissions, in addition to the domestic emissions. Not only would this provide an accu-rate assessment of a country’s total carbon responsibility, but would also facilitate greatertransparency and assist policy-makers in devising effective emissions abatement strategies.

1 See Munksgaard and Pedersen (2001), Bastianoni et al. (2004), Guan et al. (2008), Peters (2008), Peters and Hertwich(2008b), Davis and Caldeira (2010), Davis et al. (2011), Kanemoto et al. (2011), Peters et al. (2012), and Barrett et al. (2013).

180 A. MALIK AND J. LAN

Acknowledgements

The authors thank Sebastian Juraszek for expertly managing our advanced computation require-ments, Charlotte Jarabak for help with sourcing of data, Keiichiro Kanemoto, Arne Geschke andDaniel D. Moran for providing technical assistance during data preparation. The authors also thankBarney Foran for providing valuable comments on an earlier version of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was financially supported by the Australian Research Council through its DiscoveryProjects [DP0985522 and DP130101293].

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