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2013-Outsourcing CO2 within China

Outsourcing CO2within China

Kuishuang Feng a,Steven J.Davis b,Laixiang Sun a,c,d,Xin Li e,Dabo Guan e,f,g,Weidong Liu h,Zhu Liu f,i,

and Klaus Hubacek a,1

a Department of Geographical Sciences,University of Maryland,College Park,MD20742;

b Department of Earth System Science,University of California, Irvine,CA92697;

c Department of Financial an

d Management Studies,School of Oriental and African Studies,University of London,London WC1H0XG, United Kingdom;d International Institut

e for Applied Systems Analysis,A-2361Laxenburg,Austria;e Sustainability Research Institute,School o

f Earth and Environment,University of Leeds,Leeds LS29JT,United Kingdom;f Institute of Applied Ecology,Chinese Academy of Sciences,Shenyang110016,China;

g St.Edmund’s College,University of Cambridge,Cambridge CB30BN,United Kingdom;h Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,Beijing100101,China;and i University of Chinese Academy of Sciences,Beijing100094,China

Edited by M.Granger Morgan,Carnegie Mellon University,Pittsburgh,PA,and approved May7,2013(received for review November19,2012)

Recent studies have shown that the high standard of living enjoyed by people in the richest countries often comes at the expense of CO2emissions produced with technologies of low ef?ciency in less af?uent,developing countries.Less apparent is that this relationship between developed and developing can exist within a single coun-try’s borders,with rich regions consuming and exporting high-value goods and services that depend upon production of low-cost and emission-intensive goods and services from poorer regions in the same country.As the world’s largest emitter of CO2,China is a prom-inent and important example,struggling to balance rapid economic growth and environmental sustainability across provinces that are in very different stages of development.In this study,we track CO2 emissions embodied in products traded among Chinese provinces and internationally.We?nd that57%of China’s emissions are re-lated to goods that are consumed outside of the province where they are produced.For instance,up to80%of the emissions related to goods consumed in the highly developed coastal provinces are imported from less developed provinces in central and western China where many low–value-added but high–carbon-intensive goods are produced.Without policy attention to this sort of inter-provincial carbon leakage,the less developed provinces will strug-gle to meet their emissions intensity targets,whereas the more developed provinces might achieve their own targets by further outsourcing.Consumption-based accounting of emissions can thus inform effective and equitable climate policy within China. embodied emissions in trade|regional disparity|

multiregional input–output analysis

A s the world’s largest CO2emitter,China faces the challenge of

reducing the carbon intensity of its economy while also fos-tering economic growth in provinces where development is lagging. Although China is often seen as a homogeneous entity,it is a vast country with substantial regional variation in physical geography, economic development,infrastructure,population density,demo-graphics,and lifestyles(1).In particular,there are pronounced differences in economic structure,available technology,and levels of consumption and pollution between the well-developed coastal provinces and the less developed central and western provinces(2). In the2009Copenhagen Climate Change Conference of the United Nations Framework Convention on Climate Change,China committed to reducing the carbon intensity of its economy[i.e.,CO2 emissions per unit of gross domestic product(GDP)]by40–45% from2005levels and to generating15%of its primary energy from nonfossil sources by2020(3).In the meantime,China’s12th5-year plan sets a target to reduce the carbon intensity of its economy by17%from2010levels by2015(4),with regional efforts ranging from a10%reduction of carbon intensity in the less developed west and19%reduction in east coast provinces.Thus,the regions that produce the most emissions and use the least advanced technologies have less stringent intensity targets than the more af?uent and technologically advanced regions(5),where the costs of marginal emissions abatement are much higher.In further recognition of such regional inequities,pilot projects are being implemented to test the feasibility and ef?cacy of interprovincial emissions trading(6–9). Additionally,progress against emissions targets could be evaluated not only by“production-based”inventories of where emissions occur,but also by“consumption-based”inventories that allo-cate emissions to the province where products are ultimately consumed(10).Such consumption-based accounting of CO2 emissions may better re?ect the ability to pay costs of emissions mitigation(11).

Details of our analytic approach are presented in Materials and Methods.In summary,we track emissions embodied in trade both within China and internationally using a global multiregional in-put–output(MRIO)model of129regions(including107indi-vidual countries)and57industry sectors,in which China is further disaggregated into30subregions(26provinces and4cities).Al-though a number of recent studies have used a similar MRIO approach to assess the emissions embodied in international trade (12–14),studies of emissions embodied in trade within individual countries remain rare due to a lack of data.Here,we use the latest available data to construct input–output tables of interprovincial trade and nest these tables within a global MRIO database.From this framework,we calculate CO2emissions associated with con-sumption in each of the30Chinese subregions as well as emissions embodied in products traded between these subregions and the rest of the world(i.e.,128regions).

Results

In2007,57%of China’s emissions from the burning of fossil fuels, or4gigatonnes(Gt)of CO2,were emitted during production of goods and services that were ultimately consumed in different provinces in China or abroad.To facilitate reporting and discussion of our results,we group30Chinese provinces and cities into eight geographical regions(for details of this grouping,see Fig.2).Fig.1, Upper Left,shows the largest gross?uxes of embodied emissions among the eight regions,with regions shaded according to net emissions embodied in trade(i.e.,the difference between production and consumption emissions)in each region.Beijing–Tianjin,the Central Coast,and the South Coast are the most af?uent regions in China,with large imports of emissions embodied in goods from poorer central and western provinces.More than75%of emissions associated with products consumed in Beijing–Tianjin occur in other regions.Similarly,the Central Coast and South Coast regions outsource about50%of their consumption emissions. Author contributions:K.F.,S.J.D.,L.S.,X.L.,and K.H.designed research;K.F.,X.L.,and K.H. performed research;K.F.,X.L.,W.L.,and Z.L.contributed new reagents/analytic tools;K.F., S.J.D.,L.S.,X.L.,D.G.,and K.H.analyzed data;and K.F.,S.J.D.,L.S.,X.L.,D.G.,and K.H. wrote the paper.

The authors declare no con?ict of interest.

This article is a PNAS Direct Submission.

Freely available online through the PNAS open access option.

See Commentary on page11221.

1To whom correspondence should be addressed.E-mail:hubacek@https://www.wendangku.net/doc/6713916022.html,.

This article contains supporting information online at https://www.wendangku.net/doc/6713916022.html,/lookup/suppl/doi:10. 1073/pnas.1219918110/-/DCSupplemental.

11654–11659|PNAS|July9,2013|vol.110|https://www.wendangku.net/doc/6713916022.html,/cgi/doi/10.1073/pnas.1219918110

The other maps in Fig.1highlight emissions embodied in prod-ucts traded within China that are triggered by different categories of GDP:household consumption (Upper Right ),capital formation (Lower Left ),and international exports (Lower Right ).People living in Beijing –Tianjin,Central Coast,and South Coast provinces have much higher per capita household consumption than do people living in other provinces.For example,household consumption per capita in Beijing –Tianjin in 2007was more than three times the consumption in the Southwest region.However,our analysis shows that higher levels of household consumption in more developed coastal regions are being supported by production and associated emissions occurring in less developed neighboring regions (Fig.1,Upper Right ).In the case of Beijing –Tianjin,household consumption causes emissions in the Northwest (34Mt)and North (29Mt)regions.Similarly,substantial emissions related to household con-sumption in the Central Coast region are outsourced to the Central (58Mt),North (42Mt),and Northwest (32Mt)regions,and household consumption in South Coast is supported by emissions in the Central (34Mt)and Southwest (33Mt)regions.Interestingly,

emissions in the North region support household consumption in more af ?uent coastal regions,but at the same time,household consumption emissions in the North region are in turn outsourced to the Central (45Mt)and Northwest (34Mt)regions.

In keeping with its rapid growth but in contrast to most countries,capital formation (i.e.,new infrastructure and other capital investments)in China represents a larger share of GDP (42%in 2007)than household consumption (36%in 2007).In addition,in less-developed western provinces such as Guangxi,Qinghai,Ningxia,and Inner Mongolia,capital formation in recent years has represented an even greater proportion of provincial GDP,for example,more than 70%in 2010.Because such capital formation often entails energy-intensive materials like cement and steel,it is also responsible for a large proportion of China ’s emissions:37%in 2007.The largest transfers of embodied emissions caused by capital formation were to the Central Coast from the Central (90Mt),North (80Mt),and Northwest regions (36Mt);and capital for-mation in Beijing –Tianjin was supported by substantial emissions produced in the North (46Mt)(Fig.1,Lower Left ).Partly

Capital formation

per capita (¥)

27,222

6,248

no data

90

37

4645

35

4035

80

36

Central

North Central Coast

South Coast Beijing-Tianjin

Tibet

Percent GDP related to International Exports

36%

3%

no data

Tibet

26,009

6,861

17,524

18,189

19%

-452

452

Northeast Southwest

Northwest

Northwest

Upper Left shows largest interprovincial ?uxes (gross)of emissions embodied in trade (megatonnes of CO and net importing regions (red).Upper Right shows the largest interprovincial ?uxes of emissions embodied regions shaded according to value of household consumption per capita (from high in red to low in green).Lower of emissions embodied in products consumed by capital formation,with regions shaded according to the value of capital formation per capita (from high in red to low in green).Lower Right shows the largest interprovincial ?uxes of emissions embodied in products destined for international export,with regions shaded according to the share of GDP related to international exports (from high in red to low in green).Note:carbon ?uxes caused by government ex-penditure are not shown separately in this ?gure but are included in the total emissions embodied in trade (Upper Left ).

Feng et al.

PNAS |July 9,2013|vol.110|no.28|11655

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in contrast to the dominant pattern of emissions embodied in in-terprovincial trade for household consumption,the emissions re-

lated to capital formation re?ect the large-scale expansion of infrastructure that is underway in relatively poor regions such as

Southwest and Northwest,such that less developed provinces are in some cases outsourcing emissions to the more af?uent regions of

eastern China.For example,in2007emissions in the North region supported capital formation in the Northwest(35Mt)and Central (45Mt)regions.

Previous studies have emphasized international exports as a pri-

mary driver of Chinese CO2emissions(15–18).According to Chi-na’s statistical yearbook,74%of China’s exports in2007originated in provinces of the Central Coast and South Coast regions(19).

However,here we?nd that40%of the emissions related to exports from these coastal regions actually occurred in other regions of China(Fig.1,Lower Right).In particular,international exports from

the Central Coast region were supported by substantial emissions in

the Central(77Mt),North(70Mt),and Northwest(38Mt)regions.

Similarly,international exports from the South Coast were sup-ported by large amounts of emissions in Southwest(59Mt),Central (45Mt),and Northwest(20Mt)regions.

Fig.2shows the balance of emissions embodied in China’s in-terprovincial and international trade.In provinces in which net

export of emissions is large(e.g.,Hebei,Henan,Inner Mongolia, and Shanxi),a substantial portion(in those cases,81–94%)of the

emissions embodied in exports were for intermediate(i.e.,un-?nished)goods traded to other provinces in China.In contrast, 38–54%of the emissions imported to Hebei,Henan,Inner

Mongolia,and Shanxi were embodied in?nished goods.In Inner Mongolia,exported emissions are also driven by the dominance of energy-intensive heavy industry(more than70%of that province’s gross industrial output in2007)and coal use(92%of its fuel mix). Meanwhile,Guangdong,Zhejiang,Shanghai,Tianjin,and Beijing are net importers of embodied emissions,with a relatively high proportion of imported emissions embodied in?nished goods: from12%in Zhejiang up to62%in Tianjin.This shows that the poorer regions export a larger share of low–value-added and im-port a larger share of high-value products.

Not surprisingly,in each province the emissions embodied in international exports exceeded emissions embodied in imports from other countries in2007(Fig.2).In coastal provinces such as Shandong,Jiangsu,Guangdong,Zhejiang,Shanghai,and Fujian, a considerable fraction of emissions produced support interna-tional exports,ranging from35%to51%in2007,whereas for central and western provinces(e.g.,Anhui,Hunan,Hubei, Yunnan,Xinjiang),this share is generally less than25%.How-ever,as discussed above,substantial emissions in these interior provinces are embodied in intermediate goods exported to coastal provinces,where they become part of?nished goods for interna-tional export.

Fig.3,row1,Left,shows the largest net domestic importers of embodied emissions produced elsewhere in China,dominated by af?uent cities and provinces along the coast such as Zhejiang, Shanghai,Beijing,Guangdong,and Tianjin.The main net domestic exporters of these emissions include mostly less developed prov-inces in the Central and Northwest regions of China such as Inner Mongolia,Shanxi,and Henan,as well as a few provinces in the North and Northeast regions such as Hebei,Shangdong,and Liaoning.Normalizing net domestic imports of emissions per unit of GDP(Fig.3,row1,Center)and per capita(Fig.3,row1,Right) further emphasizes the disproportionate outsourcing of emissions from rich coastal cities such as Shanghai,Beijing,and Tianjin.In the case of net domestic exports of emissions per unit GDP(Fig.3, row2,Center),we?nd that the carbon intensity of net domestic exports is greatest in Inner Mongolia(247g of CO2embodied in net exports per¥GDP),Shanxi(164g per¥GDP),and Hebei(144 g per¥GDP)due to the prevalence of heavy industry and/or energy products(i.e.,coal and electricity)exported from these provinces. Overall consumption-based emissions are greatest in large and rich coastal provinces such as Shangdong,Jiangsu,Guandong, Zhejiang,Hebei,Liaoning,and Shanghai,as well as populous provinces such as Henan and Sichuan(Fig.3,row3,Left).However, the provinces with the lowest consumption-based emissions include the least developed provinces in the Central,Northwest,and Southwest regions as well as cities or provinces with relatively small populations(e.g.,Tianjin)(Fig.3,row4,Left).However,the con-sumption-based carbon intensity(emissions per unit GDP)is

Emissions Embodied in Exports (Mt CO2)Emissions Embodied in Imports (Mt CO2)

Mining

Fig.2.Emissions embodied in interprovincial and international trade for30provinces.Colors represent trade in domestic?nished goods by industry sector. Traded domestic intermediate goods(dark gray)are those used by industries in the importing provinces to meet consumer demand for domestic goods. Internationally traded goods(light gray)are those goods purchased from or sold to international markets.Italicized labels at the right of each bar indicate to which of the eight aggregated regions the province or city has been assigned.

11656|https://www.wendangku.net/doc/6713916022.html,/cgi/doi/10.1073/pnas.1219918110Feng et al.

greatest in provinces of the Central,Northwest,and Southwest regions where coal use and energy-intensive activities such as the production of capital infrastructure are dominant,and economies are growing rapidly(Fig.3,row3,Center).In contrast,the high GDP and more established economies in coastal provinces are among the least carbon intensive(Fig.3,row4,Center).For example,Ningxia, in the less developed Northwest region,has the highest consump-tion-based carbon intensity,527g of CO2per¥GDP,which is more than four times the intensity of Guangdong,in the rich South Coast region,where carbon intensity reaches a low level of126g of CO2 per¥GDP.Similarly,per capita consumption-based carbon emis-sions in the most af?uent cities of Shanghai,Beijing,and Tianjin (10.8–12.8tons per person;Fig.3,row3,Right)are more than four times that of interior provinces such as Guangxi,Yunnan,and Guizhou(2.4–2.6tons per person;Fig.3,row4,Right). Discussion

Our results demonstrate the economic interdependence of Chi-nese provinces,while also highlighting the enormous differences in wealth,economic structure,and fuel mix that drive imbalances in interprovincial trade and the emissions embodied in trade. The highly developed areas of China,such as Beijing–Tianjin, Central Coast,and South Coast regions,import large quantities of low value-added,carbon-intensive goods from less developed Chinese provinces in the Central,Northwest,and Southwest regions.In this way,household consumption and capital formation in the developed regions,as well as international exports from these regions,are being supported by emissions occurring in the less developed regions of China(20).Indeed,the most af?uent cities of Beijing,Shanghai,and Tianjin,and provinces such as Guangdong and Zhejiang,outsource more than50%of the emissions related to the products they consume to provinces where technologies tend to be less ef?cient and more carbon intensive. The carbon intensity of imports to the af?uent coastal prov-inces is much greater than that of their exports—in some cases by a factor of4,because many of these imports originate in western provinces where the technologies and economic structure are energy intensive and heavily dependent on coal.Provinces

such Bottom 10

Consumption

Emissions

Top 10

Consumption

Emissions

Top 10

Net Domestic

Export

of Emissions

Top 10

Net Domestic

Import

of Emissions

by Province per ¥GDP

Mt CO

2

/ y

Mt CO

2

/ y

Mt CO

2

/ y

Mt CO

2

/ y

g CO

2

/¥GDP

g CO

2

/¥GDP

g CO

2

/¥GDP

126

182

195

196

196

200

205

g CO

2

/¥GDP t CO

2

/ person / y

t CO

2

/ person / y

t CO

2

/ person / y

per capita

t CO

2

/ person / y

GDP per capita

7,28865,602

Fig.3.The top10provinces by net domestic imports(row1),net domestic exports(row2),and consumption emissions(row3),and the bottom10provinces

by consumption emissions(row4),all presented as regional totals(left column),per unit GDP(center column),and per capita(right column).The color of bars corresponds to provincial GDP per capita from the most af?uent provinces in red to the least developed provinces in green(see scale).

Feng et al.PNAS|July9,2013|vol.110|no.28|11657

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as Inner Mongolia and Shanxi,which together produce more than80%of coal burned in China and export23%and36% of the electricity they generate to other provinces,respectively, are locked into energy-and carbon-intensive heavy industries that account for more than80%of their total industrial output. At present,China’s carbon policy seeks to address regional differences within China by setting higher targets for reducing emissions in Central Coast(reduction by19%),Beijing–Tianjin (18–19%),South Coast(17.5–19.5%),except Hainan(11%),which is a tourist region,and North(18%);medium targets in Northeast (16–18%)and Central(17%);and lower targets in Northwest(10–16%)and Southwest(11–17.5%)by2015(4).However,provinces in the central and western parts of China will struggle to achieve even these more modest reduction targets if no funds are provided for updating their infrastructure and importing advanced technol-ogies.Moreover,the more ambitious targets set for the coastal provinces may lead to additional outsourcing and carbon leakage if such provinces respond by importing even more products from less developed provinces where climate policy is less demanding. However,the marginal cost of emissions reductions are sub-stantially lower in interior provinces such as Ningxia,Shanxi,and Inner Mongolia,where produced emissions,energy intensity, and coal use are all high relative to the cities and provinces along the central coast.The emissions trading scheme being tested now (6)may help achieve least cost emissions reductions through technology transfer and capital investment from the coast to the interior.However,this study provides another justi?cation for such a scheme:the economic prosperity of coastal provinces is being supported by the industry and carbon emissions produced in the central and western provinces.For instance,if a uniform price were imposed on carbon within China,larger emissions reductions would occur in western provinces where marginal costs are lower,and the cost of these reductions would be shared by af?uent consumers in coastal China who would pay more for the goods and services imported from the interior.In contrast, more lenient intensity targets in the western provinces will ne-cessitate more expensive emissions reductions in coastal prov-inces,and will encourage additional outsourcing to the western provinces.Consumption-based accounting can thus inform ef-fective and equitable policies to reduce Chinese CO2emissions. Materials and Methods

In this study,we include26provinces and4cities(in total,30regions)except Tibet and Taiwan.The results are based on30regions,but for easier un-derstanding the results and discussions are organized in8Chinese regions: Northeast(Heilongjiang,Jilin,Liaoning),Beijing–Tianjin(Beijing,Tianjin), North(Hebei,Shandong),Central(Henan,Shanxi,Anhui,Hunan,Hubei, Jiangxi),Central Coast(Shanghai,Zhejiang,Jiangsu),South Coast(Guangdong, Fujian,Hainan),Northwest(Inner Mongolia,Shannxi,Gansu,Ningxia,Qinghai, Xinjiang),and Southwest(Sichuan,Chongqing,Yunnan,Guizhou,Guangxi).

The2007input–output tables(IOTs)for each of China’s30provinces ex-cept Tibet are compiled and published by the National Statistics Bureau(21). The of?cial IOTs have42sectors and the?nal demand category in the tables consists of rural and urban household consumption,government expendi-ture,capital formation,and exports.The IOTs also report the total value,by sector,that is shipped out of each province and the total value,also by sector,that enters into each destination province.This set of sector-level domestic trade?ow data provides the basis for constructing the in-terregional trade?ow matrix with both sector and province dimensions.In terms of the core methodology for the construction,we adopt the best-known gravity model of Leontief and Strout(22)and augment it in line with LeSage and Pace(23)and Sargento(2009)(24)to accommodate the spatial dependences of the dependent variable.Because the calibration of the augmented gravity model for each sector needs a known trade matrix of dominant/representative commodities in the sector(e.g.,grain and cotton in the agricultural sector)and because such detailed data are not available for some small sectors,we aggregate the provincial tables into30sectors to accommodate this data constraint.

In the standard Leontief–Strout gravity model,the sector-speci?c in-terregional trade?ows are speci?ed as a function of total regional out?ows,total regional in?ows,and the cost of transferring the commodities from

one region to another.This cost is typically proxied by a distance function.In the augmented gravity model,the equation also includes three variables re?ecting the spatial dependences of the dependent variable:The origin-based one is de?ned as the spatially weighted average of?ows from the neighbors of each region of origin to each destination region;the destina-tion-based one is the spatially weighted average of?ows to the neighbors of each destination regions,which are from the same region of origin;the mixed origin-destination–based one is de?ned as the spatially weighted average of?ows from the neighbors of each region of origin to the neighbors of each destination region.The mathematical simplicity and in-tuitive nature of the gravity model and more importantly the reasonability of its empirical results grant it popularity and success in calibrating trade ?ows(25,26).The comparative assessment of Sargento(24,27)on alter-native models further indicates that the gravity model is well suited to ex-plain trade?ow behavior.A technical speci?cation of our augmented gravity model is presented in SI Text1.

We run regressions of the augmented gravity model based on the known trade matrix of dominant/representative commodities in5primary sectors,16 manufacturing sectors,and1electricity sector.The regressions for agriculture, chemistry,and electronics are presented in SI Text1as three illustrative examples.The regressions give us the estimated values of the model param-eters.Substitution of the known values of the total regional out?ows,total regional in?ows,and distance function into the augmented gravity model with known parameters gives us the initial trade matrix for the5primary sectors(sectors1–5),16manufacturing sectors(sectors6–21),and1electricity sector(sector22).For gas and water production(sector23),construction (sector24),and all service sectors(sectors25–30),we do not have quali?ed sample data of dominant/representative commodities.To get the initial matrix for these sectors,a simple data pooling method of Hulu and Hewings(28)is adopted with an augmentation as follows.Sixty percent of the out?ow of each province is distributed to other provinces in proportion to the inverse of distance,and the remaining40%is distributed according to the ratio of a province’s in?ow to the sum of all provinces’in?ows.The initial trade?ow matrix produced above,which excludes intraregional?ows,does not meet the “double sum constraints”in that the row and column totals match with the known values given in the2007IOTs.To assure agreement with the sum constraints,we apply the well-known iterative procedure of biproportional adjustment of the RAS technique.The RAS procedure tends to preserve as much as possible the structure of the initial matrix,with the minimum amount of necessary changes to restore the row and column sums to the known values (29,30).To complete with the system boundary,we connect the Chinese MRIO 2007to global trade database version8(based on2007trade data)published by Global Trade Analysis Project(GTAP)(31)(description of connecting to the GTAP database is included in SI Text2).

China does not of?cially publish annual estimates of CO2emissions.We estimate CO2emissions of the30provinces based on China’s provincial en-ergy statistics.We adopt the Intergovernmental Panel on Climate Change reference approach(32)to calculate the CO2emissions from energy com-bustion as described by Peters et al.(17)and applied in previous work on China by three of the authors(2,15,16).We applied the method to calculate emissions for all provinces in2007.The inventories include emissions from fuel combustion and cement production.Total energy consumption by production sectors and residents provide the basis for calculating the energy combustion CO2emissions(21).We construct the total energy consumption data for production purposes based on the?nal energy consumption(ex-cluding transmission energy loss),plus energy used for transformation(pri-mary energy used for power generation and heating)minus nonenergy use. The transmission energy loss refers to the total of the loss of energy during the course of energy transport,distribution,and storage,and the loss caused by any objective reason in a given period(26).The loss of various kinds of gas due to discharges and stocktaking is excluded(26).We understand there are two different of?cial and publicly available energy data sources in China between provincial and national statistics and the discrepancy is up to18% (33).We adopted the provincial energy statistics to compile the emission inventories for every Chinese province as it more closely represents energy consumption at the provincial level.

In a MRIO framework,different regions are connected through inter-regional trade.The technical coef?cient submatrix A rs=ea rs ijTis given by a rs

ij

=z rs

ij

=x s

j

,in which z rs

ij

is the intersector monetary?ow from sector i in region r to sector j in region s;x j s is the total output of sector j in region s.

The?nal demand matrix is F=ef rs

i

T,where f rs

i

is the region’s?nal demand

for goods of sector i from region r.Let x=ex s

i

https://www.wendangku.net/doc/6713916022.html,ing familiar matrix nota-tion and dropping the subscripts,we have the following:

11658|https://www.wendangku.net/doc/6713916022.html,/cgi/doi/10.1073/pnas.1219918110Feng et al.

A=2

66

66

4

A11A12?

A21A22?

???

A1n

A2n

?

A n1A n2?A nn

3

77

77

5

;F=

2

66

66

4

f11f12?

f21f22?

???

f1n

f2n

?

f n1f n2?f nn

3

77

77

5

;x=

2

66

66

64

x1

x2

?

x n

3

77

77

75

:

Consequently,the MRIO framework can be written as follows:x=Ax+F,and we have x=(I–A)–1F,where(I–A)–1is the Leontief inverse matrix,which captures both direct and direct inputs to satisfy one unit of?nal demand in monetary value;I is the identity matrix.To calculate the embodied emissions in the goods and services,we extend the MRIO table with environmental extensions by using CO2emissions as environmental in-dicator:CO2=k(I–A)–1F,where CO2is the total CO2emissions embodied in goods and services used for?nal demand;k is a vector of CO2emissions per unit of economic output for all economic sectors in all regions.The applica-tion detail of the MRIO framework to our research is presented in SI Text1.

ACKNOWLEDGMENTS.D.G.was supported by Research Councils UK(RCUK) and National Natural Science Foundation of China Grant71250110083;W.L. was supported by National Natural Science Foundation of China Grant 41125005;and Z.L.was supported by National Natural Science Foundation of China Grant31100346.

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