CO2 multipliers in multi-region input-output models
Manfred Lenzen1, Lise-Lotte Pade2 and Jesper Munksgaard3
1 Introduction
All societies have a responsibility for abating climate change according to, amongst other criteria,
their contribution to global greenhouse gas emissions. Through goods and services trade in a
globally interdependent world, the consumption in each region is linked to greenhouse gas
emissions in other regions. Although the ’greenhouse gas responsibility’ of the inhabitants of a
region is determined by their consumption of both domestically produced and imported
commodities (Proops et al., 1993), international negotiations on emission reductions focus solely on
territorial emissions. Greenhouse gases embodied in international trade are often neglected. It has
been argued that, in order to achieve equitable reduction targets, international trade has to be taken
into account when assessing nations’ responsibility for abating climate change (Wyckoff & Roop,
1994, Imura & Moriguchi, 1995, and Munksgaard & Pedersen, 2000).
Especially for open economies such as Denmark, taking into account the greenhouse gases
embodies in internationally traded commodities can have a considerable influence on national
balance of greenhouse gases. Increased export of commodities produced in Denmark, for example,
increase Danish energy consumption and greenhouse gas emissions, while the opposite holds for
imports into Denmark. Munksgaard and Pedersen (2000) report that a significant amount of power
and other energy-intensive commodities are traded across Danish borders, and that between 1966
and 1994 the Danish foreign trade balance in terms of CO2 developed from a 7 Mt deficit to a 7 Mt
surplus, compared to total emissions of approximately 60 Mt. In particular, (hydro-)electricity
traded between Norway, Sweden and Denmark is subject to large annual fluctuations due to varying
rainfall in Norway and Sweden, with corresponding fluctuations of CO2 embodiments. As a result,
the official Danish emissions inventory includes a correction for electricity trade (Danish
Environmental Protection Agency, 1998). Remaining emissions figures to be reported to the
Intergovernmental Panel on Climate Change (IPCC) do not reflect national ‘greenhouse gas
responsibilities’, but simply refer to territorial emissions. Consequently, reaching national emissions
targets is becoming more difficult for Denmark, since an increasing part of greenhouse gas
emissions from Danish territory is caused by foreign demand.
Similarly, Subak (1995) investigates methane (CH4) embodiments in the most CH4-intensive
agricultural goods, and the possibility of CH4 leakage. Subak points out that if greenhouse gases
embodied in trade flows were more closely monitored, leakage would be avoided and moreover,
trade could serve as a de facto abatement control, since countries with insufficient greenhouse gas
credits from emissions trading schemes would import commodities from countries with more
efficient industries.
1
School of Physics, The University of Sydney NSW 2006, Australia
e-mail m.lenzen@physics.usyd.edu.au, fax +61 / 2 / 9351-5985.
2
AKF, Institute of Local Government Studies, Nyropsgade 37, DK-1602 København V, Denmark
e-mail LPH@AKF.dk, fax +45 / 33 15 28 75.
3
AKF, Institute of Local Government Studies, Nyropsgade 37, DK-1602 København V, Denmark
e-mail JMU@AKF.dk, fax +45 / 33 15 28 75.
1
These two examples highlight the relevance of a revision of greenhouse gas accounting practices for
the Conferences Of Parties (COP). In Kyoto in 1998, differentiated emission reduction targets for
industrialised nations were negotiated for the first time. However, these targets were set without
consideration of international trade.
One way of dealing with international trade in regard to CO2 emission is applying input-output
models. Some studies dealing with environmental factors in a generalised input-output framework
assume closed economies. Most studies employ single-region models where imports are either
treated as exogenous (Schaeffer & Leal de Sá, 1996, Wyckoff & Roop, 1994, and Common &
Salma, 1992), or endogenous (Lenzen, 1998, Pedersen, 1996, and Denton, 1975). In both cases,
however, factor embodiments in imported commodities are determined by the applying domestic
production recipe and energy use structure.
Using a single-region input-output model and assuming that factor uses of foreign industries are
identical to those of domestic industries can introduce an error into the CO2 multipliers (the amount
of CO2 embodied in a value unit of commodities produced) and hence into CO2 embodiments in
internationally traded commodities. This error, which to our knowledge has not been investigated
quantitatively so far, will be estimated in this work for the example of trade into and out of
Denmark.
In order to arrive at more realistic estimates of the amount of CO2 embodied in commodities traded
internationally we further develop the methodology used by Munksgaard et al. (2000) from a
single-country model into a multi-region model including multidirectional trade flows. We
introduce a five-region input-output model including Denmark, Germany, Sweden, Norway and the
rest of the world in order to calculate CO2 multipliers for Danish, German, Swedish and Norwegian
final demand. These countries have been separated from the rest of the world because our analysis
focuses on Denmark, and Germany, Sweden and Norway are among Denmark’s most important
trading partners.4 We only consider CO2, because it represents 79% of global greenhouse gas
emissions, and because it is at the centre of COP negotiations.
This article is structured as follows: Section 2 gives a detailed literature review of earlier analyses
of the link between greenhouse gas emissions and trade, with an emphasis on input-output models.
In Section 3 the input-output methodology is introduced and the model for the empirical analyses is
explained. The data sources are described in Section 4, results are presented in Section 5, and
Section 6 concludes.
2 Literature review
Analyses of issues relating to trade and greenhouse gases have been carried out using mainly three
techniques: Process analysis, input-output models, and general equilibrium (GE) models.
2.1 Process-type studies
4
Of US$bn 44.5 of imports into Denmark in 1997, Germany accounted for 5.6%, Sweden for 18.0%, and Norway for
5.7% (Organisation for Economic Co-operation and Development (2001) ITCS International Trade
by Commodity statistics, Harmonised System 88, 1990-2000, 4 CD-ROM,.).
2
The effect of explicitly considering imports and foreign emissions on the life-cycle CO2 emissions
of German production was examined by Wenzel (1999). This author carries out a life-cycle analysis
of the CO2 requirements of passenger cars, computers and food items. His assessment involves the
identification of countries of imports origins, transport distances and modes, and foreign energy
production processes, in particular electricity. While transport emissions are modeled on the basis
of process-chain data, embodied emissions are calculated using input-output analysis. Wenzel finds
that, in spite of long distances, CO2 emissions from transport forms a relatively minor part of total
emissions (1-2% for cars and computers, and around 6% for food items). If, however, foreign
energy production was explicitly taken into account, CO2 requirements changed significantly (9%
for cars and computers; food was not examined). Wenzel concludes that within effects of trade on
CO2 emissions, and within reduction potentials, differences in production structure are more
important than increased transport requirements.
Following up a study of CO2 embodied in international trade flows of six large OECD countries by
Wyckoff and Roop (1994), Subak (1995) investigates methane (CH4) embodiments in the most
CH4-intensive agricultural goods – rice, meat and dairy products – traded from developing countries
to the USA, UK, Germany, Japan, France and Canada, and thus the possibility of CH4 leakage.
However, she considers only emissions occurring on-site in the respective agricultural industries.
Thus, she arrives at a lower limit of 1200 kt CH4 embodied in agricultural products supplied to the
above six countries, representing between 1-2% (Canada, USA) and 14-23% (Germany, Japan, UK)
of national CH4 emissions. These leakage figures are likely to increase if subsidies to agricultural
sectors in industrialised countries are removed, and exports of agricultural products by developing
countries increase. Hence, future CH4 leakage may be of a scale sufficient to undermine regional
abatement goals. Subak (1995) states that “an estimation method based on attributing all embodied
emissions to the country of import opens up a range of new technical difficulties”. These difficulties
can, however, be overcome by using (multi-regional) input-output models.
In a study by Eurostat (2001) the CO2 contents in imports to the UK have been estimated by
applying three alternative multi-regional approaches. While the first approach uses only UK energy
data, approaches two and three include foreign energy use and emissions data. The calculations are
based on sectoral trade and fuel use statistics of the exporting countries. Comparing the results of
the three approaches indicates that UK production is more CO2-intensive than that of other
countries, for example Denmark.
2.2 Single-region input-output models
An early study attempting to quantify “environmental loading” of traded products is Walter's (1973)
examination of the pollution content of American trade. Even though the author uses input-output
coefficients to allocate environmental control cost to industries, the analysis falls short of all but
first-round effects, since no matrix inversion is carried out. The first authors to use the Leontief
inverse in order to investigate embodied-factor trades was Fieleke (1975), who determined the US
trade deficit in embodied energy. Shortly after, Bourque (1981) calculated the embodied-energy
trade balances between Washington State and the rest of the United States. Due to a lack of sectoral
data in physical units, he uses “energy equivalents”, that is dollars of output and the number of
employees in energy-producing industries.
Using a single-region input-output model, Jacobsen (2000) examines the relation between trade
patterns and the energy consumption in Danish manufacturing industries. His results show that
manufacturing sectors, such as chemicals or paper production, can be affected in opposite directions
as a result of changes in trade patterns. Another interesting aspect of his analysis is the dependence
3
of the results on the aggregation level of the input-output data: impacts of trade pattern on energy
consumption obtained from a 27-sector model differ significantly from those obtained from a 117sector model. This dependence is due to the aggregation of disaggregated sectors with very different
trade developments and energy intensities.
Gale (1995) investigates the effect of Mexico’s participation in the NAFTA agreement on CO2
emissions, by estimating changes in Mexican imports, exports and import-competing goods, and
subsequently inserting adjusted figures into an augmented input-output model. Gale’s preliminary
results show that even though tariff elimination gives rise to an overall 12% increase in Mexican
CO2 emissions, half of this increase is compensated by shifts in the production structure away from
pollution-intensive sectors.
In a study of international trade flows of six large OECD countries, Wyckoff and Roop (1994)
revealed that about 13% of these countries’ total CO2 emissions was embodied in manufactured
products. They concluded that measures of greenhouse gas abatement policies, which solely rely on
domestic emissions will be less effective. The problem of ‘territorial’ or ‘attributable’ emissions
(Proops et al., 1993) is also addressed by Kondo et al. (1996) and Munksgaard and Pedersen
(2000), who demonstrate the differences between CO2 accounts assuming producer and consumer
responsibility. The latter authors highlight the significant changes that Denmark’s CO2 trade
balance underwent between 1989 and 1994. Eder and Narodoslawsky (1999) examine several
criteria for inter-regional consumer and producer responsibility in their augmented input-outputbased case study of a small Austrian region. Energy and/or CO2 emissions embodied in imports
have also been estimated by Common and Salma (1992), Schaeffer and Leal de Sá (1996), Bicknell
et al. (1998), and Frickmann Young (2000). The latter author uses results on CO2 emissions
embodied in exports to examine the ‘race-to-the-bottom’ hypothesis.
Most authors listed above carry out an input-output analysis of a closed economy, and subsequently
apply multipliers obtained from this model to exports and imports. In this approximation, the
imports structure does not enter the direct requirements matrix, and is hence not reflected in the
multipliers.
The environmental impact of Swedish trade in terms of SO2 and NOx has been investigated in a
study by Statistics Sweden (2000). Like in Eurostat's (2001) study on the CO2 contents of UK
imports, three different approaches are compared, but this time founded in single-region inputoutput models. The most sophisticated model uses foreign sectoral emission coefficients where such
data is available, while aggregate emissions coefficients are used for the remaining countries. In
addition, emissions from Swedish exports are estimated. The results show that the production in
countries exporting to Sweden is more CO2-intensive than the production in Sweden. The CO2
intensity of Swedish exports is estimated to be 0.03 kg/SEK, whereas the intensity of imports is
twice as high at about 0.06 kg/SEK. Even though in this study correct factor intensities were used
for imports, the single-region model does not capture feedback effects, which describe increases in
production in one region that result from increases in intermediate demand in another region, which
are in turn brought about by demand increases in the first region (see Miller, 1969, p. 41). The error
associated with this assumption can be overcome by employing multi-regional input-output
frameworks.
2.3 Multi-region input-output models
After Isard's (1951) introduction of input-output analysis into regional science, multi-regional
approaches were first applied to regions in Italy by Chenery (1953, as cited by Polenske, 1989) and
4
in the USA by Moses (1955, as cited by Polenske, 1989). Polenske (1976, 1980) examines the
economic interactions and repercussions between the coal mining, freight transport and electricity
generation sectors in 9 regions of the USA.
Suh and Huppes (2001) outline a multi-regional generalised input-output approach for compiling
life-cycle inventories for the industrialised world. Imura and Moriguchi (1995) employ a multiregional input-output framework in order to calculate trade balances in terms of energy. However,
their calculation only treats Japanese industries in sectoral detail, but other countries are assumed to
only produce one commodity with an average energy intensity. Using an input-output model,
Hayami et al. (1999) assess the bilateral trade in greenhouse gases between Japan and Canada. They
employ a number of matrices in order to convert between the commodity classifications of different
trade statistics, and also between free-on-board (f.o.b.) and cost-insurance-freight (c.i.f.) valuation
(see Section 3.3 in this article). An interesting finding in their study is that almost all CO2 embodied
in Japanese exports is itself induced by imports, while emissions from Canada’s exports were
generated by the respective exporting industries. In their bi-regional analysis of energy and air
pollutants in Japan and China, Hayami and Kiji (1997) examine fuel, CO2, and SOx intensities, and
also calculate indices of power and sensitivity of dispersion, in order to establish whether energyand pollution-intensive industries are also strongly interlinked within the economy. Petri (1976)
suggests extending a bilateral trade quantity model with the dual price model. Such an approach is
however quite data-intensive.
Proops et al. (1999) use regional trade flow data from the United Nations’ Statistical Yearbook in a
multiregional input-output analysis to examine a sustainability criterion. These authors
quantitatively compare the closed-economy, or single-region approach with the multiregional
approach. They demonstrate that for countries with resource-intensive imports, such as the USA
and many European nations, the sustainability index decreases when the economies are assessed in
a multiregional framework. Similarly, Battjes et al. (1998) test the assumption of identical domestic
and foreign factor intensities by examining the differences between energy intensities from a
multiregional input-output system and the corresponding single-region systems. Using the
consolidated input-output tables of a number of European-Union countries compiled by van der
Linden and Oosterhaven (1995), they show that single- and multi-region energy intensities for
Germany are equal, but that single-region energy intensities are lower/higher for the
Netherlands/Ireland than multi-region energy intensities. The latter effect results from a sectoral
over- and underestimation of energy embodiments in imports.
2.4 Feedback-loop analysis
A variety of multi-regional input-output analysis is feedback-loop input-output analysis (Round,
1985, Sonis & Hewings, 1998, Sonis et al., 1995, Sonis et al., 1993). This concept is based on a
decomposition of the Leontief inverse matrix into submatrices describing the disjoined
interdependence of two sectoral or regional subgroups in terms of internal and external multipliers,
which was introduced by Miyazawa (1966), and then further developed by Cella (1984) and
Clements (1990). Reinert and Roland-Holst (2001) utilise a social accounting matrix to examine
industrial pollution feedbacks between NAFTA member countries. Their analysis treats air
pollutants such as CO, SO2, NO2 and volatile organic compounds, but not CO2.
Miller (1969) examines the deviation of single- and multi-region models in his “experimental”
studies of Kalamazoo County and the USA, and of the USA and India. He concludes that
(monetary) feedback effects are very small, typically affecting multipliers to less than 1%. Those
results were confirmed in other studies, as summarised by Richardson (1985, p. 631). However, in
5
Greytak's (1970) results, interregional feedbacks appear to be significant. Gillen and Guccione
(1980), and later Miller (1985) show that the magnitude of feedbacks depends critically on the norm
of the A matrices (reflecting the interconnectedness of industries in the respective regional
economies), and on the proportion of interregional trade in intermediate demand (ie the selfsufficiency of the economies). There are, however, no studies examining feedback effects in a
generalised input-output framework incorporating factors such as labour, energy, or pollutants.
2.5 General equilibrium modeling
A number of general equilibrium studies attempt to quantify the amount of carbon leakage as a
result of restrictive measures in OECD countries (for example Oliveira-Martins et al., 1992, Perroni
& Rutherford, 1991, Pezzey, 1992). While Perroni and Rutherford (1991) and Oliveira-Martins et
al. (1992) determine carbon leakages of not more than 10% and 16% of the initial emission
reduction, respectively, Pezzey (1992) finds a 70% leakage offset of the carbon reduced by
unilateral OECD action, thus rendering unilateral action largely ineffective in environmental terms.
Nevertheless, Pezzey concedes that these differences “no doubt reflect the very different modelling
assumptions [...] about world energy markets, as well as different data on energy supply
elasticities”. This is confirmed by Paltsev (2001) who finds that not regional sector aggregation but
“fossil fuel supply elasticities and trade substitution elasticities are the crucial determinants” in his
static, multiregional GE model of the carbon leakage resulting from the Kyoto protocol.
Kainuma et al. (2000) calculate embodied carbon emissions using a GE model, and compare their
results with embodiments calculated via input-output analysis for an open and a closed economy
(that is, including and excluding factor inputs from foreign economies, respectively). Employing the
GTAP database and IEA/OECD energy statistics, these authors show that changing from a closedto an open-economy model increases the emissions responsibility of Japan, USA and the European
Commission, but decreases the emissions responsibility for Australia, Former Soviet Republics and
Eastern Europe, China and India. However, input-output- and CGE-based embodiments are only
comparable within limits, since they address slightly different questions (static and dynamic,
average and marginal analysis; personal communication with author).
3 Methodology
Input-output analysis deals with inter-industrial relationships. The basic model is introduced, for
example, in an article by Leontief and Ford (1972). It can be extended to describe inter-regional
trade flows by applying a multi-regional model. One particular application of generalised inputoutput models is in the field of environmental economics, where they have proven useful for
examining the embodiment of production factors in commodities.
In this work we apply a combined multi-region generalised input-output analysis in order to trace
CO2 flows through a system of trading economies. The starting point for using this technique is to
arrange economic data for different countries in a compound Social Accounting Matrix (SAM) with
the following general layout (example for Denmark, Germany and the rest of world, for a
generalised formulation see Furukawa, 1986, p. 10):
The R matrices describe the national production recipes on the diagonal, while the off-diagonal
matrices describe trade flows between the countries. The vectors y represent final consumption by
households of commodities produced in different regions. g stands for government consumption,
6
while x is total output, which is equal to total input, which in turn is made up of wages w (only
domestic contributions) and taxes t. Within this part of the SAM, accounting balances hold both
vertically and horizontally. E and E’ denote matrices containing the consumption of energy carriers
by industries and households, respectively, while total CO2 emissions C are a sum of total CO2
emissions from fuel combustion by fuel (Cf), and from non-energy sources (Cr).
Denoting the bold-framed parts of the SAM in Tab. 1 with R*, and setting
 y DK DK   y DK D 
 g DK 
 x DK 

 





        ...      y* ,     x* ,
 E' DK   E' D 
 E' g 
 E 

 





 0   0 
 0 
 C 
(1)
and A*ij = R*ij / x*j, we find the standard input-output equation for multi-regional systems
A* x* + y* = x*  x* = ( I – A* )-1 y* ,
(2)
where I is a unity matrix. The compound Leontief inverse ( I – A* )-1 contains compound total
multipliers of intermediate demand, trade, energy consumption and CO2 emissions. x* contains
total CO2 embodiments in its last element.
We will investigate three scenarios (see Fig. 1) that are reflected in three different A* matrices:
I.
Five autonomous regions that are completely decoupled with regard to inter-regional trade:
imports are treated as domestic production.
II.
Unidirectional trade into Denmark: the introduction of foreign production, energy and CO2
data relaxes the assumption inherent in scenario 1 that foreign industries exhibit factor
multipliers that are identical to those of Danish industries.
III.
Multidirectional trade between Denmark, Germany, Sweden and Norway, and from the rest
of the world5: inclusion of feedback loops and capture of direct, indirect, and induced effects
of trade.
3.1 Scenario I: Autonomous economies
The starting point of our analysis is a five-region input-output account incorporating Denmark,
Germany, Sweden, Norway, and the rest of the world. Danish (German, Swedish, Norwegian)
imports are assumed to be produced using the Danish (German, Swedish, Norwegian) production
recipe, energy inputs and specific CO2 emissions.
Except for the energy and CO2 intensities of a few key industries, the component of A* representing
the rest of the world was modeled on the basis of Australian input-output, energy and CO2 statistics.
This decision was mainly guided by data availability and quality, and is of course debatable.
Nevertheless, this approximation is not unreasonable, since Australia features an economy that
produces primary resources, manufactured goods and services. Australia exports significant
volumes of coal, crude oil, natural gas, gold, iron ore, wheat, beef, alumina, aluminium, nickel,
copper, iron and steel, wool, cotton, sugar, wine and woodchips. We assume that Australian energy
5
The trade from Denmark, Germany, Sweden and Norway to the rest of the world was assumed to be negligible in
terms of total imports of the rest of the world.
7
and CO2 inputs reflect world-average production conditions, except for beef cattle grazing and
forestry, where CO2 emissions from land use changes were excluded, and except for electricity
generation, aluminium, basic iron and steel manufacturing, for which energy and CO2 intensities
were derived from previous studies (Lenzen & Dey, 2000, Michaelis et al., 1998, Wenzel et al.,
1999, Worrell et al., 1997, World Bureau of Metal Statistics, 2001).
Note that absolute figures (R, y, g, x, E, C) are of course not available for the rest of the world, and
therefore this region is not explicitly, but only structurally modeled. The A* components of all
regions are represented in their make-use formulation (see Madsen & Jensen-Butler, 1999). A reclassification into a common commodity scheme was not carried out, since this would have resulted
in a considerable loss of detail. The number of industries is in general different from the number of
commodities, leading to rectangular components in an overall square model (Tab. 2).
The lowercase letters in Tab. 2 represent variables per unit of domestic total output xr of the
receiving economy:
uijr 
U ijr
x rj
, v ijr 
Vijr
x rj
, mijr 
M ijr
x rj
, e rfj 
E rfj
x rj
and c rj 
C rj
x rj
,
(3)
where Vr, Ur, Mr, Er and Cr are make, use, imports, and energy use matrices, and CO2 emissions
vectors of regions r, covering nr industries, mr commodities, and fr fuels. In addition, ck = Ck / Ek are
specific CO2 contents of all fr energy carriers.6 While Danish, German and Swedish input-output
data were used unmodified, Australian data were augmented from 106 to 134 commodities (see
Lenzen, 2001), and Norwegian data were compressed from 1309 to 229 commodities. A* as in Tab.
2 has dimensions 1199×1199 (for further details see Tab. 3).
CO2 emissions due to Danish, German, Swedish and Norwegian final demand calculated using A*
as in Tab. 2 depend only on the structure of domestic intermediate demand, and domestic energy
and CO2 intensities. The multipliers calculated in this scenario are equivalent to those that would
result from separate single-region input-output models.
3.2 Scenario II: Unidirectional trade into Denmark
As a first improvement of scenario I, imports mDK are decomposed into contributions from
countries of imports origin. It is assumed that the imports into Denmark from Germany, Sweden,
Norway and the rest of the world are produced using German, Swedish, Norwegian, and worldaverage production technology, energy intensities, and CO2 intensities, respectively. The imports
into Germany, Sweden and Norway are still assumed to be produced using a domestic production
recipe (see Tab. 4).
CO2 emission due to Danish final demand now depend additionally on foreign production, energy
use and CO2 emission structures. For Germany, Sweden and Norway, CO2 embodiments and
multipliers are identical to those obtained from scenario I.
6
We account only for CO2 emissions from fuels with a recovery period (life cycle) of more than one year. Other fuels
(for example straw and waste) were considered as long-term zero-emitters.
8
3.3 Scenario III: Multidirectional trade
In this scenario, all trade volumes are assumed to be produced using the production, energy use and
CO2 emissions structure of the economy of origin (Tab. 5)
CO2 embodiments obtained in scenario III are all different from those obtained from previous
scenarios. We assume that
mr  RW
0
xRW
(4)
and set the exports from Denmark, Germany, Sweden and Norway to the rest of the world to zero.
4 Data sources and preparation
Constructing a multi-regional input-output table involves several practical challenges, which are
treated in the following four Sections.
4.1 Re-classification
Input-output classifications differ between countries, because their different economic structures are
governed by particular commodity groups: agricultural, manufacturing and service sectors have
different importance, and some inputs and outputs (such as coffee, district heat, uranium) might
even only exist in a few countries. All off-diagonal flow matrices therefore have to be re-classified
with respect to the usage direction (industries) into the national classification of the receiving
economy, resulting in rectangular matrices. Re-classification is usually – and also in this work –
accomplished by scrutinising handbooks detailing the definition of national industry sectors.
4.2 Currency conversion
Currency conversion is necessary in models examining monetary flows in order to make the
transaction entries in the monetary matrices and vectors (A*, y*) comparable between countries.
Some authors found it convenient to either choose an important trade currency (US$, ₤, €, ¥), or the
currency of the main country appraised in the study as the common model currency. In this work,
we apply a mixed-units approach, in which we keep the national production and final demand data
including exports (rows) in national currency units (Danish Crowns, DKK; German Marks, DM;
Swedish Crowns, SEK; Norwegian Crowns, NOK). The rest of the world is modeled in units of
Australian Dollars (A$). Diagonal (domestic production) blocks Arr are then in units (DKK/DKK
etc), while off-diagonal (trade) blocks Ars are in mixed units (DM/DKK, NOK/SEK etc). Note that
units are constant across any one row of the SAM in Tab. 1, but not across any column. 7 Currency
conversion rates were obtained from OECD trade data (OECD 2001).
4.3 Valuation
7
This requirement for mixed-units models was established by Leontief and Ford (1970), after several early ecologicaleconomic models proved unoperational.
9
Input-output tables come in different formats. In the use matrix, trade can be treated in two ways:
(1) it can be incorporated into the domestic transactions matrix, treating imports as competitive, and
(2) it can be separated from domestic transactions, generating a separate non-competitive imports
matrix. The later is an essential requirement for multi-regional frameworks. If only a use matrix of
type (1) exists for a country (such as for Denmark and Norway), imports flows can only be
estimated from total imports by commodity, assuming that the usage pattern across domestic
industries is identical to that of domestically produced commodities. For many developed
economies, however, there is separate information on domestic and imported flows available.
Furthermore, entries in trade statistics or input-output tables will differ depending on whether
transactions are valued at the point of producers shipping their products, or at the point of
purchasers receiving them. The different valuations are referred to as basic prices and purchasers’
prices. While in general, domestic A matrices are provided in both valuation systems by national
statistical bureaux, exports are often only available in f.o.b. (free-on-board) valuation
(corresponding to basic prices plus domestic commodity taxes and commercial margins), and
imports only in c.i.f. (cost-insurance-freight) valuation. The latter includes insurance and transport
cost. Purchasers’ prices then consist of the c.i.f. tariffs plus domestic import duties and trade
margins (see Hayami et al., 1999). Matrices valued in basic prices show generally a higher stability
over time, because they are not subjected to potentially drastic changes in taxes and margins, and
are therefore mostly used for analytical purposes (Furukawa, 1986), and also in this work.
This work is mainly concerned with tracing physical and not monetary flows. Since all energy and
CO2 intensities in the compound A* matrices (Tabs. 3 to 5) refer to a unit of domestically produced
total output in basic prices, trade flows – as with domestic intermediate demand – have to be
expressed in basic prices. In the absence of information on margins and tax flows, and on imports
flows in both f.o.b. and c.i.f. terms, a way to estimate imports matrices valued in basic prices is to
(1) reduce entries in an existing c.i.f. matrix homogeneously using aggregated f.o.b./c.i.f. ratios, as
well as tax rate and trade margins of the exporting country, for a whole commodity group, before
applying the trade coefficients, and (2) to apply a RAS technique for balancing the resulting multiregion input-output table (Boomsma et al., 1991, pp. 22-24; see also Round, 1978a). These
procedures were applied in a compilation of an inter-country input-output table for the European
Community, and a subsequent analysis of spillover effects by van der Linden and Oosterhaven
(1995). Economy-wide basic price/f.o.b./c.i.f. ratios were used in this work in order to express
imports in scenarios I and II in basic prices.
4.4 Estimation of trade flow matrices
There is a range of international trade statistics that specify trade volumes in both f.o.b. and c.i.f.
valuation. However, these statistics only detail the amounts of commodities traded between
countries (a vector {mirs} of commodities i exported by country r and imported by country s, see
summary in Tab. 6), but not their usage by industries (a flow matrix {Aijrs}). In other words, it is in
general not possible to find information on the spatial origin of every intermediate and final import,
disaggregated according to the consuming sector in the country of destination (see also Boomsma et
al., 1991, pp. 7-8). This is mainly because of the considerable cost, time and resources that are
associated with conducting international industry surveys (Round 1978a, 1978b).
One solution to the estimation problem for off-diagonal trade flow matrices is to use trade
coefficients (a non-survey approach)
10
cirs 
mirs
rs
r mi
with r cirs  1 ,
(5)
describing the percentage of imports of commodity i into country s that come from country r. These
trade coefficients can then be applied to an entire row of the national imports matrices Mijs and
imported final demand vectors yis in order to yield breakdown according to country of origin:
Aijrs  cirs M ijs and yirs  cirs yis .
(6)
This procedure assumes that the trade coefficients are identical for all entries along a row of the
imports matrix, that is for all using domestic industries.
Using trade coefficients as well as OECD trade data (OECD 2001), Hayami et al. (1999) give a
detailed recipe for converting exports xr from r (in r’s input-output classification) into imports ms to
s (in s’s input-output classification) in a six-step process8:
xrs = (Pr)-1 CIFrs Tr←HS THS←s Ps ĉrs ms = Zrs ĉrs ms .
(7)
The matrices in the above equations are
−
ĉrs is a diagonalised matrix of trade coefficients cirs, which converts s’s total imports into
those originating from r;
−
Ps converts imports from the classification in s’s input-output system (c.i.f. + duty) to the
classification in s’s trade statistics (c.i.f.);
−
THS←s converts imports from r to s from s’s national trade statistics into s’s trade statistics in
the Harmonised ITCS System;
−
Tr←HS converts exports from r to s from r’s trade statistics in the Harmonised ITCS System
into r’s national trade statistics;
−
CIFrs adjusts export data in r’s trade statistics from c.i.f. to f.o.b.;
−
(Pr)-1 converts exports from the classification in r’s trade statistics (f.o.b. = basic prices +
margins and commodity taxes) to the classification in r’s input-output system (basic prices).
In this work it is not necessary to convert imports from purchasers’ to basic prices, but only to
allocate exports across using foreign industries (via cirs and Mijrs) and to convert the commodity
classifications (using Pr, Tr←HS and THS←s). The result of this procedure are trade coefficients Aijrs
that represent exports of commodities i (in basic prices of country r) into country s’s industries j per
unit of country s’s industries j’s total output (in basic prices of country s). Physical flows are then
propagated in appropriate units. For example, the portion E of the energy expended in r’s industry i
that becomes embodied in the delivery of commodity i from r into s’s industry j, which in turn uses
this commodity to satisfy household consumption yjs of its own product j, is E = Eir Aijrs yjs. This
energy E is expressed in consistent units of MJ/$bpr × $bpr/$bps × $bps = MJ.
8
Hayami et al. (1999) then set up a trade interactions matrix
Qrs = Lr Zrs ĉrs Ms Ls Zsr ĉsr Mr
to describe a full loop of imports Zsr ĉsr Mr from s into r, the induced production Ls in s, the imports requirements Zrs ĉrs
Ms from r for Ls in s, and the induced production Lr in r. Qrs ys hence describe the first- and second-round requirements
in r that arise out of a final demand bundle ys is s. Lr = [I – Arr]-1 denotes the Leontief inverse of the respective
economy r. The total requirements in r are then calculated via [I – Qrs]-1, where I is the unity matrix.
11
For this work, margins and tax flow matrices using Pr were unavailable. Consequently, our
conversion matrices Zrs consist of only of Zrs = Tr←HS × THS←s, with the T matrices supplied by the
national statistical bureaux. A shortcoming of this procedure is that these Zrs convert trade data in
f.o.b. terms but not – as required – in basic prices. Trade coefficients cirs were mostly taken directly
from OECD export statistics (OECD 2001), and converted from f.o.b. valuation into basic prices.
However, the OECD data does not cover some goods and most services. Economy-wide constant
trade coefficients
 mi
rs
c 
rs
i
(8)
 mi
rs
r ,i
were therefore applied to all remaining commodities (mostly services). Elements of the conversion
matrices Zrs for these rows were prorated from total imports shares (see Tab. 6) according to total
exports by commodity:
Z ijrs 
rp
qs
 mi m j
p ,q
rp
qs
 mi m j
.
(9)
p ,q , j
Imports from the rest of the world were calculated residually by subtracting imports from Denmark,
Germany, Sweden and Norway from total imports as listed in the respective input-output tables.
In a multi-region input-output framework, conversions are conveniently done using matrix
formulation and operations:
 0
 21
A
 
 n1
A
A12
0

A n2
 A1n   0
 
A 2 n   Z 21 ĉ 21

    
 
 0  Z n1 ĉ n1
Z12 ĉ 12
0

Z n 2 ĉ n 2
 Z1n ĉ 1n 

Z 2 n ĉ 2 n 

 


0 
M1

 0
 

 0
M

0
0 

0 
.
  

 Mn 

0
2
(10)
As a consequence of using the trade coefficients ĉrs, the off-diagonal elements in the compound A
matrix have incomplete, extrapolated and therefore uncertain information, while the diagonal
elements are the original intra-country direct requirements matrices containing full, original
infomation (Boomsma et al., 1991, p.9). In addition, the trade statistics mentioned above bear a
range of uncertainties (Furukawa, 1986), which are due to (1) time lags between shipping of exports
and receipt of imports, (2) differences in commodity classification (see 2.6.1), (3) reporting errors,
(4) losses due to accidents in transits, and (5) discrepancies of origin and destination country due to
commodity re-export. The largest discrepancies found in this work were due to systematic
differences in overall import volumes as documented by national input-output tables and in the
OECD trade database. A major stumbling block was the fact that the latter source does not cover the
trade of services. Factors such as these add to inconsistencies in multi-region input-output
frameworks estimated using the prorata techniques outlined above (Boomsma et al., 1991, pp. 1017).
12
5 Results
5.1 CO2 multipliers for selected industries
Significant changes in CO2 multipliers during the transition from scenario I to scenarios II and III
are likely to be facilitated by feedbacks via a small number of industries that exhibit (1) large CO 2
intensities, (2) large differences in regional CO2 intensities, and (3) strong forward linkages to
downstream, using industries. Examples for such key industries are electricity generation, basic
chemicals, and basic metals. Industries such as commercial fishing, and manufacturing and services
are unlikely to be the origin of significant CO2 feedback loops even if they exhibit large magnitudes
differences in their CO2 intensity across countries (see Tab. 7), because of their weak forward
linkages and small CO2 emissions, respectively. Given the imports shares listed in Tab. 6, CO2intensive primary and secondary industries in the rest of the world are likely to exert the strongest
influence within the multi-regional model, and hence their appropriate representation is a crucial
requirement for obtaining realistic results.
In the following, the CO2 multipliers of a number of key industries will be presented for all
scenarios. These industries were selected either because of their CO2-intensiveness, or because of
their importance for final demand in monetary terms. As a starting point, differences in direct CO 2
intensities (Tab. 7) are most prominent in primary and some manufacturing industries, and are due
to, for example, varying penetration of non-fossil energy sources (nuclear power in Sweden’s
electricity generation, hydropower in Norway, see Wenzel et al., 1999), or to varying penetration of
scrap metal recycling and steel making routes (see Worrell et al., 1997).
5.1.1 Scenario I: Autonomous economies
The inclusion of indirect CO2 requirements leads to a general increase of multipliers (compare
Tabs. 7 and 8). This increase is minor for primary industries, because most of these do not feature
CO2-intensive upstream suppliers. For some manufacturing and service industries, however, tenfold and higher increases of multipliers are common. The associated indirect effects are facilitated
via complex supply chains, which in general link CO2 sources in primary industries, steel and
chemicals manufacturing, refining, or electricity generation to further downstream manufacturing
and service industries.
5.1.2 Scenario II: Unidirectional trade into Denmark
Once the assumption that Danish imports are produced using the Danish production structure is
relaxed, Danish CO2 multipliers undergo changes that reflect the CO2 intensity of Denmark’s
trading partners’ industries relative to that of Danish industries. Since the rest of the world as well
as one of Denmark’s important trading partners – Germany – exhibit domestic CO2 intensities that
are higher than Denmark’s, Danish CO2 multipliers increase when countries of imports origin are
modeled explicitly. Note that CO2 multipliers of countries other than Denmark are the same in
Tabs. 8 and 9, since these countries were assumed to be autonomous in both scenario I and II.
5.1.3 Scenario III: Multidirectional trade
13
Incorporating multilateral trade relationships once again refines the CO2 intensities obtained from
previous scenarios. The feedback loops captured in a truly multi-regional analysis generally work
towards evening out differences between CO2 intensity levels of different economies: those
featuring CO2-intensive industries (for example Germany) experience reductions during the
transition from scenario II to III, and vice versa (for example Denmark and Norway).9 In our
example, feedback loops induce changes in multipliers of around 20%. This confirms that, in
contrast to purely monetary flows, generalised feedback loops can be significant, because they are
often amplified by large physical factor contents (for example energy, CO2, etc).
5.2 CO2 balances
Applying the CO2 multipliers obtained from the compound Leontief inverses of scenarios I to III to
final demand categories, it is possible to determine a national CO2 balance (see C in Equation 1). In
contrast to conventional emissions accounts, this balance is expressed in terms of embodiments in
final demand, and includes CO2 embodied in imports. It thus reflects consumer rather than producer
responsibility.
In the following Sections we present a balance for each scenario, and compare these to the
‘baseline’ of territorial emissions as reported to the Intergovernmental Panel on Climate Change.
Only industrial emissions are considered, since emissions caused directly by final consumers
(households and governments) are not affected by international trade, and are therefore not subject
of this type of study.
5.2.1 Territorial emissions
In 1997, 59.2 Mt of CO2 were emitted from industries operating on Danish territory. This and the
following figures exclude emissions originating directly from households, since they are irrelevant
for trade balances.
5.2.2 Scenario I: Autonomous economies
The transition from producer to consumer responsibility reduces the CO2 emissions associated with
Danish production by about 20% or 12.0 Mt to an overall 47.2 Mt. While 18.0 Mt are embodied in
imports into Denmark (11.2 Mt into Danish industries, and 6.8 Mt directly imported by Danish
households and governments), 30.1 Mt become embodied in exports, so that Denmark exhibits a
negative trade balance of 12.0 Mt. This agrees with Munksgaard and Pedersen's (2000) finding of a
surplus of 7 Mt in 1994. Territorial emissions can be found in the lower right corner of Tab. 11.
5.2.3 Scenario II: Unidirectional trade
Considering that Danish CO2 multipliers increased during the transition from scenario I to II (see
Section 5.1.2), it is not surprising that the CO2 responsibility of Danish final demand increases as
well (from 47.2 Mt to 56.0 Mt, see Tab. 12). However, the effect on multipliers of the explicit
modeling of imports into Denmark is positive as well, because generally, foreign industries are
more CO2-intensive than Danish industries, thus increasing overall imports embodiments from 18.0
The fact that Swedish CO2 multipliers increase is in agreement with Statistics Sweden's (2000) finding that Sweden’s
imports carry more CO2 than its exports.
9
14
Mt to 34.4 Mt. As a result, the trade balance is still negative at 3.2 Mt. Most of the CO2 embodied
in imports originated in the rest of the world, followed by Germany and Sweden.
5.2.4 Scenario III: Multidirectional trade
Taking into account the multilateral trade structure of all regions changes the picture only slightly.
Once again, effects work towards the evening-out of regional differences: Relatively CO2-intensive
economies such as Germany experience a decrease in export embodiments, while the opposite holds
for Sweden and Norway (see Tab. 10). Since imports into Denmark are larger from Sweden and
Norway than from Germany (see Tab. 6), the increase of Swedish and Norwegian CO 2 intensities
outweighs the decrease of German CO2 intensities, and Danish imports embody more CO2 than in
scenario II (compare Tabs. 12 and 13). Similarly, Danish CO2 intensities increase, and with them
export embodiments. Due to the commodity and trade partner mix, imports increase more than
exports. The Danish CO2 responsibility is now 56.9 Mt, while the trade balance is –2.3 Mt.
6 Conclusions
The appropriate apportioning of national contributions to climate change entails the calculation of
greenhouse gas emissions embodied in international trade. The latter can be carried out
conveniently using a multi-region input-output model. The results from these models impact on
concepts of equity, and on producer and consumer responsibility, and therefore hold implications
for negotiations at the Conferences of Parties (COP).
This work has shown that it is important to explicitly consider the production recipe, energy use
structure and CO2 emissions of trading partners, in order to arrive at realistic figures for CO2
embodied in trade, and hence for the national contribution to emissions, based on consumer
responsibility. Considering trade in the Danish case, the CO2 trade surplus shrinks from 12.0 Mt to
about 3 Mt.
At present, generalised multi-regional frameworks covering the OECD or the entire world are
mostly restricted to a few tens of industry sectors. At this level of aggregation, initially varying
energy and CO2 multipliers average out across sub-sectors, and differences in scenarios and
feedback loops are likely to be less pronounced. Given the complex international division of
production, multi-regional input-output models that are disaggregated yet manageable will only be
possible for regional applications.
Acknowledgements
This work was carried out in the scope of the research project "Energy and Greenhouse Gases
Embodied in Trade - Implications for Future Climate Negotiations". The project was generously
supported by the Danish Energy Agency.
15
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2
19
Scenario I:
Autonomous
economies
Scenario II:
Unidirectional
trade
DK
DK
RW
D
SV
Scenario III:
Multidirectional
trade
NO
DK
RW
D
SV
NO
RW
D
SV
NO
Fig. 1: Schematic of three trade scenarios
(DK = Denmark, D = Germany, SV = Sweden, NO = Norway, RW = rest of the world).
20
Final
demand
Intermediate
demand
Intermediate demand
Government
Total
input
Fuels
CO2
Denmark
Germany
RW
Denmark
Germany
RW
Final demand
Government
Total
output
Denmark
Germany
RW
Denmark
Germany
RW
RDK DK
RDK D
RDK RW
yDK DK
yDK D
yDK RW
gDK
xDK
RD DK
RD D
RD RW
yD DK
yD D
yD RW
gD
xD
RRW DK
RRW D
RRW RW
yRW DK
yRW D
yRW RW
gRW
xRW
E’DK
E’D
E’RW
E’g
E
C
Fuels
CO2
wDK
wD
wRW
tDK
tD
tROW
xDK
xD
xRW
EDK
CDK
ED
CD
ERW
CRW
Cf
Tab.1: A multi-regional Social Accounting Matrix (SAM), extended with energy and CO2 data
(DK = Denmark, D = Germany, RW = rest of the world).
21
ind
ind
com
ind
com
ind
com
ind
com
ind
com en CO2
vDK
com uDK+mDK
ind
vD
com
uD+mD
ind
vSV
com
uSV+mSV
ind
vNO
com
uNO+mNO
ind
vRW
com
uRW+mRW
en
eDK
eD
eSV
eNO
eRW
CO2
cDK
cD
cSV
cNO
cRW
Tab. 2: A* matrix for the autonomous economies scenario
(ind = industries; com = commodities, en = energy).
22
c
Region
Denmark
Germany
Sweden
Norway
Rest of the world
nr
133
59
39
118
106
mr
128
59
39
229
134
Input-output data
Source
Danmarks Statistik, 1999
SBA 2002b
Statistiska Centralbyrån, 2002
Statistisk Sentralbyrå, 2002
ABS 2001
fr
40
37
23
23
29
Energy and CO2 data
Source
Danmarks Statistik, 1999
SBA 2002a
Statistiska Centralbyrån, 2002
Statistisk Sentralbyrå, 2002
ABARE 2000, AGO 1999
Tab. 3: Dimensions of regional components in A*, and data sources.
23
ind
ind
com
com
ind
ind
com
ind
mDDK
uD+mD
vSV
mSVDK
uSV+mSV
vNO
mNODK
uNO+mNO
ind
com
com en CO2
vD
ind
com
com
uDK
ind
com
ind
vDK
ind
com
com
vRW
mRWDK
uRW+mRW
en
eDK
eD
eSV
eNO
eRW
CO2
cDK
cD
cSV
cNO
cRW
Tab. 4: A* matrix for the unidirectional trade scenario
(ind = industries; com = commodities, en = energy).
24
c
ind
ind
com
com
ind
ind
uDK
mDKD
ind
mDKSV
mDKNO
mDDK
uD
mDSV
mDNO
ind
mSVDK
mSVD
uSV
mSVNO
vNO
mNODK
mNOD
uNO
mNOSV
ind
com
com en CO2
vSV
ind
com
com
vD
ind
com
com
vDK
ind
com
com
vRW
mRWDK
mRWD
mRWSV
mRWNO
uRW+mRW
en
eDK
eD
eSV
eNO
eRW
CO2
cDK
cD
cSV
cNO
cRW
Tab. 5: A* matrix for the multidirectional trade scenario
(ind = industries; com = commodities, en = energy).
.
25
c
DK
Trade volume
DK
D
SV
NO
RW
Total imports
Exchange rate
to 1997 US$
Share
DK
D
SV
NO
RW
D
SV
0 10,763,270
3,019,740
0
8,646,294
5,139,813
6,210,906 10,789,391
23,861,873 205,099,493
41,738,812 231,791,966
NO
6,394,554
3,817,851
0
14,463,094
39,193,576
63,869,075
3,394,513
1,544,241
2,672,364
0
36,259,020
43,870,137
0.152
0.577
0.131
0.142
7.2%
20.7%
14.9%
57.2%
4.6%
0.0%
2.2%
4.7%
88.5%
10.0%
6.0%
0.0%
22.6%
61.4%
7.7%
3.5%
6.1%
0.0%
82.7%
Tab. 6: Trade volumes (‘000 US$ f.o.b.), exchange rates and imports shares
(derived from OECD 2001).
26
Industry
DK
Commercial fishing
Electricity, gas and district heat
Basic iron and steel
Basic non-ferrous metals
Basic chemicals
Vehicle manufacturing
Electronic equipment, computers
Construction
Communication
Financial services
1.03
5.60
0.29
0.05
0.17
0.04
0.02
0.06
0.02
0.01
Direct CO2 intensity (kg/US$)
D
SV
NO
0.34
10.09
1.42
1.58
2.49
0.57
1.52
0.07
0.03
0.17
0.06
0.03
0.06
0.02
0.02
0.08
0.01
0.004
Tab. 7: Direct CO2 multipliers for important industries.
27
1.16
0.00
1.99
0.03
1.60
0.04
0.00
0.03
0.02
0.01
RW
0.53
6.04
1.97
0.63
1.50
0.03
0.02
0.06
0.04
0.001
Industry
Commercial fishing
Electricity, gas and district heat
Basic iron and steel
Basic non-ferrous metals
Basic chemicals
Vehicle manufacturing
Electronic equipment, computers
Construction
Communication
Financial services
Total CO2 multiplier (kg/US$)
D
SV
NO
1.90
1.65
1.24
11.70
1.84
0.04
2.33
6.48
1.12
0.09
4.13
0.30
2.24
1.34
0.22
0.28
0.79
0.21
0.03
1.19
0.29
0.19
0.38
0.11
0.09
1.00
0.06
0.09
DK
1.33
5.79
1.29
0.40
0.87
0.35
0.21
0.29
0.14
0.07
Tab. 8: Total CO2 multipliers calculated for autonomous economies.
28
RW
1.23
9.31
3.56
2.29
3.01
0.98
0.85
0.76
0.38
0.15
Industry
Commercial fishing
Electricity, gas and district heat
Basic iron and steel
Basic non-ferrous metals
Basic chemicals
Vehicle manufacturing
Electronic equipment, computers
Construction
Communication
Financial services
Total CO2 multiplier (kg/US$)
D
SV
NO
1.90
1.65
1.24
11.70
1.84
0.04
2.33
6.48
1.12
0.09
4.13
0.30
2.24
1.34
0.22
0.28
0.79
0.21
0.03
1.19
0.29
0.19
0.38
0.11
0.09
1.00
0.06
0.09
DK
1.40
5.88
1.74
0.72
1.25
0.62
0.34
0.38
0.17
0.08
RW
1.23
9.31
3.56
2.29
3.01
0.98
0.85
0.76
0.38
0.15
Tab. 9: Total CO2 multipliers calculated for unidirectional trade into Denmark.
29
Industry
Commercial fishing
Electricity, gas and district heat
Basic iron and steel
Basic non-ferrous metals
Basic chemicals
Vehicle manufacturing
Electronic equipment, computers
Construction
Communication
Financial services
Total CO2 multiplier (kg/US$)
D
SV
NO
1.44
1.79
1.51
11.29
2.13
0.16
2.96
5.33
1.39
0.27
3.33
0.59
2.85
1.03
0.44
0.91
0.66
0.37
0.45
0.99
0.40
0.43
0.32
0.16
0.19
0.79
0.08
0.24
DK
1.42
5.88
1.74
0.86
1.33
0.63
0.35
0.38
0.17
0.08
Tab. 10: Total CO2 multipliers calculated for multidirectional trade.
30
RW
1.23
9.31
3.56
2.29
3.01
0.98
0.85
0.76
0.38
0.15
Domestic suppliers
Foreign suppliers
Total
Trade balance
Dom.final demand + Exports - Imports = Domestic production
40.4
30.1
11.2
59.2
6.8
6.8
0.0
47.2
30.1
18.0
-12.0
59.2
Tab. 11: CO2 balance for Denmark, autonomous economies.
31
Domestic suppliers
Foreign suppliers
Total
Trade balance
Dom.final demand + Exports - Imports = Domestic production
46.3
37.6
24.7
59.2
9.7
9.7
0.0
56.0
37.6
34.4
-3.2
Tab. 12: CO2 balance for Denmark, unidirectional trade.
32
59.2
Domestic suppliers
Foreign suppliers
Total
Trade balance
Dom.final demand + Exports - Imports = Domestic production
46.8
38.4
25.9
59.2
10.1
10.1
0.0
56.9
38.4
36.0
-2.3
59.2
Tab. 13: CO2 balance for Denmark, multidirectional trade.
33