Key sectors in greenhouse gases emissions in Switzerland:                            An input-output approach             ...
E.Jodar(2011)                                        21. IntroductionGreenhouse gases (GHG) have received increasing atten...
E.Jodar(2011)                                                    3Office of Statistics (SFOS) together with the Swiss Fede...
E.Jodar(2011)                                         4embodied in imports and exports. Indeed, such a depiction will, by ...
E.Jodar(2011)                                        5that the matrix used to calculate forward linkages ought to be the “...
E.Jodar(2011)                                                     6result of combining augmented models to sector classifi...
E.Jodar(2011)                                                 73. DataThe first data set needed for my research is the Inp...
E.Jodar(2011)                                                   8it means that to engage in production, sectors must relea...
E.Jodar(2011)                                          9PFCs, SF6). This vector is disaggregated into 42 sectors, which co...
E.Jodar(2011)                                                10where       is the “make matrix”, that is, the supply matri...
E.Jodar(2011)                                            11the system while the Ghosh inverse starts at the beginning of t...
E.Jodar(2011)                                      12The last equation is a converted Leontief model which instead of conn...
E.Jodar(2011)                                        13              .                                                    ...
E.Jodar(2011)                                                    145. ResultsFigure 1 presents results on key sectors in G...
E.Jodar(2011)                                     15This first outlook on the 10 two-side key sectors is not very surprisi...
E.Jodar(2011)                                           16mitigate their demand with a tax or focus on the sectors they de...
E.Jodar(2011)                                            17hand, policy interventions should focus on sectors that use the...
E.Jodar(2011)                                       18to pollute while they are considered to be harmless from what is rec...
E.Jodar(2011)                                                     19concentrated exclusively in sector 24, say, because of...
E.Jodar(2011)                                               20One drawback of the “producer responsibility principle” is t...
E.Jodar(2011)                                                  21Results are shown in Table 2 (cf. Appendix). Negative num...
E.Jodar(2011)                                       22ConclusionThe Generalized IO analysis used to assess key sectors in ...
E.Jodar(2011)                                   23References    Alcántara, Vicent. Pablo del Río and Félix Hernández (201...
E.Jodar(2011)                                   24   Palutikof, P.J. van der Linden and C.E.Hanson, Eds., Cambridge Univer...
E.Jodar(2011)                                      25   http://www.bfs.admin.ch/bfs/portal/fr/index/news/publikationen.htm...
E.Jodar(2011)                                              26AppendixTable 1 Classification of Sectors.                   ...
E.Jodar(2011)                                              27Table 4 Sectors of the Swiss Economy. 1        Products of ag...
Table 5 Ranking of Top Polluters.                    Table 6 Sectoral Trade Balance.            Ranking of Top Polluters  ...
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Key Sectors in greenhouse gases emissions in Switzerland: An input-output approach

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This paper undertakes a Generalized Input-Output analysis on the Swiss economy in order to identify key sectors in greenhouse gases (GHG) emissions. The analysis reveals the actual relevant sectors by taking into account indirect emissions. In order to refine results on key sectors, a sectoral calculation of GHG embodied in Swiss trade is undertaken. Results reveal that some sectors such as food products, machinery rentals and basic metals play an unexpected role in GHG emissions.

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Key Sectors in greenhouse gases emissions in Switzerland: An input-output approach

  1. 1. Key sectors in greenhouse gases emissions in Switzerland: An input-output approach Etienne Jodar* June 2011AbstractThis paper undertakes a Generalized Input-Output analysis on the Swiss economy in order toidentify key sectors in greenhouse gases (GHG) emissions. The analysis reveals the actualrelevant sectors by taking into account indirect emissions. In order to refine results on keysectors, a sectoral calculation of GHG embodied in Swiss trade is undertaken. Results revealthat some sectors such as food products, machinery rentals and basic metals play anunexpected role in GHG emissions.Keywords: embodied emissions; greenhouse gases; input-output; key sectors; Switzerland.JEL codes: D57, F18, Q53.*Student in applied economics at the Autonomous University of Barcelona.
  2. 2. E.Jodar(2011) 21. IntroductionGreenhouse gases (GHG) have received increasing attention in the last decades because of itsrole in global warming. Since the beginning of the Industrial Revolution, the burning of fossilfuels has contributed to an increase of GHG concentration in the atmosphere. As a result, thegreenhouse effect has progressively intensified, leading to global warming and consequentclimate change.Global warming itself is seen as a major issue for the health of our planet and nextgenerations. Indeed, climate change, together with global warming, have already harmedmany populations through floods, droughts, increased sea level, melting of ice caps andnatural disasters. The increase in intensity and frequency of these changes is seen by scientistsas a consequence of global warming, which is in turn explained by the amount of GHGaccumulated in the atmosphere (IPCC, 2007).Thus, in the last decades, mitigation of GHG emissions has become one of the hottest issuesin sustainable development and is in the agendas of most decision makers of each country.Nations from all around the world have recognized the issue of emissions but do not haveincentives to act alone since it is a global problem. The problem of GHG emissions hasreached such proportions that an international protocol was designed in Kyoto, Japan in acollaborative, multi-state effort to mitigate GHG. This Protocol was initially adopted onDecember 11, 1997 and today 192 countries have signed and ratified this agreement whichentered into force on February 16, 2005.Concerning the sources of pollution, a field of research has tried to identify, since the end ofthe eighties, which industries are the biggest contributors to GHG emissions within adeterminate national economy.In Switzerland, plenty of studies have inventoried GHG emissions and evaluated trends datingback to 1990. Indeed, Switzerland has taken this matter as a central issue with periodic reportson GHG releases published by the Swiss Federal Office of Environment. Some studies haveidentified emissions from isolated activities while others have assessed impacts of reducingthe use of some energy types (Werner et al., 2006; Siller et al., 2007; Hartmann et al., 2008;Perch-Nielsen et al., 2010). However, not much has been done at a macroeconomic level withthe aim of attributing responsibility of GHG emissions towards the diverse sectors of theSwiss economy. An scattered attempt to do so has been undertaken by the Swiss Federal
  3. 3. E.Jodar(2011) 3Office of Statistics (SFOS) together with the Swiss Federal Office of Environment with astudy published in 2005 (SFOS, 2005). This study analyses GHG emissions by economicbranches using the NAMEA (National Accounting Matrix including EnvironmentalAccounts) data to assign responsibility of various pollutants to the different sectors of theeconomy. 1Although such studies have been very enlightening in assessing highly polluting activities inthe Swiss economy, some knowledge gaps still remain concerning classification of relevantsectors within the Swiss economy. Indeed, nothing has been done to assess key sectors with avertically integrated economy concern; that is, taking into account indirect GHG emissionsfrom each economic sector (emissions that are not actually emitted but rather induced). Nostudy has assessed key sectors in GHG emissions using an Input–Output (IO) frameworkwhich allows seeing linkages between sectors and thus gives a realistic view of the complexconsequences of changes in demand for a determinate product.The aim of this paper is to assess key sectors in GHG emissions in Switzerland from aninterdependent understanding of the economic sectors. My research question will be: Whichsectors play a key role in GHG emissions in the Swiss economy. My hypothesis is that somesectors are much more relevant than what is prima facie thought. The knowledge of keyemitter sectors with a concern for indirect emissions is of primary interest in order to achieveGHG reductions targets. Such new knowledge could help to reduce certain policies that mayseem harmless in emitting GHG but actually pose a threat to the environment (such aspolicies that promote the development of a determinate industry).In order to answer my research question and test my hypothesis, I will use a Generalized IOmodel to see how linked in terms of GHG emissions are the different sectors of the Swisseconomy. The magnitude of this linkage will serve me as a proxy of relevance. I willundertake an IO analysis to trace backward (and forward) the total GHG emissions “needed”in order for every single sector to produce one more unit of its product. Results will give methe relevance of each sector. Such an approach will be based on a pioneering paper written byWassily Leontief (1970).Moreover, in order to improve the results of the IO identification of key sectors, I will utilizea sector-by-sector description of Switzerland’s international foreign trade in terms of GHG1 NAMEA is a statistical tool that relates environmental data to economic data. Environmental data is compiledin such a manner that is compatible with the presentation of economic activities in national accounts. This toolhas first been designed by the Dutch after having been developed by EUROSTAT.
  4. 4. E.Jodar(2011) 4embodied in imports and exports. Indeed, such a depiction will, by means of a simple andplausible assumption, corroborate the standard key sectors assessment. Such a description willbring light on the international responsibility that each Swiss economic sector has on GHGemissions and will help to give a more realistic view of each sector’s relevance.The rest of the paper will be as follows: Section 2 depicts the literature on the topic; Section 3presents the data needed for the analysis and the problems encountered; Section 4 presents themethodological framework; Section 5 answers the central research question by presentingresults and exposes some policy implications that come from them; Section 6 refines resultsbringing an extra concern, and Section 7 concludes.2. Literature ReviewThe concept of key economic sectors did not emerge in environmental Input-Output (IO)models. In fact, this concept was born before IO models were adapted for environmentalpurposes, in a more general context, in the writings of Rasmussen (1957) and Hirschman(1958). This concept emerged naturally from comments made on the elements of the famousinverse matrix of Leontief. Rasmussen realized that the column sum of the “Leontief inverse”would be a measure of the power of dispersion of each corresponding sector while the rowsum of the matrix would be an index of sensitivity of dispersion. Based on these indices, hederived the concept of “key industry” for sectors with large indices. Since the power ofdispersion measures how deeply a certain sector relies on the whole system, Rasmussenconsidered “natural” to characterize sectors with large indices as key sectors. This influentialconcept of identifying key sectors in a certain economy, although far from environmentalconcerns was later applied to them and will serve as a starting point for this paper.Later Hazari (1970), in an empirical work on the Indian economy, brought a plus in theframework of Rasmussen’s linkages. In order to identify key sectors, he chose to weightRasmussen’s indices (that would later commonly be called multipliers) to the relativemagnitude of sectors’ deliveries to final demand. He considered that in order to bring out therelative importance of each sector in the national economy, multipliers had to be weighted.Since its introduction, this weighted classification of relevant sectors has repeatedly been usedand will be implemented in the present paper.Later on, Jones (1976), in a classic and seminal paper, brought some clarification on thematrices that should be employed to accurately measure forward linkages. Basically, he stated
  5. 5. E.Jodar(2011) 5that the matrix used to calculate forward linkages ought to be the “output inverse” (which isthe basic matrix in the supply-driven model founded by Ghosh) instead of the Leontiefinverse, which was used since Rasmussen. From that point in time, there has been a generalapproval among regional economists to use the row sum of that matrix in order to assessforward connectedness between sectors. Here too, the subsequent assessment of key sectorswill follow Jones’ recommendation.Later on the history of linkages between sectors, and still with the aim of identifying keysectors, a concern rose in order to calculate “total” linkage of a determinate sector and notonly backward or forward connectedness. As a result, the approach of “hypotheticalextraction” has been developed by various authors such as Cella (1984), among others. Thisapproach consists on evaluating the relevance of a sector by calculating the total productionthat would be achieved without it. Practically, this method consists of removing (or replacingby zeros) from the matrix of technical coefficient, the row and column of each sector to seehow the production varies without a definite sector. Although this is also used in someempirical studies, I will not follow this methodological branch to identify key sectors since itis not superior and requires more formalization and much more calculation than the“classical” method initiated by Rasmussen (1957).Concerning IO models to account for pollution externalities, the first application has beeninitiated by the IO model founder himself: Wassily Leontief. In a first attempt to incorporateconcerns on pollution in a production framework, Leontief (1970) introduced a row showingthe sectoral pollution in his 1936 basic IO framework. Such a model received the name ofaugmented model in reference to this additional non-economic row. Although assessing keysectors on pollution grounds was not Leontief’s aim in this pioneering paper, the combinationof the IO model and sectoral pollution was a first step in attributing pollution to economicsectors. The augmented Leontief model was widely extended further; indeed, the idea ofadding data to the initial model has deserved many applications. As a result, augmentedmodels have been implemented in other areas such as energy consumption and employmentand they ended up being called Generalized IO models.The combination of augmented models à la Leontief with literature on key sectors hasstreamed from the years 1970 according to the availability of the data in the countries understudy. This combination permits to assess key sectors no more on production grounds asbefore, but on pollution generation concerns. Thus, many authors such as one of the first: Just(1974) or recently Alcántara (2010) have carried out studies in different countries. The typical
  6. 6. E.Jodar(2011) 6result of combining augmented models to sector classification à la Rasmussen is thatrelevance is assigned to sectors that were not considered important at first sight. Indeed, theIO analysis makes possible to show up the complex effects and impacts of a productionincrease in a determinate sector not only on production but also on pollution grounds. For thatreason, I expect this paper to bring light on the relevance in GHG emissions of the sectors ofthe Swiss economy. Likewise, I hypothesize that some sectors are more relevant than what isprima facie thought.Later, in the nineties, another branch of literature arose with the aim of identifying CO 2emissions at an over-boundaries level. This literature started by Proops et al. in 1993 andfollowed, among others, by Machado et al. (2001), attempted to take into account CO2embodied in imports and exports to assess the national balance in GHG emissions. Followingthat line, Munksgaard and Pedersen (2001) developed, by means of an IO model, the conceptof “trade balance”, which helps to understand flows of embodied GHG in a country’s trade.Sánchez-Chóliz and Duarte (2004) developed this concept, disaggregating it in a sectoralmanner which reveals each sector’s importance in GHG embodied in trade.Concerning Swiss studies, much has been done on GHG emissions, however, nothing to dateusing a Generalized IO model with a key sector assessment. 2 Thus, a lot is known aboutintense activities within Switzerland, and, say, costs of abandoning an energy type to achieveCO2 targets, but gaps still remain in understanding the influence of one sector over the other.In 2005, an important study about sectoral GHG releases was published by the Swiss FederalOffice of Statistics (SFOS, 2005). This publication, based on the estimation of a NAMEA forthe year 2002, has helped to recover the idea of key playing actors in GHG emissions.Although honorable (since pioneering for Switzerland), this study fails to consider indirectemissions from economic sectors. Indeed, the assessment of sectors with large shares ofnational pollution releases is based only on direct emissions. In addition to that limitation, theaforementioned study did not offer a high level of disaggregation of the economy that wouldallow for accurate policy interventions. Thus, the purpose of the present paper, namely,assessing key sectors from an IO perspective, takes its motivation from the incomplete viewon relevant sectors.2 Indeed, Switzerland does not have a long history in estimating IO tables. This lack is twofold. First because ofmissing important data and second due to lack of political pressure for compilation of IO tables. Thus, the firsttrustworthy table and “sufficiently” disaggregated was released in 2006.
  7. 7. E.Jodar(2011) 73. DataThe first data set needed for my research is the Input-Output (IO) tables. This set is availableat the SFOS. This set contains 3 tables: a use, a supply and a Symmetric IO Table (SIOT).Transactions within the economy are disaggregated into 42 sectors and the model is openwith respect to households. The use and supply table come in a squared fashion. Twopackages of data on IO tables corresponding to the years 2001 and 2005 are available. Thefollowing exercise will be based on the latter one. 3Unfortunately, the data do not provide either an IO table for domestic output or a use table forimports. Thus, the separation of domestically-produced and imported goods and serviceswhich is of great importance for my analytical purposes is not directly available and atreatment of the data will be necessary.Indeed, IO data provided by the SFOS, like some other countries, include imports in thetransaction matrix ( ) in such a way that it is impossible to differentiate if a purchasing sectoris using domestically produced or imported inputs. 4 Data compiled in such a way, that is,including imports from other countries, is useful if the purpose of a study is to makecomparisons between the structures of production of different countries. However, to analyzekey sectors based on linkages between economic sector, imports must be “scrubbed” since itis the impact on the domestic economy that is of interest (Miller and Blair, 2009). Thisconcern has to be taken into account independently of the country, but even more so for asmall country such as Switzerland that has a big foreign trade. In the same line, Jones (1976),Dietzenbacher et al. (2005), Eurostat (2008) recommend regional economists to infer adomestic model when data is collected in such a manner. Thus, I operated the data.The process of inferring a domestic model from a total model (with imports) has commonlybeen called “domestication” (Lahr, 2001). My first attempt to net out imports from thetransaction matrix of the SIOT was based on the methodology presented in Miller and Blair(2009; pp. 150-154). This technique consists of removing imported inputs from the matrix.When implementing that method to scrub imports, 7 rows of the resultant domestic matrix ofintermediate consumptions ( ) appeared with negative elements. As a consequence, thedomestic direct input coefficient matrix (or technical coefficient matrix), which is essentialto my research, had negative values as well. From an economic point of view this is absurd as3 A description of sectors is available in the appendix (cf. Table 4).4 The transaction matrix, matrix of intermediate consumptions or matrix of flows shows the sales andpurchases between sectors.
  8. 8. E.Jodar(2011) 8it means that to engage in production, sectors must release rather than consume inputs fromother sectors.5In order to overcome that first drawback I looked for other methods to domesticate dataaccounting for trade. These other techniques were based on the supply and use tables, not on as previously mentioned. Thus, I followed Lahr (2001) to obtain domestic technicalcoefficient matrices from the use and supply tables. I extrapolated domestic data followingtwo different techniques proposed by St Louis (1989) and Jackson (1998), respectively. Theformer technique assumes implicitly that there are “re-exports” in the export vector (that is,imports that are exported without processing). The latter assumes no re-exports at all. Bothways of domesticating the data gave matrices exempt of negative values. I chose to retainJackson’s method of domesticating and the subjacent assumption that goes with it since theexport vector given by the use table should not, in principle, include re-exports.The domestic direct input coefficient matrix ( ) obtained following Jackson’s (1998)procedure assumes an Industry Based Technology (IBT). This assumption asserts that sectorshave only one input mix in producing different types of commodities. This assumption isopposed to the Commodity Based Technology (CBT) which assumes that commodities areproduced with the same input structure irrespective of the sector where they are produced.Although the CBT seems more realistic, I chose to retain the IBT in order to get the technicalcoefficient matrix ( ) that I needed. Indeed, the CBT assumption could have led to negativeelements on the domestic technical coefficient matrix but, as mentioned before, that isunrealistic from an economic point of view. Consequently, it would have been impossible tointerpret CBT as a demand-driven economic circuit (de Mesnard, 2004).Another concern rises when “domesticating” the data to get a technical coefficient matrixfrom the supply and use tables: the choice between inferring a commodity-by-commoditytable or an industry-by-industry one. Since the GHG vector needed to compute my model isavailable by industry I decided to retain the latter feature.6 I, thus, finally got a domestic directinput coefficient matrix with dimensions industry-by-industry that assumes an IBT.The second essential data to assess key emitting sectors in a Generalized IO model is thevector of GHG emissions which I obtained from the SFOS as well. This vector collects, inCO2 equivalent, sectoral emissions of different greenhouse gases (CO2, N2O, CH4, HFCs,5 This result comes from the fact that 7 economic sectors, have more imports than domestic production.6 Moreover, most statistics are available in an industry format (employment, value added generated, etc.) thus,the technical coefficient matrix could be used for other purposes.
  9. 9. E.Jodar(2011) 9PFCs, SF6). This vector is disaggregated into 42 sectors, which corresponds to the sectorsfrom the IO tables and represents the total amount of pollution emitted during the year 2005.These emissions are collected in line with the National Accounting Matrix includingEnvironmental Accounts (NAMEA) that serves as a basis for European Union countries.4. Methodological FrameworkMy approaches to identify key sectors are taken from the abundant literature of linkages andare based on the inverse matrix of Leontief (or total requirements matrix) and the inversematrix of Ghosh (or output inverse). In the first stage of assessing the relevance of a sector, Iwill consider the magnitude of its pulling effect. By pulling effect, I mean the backwarddependency of the sector; the necessity of inputs provided by other sectors in order for it toproduce. A sector with a large backward effect will “demand” from other sectors. Such asector will induce other sectors to produce inputs for it when expanding its production. Fromwhere, a sector with a large backward linkage will be considered relevant as in Rasmussen(1956). In order to assess relevance of the various sectors from this demand perspective, I willuse the Leontief “demand-driven” model.As previously said, I am following the line of the Generalized IO models. The methodologyherein is to convert the inverse matrix of Leontief into a matrix that contains emissions ratherthan production worth. Indeed, the Leontief inverse represents production inmonetary terms where each element gives the total (direct and indirect) increase in sector s production needed for an additional Swiss Franc’s worth of sector s production.Since it is not the production which is of primary interest in this study, I will convert theLeontief inverse in a matrix that instead of representing production will show emissions. Inorder to do it I will follow a methodology used by Alcántara (2007).In the following lines, matrices and vectors will appear in bold with capital and normal lettersrespectively. The “diagonalization” of a vector will figure with a “hat” and the transpositionof a column vector by a prime.Define: . (1) . (2)
  10. 10. E.Jodar(2011) 10where is the “make matrix”, that is, the supply matrix transposed and is the use matrix.Any element of the supply matrix represents the amount of commodity produced byindustry while any element of the use matrix represents the amount of commodityabsorbed as an input by industry . Vectors and are total commodity output and totaloutput of industries respectively.A total direct input coefficient matrix with dimensions industry-by-industry and IBT can becalculated following Miller and Blair (2009, p. 193). However, as mentioned previously, atotal technical coefficient matrix is not convenient to assess key sectors. Hence, letdomesticate the data following Jackson’s trade adjustment contribution presented in Lahr(2001) as: . (3)Where (42 x 42) is a domestic technical coefficient matrix adjusted for trade. Letters are vectors of output, imports and exports by products respectively.And, from the Leontief demand-driven model: (4)with the identity matrix, the final demand and the production we get the totalrequirement matrix necessary for our analysis which relates changes in demand tochanges in production. As suggested by Jones (1976), the column sum of this matrix shouldbe used to represent direct plus indirect backward linkages.Another approach, taken from the Ghosh model developed in 1958, is often used to identifykey sectors. In that year, Ghosh proposed with the same data needed for the Leontief model,an input-output model with a supply approach. This “supply-driven” model relates sectoralgross production to primary inputs by the output inverse matrix.7Any element of this matrixgives in a single number the total production that sector has to do in order to exhaust aninitial increase from one Swiss Franc’s worth of production from sector . The Ghosh inversematrix can be calculated from the matrix of intermediate consumptions or extrapolated fromthe Leontief inverse following Miller and Blair (2009, p. 548). The difference between thesetwo matrices (Ghosh and Leontief) is that the Leontief inverse matrix starts at the end of theproduction process, with an increase in final demand, and traces the effect backward through7 Primary inputs are collected within the value added vector which represents labor and capital that economicbranches need, beside the inputs from other industries, in order to produce.
  11. 11. E.Jodar(2011) 11the system while the Ghosh inverse starts at the beginning of the production process, with anincrease in primary inputs, and traces the effect forward through the system.Thus, in the second stage of identifying key sectors, I will use the supply-driven model ofGhosh. The approach consists in considering a sector to be “important” if its pushing effect isrelevant. By pushing effect, I mean the capacity of a sector to induce other sectors to produce(by making them exhaust its output). The strength of that effect will directly depend on theforward connectedness of sectors. In order to assess what sectors have a high forward linkage,I will use the row sum of the output inverse as suggested by Jones in 1976.Extrapolating from the Leontief inverse (as in Miller and Blair, 2009, p. 548) we find theGhosh inverse that will allow for assessment of key sectors in a supplyperspective.Let write the Ghosh supply-driven model (where is not the one from equation 2 and 3) as: 8 (5)Introduce (42x1), the vector of emissions measured in tons of CO2 equivalent (GHG). Letbe the vector (42x1) of production measured in million of Swiss Francs (CHF). Then, is a row vector of emission coefficients with units: tons of GHG by millions ofCHF of production. From the previous equation we can deduce that, . (6)Let now rewrite the well known Leontief model. . (7)Substituting (in equation 6) by its value in the Leontief model, we get: . (8)Define: . (9)Then . (10)8 Where is the vector of value added from the use table which will be called “primary inputs”.
  12. 12. E.Jodar(2011) 12The last equation is a converted Leontief model which instead of connecting the final demandto production relates it to emissions. The matrix is fundamental in evaluating key sectors inGHG emissions and therefore to test my hypothesis. Any element of gives the GHGemissions of sector , needed, to sustain an additional unit of product . Thus, if we sum theelements of column we will get a multiplier effect of a marginal increase in final demand forsector j ;Column sum of is therefore a measure of backward linkage on emissions.Formally, with a summation vector will give the output multipliers for the 42sectors; the sectoral backward linkage.Several weights can be applied for bringing out the relative importance of the various sectorsin the national economy. Let weight multipliers according to the greater or lower relevance ofsectors in the final demand, as in Hazari (1970), since unweighted multipliers are potential,not effective multipliers. Define as a vector of weighted final demands such that .Thus, will represent the weighted output multipliers.In order to get an accurate measure of each sector’s backward dependence to the “rest” of theeconomy, the on-diagonal elements of should be omitted because it represents internallinkages (Miller and Blair, 2009, p. 577).9 Thus, let net out from the summation the on-diagonal elements of splitting between pure an own backward effects corresponding toexternal and internal linkages respectively. Following Alcántara’s procedure (2010), let leavethe matrix notation for a little while: . (11) . (12)The methodology for assessing forward linkages is in the same vein. Let post-multiply theGhosh model from equation (5) by the emission coefficient vector to get as left-hand sidevariable emissions rather than production worth. . (13)Define: . (14)Then,9 Those internal linkages are seen as “own-consumption” by sectors.
  13. 13. E.Jodar(2011) 13 . (15)The last equation is a converted Ghosh model. Instead of connecting primary inputs toproduction, it relates primary inputs to emissions. Any element of gives the GHGemissions of sector , needed, to exhaust the additional unit of . Thus, if we sum the elementsof row , we will get a multiplier effect of a marginal increase in primary inputs of sector ;Row sum of is therefore a measure of forward linkage on emissions. Formally,with a summation vector will give the supply multipliers for the 42 sectors; the sectoralforward linkage.Several weights can be applied for bringing out the relative importance of the various sectorsin the national economy. Herein multipliers will be weighted according to the share ofprimary inputs needed for production of the different economic branches. Define as thevector of weighted primary inputs such that . Thus, will represent theweighted supply multipliers. As with the demand model, in order to get an accurate measureof each sector’s forward linkage to the “rest” of the economy, we can separate the sectoralinternal linkages that are located on the on-diagonal elements of matrixLeaving the matrix notation for a little while: . (16) . (17)Let now categorize sectors into 4 groups according to the relative strength of their linkages. Asimple way to do so is by comparing each weighted multiplier to the average weightedmultiplier. Define the average weighted multiplier as: . (18)With this benchmark, let classify sectors following Table 1 (cf. Appendix).Other taxonomies will be useful to assess backward and forward linkages in more detail,separating for pure and own linkages. This distinction is useful in terms of policy implicationas will be seen in the following section. Let, thus, categorize sectors into 4 groups accordingto their own and pure backward/forward linkages as in table 2 and 3 (cf. Appendix). Themultipliers will be compared with respect to each effect’s (own and pure) specific average.
  14. 14. E.Jodar(2011) 145. ResultsFigure 1 presents results on key sectors in GHG emissions in Switzerland for the year 2005.Weighted multipliers reveal that 15 sectors are key in a demand side perspective, 14 in asupply side perspective and 10 sectors out of the 42 are key from both a demand- and asupply-side perspective. Thus, the IO analysis implemented to emissions reveal that both-perspective key sectors (“two-side”) in total CO2 equivalent are products of agriculture,forestry and fishing (1), coke, refined petroleum products (10), other non-metallic mineralproducts (12), construction work (24), wholesale trade and commission trade services (26),transport services (28), public administration and defense services, compulsory social securityservices (37), education services (38), health and social work services (39) and sewage andrefuse disposal services (40). 10 0.008 1 0.007 28 0.006 26 40 34 0.005 Supply side 0.004 12 0.003 37 31 24 10 0.002 30 23 38 39 33 27 3 0.001 13 15 0 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 Demand side Figure 1 Key Sectors in GHG Emissions.Note: This figure classifies the 42 sectors of the Swiss economy into 4 groups according to each sector’s weighted multipliersof converted demand and supply-driven model. The vertical and horizontal axes show the average weighted multipliers of thedemand and supply-driven model respectively and divide the plane into four regions. The first quadrant presents key sectorsin GHG emissions from both a demand and a supply-driven model. The second quadrant shows key sectors from a supply-driven model exclusively while the fourth quadrant presents them from a demand-driven model exclusively.10 Without weighting multipliers by their relative importance in the economy results are totally different. Thus, ifwe assume that all sectors are equally important only five sectors are “key” from both perspectives and the sectorof sewage (40) has a humongous effect. This result shows the importance of bringing out every sector’s relativeimportance in the economy. In light of that result all following assessments in this work will be weighted.
  15. 15. E.Jodar(2011) 15This first outlook on the 10 two-side key sectors is not very surprising since these sectorswere already known to be serious polluters. The most startling result might be the wholesaletrade sector (26) that is in the top 4 two-side key sectors though it is considered not “that”relevant in GHG emissions data (cf. Appendix, Table 5).Notice from Figure 1 that although not relevant from a supply-side analysis, sector 3, namelyfood products, is highly relevant from a demand-side perspective and thus deserves attention.Indeed, when we look at the data (cf. Table 5), sector 3 does not appear to be a big polluter.Similarly, sector 34, that is, renting of machinery and equipment, is relevant from a supply-side analysis but not from a demand-side.Let now analyze in detail backward and forward linkages, distinguishing between own andpure effects. Such a separation will help to understand why some sectors are relevant in theIO analysis though not appearing so in the data on direct emissions. Furthermore, such ananalysis is of primary interest in order to fight cleverly against GHG emissions. Indeed, interms of policy implications, branches with large own backward linkage must be treateddifferently from those with high pure backward linkage. The underlying reason for it is thatsectors with high own linkages exclusively pollute themselves while sectors with large purelinkage do not pollute much but require others to pollute. Consequently, the policyimplications will have to be different. Results are presented in Figures 2 and 3.Backward LinkagesA quick outlook on Figure 2 is sufficient to see that sectors with high backward linkage canbehave following really different patterns. A fascinating example is given by sectors 3 and 10that are similar in the key sector assessment (Figure 1) but are driven by two different forces.Accordingly, two groups with different policy implications appear from Figure 2. The first iscomposed of sectors 1, 28, 40 and exhibits high direct effects (own) but low pulling effects(pure) upon other sectors. Hence, a strategic policy should aim at adopting measures to reduceGHG emissions, especially on these sectors. This can be done, for instance, by forcing themto adopt better technologies. The second group is composed of sectors 3 and 24 and exhibitssubstantial indirect effects. This group has to be treated differently from the first groupaforementioned. Here, better technologies do not matter so much since the final polluter isneither sector 3 nor sector 24; these sectors are not directly responsible for GHG emissions. Ifa decision maker wants to mitigate GHG emissions due to these sectors, he should either
  16. 16. E.Jodar(2011) 16mitigate their demand with a tax or focus on the sectors they demand inputs from in order tomake them adopt better technologies. 0.006 3 0.005 0.004 Pure Backward 0.003 24 10 0.002 27 26 15 39 18 33 0.001 37 38 28 13 0 12 40 1 0 0.001 0.002 0.003 0.004 0.005 0.006 Own Backward Figure 2 Backward Effects.Note: This figure classifies the 42 sectors of the Swiss economy into 4 groups according to each sector’s weighted multipliersof own and pure backward linkage. The vertical and horizontal axes show the average weighted multiplier for the own andpure backward effects respectively and divide the plane into four regions. The pure backward multiplier consists in thecolumn sum of the F matrix netted out from the on-diagonal element. The own backward is measured by the on-diagonalelement of the matrix and represents internal linkages. The first quadrant presents relevant sectors in GHG emissions for bothown and pure backward effects. The second quadrant shows relevant sectors in pure backward linkage, that is, to the rest ofthe economy while the fourth quadrant presents them from an own effect consideration exclusively.Forward LinkagesAn illustrated insight is given by Figure 3. Here again, the results are enlightening. Adecomposition of the forward effects between own and pure forward unveil the grounds for asector to be relevant from an IO analysis. Sector 1, 28 and 40 are relevant in GHG emissionsbecause of their own effects while sectors 26 and 34 and, in a lesser extent, sector 31 appearrelevant because their production will make others to pollute by providing them inputs.Strategic policy implications to reduce GHG emissions in sectors 1, 28, and 40, here again,should focus on the adoption of cleaner technologies. Policy implications for sectors 26 and34 are as follow. On the one hand, their production should be mitigated (for example via atax). Indeed, since their relevance in GHG emissions come from other economic sectors,measures to reduce their production will achieve GHG reduction in other sectors. On the other
  17. 17. E.Jodar(2011) 17hand, policy interventions should focus on sectors that use their output as inputs in order tomake them adopt better technologies. 11 0.005 34 0.004 26 Pure Forward 0.003 31 0.002 37 25 24 0.001 28 10 1 12 40 0 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 Own Forward Figure 3 Forward Effects.Note: This figure classifies the 42 sectors of the Swiss economy into 4 groups according to each sector’s weighted multipliersof own and pure forward linkage. The vertical and horizontal axes show the average weighted multiplier for the own and pureforward effects respectively and divide the plane into four regions. The pure forward multiplier consists in the row sum of theH matrix netted out from the on-diagonal element. The own forward is measured by the on-diagonal element of the matrixand represents internal linkages. The first quadrant presents relevant sectors in GHG emissions for both own and pureforward effects. The second quadrant shows relevant sectors in pure forward linkage, that is, to the rest of the economy whilethe fourth quadrant presents them from an own effect consideration exclusively.Contribution from the IO analysisSome sectors appear to be much more relevant in GHG emissions than what is commonlythought; what is shown by data on sectoral emissions. Such a feature is in line with thehypothesis of the present paper that certain branches have a role in GHG emissions that is notvisible just with data on sectoral emissions and that is highlighted by an IO analysis. Themain result of the papers is the following: some sectors are much more relevant in GHGemissions than what is prima facie thought. Indeed, certain sectors strongly pull/push others11 A policy that mitigates demand for sector 1, 28 and 40 has evidently also a positive effect on GHG emissionsmitigation. The logical reason for it is that demand of other sectors for, say, transport services (28) leads to asubstantial increase in transport services from the transport services sector itself. The underlying assumption tofocus on a policy that aims at revising the sector’s technology is that it is considered as more effective.
  18. 18. E.Jodar(2011) 18to pollute while they are considered to be harmless from what is recorded in the data. Thus,some sectors deserve several comments.Sectors 3 and 34, namely food products and machinery rentals respectively are representativeof the apparent harmlessness issue. On the one hand, sector 34 does not appear in the top tenemitting sectors revealed by NAMEA data (cf. Table 5), indeed, its emissions are not thatimportant compared to other sectors on the top of the list. On the other hand, this sector has astrong indirect effect on other sectors from a supply perspective. Consequently, it could beeasy to override this sector in a policy that aims at reducing GHG emissions.Sector 3 is also of particular interest. Its supply dependence is quite substantial, thus pullingmany sectors to pollute when it expands its production. However, surprisingly, its ranking inthe most harming sectors for GHG emissions stands in the ninth position (cf. Table 5).To give a taste of what these results mean, let us have an example. Using our IO framework,let imagine a 1% increase in final demand (=1% increase in GDP). Let first imagine that thisincrease is due exclusively to an increase in wholesale trade services demand (26) in a waythat all other sectors remain with the same demand. Formally: . (19)Using the augmented Leontief model to analyze the impact of that demand change, it comesout that this 1% increased GDP concentrated in sector 26 will increase GHG emissions by0.66%.Let us now contrast this with an equal 1% GDP increase now exclusively due to the foodproducts sector (3), say, because of an increase in exported Swiss products such as cheese,chocolate, etc. Let us look at the impact on GHG emissions that would follow in the Swisseconomy. Using the augmented Leontief model to assess the environmental impact of thisdifferent growth path, it comes out that this 1% increased GDP concentrated in sector 3 willraise GHG emissions by 2.48%.This result is definitely striking when we consider that sector 26 emits almost twice as muchas sector 3 according to public data (c.f Table 5).In the same vein, but with the augmented supply-driven model à la Ghosh, let show how anexpansion of primary inputs in sector 34 is relevant. Let imagine a 1% increase in GDPmeasured by the value added. Consider in a first stage that this increase in “primary inputs” is
  19. 19. E.Jodar(2011) 19concentrated exclusively in sector 24, say, because of an inflow of immigrant workers.Formally, . (20)Using the augmented Ghosh model to calculate the impact of this change, it comes out thatthis 1% increased GDP concentrated in sector 24 will increase GHG emissions by 0.64%. Letnow imagine the same increase in GDP (1%) but now due exclusively to more primary inputsentering in sector 34. The environmental consequence will be that this 1% increased GDPconcentrated in sector 24 will increase GHG emissions by 1.13%.These unusual results illustrate the general fact that even though the direct environmentalimpact of production from a definite sector can be small, the real-world impact can be large.This will be the case, particularly, if a sector gets its inputs from activities that pollute a lot.Thereby, and in order to conclude this section, we see that some sectors that are apparentlyharmless to GHG releases are actually more relevant than what is prima facie thought (what isrevealed from public data). Indeed, changes in final demand for commodities and changes insupply of primary inputs can affect very differently the environment according to the sectorthat undergoes the change. 126. Refinement of the key sector assessmentAn exhaustive concern to assess GHG key sectors in an open economy should take intoconsideration imports and exports of goods, services and inputs. Indeed, a country couldavoid pollution by importing (its imports serving for both the final demand and industriesinputs). Thus, to reach a CO2 target, a country could reduce pollution simply by importinginputs that would have required substantial emissions (Machado et al. 2001). The subjacentquestion of this concern is one of responsibility. Who imputing responsibility for emissions?The producer or the consumer? A recent literature on this matter distinguishing “CO 2emissions” from “CO2 responsibility” and was first proposed by Proops et al. (1993). Thisliterature proposes two principles to attribute responsibility. On the one hand, one couldconceive that only the producer should be hold responsible for GHG emissions. On the otherhand, one could consider that the responsibility should fall on the final consumer.12 Obviously, those surprising results are subject to the underlying assumptions of both demand and supply-driven models. The former assumes no input substitution, that is, fixed input coefficient, the latter fixed outputcoefficient, that is, if sector double its output, then the sales from to each of the sectors that purchase fromwill also be doubled.
  20. 20. E.Jodar(2011) 20One drawback of the “producer responsibility principle” is that it does not differentiatebetween emissions to provide goods, services and inputs intended for other countries(exports) from emissions for domestic demand. In spite of this, the producer principle (orterritorial principle) is the one adopted by the Kyoto agreement. This weakness of the Kyotoagreement harms exporting countries and forces them to make an extra effort to reach CO 2targets (Munskgaard and Pedersen, 2001).In contrast, the “consumer responsibility principle” would impute the responsibility on theconsumer. In our case, Switzerland would be held responsible for the GHG emissionsembodied in its imports. A shortcoming to this approach is that nothing can be done by theimporting country to improve technologies abroad and thus reduce emissions.The calculation of the ecological footprint of a country varies depending on what principle isused to calculate total GHG emitted; these two principles lead to different valuation of theimpact of a determinate country on the environment. This concern led Munksgaard andPedersen (2001) to introduce the concept of “trade balance” in order to show the difference inCO2 emissions embodied in total imports and exports.In order to complement the previous analysis of key sectors in GHG releases within the Swisseconomy (which is the aim of this paper) and to obtain a more realistic view of the ecologicalfootprint of the 42 industries, I will undertake an IO trade balance calculation for the Swisseconomy. Such an analysis will tell if some sectors that did not get a large relevance in theprevious analysis have actually a deep weight at a global level. Furthermore, in calculating theaggregated trade balance, that is, the sum of all sectors’ trade balance, I will unveil ifSwitzerland is a freeloader of the Kyoto agreement (by importing more “GHG intense” inputsand goods than exporting). By doing that, I will naturally discover if Switzerland is a winneror a loser of the Kyoto agreement. A negative trade balance would indicate that the country isavoiding CO2 releases in some extent by importing. This calculation will follow the basicmethodology of Munksgaard and Pedersen (2001) but detailed in a sectoral disaggregationfollowing the methodology of Sanchez-Chóliz and Duarte (2004).Let the trade balance of GHG pollution vector be: 13 . (21)13 Where and are the total and imported direct input coefficient matrices respectively. The former matrixis calculated as: while the latter as: . Furthermore, , the vector of imports for final demand hasbeen computed as: .
  21. 21. E.Jodar(2011) 21Results are shown in Table 2 (cf. Appendix). Negative numbers indicate that sectors are netimporters of GHG from the outside while positive values indicate that sectors export moreGHG than import.Assuming that not all foreign providers are restrained by GHG targets as, for instance, by theKyoto protocol, one should give a “relevance premium” to sectors with negative values insight of the GHG leakages that can occur in global accounting. Indeed, in the previous IOanalysis, imports were not taken into account because of the data domestication that wasrequired to assess key sectors. Consequently, no weight was given to the amount of imports inthe key sector assessment and the “territorial principle” was thus implicit. Nonetheless, ifSwiss imports come from countries that are exempt from GHG releases restrictionspurchasers’ sectors should get a sense of responsibility for the carbon embodied in theirimports. With that additional consideration, let review upward the relevance in GHGemissions of sector 1; 12; 13 among others.Sector 13, namely basic metals, is a nice example of the contribution of this calculation.Indeed, this sector did not show up to be a relevant sector in the previous IO analysis.However, this sector is known to be harmful on GHG emissions in most countries of theworld. In Switzerland, by substituting domestic production by imports, this sector shifts theburden of acquiring inputs to other countries. If provider countries register their emissions,there is no point on blaming sector 13 in Switzerland since no carbon leakage will occur.Nevertheless, if Switzerland imports basic metal commodities from countries that are exemptfrom GHG emissions listing (as could be the case) then, sector 13 should get an “extrarelevance”.It is worth mentioning that on aggregated terms, Switzerland is not a freeloader of GHGemissions. Switzerland appears to be a loser of the Kyoto agreement. Thus, in order toachieve GHG emissions targets, Switzerland has to make an extra effort. 14The emission coefficient vector is domestic. Thus, this calculation assumes implicitly that provider countrieshave the same production technology as Switzerland; the same amount of emission by unit of production.14 It is worth mentioning that this analysis has the only purpose of informing of the relevance of sectors in aworld economy, key sectors having already been defined in the previous section. If we assume that all countriesthat provide Swiss imports signed the Kyoto agreement then they are hold responsible for their emissions andthere is no point in calculating GHG embodied in imports since it has already been accounted on the exportingcountry. In contrast, this analysis is of particular interest if the foreign providers of the Swiss economy are notpart of the Kyoto agreement since the emissions are not accounted for by the exporting country. In this case, thenet trade balance calculation helps to give a more realistic view of key sectors that has been lost bydomestication of the data.
  22. 22. E.Jodar(2011) 22ConclusionThe Generalized IO analysis used to assess key sectors in GHG emissions for Switzerland in2005 shows that some sectors are more relevant than what is commonly thought. Indeed, theIO analysis has allowed interdependencies between sectors to be taken into account and, thus,the pulling and pushing effects that occur when a determinate sector increases its production.Results show that the food products sector (3) and machinery rentals sector (34) present highindirect effects and are thus not harmless to the environment when their productions increase.Moreover, a refinement of the key sector assessment has been undertaken by a calculation ofGHG embodied in trade. This refinement has the purpose of recovering a dimension lost bythe domestication of the data needed for the key sector assessment, namely, the concern forGHG embodied in imports. By assuming that not all foreign Swiss providers have emissionrestrictions, an “extra responsibility” has been assigned to sectors that import more GHG thanexport. This extension of the key sector assessment shows that some sectors, such as basicmetals (13), among others, should get an extra relevance in order to accurately assess theirresponsibility in GHG emissions.In terms of policy implications, the aforementioned food products sector (3) and machineryrentals sector (34) should receive a demand and production mitigation policy, respectively.Indeed, such interventions will achieve deep GHG mitigations in the whole Swiss economybecause these sectors make others to pollute when they expand their production.AcknowledgmentsThis work is the outcome of an applied economic master’s dissertation project. I am gratefulto the Swiss Federal Office of Statistics team and Michael Lahr for answering questions aswell as my supervisor Emilio Padilla and Vicent Alcántara for their precious comments. Allerrors are mines.
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  26. 26. E.Jodar(2011) 26AppendixTable 1 Classification of Sectors. Forward Linkage Supply perspective Irrelevant Sectors Key Sectors Backward Linkage Demand Two-side Key perspective Key Sectors SectorsNote: Table 1 classifies sectors according to a benchmark which is the average weighted multiplier. Theweighted multiplier is a measure of sectoral connectedness weighted according to the relevance of the sector inthe whole economy. Sectors with high backward linkage exclusively are key in a demand perspective. Sectorswith high forward linkage exclusively are key in a supply perspective. Sectors with both a large backward andforward linkage are key from both perspectives. Results are shown in Figure 1.Table 2 Classification of Backward Linkages. Own Backward Relevant Sectors in Irrelevant Sectors own Backward Pure Backward Linkage Relevant Sectors in Relevant Sectors in pure Backward both own and pure Linkage Backward LinkageNote: Table 2 takes backward linkages and split them into own and pure backward. Each effect (own and pure)is compared to its own average. Results are shown in Figure 2.Table 3 Classification of Forward Linkages. Own Forward Relevant Sectors in Irrelevant Sectors own Forward Pure Forward Linkages Relevant Sectors in Relevant Sectors in pure Forward both own and pure Linkages Forward LinkagesNote: Table 3 takes forward linkages and split them into own and pure forward. Each effect (own and pure) iscompared to its own average. Results are shown in Figure 3.
  27. 27. E.Jodar(2011) 27Table 4 Sectors of the Swiss Economy. 1 Products of agriculture, forestry and fishing 2 Products of mining and quarrying 3 Food products, beverages and tobacco products 4 Textiles 5 Wearing apparel, furs 6 Leather and leather products 7 Wood and products of wood and cork (except furniture); articles of straw and plaiting materials 8 Pulp, paper and paper products 9 Printed matter and recorded media10 Coke, refined petroleum products and nuclear fuel; chemicals and chemical products11 Rubber and plastic products12 Other non-metallic mineral products13 Basic metals14 Fabricated metal products, except machinery and equipment15 Machinery and equipment n.e.c.16 Office machinery, computers and electrical machinery n.e.c.17 Radio, television and communication equipment and apparatus18 Medical, precision and optical instruments, watches and clocks19 Motor vehicles, trailers and semi-trailers20 Other transport equipment21 Furniture; other manufactured goods n.e.c.22 Secondary raw materials Electrical energy, gas, steam, hot water; collected and purified water and distribution services of23 water24 Construction work Trade, maintenance and repair services of motor vehicles and motorcycles; retail sale of automotive25 fuel Wholesale trade and commission trade services, except of motor vehicles and motorcycles, Retail26 trade services, except of motor vehicles and motorcycles; repair services of personal and household goods27 Hotel and restaurant services28 Transport services29 Supporting and auxiliary transport services; travel agency services30 Post and telecommunication services31 Financial intermediation services, except insurance and pension funding services Insurance and pension funding services, except compulsory social security services (includes also32 part of CPA 67)33 Real estate services (incl. private households) Renting of machinery and equipment without operator and of personal and household goods; other34 business services35 Computer and related services36 Research and development services37 Public administration and defense services; compulsory social security services38 Education services39 Health and social work services40 Sewage and refuse disposal services, sanitation and similar services41 Membership organization services n.e.c.; recreational, cultural and sporting services42 Other services; private households with employed persons
  28. 28. Table 5 Ranking of Top Polluters. Table 6 Sectoral Trade Balance. Ranking of Top Polluters Trade BalancePosition Sector Tons of GHG emissions Sector b 1 1 6435 1 -1761 2 28 5969 2 -101 3 40 5526 3 -48 4 12 3403 4 -69 5 10 2653 5 -70 6 26 1746 6 -46 7 37 1526 7 -92 8 24 1205 8 65 9 3 937 9 7 10 39 933 10 1346 11 27 876 11 26 12 34 803 12 -557 13 23 775 13 -425 14 8 768 14 114 15 7 642 15 126 16 13 628 16 -23 17 30 481 17 -25 18 14 388 18 134 19 15 362 19 -97 20 29 350 20 -10 21 25 295 21 -60 22 31 271 22 -10 23 9 238 24 16 225 23 105 25 41 221 24 14 26 18 206 25 20 27 35 188 26 377 28 11 136 27 -18 29 4 136 28 998 30 38 132 29 76 31 32 128 30 49 32 21 117 31 108 33 17 117 32 14 34 42 116 33 10 35 33 82 34 110 36 2 63 35 11 37 36 59 36 23 38 20 40 37 16 39 22 35 38 19 40 5 23 39 22 41 19 15 40 274 42 6 12 41 -8 42 2 Total 39261 National Balance 644Note: Sectors are ranked according to their amountof GHG emissions for the year 2005. Data come Note: This table shows the result of the calculationfrom the NAMEA 2005. of equation 21. The trade balance is disaggregated at a sectoral level. A positive value means that a definite sector exports more GHG than receive by its imports from abroad.

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