Mapping Flows of Embodied Emissions in the Global Production System

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Mapping Flows of Embodied Emissions in the Global Production System Andrew Skelton,† Dabo Guan,*,‡,§ Glen P. Peters,|| and Douglas Crawford-Brown† †

)

Cambridge Centre for Climate Change Mitigation Research, Department of Land Economy, University of Cambridge, Cambridge CB3 9EP, U.K. ‡ School of Earth and Environment, University of Leeds, Leeds LS2 9JT, U.K. § St. Edmund’s College, University of Cambridge, Cambridge CB3 0BN, U.K. Center for International Climate and Environmental Research Oslo (CICERO), N-0318 Oslo, Norway

bS Supporting Information ABSTRACT: Environmentally extended multiregional input-output (MRIO) analysis can be used to investigate final production and consumption attributions of emissions. As the distinction between the two attributions has been brought to the attention of policy-makers, there is an ever greater need to understand how and why they differ, by analyzing the connections between production and consumption activities. Seeking to meet this need, we present an approach for mapping flows of embodied emissions through a Leontief production system. The approach, seen as an extension of Structural Path Analysis (SPA), provides an exhaustive map of supply chain linkages between final production and consumption attributions of emissions. Whereas SPA is traditionally used to extract and rank individual supply chains according to the emissions occurring at the end of each chain, the mapping approach considers emissions embodied in the flows of intermediate products linking different economic sectors along supply chains. Illustrative results are presented from a global MRIO model and CO2 emissions for 2004. The emissions embodied in a sector’s total output of products is also of interest: a method for calculating this is presented and shown to provide further insight into where in the production system a sector’s overall emissions impact is concentrated.

1. INTRODUCTION Assuming that all anthropogenic greenhouse gas (GHG) emissions can ultimately be attributed to consumption activity, environmentally extended multiregional input-output (MRIO) models have been used to investigate annual final consumption attributions of emissions caused by national final demand of products (i.e., goods and services),16 and, subsequently, to assess the emissions embodied in internationally traded products.711 In contrast, final production attributions of emissions detail only the total direct (Scope 1) emissions released by producing entities. The results of the MRIO studies offer a reattribution of emissions from producers to final consumers (i.e., households and governments) and investors in capital (that enables further production in later years).12,13 Such an approach supports policies aimed at reducing or shifting consumer demand, at helping consumers understand the composite GHG implications of their choices, or at ensuring that costs of, and responsibilities for, climate change mitigation are allocated to entities and regions based on their roles in driving production processes through consumption. Final consumption attributions quantify the emissions virtually embodied in products purchased by final demand the direct r 2011 American Chemical Society

emissions released during the assembly of final products and all the indirect emissions released by the producers of intermediate products processed along associated supply chains. A final consumption attribution may, for example, reveal that automobiles purchased by final demand are high in embodied emissions (perhaps due to emissions occurring in supply chain steel and electricity production); while the automotive sector’s final production attribution of emissions might be very low. Discrepancies between final production and consumption attributions raise questions such as the following: where have the emissions embodied in final products come from; conversely, where have the production emissions from economic sectors gone. Structural Path Analysis (SPA) has helped address these questions.1419 Within the context of a Leontief input-output (IO) model, purchases of intermediate products, instigated by final demand purchases of final products, can be traced through layers of the production system (e.g., consumers purchase from Received: July 19, 2011 Accepted: November 3, 2011 Revised: October 24, 2011 Published: November 03, 2011 10516

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Environmental Science & Technology product manufacturers, who purchase from primary manufacturers, who in turn purchase from resource extractors). SPA extracts individual supply chains instigated by final demand. When coupled with emissions data, an SPA quantifies the emissions at the end of each supply chain (e.g., emissions occurring in metal production used as input into automobile production, or, for a longer supply chain: emissions occurring in electricity production used as input into metal production used as input into automobile production). The results of an SPA transform the embodied emissions, detailed in a final consumption attribution, into a tree-like structure of emissions occurring in different economic sectors at different points in the production system. However, the interdependence between sectors in an IO framework (e.g., where one sector’s products are used by another sector to produce products used by the initial sector) means that a final demand purchase theoretically propagates an infinite number of supply chains through the economy. Still, there is a practical limit to the number of supply chains that can be extracted, both in terms of computational requirements and sensible interpretation.7 Studies using SPA have therefore found it impossible to exhaustively quote SPA results and provide a comprehensive assessment of the connections between final production and consumption attributions, focusing instead on the identification of emissions hotspots by ranking only the most important supply chains.15,19 The problem with this approach, however, is that while the majority of less important supply chains represent only a small fraction of total emissions, to the point of rendering them individually negligible, they can together represent a considerable share of total emissions.8 As differences in the apparent roles and responsibilities of actors when viewed through final production and consumption attributions of emissions increasingly come to the attention of policy-makers,9 there is an ever greater need to understand how and why such differences occur. We suggest that part of this need can be met by investigating and illustrating the connections between final production and consumption attributions in an exhaustive manner. To achieve this, three problems need to be addressed: (i) how to examine a large number of supply chains linking sectors at different stages in the production system; (ii) how to show the relative importance of different supply chains in regard to GHG emissions; and, (iii) how to account for the majority of supply chains that each constitute negligibly small emissions sources, but that collectively make up a considerable share of total emissions within an economy. In addressing these problems, the primary contribution of this paper is the introduction of a methodological and diagrammatic approach for mapping flows of embodied emissions through a production system as described by a Leontief input-output model. This mapping approach builds on SPA to enable the exhaustive depiction of the connections between final production and consumption attributions of emissions. To illustrate the approach, results are presented from an exploratory application to a global MRIO model and CO2 emissions for 2004. The mapping approach provides intermediate consumption attributions the emissions embodied in a sector’s output of intermediate products purchased at a particular layer within the production system. In addition, we present a method, based on the Pure Backward Linkage measure,20 for calculating a sector’s total consumption attribution the emissions embodied in a sector’s total output of products (i.e., all intermediate and final products). The secondary contribution of this paper is to show how knowledge of a sector’s total consumption attribution

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provides further insight into where in the production system a sector’s overall emissions impact is concentrated and to show to what extent final production and consumption attributions actually account for a sector’s overall impact.

2. METHODOLOGY MAPPING FLOWS OF EMBODIED EMISSIONS The fundamental equation of the Leontief model (consisting of N economic sectors) links an exogenous N 1 final demand vector y with an N 1 total output vector x via x ¼ ðI AÞ1 y ¼ Ly

ð1Þ

where A is an N N matrix of sector intermediate purchases (or direct requirements matrix; an element Apq of A measures direct output from sector p necessary to satisfy unit output from sector q); I is the N N identity matrix; and L is the N N Leontief inverse (or total requirements matrix; an element Lpq of L measures total (i.e., direct and indirect) output from sector p necessary to satisfy unit output from sector q).18 The environmental extension of the model introduces a 1 N intensity vector f of sector direct emissions per unit output, such that total direct emissions (i.e., the final production attribution) for sector s = fsxs. For a comprehensive introduction to IO is given by pfinal s theory and techniques see Miller and Blair.21 An advantage of the Leontief model is the ability to trace chains of intermediate purchases through layers of a production system instigated by final demand. This is achieved by unravelling the Leontief inverse using its power series approximation, as shown in eq 221,23 L ¼ ðI AÞ1 ¼ I þ A þ A 2 þ A 3 þ A 4 þ 3 3 3 provided that limt f ∞ A t ¼ 0

ð2Þ

We define a production layer (PL) as each term in the power series expansion, PLt = At. Each additional layer, PLt+1 = PLtA, represents the production of intermediate products used as inputs into the preceding layer. For example, the assembly of automobiles (purchased by final demand) occurs at PL0, the manufacture of metal products used as inputs into the automotive sector occurs at PL1, which requires inputs from metal production occurring at PL2. In effect, the power series approximation irons-out interdependencies between sectors into linear intersector supply chains. This representation of a production layer is somewhat abstract as each sector is an aggregation of many industries, factories, and processes, and some sectors may be more aggregated than others. In many IO models, for example, agriculture is highly aggregated; thus, internal agricultural supply chains (e.g., seed production as input to vegetable growing) would be represented within a given layer, rather than across several layers were it more disaggregated. Our representation of the production system as a structure of linear intersector supply chains will become more realistic as the number of sectors increases (e.g., toward the resolution of LCA process data) and less realistic for highly aggregated IO models. In addition, an N N emissions multiplier matrix M can be calculated according to M ¼ ^f ðI AÞ1 ¼ ^f L

ð3Þ

where ^f is the diagonal of the intensity vector f.15,22 Similar to an element Lpq, an element Mpq measures direct and indirect emissions released from sector p that have been induced by the 10517

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Table 1. Direct, Consumption, and Production Attribution Equations for PL0 to PL3 final attribution (i.e., at PL ) 0

intermediate attribution at PL

direct

consumption

production

= fiyi

E0i E1j

= mi y i

P0i = Mi:y

= mjAj:y

P1j = Mj:Ay

= fjAj:y

to sector at PL0 from sector from sector at PL

D2k = fkAk:Ay

E2k = mkAk:Ay

P2k = Mk:A2y

at PL D3l = flAl:A2y

E3l = mlAl:A2y

P3l = Ml:A3y

at PL3

production of unit output from sector q (i.e., the emissions from sector p that have become embodied in the unit output from sector q). Manipulation of multiplier matrix M allows two different measures of embodied emissions, Type I and Type II, to be calculated: Type I Embodied Emissions. A 1 N multiplier row vector, m, can be defined as the column sum of M, which can also be obtained directly from eq 4 m ¼ f ðI AÞ1 ¼ f L

to sector at PL2

E1f0 = mjAjiyi ji

-

-

E2f0 kji = mkAkjAjiyi

E2f1 = mkAkjAj:y kj

-

E3f0 lkji = mlAlkAkjAjiyi

E3f1 lkj = mlAlkAkjAj:y

E3f2 = mlAlkAk:Ay lk

ð4Þ

An element mq measures emissions from all sectors that have become embodied in unit output from sector q. This measure includes direct emissions (released during the production of sector q’s unit output) and indirect emissions (caused by intersector requirements and feedback effects in the supply chain). Postmultiplication of mq by an elemental final or intermediate demand for sector q’s output measures emissions from all sectors that have become embodied in that demand quantity. For the particular case of final demand, we define this measure as sector q’s final consumption attribution. For the case of intermediate demand at PLt, we define this measure as sector q’s intermediate consumption attribution at PLt. Type II Embodied Emissions. Any 1 N row vector, Mp:, extracted from the overall multiplier matrix M measures emissions from sector p that have become embodied in unit output from each sector. Postmultiplication of Mp: by an N 1 column vector of final or intermediate demand for output from each sector will then measure emissions from sector p that have become embodied in that basket of demand quantities. For the particular case of final demand, we define this measure as sector p’s final production attribution (i.e., Mp:y = Pfinal p = fpxp, since sector p’s total direct emissions must have been released by the end of the production system). For the case of intermediate demand at PLt, we define this measure as sector p’s intermediate production attribution at PLt (i.e., the portion of sector p’s final production attribution of emissions that has been released by PLt). By combining eqs 2-4 we arrive at three sets of equations, which for an example of 4 production layers (PL0 to PL3) are given in Table 1. Where i, j, k, and l denote sectors at PL0, PL1, PL2, and PL3, respectively, and ‘:’ denotes all sectors (e.g., Aj: is the 1 N row vector of all intermediate purchases from sector j). An element Dts represents direct emissions released from sector s at PLt (e.g., D1j measures direct emissions released from sector j at PL1 in producing output required to meet intermediate demand given by Aj:y). An element Ets represents emissions that have become embodied in the output of sector s at PLt (e.g., E2k measures emissions from all sectors that have become embodied in the output of sector k at PL2 required to meet the intermediate demand given by Ak:Ay). An element Pts represents emissions

2

from sector

2

intermediate attribution

to sector at PL1

at PL1

1

intermediate attribution at PL

D0i D1j

Table 2. Embodied Emissions Flow Equations down to PL3

3

from sector s that have become embodied in the output of all sectors at PLt (e.g., P3l measures emissions from sector l that have become embodied in the output of all sectors at PL3 required to meet the basket of intermediate demand given by A3y). Further sets of equations can be specified that measure the emissions from all sectors that have become embodied in the output of a given sector required to meet demand from a chain of final and intermediate purchases (i.e., the flows of embodied emissions between sectors at different layers) as presented in Table 2. 3f2 , E2f1 measure the emissions that have become E1f0 ji kj , and Elk embodied in the flow of intermediate products between sectors at measures emissions from all adjacent layers. For example, E1f0 ji sectors embodied in the output of sector j at PL1 purchased by sector measures emissions from all sectors i at PL0; similarly, E2f1 kj embodied in the output of sector k at PL2 purchased by sector j at PL1 to meet final demand requirements from all sectors at PL0. The remaining equations provide more specific measuremeasures emissions from all sectors ments. For example, E2f0 kji embodied in the output of sector k at PL2 purchased by sector j at PL1 to meet final demand requirements from sector i at PL0. The set of embodied emissions flow equations terminating at PL0 have a similar form to those used in SPA;24 however, the SPA equations are used to calculate direct emissions at the end of a supply chain rather than embodied emissions. For example the = SPA equations for PL1, PL2, and PL3 can be denoted D1f0 ji 3f0 fjAjiyi, D2f0 kji = fkAkjAjiyi, and Dlkji = flAlkAkjAjiyi, respectively. Equation results from Tables 1 and 2, proposed here as an extension of SPA, can be used to map flows of embodied emissions through the production layer expansion of the Leontief production system. Figure 1 illustrates a map for a simple system consisting of just two sectors, s1 and s2. The left-hand side of Figure 1 provides each sector’s final production attribution of emissions, Pfinal s , while the right-hand side provides each sector’s final consumption attribution of emissions, E0i . Between these two final attributions, the diagram elaborates: each sector’s intermediate consumption attribution at PL1, PL2, and PL3 , E1j , E2k, and E3l respectively; the release of direct emissions from each sector at PL0 to PL3 D0i , D1j , D2k, and D3l , respectively; and the flows of embodied emissions from sectors at PL1 to PL0, at 3f2 , E2f1 PL2 to PL1, and at PL3 to PL2 E1f0 ji kj , and Elk , respectively. Contributions from higher order layers have been combined to provide a comprehensive depiction of the system. As one would anticipate, the sum of final production attributions is equal to the final 0 = ∑N sum of final consumption attributions, i.e., ∑N s=1Ps i=1Ei . Conceptually we can envisage final demand purchases of final products triggering cascades of intermediate product purchases that flow upstream through the layers of the production system. Within each sector, at each layer, the processing of products entails the release of emissions into the atmosphere. Similarly, we can envisage cumulative flows of embodied emissions running in the opposite direction, from the depths of the production system to the point of consumption of final products (the right-hand side of Figure 1), 10518



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total output of products from sector s (i.e., component (a) from , can then be above). The total consumption attribution, Etotal s calculated by adding to this the final production attribution for sector s, Pfinal s Etotal ¼ EPBLs þ Pfinal s s

ð6Þ

Presenting a sector’s final and intermediate production and consumption attributions as a percentage of its total consumption attribution allows us to identify where in the production system the sector’s overall emissions impact is concentrated. Furthermore, this allows us to show to what extent the standard final consumption and production attributions account for a sector’s overall impact. A graphical means of investigating these relationships is presented in the Application section below. Figure 1. Illustrative map of embodied emissions flows for a simple two sector economy. Diagram flows from left to right and is of a Sankey-type, such that the widths of indicated flows represent their magnitude.25,26 For example, the emissions embodied in the output of final products from sector s1 at PL0 to meet final demand is given by flow (a), E0s1, the final consumption attribution for sector s1. The emissions embodied in the output of intermediate products from sectors s1 and s2 at PL1 purchased 1f0 by sector s1 at PL0 are given by flows (b), E1f0 s1,s1 , and (c), Es2,s1 , respectively. The production processing of these inputs also results in the release of direct emissions from sector s1 at PL0, given by flow (d), D0s1.

picking up additional emissions along the way as intermediate products are processed. While sources of direct emissions are not conceptually understood as flows, they have been represented as such in Figure 1 so as to clearly illustrate how the final production attribution can be mapped through to the final consumption attribution.

3. METHODOLOGY CALCULATING SECTOR TOTAL CONSUMPTION ATTRIBUTIONS The mapping approach provides a sector’s final consumption attribution (i.e., at PL0) and intermediate consumption attributions at earlier layers. To calculate the total consumption attribution of a sector s (i.e., the emissions from all sectors that have become embodied in the total output, xs, of sector s), we need to somehow sum final and intermediate consumption attributions across all layers, taking care to avoid double-counting, resulting in a measure that consists of two components: (a) the emissions from all sectors, excluding sector s, that have become embodied in the total output of sector s and (b) the final production attribution of emissions for sector s. The IO literature on key sectors and economic linkages provides an algebraic method, introduced by Sonis et al.,20 for calculating economic Pure Backward Linkage (PBL), which gives the upstream impact on the economy from the total output of a given sector that is free from intraindustry demands and feedbacks from the rest of the economy. The PBL calculation can be extended to give the Emissions Pure Backward Linkage (EPBL) of a sector s EPBLs ¼ f ðI A Þ1 A:s xs

ð5Þ

where xs is the total output of sector s; A:s is the N 1 column vector of all intermediate purchases made by s; A* is the N N matrix of intermediate purchases, with purchases by and from s set to zero; and, f* is the 1 N intensity vector of sector direct emissions per unit output, with the emissions intensity for sector s set to zero. The EPBL measure gives the emissions released from all sectors, excluding sector s, induced by the production of the

4. APPLICATION TO GLOBAL SECTOR CO2 EMISSIONS Here we apply the methodological developments introduced above to the case of global economic sector CO2 emissions by using an MRIO model constructed from 2004 global economic data, disaggregated across 113 regions and 57 sectors. Balanced economic data and sector CO2 emissions data were taken from Version 7 of the Global Trade Analysis Project (GTAP)27 and converted into an MRIO table through the proportional allocation of bilateral trade data across interindustry requirements and final consumption of imported products.2,13,28 For a discussion of MRIO model uncertainty, see Wiedmann and Lenzen et al.12,29 This particular study has been designed to illustrate the application and interpretation of the methodological developments presented in this paper. 4.1. Mapping Embodied Emissions through the Global Production System. Figure 2 presents a map of embodied CO2

emissions flows for global sectors, constructed in the following manner: • The mapping approach introduced in Section 2 was applied to the MRIO model and sector CO2 emission intensities described above. This provided final and intermediate consumption attributions and direct emissions results for each of the 57 sectors in each of the 113 regions at PL0, PL1, PL2, and PL3, and embodied emissions flow results from regional sectors at PL1 to PL0 and at PL2 to PL1. • Results were then aggregated across the 113 regions, reducing the complexity of the system, to give global sector results. • To further simplify the system, results for the 57 global sectors were aggregated to 31 sectors according to the concordance table provided in the Supporting Information (Table SI1). Aggregating to global sectors at a postcalculation stage ensured that regional variation in sector emissions intensities and full implications of international trade (including feedback effects) were accounted for within the study. We can contrast, for example, the components of the construction sector’s final consumption attribution with those of electricity production and distribution. For construction, we find direct emissions at PL0 account for only 7.7% (0.28 Gt CO2), while the inputs purchased from PL1 have very high embodied emissions associated with them: for example, nonmetallic mineral products accounts for 37.0% (1.35 Gt CO2), metal production and casting 16.3% (0.59 Gt CO2), and fabricated metal products 7.7% (0.28 Gt CO2). For the electricity sector the story is quite different: direct emissions at PL0 account for 88.0% (2.73 Gt CO2), with a further 5.6% (0.17 Gt CO2) from intraindustry 10519



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Figure 2. Map of embodied CO2 emissions flows for global sectors. Diagram flows from left to right and is of a Sankey-type, such that the widths of indicated flows represent their magnitude in Gt CO2. The left-hand side of the map shows the final production attributions of CO2 emissions, totalling 22.8 Gt CO2 (although outside the scope of this study, household CO2 emissions are estimated in the model to be 4.5 Gt CO2, giving total global CO2 emissions of 27.3 Gt CO2 for 2004). Contributions from the top 10 sectors are identified individually, while contributions from the remaining 21 sectors are combined into a single measure. Global electricity production and distribution dominates the final production attribution, accounting for 45% (10.1 Gt CO2). Other major sectors include the following: nonmetallic mineral products at 9% (2.0 Gt CO2); road, rail, and other transport at 8% (1.9 Gt CO2); and metal production and casting at 7% (1.6 Gt CO2). The right-hand side of the map presents the final consumption attributions, again totalling 22.8 Gt CO2, for each sector. Again contributions from the top 10 sectors are identified: with the construction sector accounting for 16% (3.7 Gt CO2); electricity production and distribution 14% (3.1 Gt CO2); government services 12% (2.7 Gt CO2); retail and trade 6% (1.4 Gt CO2); and food and beverage products 6% (1.4 Gt CO2). The central part of the diagram reveals the intermediate consumption attributions for each sector at PL1 and PL2. Again the top 10 sectors at each layer are identified. Direct emissions released by each sector at PL0, PL1, and PL2 are indicated by dark gray ‘flows’ linking back to the final production attribution. The top 20 embodied emissions flows between the top 10 sectors at PL1 and PL0 and between those at PL2 and PL1 have been extracted. All remaining flows have been merged and dropped to the background. Finally, contributions from PL3 and all earlier layers have been combined to provide a comprehensive view of the system.

inputs, and the remaining 6.4% (0.20 Gt CO2) from inputs from other sectors at PL1. We should note here that while construction products are heavily purchased by final demand, this is not to say that it is household consumers that are making these purchases, but rather, in this case, it is primarily final demand from investment in capital. Final construction products can then be seen as enabling further production in later years. 4.2. Normalized Evolution of Production and Consumption Attributions. Using the same MRIO model as the above analysis, here we apply eqs 5 and 6 to calculate each sector’s total consumption attribution as the sum of two components: (a) the emissions from all other sectors that have become embodied in the sector’s total output and (b) the sector’s final production attribution. These results are presented in Figure S1 in the SI and used to normalize results in Figure 3. Figure 3 shows how closely measures of final production and consumption attributions and intermediate

production and consumption attributions at PL1, PL2, and PL3 account for a sector’s total consumption attribution. First, inspecting the data points at the end of each line in Figure 3, we find that for the sectors motor vehicles, construction, recreation and other services, food and beverage products, and government services, over 75% of their total consumption attribution can be accounted for by their final consumption attribution. For electricity production and distribution and nonmetallic mineral products, the same can be said for their final production attribution. However, for all other sectors, neither final consumption nor production attributions account for more than 75% of their total consumption attribution. The most extreme case shown is that of fabricated metal products, where its final consumption attribution accounts for 19%, and its final production attribution only 8%, of its total consumption attribution. This suggests that the analysis of a sector’s emissions impact in terms of only final production and consumption attributions runs the risk of underestimating the scale 10520



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Figure 3. Evolution of sector consumption and production attributions of CO2 emissions from PL3 to PL0 normalized as a percentage of sector total consumption attribution. Solid markers at the end of each line give a sector’s final consumption attribution (read off the y-axis) and final production attribution (read off the x-axis) as a percentage of total consumption attribution. Data points marked with a star indicate intermediate consumption and production attributions at PL1, PL2, and PL3, with PL3 at the start of the line, again as a percentage of total consumption attribution. For example, for the metal production and casting sector: point (a) gives the final production attribution (1.56 Gt CO2) as 47% and final consumption attribution (0.09 Gt CO2) as 3% of the sector’s total consumption attribution (3.30 Gt CO2); point (b) gives intermediate production attribution at PL1 (1.53 Gt CO2) as 46% and intermediate consumption attribution at PL1 (1.56 Gt CO2) as 47% of total consumption attribution; point (c) gives intermediate production attribution at PL2 (1.02 Gt CO2) as 31% and intermediate consumption attribution at PL2 (1.31 Gt CO2) as 40% of total consumption attribution; and point (d) gives intermediate production attribution at PL3 (0.60 Gt CO2) as 18% and intermediate consumption attribution at PL3 (0.80 Gt CO2) as 24% of total consumption attribution. Solid lines connecting data points are shown to aid the visual inspection of discrete measurements and do not represent a continuous series.

of the sector’s overall impact. This is particularly the case for sectors that produce intermediary products from emissions intensive inputs, such as fabricated metal products and chemical and plastics products. Second, we can inspect the evolution of production and consumption attributions for a particular sector to help us understand where in the production system its overall emissions impact is concentrated and whether this takes the form of direct emissions or inducing upstream emissions from other sectors. Figure 3 shows, for example, that a large share of total direct emissions from the nonmetallic mineral products sector are released at PL1, as indicated by the relatively large difference between data points at PL2 and PL1 read off the x-axis these direct emissions alone account for 44% of the total consumption attribution. Furthermore, over two-thirds of the normalized intermediate consumption attribution at PL1 is due to direct emissions released at PL1. We already know (from Figure 2) that the majority of these emissions are in fact passed on to the construction sector at PL0. The sudden drop from intermediate consumption at PL1 to final consumption at PL0, coupled with a small increase in the production attribution, can be explained by the fact that few products from the nonmetallic mineral products sector are purchased by final demand. We can therefore surmise that the overall emissions impact of the nonmetallic mineral products sector is relatively concentrated as direct emissions released from the sector at PL1. We can contrast this with the metal production and casting sector, which looks to have a similar profile but closer inspection shows otherwise. Again there is a sudden drop in the consumption attribution from PL1 to PL0, coupled with negligible increase in the production attribution, indicating that activity at PL0 does

not significantly raise the overall emissions impact. Similar levels of direct emissions are released from PL1 and PL2, and these attributions each account for a third of the normalized consumption attribution at the respective layers. In addition, none of the final or intermediate attributions for this sector account for more than 50% of total consumption attribution. We can therefore say that the overall emissions impact of the metal production and casting sector is diffused across the production system, with the exception of PL0, and that both direct emissions and upstream emissions from other sectors are important. In general, three broad groups of sectors can be identified and are indicated in Figure 3 by different solid markers at the end of each line. First, the ‘primary producer’ sectors, such as nonmetallic mineral products and metal production and casting, which are characterized by a sudden drop in their consumption attribution and minimal increase in their production attribution at PL0, as they tend to supply few products to final demand. Second, the ‘comprehensive producer’ sectors, such as road, rail, and other transport and electricity production and distribution, which are characterized by steadily increasing production and consumption attributions across all layers. These sectors tend to supply products to both final demand and the intermediate demand of a wide range of other sectors. And finally, the ‘consumer facing’ sectors, such as construction and food and beverage products, which are characterized by a low final production attribution and a sudden jump in their consumption attribution at PL0. These sectors tend to only supply products to final demand that require the processing of typically emissions intensive inputs. 10521



5. DISCUSSION The methodological developments introduced in this paper are an extension to SPA that reveal how discrepancies between production and consumption attributions of emissions arise. The central concept is that any final or intermediate demand for products, within a Leontief production system, can be evaluated in terms of two different emissions quantities: (a) direct emissions released from the production of the purchased products and (b) emissions from all sectors that have become embodied in the purchased products. The mapping approach can then be thought of as a toolkit for evaluating flows of emissions embodied in the transactions of intermediate products between economic sectors and ultimately the supply of products to final demand. Large input-output models, in particular MRIO models, represent an enormous number of transactions between economic sectors. While it is possible, for a given production layer, to calculate the emissions embodied in each individual transaction (indeed this was the first step taken in constructing Figure 2), the visual presentation of all these results is impractical. Furthermore, the production layer expansion form of a Leontief model provides an infinite series of production layers. Hence judgment, on behalf of the analyst, in terms of which flows to illustrate individually and which to aggregate or omit, is required in constructing a map that links final production and consumption attributions of emissions. Such judgment would need to reflect the issue under investigation. An analyst may, for example, be interested in understanding the linkages between final production and consumption in three world regions, China, the US, and the rest of the world: the resulting map may then focus on the flows of embodied emissions exchanged between these world regions without need to elucidate the role played by individual economic sectors. Plots showing the normalized evolution of production and consumption attributions, such as Figure 3, could be used as an effective tool for comparing the emissions impact of sectors across different countries or, using time-series IO data, to show how a sector’s emissions impact profile has changed over a certain time period. It is anticipated that future application of the mapping approach and normalized evolution plots could provide supportive evidence for developing regional policies that target final and intermediate consumption needs that currently trigger large cascades of emissions though global supply chains. Finally, the approach could be used by firms to understand the importance of estimating emissions from their supply chains (i.e., upstream Scope 3 emissions) and benchmark against average sector performance, while future developments (e.g., adaption for use with hybrid LCA models and extension to evaluate downstream emissions impacts) could help interpretation of sector and corporate carbon-footprints: an aid that would not only benefit corporate social responsibility initiatives but also assist core business strategy as firms seek to ameliorate risks from current and future carbon price and regulation. ’ ASSOCIATED CONTENT

bS

Supporting Information. Application concordance table (Table S1) and chart showing sector total consumption attributions (Figure S1). This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected].

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