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Structural factors affecting land-transport CO2 emissions: A European comparison
Transportation Research Part D 12 (2007) 239–253 www.elsevier.com/locate/trd
Structural factors affecting land-transport CO2 emissions: A European comparison ´ ngel Taranco´n Mora´n Miguel A a
a,*
, Pablo del Rı´o Gonza´lez
b
Faculty of Social Sciences and Law, University of Castilla - La Mancha, Ronda de Toledo, s/n 13071 Ciudad Real, Spain b Department of Spanish and International Economics, Econometrics and History and Economic Institutions, Faculty of Social Sciences and Law, University of Castilla-La Mancha, Spain
Abstract The increase of CO2 emissions generated by land-transport is a major policy concern of the European Union but the upward trend in transport use makes it difficult for member states to comply with Kyoto Protocol targets. This paper develops an input–output methodology to analyse the structure of CO2 emissions from land-transport and applies this to several European Union countries. It shows how production linkages between sectors and the structure of final demand affect land-transport emissions in these countries. The paper confirms the relevance of the emissions-intensity factor to explain differences in the emissions of the transport sector across countries, but also shows the importance of technology-production linkages between sectors in an economic system that has usually been neglected in the past. 2007 Elsevier Ltd. All rights reserved. Keywords: Land transport; CO2 emissions; Input–output; Structure
1. Introduction Transport is the second largest source of greenhouse gas (GHG) emissions in the EU; accounting for 26% of CO2 emissions in 2003 in the European Union (EU) and this share is growing, mainly due to continuous increases in road transport volume (both passenger and freight). This paper focuses on emissions from landtransport (road and rail). Within the transport sector, road transport is the largest source of emissions in the EU – 84% in 2003 – followed by air (13%), inland navigation (1.7%) and rail (0.8%) (European Commission, 2005). It is also the mode experiencing the second fastest growth rate (25% accumulated increase between 1990 and 2003), behind air transport (55%). The increase of CO2 emissions from transport is a major policy concern in the EU. In this context, an analysis of the factors affecting transport emissions is conducted to facilitate the development of effective emissions mitigation policies. A methodology is developed to analyse the structure of CO2 emissions in the land-transport sector across several EU member states (France, Germany, Italy, Spain and the Netherlands). Traditionally, *
Corresponding author. ´ . Taranco´n Mora´n). E-mail address: [email protected] (M.A
1361-9209/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.trd.2007.02.003
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the analysis of factors affecting emissions in specific sectors has taken into account the scale of the sector’s activities and its specific emissions (i.e., tons of CO2 per unit of output), that depend on the technology used. We go further by considering the input–output (I–O) relationships within an economy. More specifically, we analyse how production linkages between sectors and their final demand affect land-transport emissions. 2. Aim, methodology and main assumptions Traditionally, emissions from the transport sector have been decomposed into a number of factors (International Energy Agency, 2000; Halsnaes et al., 1999; Schmutzler, 2005; Holden and Høyer, 2005). This is useful when seeking methods to implement mitigation methods and is used here. Assuming the existence of q different transport modes, transport CO2 emissions (etransport) result from aggregating the emissions of each mode (em): etransport ¼
q X
em
ð1Þ
m¼1
and these can be decomposed into: em ¼ ntripm
distm fuelm em ntripm distm fuelm
ð2Þ
where ntripm is the number of trips by individuals or freight carriers (mode m), distm is the distance by m, and fuelm is the consumption of fuel by m. The second element on the right side of Eq. (2) is the average distance of each trip, the third factor is the average fuel intensity (fuel used per unit of distance), which is the inverse of fuel efficiency, and the fourth represents CO2 emissions per unit of fuel. However, other factors are often neglected, particularly, the input–output relationships within an economy. The economic system can be viewed as a complex web of linkages between sectors; a systemic approach is highly relevant for the analysis of CO2 land-transport emissions because each economic sector has a demand for a specific transport mode m and, thus, it has an influence on its output and CO2 emissions. The relationship between the demand for transport mode m and its emissions can thus be reformulated as: em ¼ xm
ntripm distm fuelm em fuelm em ¼ xm xm ntripm distm fuelm xm fuelm
ð3Þ
where xm is the output value of transport mode m. The increase in the output of mode m is related to an increase in the final demand of all economic sectors that, in turn, increase the demand for transport services. This linkage between final sectoral demand and transport output can be either direct or indirect. Direct demand for land-transport rises when the demand for the products of a certain sector i increase because products have to be distributed to final consumers. In addition, an increased demand for the products of a specific sector i also increases the demand for transport services indirectly and, thus, land-transport emissions. More inputs (labour, capital and materials) will be needed by sector i to cover this higher demand, increasing the demand for products in other ‘‘intermediate’’ sectors and, thus, their need for transport services. These factors, related to the techno-economic linkages between sectors, should be considered when analysing the structure of CO2 emissions in a specific transport mode. The I–O method, based on the use of input–output tables (IOT), allows us to analyse in a disaggregated manner the techno-economic relationships between production sectors in an economic system. An IOT is an exhaustive information system, since it provides detailed information on the purchases and sales of goods and services between socioeconomic agents. Within an I–O framework, a transport mode (m) can be considered as an economic sector that produces services to meet the demand of other sectors as well as its own final demand. Therefore, its output can be calculated as: xm ¼
n X j¼1
xmj þ y m
ð4Þ
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where xmj represents purchases of the services of transport mode m made by the jth sector and ym represents the services of transport mode m which are bought by final demand. We can therefore define the technical coefficients as the ratio between each element of the intermediate transaction matrix and the output of the corresponding activity branch: xij xj
aij ¼
with i ¼ ð1; 2; . . . ; nÞ and j ¼ ð1; 2; . . . ; nÞ
ð5Þ
Similarly, the final demand coefficients can be calculated as: hi ¼
yi g
with i ¼ ð1; 2; . . . ; nÞ
ð6Þ
P where g ¼ np¼1 y p is the value of the final demand. If we integrate Eqs. (5) and (6) into Eq. (4), then the output from transport mode m can be rewritten as: xm ¼
n X
bmq hq g
ð7Þ
q¼1
where bmq are the m row elements of B = (I A)1, which is the Leontief inverse matrix identifying the direct and indirect output of mode m that is needed to meet a unit of final demand from sector q. A is the matrix of technical coefficients (aij). Expression 3 can be written as: ! n X fuelm em em ¼ bmq hq g ð8Þ xm fuelm q¼1 Finally, we can integrate the last two factors in the right-hand part of Eq. (8) into an ‘‘emissions-intensity coefficient’’ that represents emissions per unit of product: cm ¼
fuelm em em ¼ xm fuelm xm
Emissions from transport m in country s would then be: ! n X s s ss s em ¼ bmq hq g csm
ð9Þ
ð10Þ
q¼1
In summary, CO2 emissions from transport can be decomposed into the ‘‘modal’’, the ‘‘intensive’’ and the ‘‘extensity’’ indicators. The modal factor shows how changing from one transport mode to another may reduce emissions. The share of each mode in Eq. (1) representing this has both technological elements (i.e., each transport mode is based on a specific technology with different CO2 emissions intensities) and behavioural elements (the choice between transport modes depends on the decisions of users). The intensive indicator accounts for specific CO2 emissions, i.e., emissions per unit of output (the csm factor in Eq. (10)). It is a technological indicator because technological progress could reduce specific emissions.1 The extensity indicator is a measure of the dimensions of the corresponding activity. It is usually treated as exogenous but here it is treated as an endogenous factor that is related to the direct and indirect demand for transport services generated by other sectors (the factors in parenthesis in Eq. (10)). Following Eq. (10), this indicator can be decomposed into: 1
The technological indicator is related to energy efficiency (i.e., the energy used per km) and the emissions-intensity of the fuel used (i.e., the amount of CO2 emitted per unit of energy consumed).
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Structural Factors
Extensive Indicator
Intensive indicator
Intensity Emissions Factor
Direct / Indirect Effects n
∑b
s mq
cmcs ms
q =1
Technology-Production Factor
aijs
Final Demand Factor
hqs
Emissions
Final Demand Volume
⎛ n s ⎞ ⋅ hqs ⋅ g s ⎟⎟ ⋅ c ms emss = ⎜⎜ ∑ bmq ⎝ q =1 ⎠
gs
Variables
Fig. 1. M-transport mode emissions within a input–output framework.
• The Scale factor, which represents the final demand (gs) in Eq. (1). • The technology-production factor. The level and evolution of land-transport CO2 emissions is related to the input–output relationships in an economy. The sales of one sector to another might lead to greater emissions from transport than its sales to other sectors. This is because some sectoral linkages are more transport-intensive than others. The column of technical coefficients aij represents the technology-mix of inputs that is needed to produce an output unit in sector j. The decomposition of the columns of coefficients in each sector will thus affect the demand for the services of transport mode m. This effect is included in the bsmq coefficients in Eq. (10). • The Final demand factor. A 1% increase in the demand for the products of one sector may cause a larger increase in transport’s emissions than an identical increase in the demand for the products of other sectors, simply because some sectors are more transport-intensive than others. This factor is represented by the hsq coefficients. Focusing on Eq. (10), therefore, the emissions of transport m in country s is an endogenous variable and a function of the level of economic activity (measured by GDP or final demand). The impact of final demand, an exogenous variable, will depend on the parameters of the equation, i.e., on the emissions-intensity coefficients ðcsm Þ, the Leontieff coefficients ðbsmq Þ and the final demand coefficients ðhsq Þ. Therefore, these coefficients represent the structural factors influencing emissions of transport mode m 2 (Fig. 1). The extensity indicator has a scale and a structural component. Tackling the scale factor to mitigate emissions is difficult because this would limit the growth of GDP leading to the view that attention should be paid to the structural components of the extensive indicator ðhsq and aij). The effectiveness of mitigation policies depends to a large extent on these structural components, i.e., on the techno-productive relations between sectors and on the sectoral composition of final demand. 2 In econometric or mathematical frameworks, the term coefficient involves structural relationships that are more or less stable over time. In other words, the structural coefficients define the rules of the economic flows in the functioning of the economic system and in its productive activity (Taranco´n and del Rı´o, 2007).
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3. The model The most efficient country is assumed to be the one that minimizes aggregate emissions and the least efficient country is the one which could reduce its transport emissions level if it adopted appropriately the production structure of other countries.3 Therefore, the production structure of the land-transport in this latter country s would be very emissions-efficient if the emissions from this sector increased as a result of the adoption of elements of the land-transport of other countries. This comparative approach is based on the idea that there is no perfect structure concerning CO2 emissions from land-transport that could be implemented in all countries. Emissions levels depend on several countryspecific factors4 that make it difficult to identify an ideal, efficient structure, i.e., a benchmark. Thus, the idea of a reference measure of absolute efficiency is rejected. Instead, a relative efficiency approach is used, i.e., the methodology allows the development of inferences concerning a set of structural factors that can explain the differences in efficiency levels between countries. 3.1. Total structural efficiency index (TSEI) The TSEI identifies what would happen when country s adopts all the productive features of country r. Only the final demand of country s (gs) remains constant, whereas the intensive and the extensive structural factors (coefficients c, b, and h) change. Therefore, if country s adopted the structure of country r, its emissions would be: ! n X r r rs s em ¼ bmq hq g crm ð11Þ q¼1 ers ess i i ess i
Hence, 100 will be positive if the productive structure of country s is more efficient than that of country r. To calculate the overall efficiency of the productive structure of country s regarding land-transport, the average of this expression is used when the coefficients of each country are applied. That is: Pd ersm essm 100 r¼1 ess s m em ¼ ð12Þ d where d is the number of countries. If the productive structure of country s is efficient with respect to sector i and the rest of countries as a whole, then es i will be positive. The structure of county s will be efficient if the substitution of the structural coefficients of s for those of other countries leads to an increase in emissions. 3.2. Emissions-intensity factor efficiency index (EIFEI) This technological factor is related to the ci coefficients defined in Eq. (9). These represent the product-specific emissions of the land-transport sector. Eq. (10) can be rewritten to compare the countries’ level of efficiency of CO2 emissions which is caused by this factor: ! n X s s rðcÞs s em ¼ bmq hq g crm ð13Þ q¼1
Therefore, it is assumed that country s adopts the emission intensity factor, c, of country r in transport mode m. Recalculating Eq. (12) we obtain the efficiency index eðcÞs m .
3
Martellato and Tarancon (2005) offer an application of this methodology in an international trade context. These include size of the productive system, physical and geographical aspects, demographic factors, consumption patterns, people’s lifestyles and cultural and behavioural aspects of socioeconomic agents. 4
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3.3. Technology-productive factor efficiency index (TPFEI) The structure of the network of productive relationships in an economic system influences the level of emissions from various economic sectors including land-transport. The technical coefficients (aij) capture these relationships. A column of coefficients represents the technology of the corresponding sector because it shows the mix of productive inputs needed per unit of output. The effect on the output of a sector (and, in turn, on its emissions) caused by the increase in the final demand for a product will depend on the technologies of all the sectors, and on the network of technological linkages in the economic system. Those effects are cumulative and encompass direct as well as indirect effects. Cumulative effects are picked up by the biq coefficients. Therefore, the relative efficiency level associated to the technology of the productive system can be calculated by replacing Eq. (10) with: ! n X r s rðaÞs s em ¼ bmq hq g csm ð14Þ q¼1
We substitute arij for asij to obtain the brmq coefficients in Eq. (14). Recalculating Eq. (12) yields the efficiency index eðaÞs m . 3.4. Final demand factor efficiency index (FDFEI) Since the share of each sector and the share of land transport-intensive sectors in output affects the level of emissions, the structure of final demand should be taken into account in the analysis of the structure of landtransport emissions in different countries. Within our input–output framework this structure is provided by the hi coefficients. The emissions related to the final demand structure of countries s and r are compared and expression 11 can be transformed into: ! n X s r rðhÞs s em ¼ bmq hq g csm ð15Þ q¼1 ðhÞs Eq. (12) is recalculated and the efficiency index em is obtained.
3.5. Interaction effects The previous indicators have been treated as if they were isolated, but the interactions between them should be considered. Starting from Eqs. (11, 13, 14), and (15), the following structural decomposition can be established: rðcÞs rðaÞs rðhÞs ers þ em þ em þ ers m ¼ em m
ð16Þ
ers m
where is the interaction between the aforementioned factors (Dietzenbacher and Los, 1998) and can be treated as an error term. If an average of this effect is calculated for all countries, we arrive at an Index of Interaction Effects (IIE). Pd rs e s em ¼ r¼1 m ð17Þ d The main elements of the overall model are summarised in Table 1. 4. Empirical analysis The methodology allows identification of European countries that are relatively more efficient in terms of their land-transport CO2 emissions and the sources of efficiency. Efficiency can be related either to the specific features of land-transport (i.e., to the intensity indicator as measured by EIFEI) or to the structural features of the productive system (i.e., to the structural factors of the extensive indicator as measured by TPFEI and
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Table 1 Summary of relative efficiency indexes to analyse land-transport emissions Factor abbreviation
Factor name
Brief description
es m
Total structural efficiency index (TSEI) Emissions-intensity factor efficiency index (EIFEI) Technology-productive factor efficiency index (TPFEI)
It shows the efficiency of countries regarding the structure of emissions in the land-transport sector. It shows the impact of product-specific emissions of the land-transport sector (emissions per unit of product) in different countries. It accounts for the level and evolution of CO2 emissions related to the input–output relationships in an economy. It shows the impact of the technologies in other sectors on the emissions of the transport sector. Those other sectors will (directly or indirectly) require more or less transport services, depending on their own technologies. It shows the impact on the demand and emissions of the land transport sector as a result of a change in the relative share of sectors in the country’s total final demand. This indicator includes the effect of the interactions of the previous factors.
ðcÞs
em
ðaÞs
em
ðhÞs
em
Final demand factor efficiency index (FDFEI)
es m
Index of interaction effects (IIE)
Table 2 CO2 Emissions by sector (EU-25, MtCO2) Year
1990 1995 2000 2001 2002 2003
Total
3775 3655 3692 3749 3750 3853
Power and heat generation
1487 1417 1426 1440 1472 1514
Industry
723 640 598 598 593 597
Transport
793 857 971 979 986 1001
Of which Road
Air
Inland navigat.
Rail
675 726 811 825 835 843
85 100 134 130 129 132
20 21 16 15 15 17
12 10 9 9 8 8
H*
S and O*
500 486 464 492 454 479
273 255 233 241 246 262
Source: European Commission (2005). *H – households; S and O – services and other.
FDFEI). The methodology is applied to five EU countries – France, Germany, Italy, Spain and the Netherlands.5 Land-transport is the largest contributor to GHG emissions in the EU (96.8% of the total in 2003) and with CO2 emissions increasing significantly; by 26% between 1990 and 2003 (European Commission 2005). Furthermore, the transport sector has the second largest share in overall emissions (behind power and heat generation) and the fastest growth rate (Table 2). Road transport is the dominant land-transport mode and by far the largest CO2 emissions source within the transport sector, accounting for 85% of these emissions in the EU-25 in 2003. It also has the second highest growth rate (after air transport). 5. Data The study is based on the use of the symmetrical I–O tables for the five selected European in 2000. The tables for each country were built from the Make-and-Use tables available in EUROSTAT to avoid the inconsistencies that may arise from using different sources. This tables are based on the product technology hypothesis that, as suggested by Ten Raa and Rueda Cantuche (2003), offers the best method for this task.6 5
Two criteria were considered in selecting countries, their size and data availability. The countries chosen were the largest in the EU and the ones with available data. The reason the UK was not included is because of problems with the data. 6 Two adjustments were made. Some sectors had to be aggregated to avoid problems related to the possible existence of negative coefficients and a RAS adjustment was applied to remove negative residual coefficients.
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Unfortunately, EUROSTAT does not provide a disaggregation of land transport into road and rail transport but given the dominance of road transport, the land-transport coefficients represent mainly road transport technology. CO2 emissions per transport mode were obtained from the GHG emissions inventories of the United Nations Framework Convention on Climate Change (UNFCCC). These estimates were complemented with data from EUROSTAT and coherence checks undertaken. 6. Results 6.1. Relative efficiency indexes by transport mode and country The TSEI shows that the Netherlands is the most efficient country regarding the structure of CO2 emissions from land-transport, whereas Spain is the least efficient (Fig. 2). The intensive (EIFEI) and the technology-production factors (TPFEI) have the greatest impact on the TSEI implying that differences in the structure of land-transport emissions between countries can be explained by product-specific emissions of the sector in countries and by the technological and productive relationships between economic sectors. A country-by-country analysis shows the negative impact of the intensity factor (EIFEI) in France and Germany that is counterbalanced by the TPFEI. In contrast, the positive influence of EIFEI in Spain and Italy is partially or totally compensated by TPFEI. Both factors have a positive impact on TSEI in the Netherlands. Finally, the impact of interaction effects on the relative efficiency level is positive in all countries, and particularly in Spain and Germany, suggesting that the assumption of separation between the structural factors is a strong one. 6.2. Emissions-intensity factor efficiency (EIFEI) Fig. 2 shows that Spain and Germany have the most efficient and the least efficient emissions-intensities respectively. This could lead to the conclusion that land-transport in Spain is relatively less carbon intensive either because its land-transport is more energy-efficient or the fuels used are less carbon intensive. The later is unlikely because land-transport technologies are highly homogenous across countries7 with fuel mostly based on oil products (see Table 3). The consumption of oil products by land-transport, which is a proxy of the energy requirements per output value, confirms that the sector is comparatively less energy intensive in monetary terms in Spain than in the other countries.8 Different shares of road and railways within land-transport could also partly explain the greater efficiency in Spain given that railways are the least carbon-intensive transport mode but data on freight and passengers transport offers no support for this. Spain has the second lowest rail/road ratio in freight transport, and the lowest in passenger transport (Table 4). Finally, differences in the intensity indicator can also be related to differences in the horsepower and speed of vehicles (Herna´ndez, 2006). Heavier, more powerful vehicles emit more CO2 per kilometre than smaller ones.9 Furthermore, the same automobile emits different quantities of CO2 at different speeds. Data on these aspects is not available.10 7
This is so because car manufacturing is highly concentrated in a few European, Japanese and Korean companies in and manufacturers have voluntarily agreed to reduce emissions levels (ACEA, JAMA and KAMA voluntary agreements). 8 Fuel oil requirements in 2000 per output value unit in land transport were €0.16 in Spain, followed by the Netherlands and Germany (€0.19), France (€0.28) and Italy (€0.36). This interpretation assumes similar relative prices across countries. 9 As reported by International Energy International Energy Agency (2000) the UNFCCC Annex 1 Expert Group (Fulton, 2000) estimated that, through 2008, as much as two-thirds of the CO2-per-kilometre reduction achievable from technology uptake could be lost to increases in vehicle size, weight, and horsepower. For example, a 25% reduction in CO2 per kilometre achieved through the use of new technologies could become a 8% to 10% reduction if consumers shift to bigger, heavier, more powerful vehicles. Such a scenario, would be consistent with consumer shifts that have occurred in recent years, especially in the US, where minivans and sport-utility vehicles have become increasingly popular. 10 The lowest energy consumption takes place at speeds of around 90–100 km/hour (Instituto para la Diversificacio´n y Ahorro de la Energı´a, 2004). Speeds above or below lead to higher emissions.
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60.00 50.00 40.00
20.00 10.00
Sp ai n
nd s et he rla
er m an y
N
-20.00
G
-10.00
Ita ly
0.00
Fr an ce
Index Values
30.00
-30.00 -40.00 TSEI
EIFEI
TPFEI Countries
FDFEI
SEI
Fig. 2. Summary of results per country and factor.
Table 3 Emissions-intensity coefficients in land transport Year: 2000
Variable
France
Germany
Italy
Netherlands
Spain
a b c = a/b
Emissions (000tCO2) Output (Unit: M€) Intensity coefficient (€/000tCO2)
129710.24 26725.00 4.85
177039.36 25928.00 6.83
111514.23 24109.96 4.63
31771.78 7448.00 4.27
77501.63 24120.80 3.21
Table 4 Coverage of rail over road transport Year: 2000
Road vs rail transport
Freight transport A Goods transport by road, million tonne-km B Goods transport by rail, million tonne-km C = b/a (Mton-Km rail/Mton-Km road) · 100 Passengers transport D Road transport of passengers, million passenger-km E Rail transport of passengers, million passenger-km F = e/b (Mill. pass. rail/mill. pass. road) · 100
France
Germany
Italy
Netherlands
Spain
266,500 554,48 20.81
347,200 76,815 22.12
184,756 22,817 12.35
45,700 3819 8.36
133,078 12,180 9.15
744,900 69,571 9.34
792,400 75,404 9.52
759,200 47,133 6.21
164,100 15,400 9.38
382,210 18,571 4.86
Source: based on Eurostat database.
6.3. Technology-productive factor efficiency index (TPFEI) TPFEI is related to the direct and indirect sales of land-transport to other sectors, i.e. it is related to the technologies implemented in other sectors that have an impact on the emissions in the transport sector. This factor is a source of relative efficiency in Germany, the Netherlands and France but relative inefficiency in Italy and Spain. The impact of a change in a technical coefficient on the emissions of the land-transport sector can be expressed as the elasticity of ei with respect to a change in akl: Dem em eem akl ¼ Da kl akl
ð18Þ
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Table 5 Main sectors related to TPFEI Pn s Pn e k¼1 em akl n l¼1 ¼ 100 n 1 Agriculture. . . 5 Manuf. food 13 Chemical products 15 Other non-metallic mineral prod. 17 Manuf. fabricated metal prod. 18 Manuf. machinery and equip. n.e.c. 20 Manuf. motor vehicles 22 Furniture, n.e.c., recycling 25 Construction 26 Trade 28 Land transport 31 Auxiliary transport activ. 32 Communications 36 Business 37 Public administration 38 Education 40 Other social and cultural services Normalized average on all sectors
France (3)
Germany (1)
Italy (4)
Netherlands (2)
Spain (5)
20.27 32.29 35.77 16.22 18.76 17.26 33.24 12.55 50.36 1465.95 1013.93 7.76 22.09 376.71 332.21 139.77 152.37
15.78 38.25 37.24 10.40 59.26 36.22 53.17 8.89 27.53 128.49 1569.60 95.91 20.22 127.87 407.79 948.53 141.47
28.50 262.22 193.49 110.24 122.58 219.00 123.77 94.74 167.21 607.07 349.31 278.52 91.43 197.55 52.21 33.13 103.66
116.36 95.95 107.34 14.82 31.48 31.70 10.55 176.48 302.30 226.51 672.69 571.92 170.77 73.88 258.15 39.89 59.22
158.38 97.65 226.89 175.51 167.56 80.54 198.50 81.76 361.91 422.00 99.16 158.71 115.13 74.22 100.96 34.69 47.04
100.00
100.00
100.00
100.00
100.00
Average elasticities normalized-by-sector. Notes: The countries TPEI rank positions are in brackets. The sectors whose normalized elasticities are lower than 150 in all countries have been omitted. Normalized elasticities higher than 200 are in bold. Superscript ‘s’ indicates country s.
If we assume a variation of 1% in akl, then Eq. (18) becomes: eem akl ¼
akl xl bmk ð1 0:01 akl blk Þxm
ð19Þ
This elasticity allows identification of those technical coefficients (akl) whose change leads to the greatest variation in the emissions from the land-transport. Hence, it shows which technology elements would significantly reduce land-transport emissions as a result of a small change in the current economic and technical structure. Table 5 shows that, in most countries, the trade and land-transport sectors are the greatest contributors to the increase in land-transport emissions. This is the result of their demand for land-transport services. This is expected because trade is a transport-intensive activity. It may also be related to the fact that high emitting countries mainly move goods on trucks. However, in the French case this interpretation is problematic because the share of road in overall freight transport is below Spain and Italy, and its share of railways is the largest among the five countries. Trade and land-transport are followed by a variety of different sectors in other countries: food industry (Italy and Spain), chemical industry (Spain), construction (Spain, Netherlands), auxiliary transport activities (Italy and the Netherlands), business (France), public administration (France, Germany and Netherlands), and education (Germany).11 The results also show that several key sectors in the Spanish economy are highly land-transport intensive – agriculture, food industry, chemical products, manufacturing of metal products and other non-metallic mineral products, manufacturing of motor vehicles, construction and, to a lesser extent, communications.
11
Sectors intensive in land-transport are referred to as ‘‘key sectors’’.
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Table 6 Direct and total requirements of land-transport services per unit of output Year: 2000
France
1 Agriculture. . . 5 Manuf. food 13 Chemical products 15 Other non-metallic mineral prod. 17 Manuf. fabricated metal prod. 18 Manuf. machinery and equip. n.e.c. 20 Manuf. motor vehicles 22 Furniture, n.e.c., recycling 25 Construction 26 Trade 28 Land transport 31 Auxiliary transport activ. 32 Communications 36 Business 37 Public administration 38 Education 40 Other social and cultural services a
Germany
Italy
Netherlands
Spain
Direct
Totala
Direct
Totala
Direct
Totala
Direct
Totala
Direct
Totala
0.0000 0.0001 0.0002 0.0001 0.0003 0.0001 0.0001 0.0003 0.0001 0.1217 0.1317 0.0001 0.0007 0.0018 0.0078 0.0059 0.0043
0.0011 0.0007 0.0009 0.0020 0.0011 0.0007 0.0008 0.0011 0.0012 0.1830 1.1542 0.0007 0.0017 0.0029 0.0100 0.0074 0.0075
0.0002 0.0001 0.0001 0.0001 0.0007 0.0001 0.0001 0.0001 0.0000 0.0021 0.1200 0.0014 0.0003 0.0002 0.0048 0.0198 0.0014
0.0005 0.0004 0.0004 0.0004 0.0012 0.0004 0.0004 0.0003 0.0003 0.0047 1.1385 0.0030 0.0006 0.0004 0.0058 0.0229 0.0020
0.0013 0.0070 0.0065 0.0087 0.0047 0.0060 0.0051 0.0045 0.0028 0.0448 0.0669 0.0436 0.0118 0.0019 0.0014 0.0020 0.0050
0.0030 0.0111 0.0116 0.0157 0.0115 0.0126 0.0100 0.0100 0.0089 0.0685 1.0997 0.0538 0.0155 0.0040 0.0043 0.0036 0.0085
0.0018 0.0002 0.0004 0.0005 0.0006 0.0004 0.0001 0.0079 0.0022 0.0141 0.0661 0.0362 0.0054 0.0000 0.0040 0.0013 0.0007
0.0026 0.0011 0.0014 0.0011 0.0015 0.0008 0.0003 0.0093 0.0037 0.0251 1.0744 0.0434 0.0072 0.0004 0.0051 0.0018 0.0010
0.0122 0.0195 0.0159 0.0300 0.0196 0.0076 0.0059 0.0125 0.0049 0.1219 0.0023 0.0434 0.0208 0.0005 0.0105 0.0045 0.0037
0.0229 0.0318 0.0267 0.0448 0.0343 0.0140 0.0158 0.0247 0.0205 0.1551 1.0280 0.0546 0.0316 0.0034 0.0162 0.0074 0.0091
The ‘‘total’’ includes the aggregation of direct and indirect requirements.
Table 7 Main sectors related to the land transport final demand factor efficiency index (FDFEI) Pn s e France (4) Germany (1) Italy (2) k¼1 em hk ¼ 100 n 5 Manuf. food 20 Manuf. motor vehicles 25 Construction 26 Trade 28 Land transport 31 Auxiliary transport activ. 37 Public administration 38 Education Normalized average
Netherlands (3)
Spain (5)
15.22 14.66 24.47 612.46 2784.22 0.81 207.43 77.44
10.79 14.26 7.97 24.77 3408.77 8.25 133.79 330.56
212.36 120.12 146.61 392.37 1650.31 105.08 61.11 38.78
32.43 3.93 84.40 54.63 3119.96 165.06 110.99 17.02
364.67 160.98 302.38 254.98 1636.63 59.75 116.87 38.61
100.00
100.00
100.00
100.00
100.00
Normalized-by-sector elasticities. Note: The rankings of countries are shown in brackets. The sectors whose normalized elasticities are lower than 0.150 in all countries have been omitted.
Table 6 shows the direct and indirect land-transport requirements per unit of output in specific, transport intensive ‘‘key’’ sectors.12 In some sectors, the direct requirements for land-transportation services represent a large share of the land-transport requirements of a key sector; e.g. trade. In such cases mitigation policies could try to reduce the need for transport services through improved logistics or a shift to less polluting modes. In contrast, a different type of policy would be needed in those sectors with low direct requirements for land-transport, as in construction. Here policies may need to be focused on those sectors which provide intermediate inputs, although this may be difficult because the CO2 intensity of intersectoral linkages would need to be regulated and some products are inherently more CO2 emissions intensive than others with the possibility of replacing these with less-polluting products being limited.
12
The technical coefficients (amj) represent the direct land-transport requirements of the key sector j, whereas bmj represent the indirect requirements.
250
Table 8 Summary of findings and possible policy responses Countries
Intensive indicator
Extensive indicator
Policy implications
is a source of relative. . .
. . .is a source of relative. . .
Key sectors leading to main differences in this factor:
Weight of directlandtransport requirements:
. . .is a source of relative. . .
Key sectors leading to main differences in this factor:
Final demand depends on. . .
France
Inefficiency
Efficiency
Trade
High
Inefficiency
Trade
Domestic demand
Innovations in land-transport technologies to reduce the dependence on energy inputs: less carbon-intensive fuels; Innovation in fuel performance of fuels and in transport technologies; Better logistics. Logistics improvements for trade. Increasing the share of freight transport by rail.
Germany
Inefficiency
Efficiency
Education
High
Efficiency
Education
Domestic demand
Innovations in land-transport technologies to reduce the dependence on energy inputs: less carbon-intensive fuels; innovation in fuel performance of fuels and in transport technologies; better logistics. Increasing the share of passengers transport by rail and collective road vehicles.
Italy
Efficiency
Inefficiency
Chemical prod.
Low
Efficiency
Manuf. motor vehicles
Domestic demand and exports
Logistics improvements for trade.
Manuf. machinery and equip. n.e.c.
Low
Auxiliary transport activities
High
Technology-productive factor
Efficiency
Efficiency
Spain
Efficiency
Inefficiency
Agriculture
Low
Manuf. Food
Low
Chemica prod.
Low
Other nonmetallic mineral prod. Manuf. fabricated metal prod. Manuf. motor vehicles Construction
Low
Low
Low
Increasing the share of freight transport by rail.
Efficiency
Auxiliary transport activities
Exports
Relatively good ratings in all indicators
Inefficiency
Manuf. Food
Domestic demand Exports
Logistic improvements in sectors selling to manufacturing industries and construction. Increasing the share of freight transport by rail.
Manuf. motor vehicles Construction
Domestic demand
M.A´. Taranco´n Mora´n, P. del Rı´o Gonza´lez / Transportation Research Part D 12 (2007) 239–253
The Netherlands
Final demand structural factor
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The policy implications of these results are thus not simple. Although eem akl identifies those changes in the input–output relationships which lead to a great change in emissions, such relationships are often rigid and difficult to manipulate, and especially the indirect ones. In addition, changing them to reduce emissions could have negative side-effects on the economy such as a loss of overall economic efficiency. 6.4. Demand factor efficiency (FDFEI) FDFEI shows the impact of the structure of final demand on land-transport emissions. The elasticity of em with respect to hkl shows how a change in the share of a sector in the country’s final demand (hkl) affects the demand for land-transport and, thus, the emissions from this sector (em): eem hk ¼
Dem em Dhk hk
ð20Þ
Assuming a variation of 1% in hkl, then the elasticity is: eem hk ¼
0:01bmk hk g xm
ð21Þ
The results of such calculations are shown in Table 7, where elasticities have been normalized by sector allowing identification for each country of those sectors where changes in their final demand coefficients leads to a great change in CO2 emissions from land-transport. The key sector where a change in final demand has the greatest impact on the output of land-transport and its emissions is the land-transport sector itself. The results show the relevance of the trade sector in France, Italy and Spain and confirm its intensive use of land-transport. In contrast, the small impact of trade in Germany and the Netherlands means, either their trade sector is not as land transport-intensive as in other countries or that it uses more lower carbon-intensive modes such as railways. As seen in Table 4, the share of rail is relatively high in both Germany and the Netherlands. A change in the share of other sectors can also lead to significant changes in the emissions in particular countries. Such sectors include the food industry (Italy and Spain), construction (Spain), auxiliary transport activities (the Netherlands), public administration (France) and education (Germany). Spain and France have a negative final demand indicator and their key sectors are industry, construction and trade, that are highly land-transport intensive. The overall situations regarding the countries examined are summarized in Table 8. This also provides some indications of the types of policy responses that may be appropriate to help mitigate the output of CO2. The focus is on the role that increasing fuel efficiency may play as well as modal shifts. The suggestions are tempered by the aggregation within land-transport of road and rail modes, and it is not possible to build in any spatial considerations – e.g. in some cases it may simply not be physically possible to switch modes. Additionally, efforts to change the structural nature of production to reduce energy consumption may be difficult because of the rigidity of production technology. 7. Conclusions This paper developed a methodology to analyse the structure of CO2 emissions in the land-transport sector and applied it to several EU countries. The analysis shows how emissions intensities of the production linkages between sectors and the structure of the final demand of a sector affect land-transport emissions in selected countries. It confirms the relevance of the emissions-intensity factor in order to explain differences in the emissions of the land-transport sector across countries, but also shows the importance of a usually neglected factor: the technology-production linkages between sectors. Acknowledgements The authors are grateful to the editor and two anonymous referees for their comments.
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Appendix A. Sectors in the European input–output tables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Agriculture, hunting and forestry Fishing, operation of fish hatcheries and fish farms; service activities incidental to fishing Mining of non metal and extraction crude Mining of metal ores, other mining and quarrying Food, beverages and tobacco manufacturing Textile manufacturing Manufacture of wearing apparel; dressing; dyeing of fur Tanning, dressing of leather and luggage manufacturing Manufacturing of wood and products of wood and cork, except furniture; manufacturing of articles of straw and plaiting materials Pulp, paper and paper products Publishing, printing, reproduction of recorded media Coke, refined petroleum products and nuclear fuel Chemicals and chemical products Rubber and plastic products Other non-metallic mineral products Basic metals Metal products, except machinery and equipment Machinery and equipment n.e.c. Office machinery, computers, communications, medical, precision Motor vehicles, trailers and semi-trailers Other transport equipment Furniture; manufacturing n.e.c. And recycling Electricity, gas, steam and hot water supply Collection, purification and distribution of water Construction Trade Hotels and restaurants Land transport and transport through pipelines Water transport Air transport Auxiliary transport activities and activities of travel agencies Post and telecommunications Financial intermediation, except insurance and pension funding Insurance and pension funding, except compulsory social security Activities auxiliary to financial intermediation Business Public administration and defence; compulsory social security Education Health and social work Other social and cultural services Private households with employed persons
References Dietzenbacher, E., Los, B., 1998. Structural decomposition techniques: sense and sensitivity. Economic Systems Research 10, 307–323. European Commission, 2005. EU Energy and Transport in figures 2005. EC, Brussels. Fulton, L., 2000. Voluntary Agreements as Drivers of Technological Change in the Transport Sector. OECD/IEA Project for the Annex 1 Expert Group on the UNFCCC, OECD Paris.
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Halsnaes, K., Callaway, J.M., Meyer, H.J., 1999. Methodological Guidelines. Main Reports: Economics of Greenhouse Gas Limitations. UNEP. Riso National Laboratory, Denmark. Herna´ndez, F., 2006. Personal Communication with Fe´lix Herna´ndez. Spanish Representative in the Intergovernmental Panel of Climate Change, New York. Holden, E., Høyer, K.G., 2005. The ecological footprints of fuels. Transportation Research Part D10, 395–403. Instituto para la Diversificacio´n y Ahorro de la Energı´a, 2004. Guı´a Pra´ctica de la Energı´a, Consumo eficiente y responsable. IDAE, Madrid. International Energy Agency, 2000. The road from Kyoto, Current CO2 and transport policies in the IEA. IEA, Paris. Martellato, D., Tarancon, M.A., 2005. Openness, dependency and productive efficiency in nine European countries. Working paper. Universtita´ Ca’Foscari, Venezia. Schmutzler, A., 2005. Transportation and the Environment-Perspective for Future Research. In: Bo¨hringer, C., Lange, A. (Eds.), Applied Research in Environmental Economics, vol. 31. Physica Verlag, Springer, Heidelberg. Taranco´n, M.A., del Rı´o, P., 2007. CO2 emissions and intersectoral linkages. The Case of Spain. Energy Policy 35, 1100–1116. Ten Raa, T., Rueda-Cantuche, J.M., 2003. The construction of input–output coefficients matrices in a axiomatic context: some further considerations. Economic Systems Research 15, 439–455.
Structural factors affecting land-transport CO2 emissions: A European comparison ´ ngel Taranco´n Mora´n Miguel A a
a,*
, Pablo del Rı´o Gonza´lez
b
Faculty of Social Sciences and Law, University of Castilla - La Mancha, Ronda de Toledo, s/n 13071 Ciudad Real, Spain b Department of Spanish and International Economics, Econometrics and History and Economic Institutions, Faculty of Social Sciences and Law, University of Castilla-La Mancha, Spain
Abstract The increase of CO2 emissions generated by land-transport is a major policy concern of the European Union but the upward trend in transport use makes it difficult for member states to comply with Kyoto Protocol targets. This paper develops an input–output methodology to analyse the structure of CO2 emissions from land-transport and applies this to several European Union countries. It shows how production linkages between sectors and the structure of final demand affect land-transport emissions in these countries. The paper confirms the relevance of the emissions-intensity factor to explain differences in the emissions of the transport sector across countries, but also shows the importance of technology-production linkages between sectors in an economic system that has usually been neglected in the past. 2007 Elsevier Ltd. All rights reserved. Keywords: Land transport; CO2 emissions; Input–output; Structure
1. Introduction Transport is the second largest source of greenhouse gas (GHG) emissions in the EU; accounting for 26% of CO2 emissions in 2003 in the European Union (EU) and this share is growing, mainly due to continuous increases in road transport volume (both passenger and freight). This paper focuses on emissions from landtransport (road and rail). Within the transport sector, road transport is the largest source of emissions in the EU – 84% in 2003 – followed by air (13%), inland navigation (1.7%) and rail (0.8%) (European Commission, 2005). It is also the mode experiencing the second fastest growth rate (25% accumulated increase between 1990 and 2003), behind air transport (55%). The increase of CO2 emissions from transport is a major policy concern in the EU. In this context, an analysis of the factors affecting transport emissions is conducted to facilitate the development of effective emissions mitigation policies. A methodology is developed to analyse the structure of CO2 emissions in the land-transport sector across several EU member states (France, Germany, Italy, Spain and the Netherlands). Traditionally, *
Corresponding author. ´ . Taranco´n Mora´n). E-mail address: [email protected] (M.A
1361-9209/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.trd.2007.02.003
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the analysis of factors affecting emissions in specific sectors has taken into account the scale of the sector’s activities and its specific emissions (i.e., tons of CO2 per unit of output), that depend on the technology used. We go further by considering the input–output (I–O) relationships within an economy. More specifically, we analyse how production linkages between sectors and their final demand affect land-transport emissions. 2. Aim, methodology and main assumptions Traditionally, emissions from the transport sector have been decomposed into a number of factors (International Energy Agency, 2000; Halsnaes et al., 1999; Schmutzler, 2005; Holden and Høyer, 2005). This is useful when seeking methods to implement mitigation methods and is used here. Assuming the existence of q different transport modes, transport CO2 emissions (etransport) result from aggregating the emissions of each mode (em): etransport ¼
q X
em
ð1Þ
m¼1
and these can be decomposed into: em ¼ ntripm
distm fuelm em ntripm distm fuelm
ð2Þ
where ntripm is the number of trips by individuals or freight carriers (mode m), distm is the distance by m, and fuelm is the consumption of fuel by m. The second element on the right side of Eq. (2) is the average distance of each trip, the third factor is the average fuel intensity (fuel used per unit of distance), which is the inverse of fuel efficiency, and the fourth represents CO2 emissions per unit of fuel. However, other factors are often neglected, particularly, the input–output relationships within an economy. The economic system can be viewed as a complex web of linkages between sectors; a systemic approach is highly relevant for the analysis of CO2 land-transport emissions because each economic sector has a demand for a specific transport mode m and, thus, it has an influence on its output and CO2 emissions. The relationship between the demand for transport mode m and its emissions can thus be reformulated as: em ¼ xm
ntripm distm fuelm em fuelm em ¼ xm xm ntripm distm fuelm xm fuelm
ð3Þ
where xm is the output value of transport mode m. The increase in the output of mode m is related to an increase in the final demand of all economic sectors that, in turn, increase the demand for transport services. This linkage between final sectoral demand and transport output can be either direct or indirect. Direct demand for land-transport rises when the demand for the products of a certain sector i increase because products have to be distributed to final consumers. In addition, an increased demand for the products of a specific sector i also increases the demand for transport services indirectly and, thus, land-transport emissions. More inputs (labour, capital and materials) will be needed by sector i to cover this higher demand, increasing the demand for products in other ‘‘intermediate’’ sectors and, thus, their need for transport services. These factors, related to the techno-economic linkages between sectors, should be considered when analysing the structure of CO2 emissions in a specific transport mode. The I–O method, based on the use of input–output tables (IOT), allows us to analyse in a disaggregated manner the techno-economic relationships between production sectors in an economic system. An IOT is an exhaustive information system, since it provides detailed information on the purchases and sales of goods and services between socioeconomic agents. Within an I–O framework, a transport mode (m) can be considered as an economic sector that produces services to meet the demand of other sectors as well as its own final demand. Therefore, its output can be calculated as: xm ¼
n X j¼1
xmj þ y m
ð4Þ
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where xmj represents purchases of the services of transport mode m made by the jth sector and ym represents the services of transport mode m which are bought by final demand. We can therefore define the technical coefficients as the ratio between each element of the intermediate transaction matrix and the output of the corresponding activity branch: xij xj
aij ¼
with i ¼ ð1; 2; . . . ; nÞ and j ¼ ð1; 2; . . . ; nÞ
ð5Þ
Similarly, the final demand coefficients can be calculated as: hi ¼
yi g
with i ¼ ð1; 2; . . . ; nÞ
ð6Þ
P where g ¼ np¼1 y p is the value of the final demand. If we integrate Eqs. (5) and (6) into Eq. (4), then the output from transport mode m can be rewritten as: xm ¼
n X
bmq hq g
ð7Þ
q¼1
where bmq are the m row elements of B = (I A)1, which is the Leontief inverse matrix identifying the direct and indirect output of mode m that is needed to meet a unit of final demand from sector q. A is the matrix of technical coefficients (aij). Expression 3 can be written as: ! n X fuelm em em ¼ bmq hq g ð8Þ xm fuelm q¼1 Finally, we can integrate the last two factors in the right-hand part of Eq. (8) into an ‘‘emissions-intensity coefficient’’ that represents emissions per unit of product: cm ¼
fuelm em em ¼ xm fuelm xm
Emissions from transport m in country s would then be: ! n X s s ss s em ¼ bmq hq g csm
ð9Þ
ð10Þ
q¼1
In summary, CO2 emissions from transport can be decomposed into the ‘‘modal’’, the ‘‘intensive’’ and the ‘‘extensity’’ indicators. The modal factor shows how changing from one transport mode to another may reduce emissions. The share of each mode in Eq. (1) representing this has both technological elements (i.e., each transport mode is based on a specific technology with different CO2 emissions intensities) and behavioural elements (the choice between transport modes depends on the decisions of users). The intensive indicator accounts for specific CO2 emissions, i.e., emissions per unit of output (the csm factor in Eq. (10)). It is a technological indicator because technological progress could reduce specific emissions.1 The extensity indicator is a measure of the dimensions of the corresponding activity. It is usually treated as exogenous but here it is treated as an endogenous factor that is related to the direct and indirect demand for transport services generated by other sectors (the factors in parenthesis in Eq. (10)). Following Eq. (10), this indicator can be decomposed into: 1
The technological indicator is related to energy efficiency (i.e., the energy used per km) and the emissions-intensity of the fuel used (i.e., the amount of CO2 emitted per unit of energy consumed).
242
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Structural Factors
Extensive Indicator
Intensive indicator
Intensity Emissions Factor
Direct / Indirect Effects n
∑b
s mq
cmcs ms
q =1
Technology-Production Factor
aijs
Final Demand Factor
hqs
Emissions
Final Demand Volume
⎛ n s ⎞ ⋅ hqs ⋅ g s ⎟⎟ ⋅ c ms emss = ⎜⎜ ∑ bmq ⎝ q =1 ⎠
gs
Variables
Fig. 1. M-transport mode emissions within a input–output framework.
• The Scale factor, which represents the final demand (gs) in Eq. (1). • The technology-production factor. The level and evolution of land-transport CO2 emissions is related to the input–output relationships in an economy. The sales of one sector to another might lead to greater emissions from transport than its sales to other sectors. This is because some sectoral linkages are more transport-intensive than others. The column of technical coefficients aij represents the technology-mix of inputs that is needed to produce an output unit in sector j. The decomposition of the columns of coefficients in each sector will thus affect the demand for the services of transport mode m. This effect is included in the bsmq coefficients in Eq. (10). • The Final demand factor. A 1% increase in the demand for the products of one sector may cause a larger increase in transport’s emissions than an identical increase in the demand for the products of other sectors, simply because some sectors are more transport-intensive than others. This factor is represented by the hsq coefficients. Focusing on Eq. (10), therefore, the emissions of transport m in country s is an endogenous variable and a function of the level of economic activity (measured by GDP or final demand). The impact of final demand, an exogenous variable, will depend on the parameters of the equation, i.e., on the emissions-intensity coefficients ðcsm Þ, the Leontieff coefficients ðbsmq Þ and the final demand coefficients ðhsq Þ. Therefore, these coefficients represent the structural factors influencing emissions of transport mode m 2 (Fig. 1). The extensity indicator has a scale and a structural component. Tackling the scale factor to mitigate emissions is difficult because this would limit the growth of GDP leading to the view that attention should be paid to the structural components of the extensive indicator ðhsq and aij). The effectiveness of mitigation policies depends to a large extent on these structural components, i.e., on the techno-productive relations between sectors and on the sectoral composition of final demand. 2 In econometric or mathematical frameworks, the term coefficient involves structural relationships that are more or less stable over time. In other words, the structural coefficients define the rules of the economic flows in the functioning of the economic system and in its productive activity (Taranco´n and del Rı´o, 2007).
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3. The model The most efficient country is assumed to be the one that minimizes aggregate emissions and the least efficient country is the one which could reduce its transport emissions level if it adopted appropriately the production structure of other countries.3 Therefore, the production structure of the land-transport in this latter country s would be very emissions-efficient if the emissions from this sector increased as a result of the adoption of elements of the land-transport of other countries. This comparative approach is based on the idea that there is no perfect structure concerning CO2 emissions from land-transport that could be implemented in all countries. Emissions levels depend on several countryspecific factors4 that make it difficult to identify an ideal, efficient structure, i.e., a benchmark. Thus, the idea of a reference measure of absolute efficiency is rejected. Instead, a relative efficiency approach is used, i.e., the methodology allows the development of inferences concerning a set of structural factors that can explain the differences in efficiency levels between countries. 3.1. Total structural efficiency index (TSEI) The TSEI identifies what would happen when country s adopts all the productive features of country r. Only the final demand of country s (gs) remains constant, whereas the intensive and the extensive structural factors (coefficients c, b, and h) change. Therefore, if country s adopted the structure of country r, its emissions would be: ! n X r r rs s em ¼ bmq hq g crm ð11Þ q¼1 ers ess i i ess i
Hence, 100 will be positive if the productive structure of country s is more efficient than that of country r. To calculate the overall efficiency of the productive structure of country s regarding land-transport, the average of this expression is used when the coefficients of each country are applied. That is: Pd ersm essm 100 r¼1 ess s m em ¼ ð12Þ d where d is the number of countries. If the productive structure of country s is efficient with respect to sector i and the rest of countries as a whole, then es i will be positive. The structure of county s will be efficient if the substitution of the structural coefficients of s for those of other countries leads to an increase in emissions. 3.2. Emissions-intensity factor efficiency index (EIFEI) This technological factor is related to the ci coefficients defined in Eq. (9). These represent the product-specific emissions of the land-transport sector. Eq. (10) can be rewritten to compare the countries’ level of efficiency of CO2 emissions which is caused by this factor: ! n X s s rðcÞs s em ¼ bmq hq g crm ð13Þ q¼1
Therefore, it is assumed that country s adopts the emission intensity factor, c, of country r in transport mode m. Recalculating Eq. (12) we obtain the efficiency index eðcÞs m .
3
Martellato and Tarancon (2005) offer an application of this methodology in an international trade context. These include size of the productive system, physical and geographical aspects, demographic factors, consumption patterns, people’s lifestyles and cultural and behavioural aspects of socioeconomic agents. 4
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3.3. Technology-productive factor efficiency index (TPFEI) The structure of the network of productive relationships in an economic system influences the level of emissions from various economic sectors including land-transport. The technical coefficients (aij) capture these relationships. A column of coefficients represents the technology of the corresponding sector because it shows the mix of productive inputs needed per unit of output. The effect on the output of a sector (and, in turn, on its emissions) caused by the increase in the final demand for a product will depend on the technologies of all the sectors, and on the network of technological linkages in the economic system. Those effects are cumulative and encompass direct as well as indirect effects. Cumulative effects are picked up by the biq coefficients. Therefore, the relative efficiency level associated to the technology of the productive system can be calculated by replacing Eq. (10) with: ! n X r s rðaÞs s em ¼ bmq hq g csm ð14Þ q¼1
We substitute arij for asij to obtain the brmq coefficients in Eq. (14). Recalculating Eq. (12) yields the efficiency index eðaÞs m . 3.4. Final demand factor efficiency index (FDFEI) Since the share of each sector and the share of land transport-intensive sectors in output affects the level of emissions, the structure of final demand should be taken into account in the analysis of the structure of landtransport emissions in different countries. Within our input–output framework this structure is provided by the hi coefficients. The emissions related to the final demand structure of countries s and r are compared and expression 11 can be transformed into: ! n X s r rðhÞs s em ¼ bmq hq g csm ð15Þ q¼1 ðhÞs Eq. (12) is recalculated and the efficiency index em is obtained.
3.5. Interaction effects The previous indicators have been treated as if they were isolated, but the interactions between them should be considered. Starting from Eqs. (11, 13, 14), and (15), the following structural decomposition can be established: rðcÞs rðaÞs rðhÞs ers þ em þ em þ ers m ¼ em m
ð16Þ
ers m
where is the interaction between the aforementioned factors (Dietzenbacher and Los, 1998) and can be treated as an error term. If an average of this effect is calculated for all countries, we arrive at an Index of Interaction Effects (IIE). Pd rs e s em ¼ r¼1 m ð17Þ d The main elements of the overall model are summarised in Table 1. 4. Empirical analysis The methodology allows identification of European countries that are relatively more efficient in terms of their land-transport CO2 emissions and the sources of efficiency. Efficiency can be related either to the specific features of land-transport (i.e., to the intensity indicator as measured by EIFEI) or to the structural features of the productive system (i.e., to the structural factors of the extensive indicator as measured by TPFEI and
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Table 1 Summary of relative efficiency indexes to analyse land-transport emissions Factor abbreviation
Factor name
Brief description
es m
Total structural efficiency index (TSEI) Emissions-intensity factor efficiency index (EIFEI) Technology-productive factor efficiency index (TPFEI)
It shows the efficiency of countries regarding the structure of emissions in the land-transport sector. It shows the impact of product-specific emissions of the land-transport sector (emissions per unit of product) in different countries. It accounts for the level and evolution of CO2 emissions related to the input–output relationships in an economy. It shows the impact of the technologies in other sectors on the emissions of the transport sector. Those other sectors will (directly or indirectly) require more or less transport services, depending on their own technologies. It shows the impact on the demand and emissions of the land transport sector as a result of a change in the relative share of sectors in the country’s total final demand. This indicator includes the effect of the interactions of the previous factors.
ðcÞs
em
ðaÞs
em
ðhÞs
em
Final demand factor efficiency index (FDFEI)
es m
Index of interaction effects (IIE)
Table 2 CO2 Emissions by sector (EU-25, MtCO2) Year
1990 1995 2000 2001 2002 2003
Total
3775 3655 3692 3749 3750 3853
Power and heat generation
1487 1417 1426 1440 1472 1514
Industry
723 640 598 598 593 597
Transport
793 857 971 979 986 1001
Of which Road
Air
Inland navigat.
Rail
675 726 811 825 835 843
85 100 134 130 129 132
20 21 16 15 15 17
12 10 9 9 8 8
H*
S and O*
500 486 464 492 454 479
273 255 233 241 246 262
Source: European Commission (2005). *H – households; S and O – services and other.
FDFEI). The methodology is applied to five EU countries – France, Germany, Italy, Spain and the Netherlands.5 Land-transport is the largest contributor to GHG emissions in the EU (96.8% of the total in 2003) and with CO2 emissions increasing significantly; by 26% between 1990 and 2003 (European Commission 2005). Furthermore, the transport sector has the second largest share in overall emissions (behind power and heat generation) and the fastest growth rate (Table 2). Road transport is the dominant land-transport mode and by far the largest CO2 emissions source within the transport sector, accounting for 85% of these emissions in the EU-25 in 2003. It also has the second highest growth rate (after air transport). 5. Data The study is based on the use of the symmetrical I–O tables for the five selected European in 2000. The tables for each country were built from the Make-and-Use tables available in EUROSTAT to avoid the inconsistencies that may arise from using different sources. This tables are based on the product technology hypothesis that, as suggested by Ten Raa and Rueda Cantuche (2003), offers the best method for this task.6 5
Two criteria were considered in selecting countries, their size and data availability. The countries chosen were the largest in the EU and the ones with available data. The reason the UK was not included is because of problems with the data. 6 Two adjustments were made. Some sectors had to be aggregated to avoid problems related to the possible existence of negative coefficients and a RAS adjustment was applied to remove negative residual coefficients.
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Unfortunately, EUROSTAT does not provide a disaggregation of land transport into road and rail transport but given the dominance of road transport, the land-transport coefficients represent mainly road transport technology. CO2 emissions per transport mode were obtained from the GHG emissions inventories of the United Nations Framework Convention on Climate Change (UNFCCC). These estimates were complemented with data from EUROSTAT and coherence checks undertaken. 6. Results 6.1. Relative efficiency indexes by transport mode and country The TSEI shows that the Netherlands is the most efficient country regarding the structure of CO2 emissions from land-transport, whereas Spain is the least efficient (Fig. 2). The intensive (EIFEI) and the technology-production factors (TPFEI) have the greatest impact on the TSEI implying that differences in the structure of land-transport emissions between countries can be explained by product-specific emissions of the sector in countries and by the technological and productive relationships between economic sectors. A country-by-country analysis shows the negative impact of the intensity factor (EIFEI) in France and Germany that is counterbalanced by the TPFEI. In contrast, the positive influence of EIFEI in Spain and Italy is partially or totally compensated by TPFEI. Both factors have a positive impact on TSEI in the Netherlands. Finally, the impact of interaction effects on the relative efficiency level is positive in all countries, and particularly in Spain and Germany, suggesting that the assumption of separation between the structural factors is a strong one. 6.2. Emissions-intensity factor efficiency (EIFEI) Fig. 2 shows that Spain and Germany have the most efficient and the least efficient emissions-intensities respectively. This could lead to the conclusion that land-transport in Spain is relatively less carbon intensive either because its land-transport is more energy-efficient or the fuels used are less carbon intensive. The later is unlikely because land-transport technologies are highly homogenous across countries7 with fuel mostly based on oil products (see Table 3). The consumption of oil products by land-transport, which is a proxy of the energy requirements per output value, confirms that the sector is comparatively less energy intensive in monetary terms in Spain than in the other countries.8 Different shares of road and railways within land-transport could also partly explain the greater efficiency in Spain given that railways are the least carbon-intensive transport mode but data on freight and passengers transport offers no support for this. Spain has the second lowest rail/road ratio in freight transport, and the lowest in passenger transport (Table 4). Finally, differences in the intensity indicator can also be related to differences in the horsepower and speed of vehicles (Herna´ndez, 2006). Heavier, more powerful vehicles emit more CO2 per kilometre than smaller ones.9 Furthermore, the same automobile emits different quantities of CO2 at different speeds. Data on these aspects is not available.10 7
This is so because car manufacturing is highly concentrated in a few European, Japanese and Korean companies in and manufacturers have voluntarily agreed to reduce emissions levels (ACEA, JAMA and KAMA voluntary agreements). 8 Fuel oil requirements in 2000 per output value unit in land transport were €0.16 in Spain, followed by the Netherlands and Germany (€0.19), France (€0.28) and Italy (€0.36). This interpretation assumes similar relative prices across countries. 9 As reported by International Energy International Energy Agency (2000) the UNFCCC Annex 1 Expert Group (Fulton, 2000) estimated that, through 2008, as much as two-thirds of the CO2-per-kilometre reduction achievable from technology uptake could be lost to increases in vehicle size, weight, and horsepower. For example, a 25% reduction in CO2 per kilometre achieved through the use of new technologies could become a 8% to 10% reduction if consumers shift to bigger, heavier, more powerful vehicles. Such a scenario, would be consistent with consumer shifts that have occurred in recent years, especially in the US, where minivans and sport-utility vehicles have become increasingly popular. 10 The lowest energy consumption takes place at speeds of around 90–100 km/hour (Instituto para la Diversificacio´n y Ahorro de la Energı´a, 2004). Speeds above or below lead to higher emissions.
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60.00 50.00 40.00
20.00 10.00
Sp ai n
nd s et he rla
er m an y
N
-20.00
G
-10.00
Ita ly
0.00
Fr an ce
Index Values
30.00
-30.00 -40.00 TSEI
EIFEI
TPFEI Countries
FDFEI
SEI
Fig. 2. Summary of results per country and factor.
Table 3 Emissions-intensity coefficients in land transport Year: 2000
Variable
France
Germany
Italy
Netherlands
Spain
a b c = a/b
Emissions (000tCO2) Output (Unit: M€) Intensity coefficient (€/000tCO2)
129710.24 26725.00 4.85
177039.36 25928.00 6.83
111514.23 24109.96 4.63
31771.78 7448.00 4.27
77501.63 24120.80 3.21
Table 4 Coverage of rail over road transport Year: 2000
Road vs rail transport
Freight transport A Goods transport by road, million tonne-km B Goods transport by rail, million tonne-km C = b/a (Mton-Km rail/Mton-Km road) · 100 Passengers transport D Road transport of passengers, million passenger-km E Rail transport of passengers, million passenger-km F = e/b (Mill. pass. rail/mill. pass. road) · 100
France
Germany
Italy
Netherlands
Spain
266,500 554,48 20.81
347,200 76,815 22.12
184,756 22,817 12.35
45,700 3819 8.36
133,078 12,180 9.15
744,900 69,571 9.34
792,400 75,404 9.52
759,200 47,133 6.21
164,100 15,400 9.38
382,210 18,571 4.86
Source: based on Eurostat database.
6.3. Technology-productive factor efficiency index (TPFEI) TPFEI is related to the direct and indirect sales of land-transport to other sectors, i.e. it is related to the technologies implemented in other sectors that have an impact on the emissions in the transport sector. This factor is a source of relative efficiency in Germany, the Netherlands and France but relative inefficiency in Italy and Spain. The impact of a change in a technical coefficient on the emissions of the land-transport sector can be expressed as the elasticity of ei with respect to a change in akl: Dem em eem akl ¼ Da kl akl
ð18Þ
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Table 5 Main sectors related to TPFEI Pn s Pn e k¼1 em akl n l¼1 ¼ 100 n 1 Agriculture. . . 5 Manuf. food 13 Chemical products 15 Other non-metallic mineral prod. 17 Manuf. fabricated metal prod. 18 Manuf. machinery and equip. n.e.c. 20 Manuf. motor vehicles 22 Furniture, n.e.c., recycling 25 Construction 26 Trade 28 Land transport 31 Auxiliary transport activ. 32 Communications 36 Business 37 Public administration 38 Education 40 Other social and cultural services Normalized average on all sectors
France (3)
Germany (1)
Italy (4)
Netherlands (2)
Spain (5)
20.27 32.29 35.77 16.22 18.76 17.26 33.24 12.55 50.36 1465.95 1013.93 7.76 22.09 376.71 332.21 139.77 152.37
15.78 38.25 37.24 10.40 59.26 36.22 53.17 8.89 27.53 128.49 1569.60 95.91 20.22 127.87 407.79 948.53 141.47
28.50 262.22 193.49 110.24 122.58 219.00 123.77 94.74 167.21 607.07 349.31 278.52 91.43 197.55 52.21 33.13 103.66
116.36 95.95 107.34 14.82 31.48 31.70 10.55 176.48 302.30 226.51 672.69 571.92 170.77 73.88 258.15 39.89 59.22
158.38 97.65 226.89 175.51 167.56 80.54 198.50 81.76 361.91 422.00 99.16 158.71 115.13 74.22 100.96 34.69 47.04
100.00
100.00
100.00
100.00
100.00
Average elasticities normalized-by-sector. Notes: The countries TPEI rank positions are in brackets. The sectors whose normalized elasticities are lower than 150 in all countries have been omitted. Normalized elasticities higher than 200 are in bold. Superscript ‘s’ indicates country s.
If we assume a variation of 1% in akl, then Eq. (18) becomes: eem akl ¼
akl xl bmk ð1 0:01 akl blk Þxm
ð19Þ
This elasticity allows identification of those technical coefficients (akl) whose change leads to the greatest variation in the emissions from the land-transport. Hence, it shows which technology elements would significantly reduce land-transport emissions as a result of a small change in the current economic and technical structure. Table 5 shows that, in most countries, the trade and land-transport sectors are the greatest contributors to the increase in land-transport emissions. This is the result of their demand for land-transport services. This is expected because trade is a transport-intensive activity. It may also be related to the fact that high emitting countries mainly move goods on trucks. However, in the French case this interpretation is problematic because the share of road in overall freight transport is below Spain and Italy, and its share of railways is the largest among the five countries. Trade and land-transport are followed by a variety of different sectors in other countries: food industry (Italy and Spain), chemical industry (Spain), construction (Spain, Netherlands), auxiliary transport activities (Italy and the Netherlands), business (France), public administration (France, Germany and Netherlands), and education (Germany).11 The results also show that several key sectors in the Spanish economy are highly land-transport intensive – agriculture, food industry, chemical products, manufacturing of metal products and other non-metallic mineral products, manufacturing of motor vehicles, construction and, to a lesser extent, communications.
11
Sectors intensive in land-transport are referred to as ‘‘key sectors’’.
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Table 6 Direct and total requirements of land-transport services per unit of output Year: 2000
France
1 Agriculture. . . 5 Manuf. food 13 Chemical products 15 Other non-metallic mineral prod. 17 Manuf. fabricated metal prod. 18 Manuf. machinery and equip. n.e.c. 20 Manuf. motor vehicles 22 Furniture, n.e.c., recycling 25 Construction 26 Trade 28 Land transport 31 Auxiliary transport activ. 32 Communications 36 Business 37 Public administration 38 Education 40 Other social and cultural services a
Germany
Italy
Netherlands
Spain
Direct
Totala
Direct
Totala
Direct
Totala
Direct
Totala
Direct
Totala
0.0000 0.0001 0.0002 0.0001 0.0003 0.0001 0.0001 0.0003 0.0001 0.1217 0.1317 0.0001 0.0007 0.0018 0.0078 0.0059 0.0043
0.0011 0.0007 0.0009 0.0020 0.0011 0.0007 0.0008 0.0011 0.0012 0.1830 1.1542 0.0007 0.0017 0.0029 0.0100 0.0074 0.0075
0.0002 0.0001 0.0001 0.0001 0.0007 0.0001 0.0001 0.0001 0.0000 0.0021 0.1200 0.0014 0.0003 0.0002 0.0048 0.0198 0.0014
0.0005 0.0004 0.0004 0.0004 0.0012 0.0004 0.0004 0.0003 0.0003 0.0047 1.1385 0.0030 0.0006 0.0004 0.0058 0.0229 0.0020
0.0013 0.0070 0.0065 0.0087 0.0047 0.0060 0.0051 0.0045 0.0028 0.0448 0.0669 0.0436 0.0118 0.0019 0.0014 0.0020 0.0050
0.0030 0.0111 0.0116 0.0157 0.0115 0.0126 0.0100 0.0100 0.0089 0.0685 1.0997 0.0538 0.0155 0.0040 0.0043 0.0036 0.0085
0.0018 0.0002 0.0004 0.0005 0.0006 0.0004 0.0001 0.0079 0.0022 0.0141 0.0661 0.0362 0.0054 0.0000 0.0040 0.0013 0.0007
0.0026 0.0011 0.0014 0.0011 0.0015 0.0008 0.0003 0.0093 0.0037 0.0251 1.0744 0.0434 0.0072 0.0004 0.0051 0.0018 0.0010
0.0122 0.0195 0.0159 0.0300 0.0196 0.0076 0.0059 0.0125 0.0049 0.1219 0.0023 0.0434 0.0208 0.0005 0.0105 0.0045 0.0037
0.0229 0.0318 0.0267 0.0448 0.0343 0.0140 0.0158 0.0247 0.0205 0.1551 1.0280 0.0546 0.0316 0.0034 0.0162 0.0074 0.0091
The ‘‘total’’ includes the aggregation of direct and indirect requirements.
Table 7 Main sectors related to the land transport final demand factor efficiency index (FDFEI) Pn s e France (4) Germany (1) Italy (2) k¼1 em hk ¼ 100 n 5 Manuf. food 20 Manuf. motor vehicles 25 Construction 26 Trade 28 Land transport 31 Auxiliary transport activ. 37 Public administration 38 Education Normalized average
Netherlands (3)
Spain (5)
15.22 14.66 24.47 612.46 2784.22 0.81 207.43 77.44
10.79 14.26 7.97 24.77 3408.77 8.25 133.79 330.56
212.36 120.12 146.61 392.37 1650.31 105.08 61.11 38.78
32.43 3.93 84.40 54.63 3119.96 165.06 110.99 17.02
364.67 160.98 302.38 254.98 1636.63 59.75 116.87 38.61
100.00
100.00
100.00
100.00
100.00
Normalized-by-sector elasticities. Note: The rankings of countries are shown in brackets. The sectors whose normalized elasticities are lower than 0.150 in all countries have been omitted.
Table 6 shows the direct and indirect land-transport requirements per unit of output in specific, transport intensive ‘‘key’’ sectors.12 In some sectors, the direct requirements for land-transportation services represent a large share of the land-transport requirements of a key sector; e.g. trade. In such cases mitigation policies could try to reduce the need for transport services through improved logistics or a shift to less polluting modes. In contrast, a different type of policy would be needed in those sectors with low direct requirements for land-transport, as in construction. Here policies may need to be focused on those sectors which provide intermediate inputs, although this may be difficult because the CO2 intensity of intersectoral linkages would need to be regulated and some products are inherently more CO2 emissions intensive than others with the possibility of replacing these with less-polluting products being limited.
12
The technical coefficients (amj) represent the direct land-transport requirements of the key sector j, whereas bmj represent the indirect requirements.
250
Table 8 Summary of findings and possible policy responses Countries
Intensive indicator
Extensive indicator
Policy implications
is a source of relative. . .
. . .is a source of relative. . .
Key sectors leading to main differences in this factor:
Weight of directlandtransport requirements:
. . .is a source of relative. . .
Key sectors leading to main differences in this factor:
Final demand depends on. . .
France
Inefficiency
Efficiency
Trade
High
Inefficiency
Trade
Domestic demand
Innovations in land-transport technologies to reduce the dependence on energy inputs: less carbon-intensive fuels; Innovation in fuel performance of fuels and in transport technologies; Better logistics. Logistics improvements for trade. Increasing the share of freight transport by rail.
Germany
Inefficiency
Efficiency
Education
High
Efficiency
Education
Domestic demand
Innovations in land-transport technologies to reduce the dependence on energy inputs: less carbon-intensive fuels; innovation in fuel performance of fuels and in transport technologies; better logistics. Increasing the share of passengers transport by rail and collective road vehicles.
Italy
Efficiency
Inefficiency
Chemical prod.
Low
Efficiency
Manuf. motor vehicles
Domestic demand and exports
Logistics improvements for trade.
Manuf. machinery and equip. n.e.c.
Low
Auxiliary transport activities
High
Technology-productive factor
Efficiency
Efficiency
Spain
Efficiency
Inefficiency
Agriculture
Low
Manuf. Food
Low
Chemica prod.
Low
Other nonmetallic mineral prod. Manuf. fabricated metal prod. Manuf. motor vehicles Construction
Low
Low
Low
Increasing the share of freight transport by rail.
Efficiency
Auxiliary transport activities
Exports
Relatively good ratings in all indicators
Inefficiency
Manuf. Food
Domestic demand Exports
Logistic improvements in sectors selling to manufacturing industries and construction. Increasing the share of freight transport by rail.
Manuf. motor vehicles Construction
Domestic demand
M.A´. Taranco´n Mora´n, P. del Rı´o Gonza´lez / Transportation Research Part D 12 (2007) 239–253
The Netherlands
Final demand structural factor
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The policy implications of these results are thus not simple. Although eem akl identifies those changes in the input–output relationships which lead to a great change in emissions, such relationships are often rigid and difficult to manipulate, and especially the indirect ones. In addition, changing them to reduce emissions could have negative side-effects on the economy such as a loss of overall economic efficiency. 6.4. Demand factor efficiency (FDFEI) FDFEI shows the impact of the structure of final demand on land-transport emissions. The elasticity of em with respect to hkl shows how a change in the share of a sector in the country’s final demand (hkl) affects the demand for land-transport and, thus, the emissions from this sector (em): eem hk ¼
Dem em Dhk hk
ð20Þ
Assuming a variation of 1% in hkl, then the elasticity is: eem hk ¼
0:01bmk hk g xm
ð21Þ
The results of such calculations are shown in Table 7, where elasticities have been normalized by sector allowing identification for each country of those sectors where changes in their final demand coefficients leads to a great change in CO2 emissions from land-transport. The key sector where a change in final demand has the greatest impact on the output of land-transport and its emissions is the land-transport sector itself. The results show the relevance of the trade sector in France, Italy and Spain and confirm its intensive use of land-transport. In contrast, the small impact of trade in Germany and the Netherlands means, either their trade sector is not as land transport-intensive as in other countries or that it uses more lower carbon-intensive modes such as railways. As seen in Table 4, the share of rail is relatively high in both Germany and the Netherlands. A change in the share of other sectors can also lead to significant changes in the emissions in particular countries. Such sectors include the food industry (Italy and Spain), construction (Spain), auxiliary transport activities (the Netherlands), public administration (France) and education (Germany). Spain and France have a negative final demand indicator and their key sectors are industry, construction and trade, that are highly land-transport intensive. The overall situations regarding the countries examined are summarized in Table 8. This also provides some indications of the types of policy responses that may be appropriate to help mitigate the output of CO2. The focus is on the role that increasing fuel efficiency may play as well as modal shifts. The suggestions are tempered by the aggregation within land-transport of road and rail modes, and it is not possible to build in any spatial considerations – e.g. in some cases it may simply not be physically possible to switch modes. Additionally, efforts to change the structural nature of production to reduce energy consumption may be difficult because of the rigidity of production technology. 7. Conclusions This paper developed a methodology to analyse the structure of CO2 emissions in the land-transport sector and applied it to several EU countries. The analysis shows how emissions intensities of the production linkages between sectors and the structure of the final demand of a sector affect land-transport emissions in selected countries. It confirms the relevance of the emissions-intensity factor in order to explain differences in the emissions of the land-transport sector across countries, but also shows the importance of a usually neglected factor: the technology-production linkages between sectors. Acknowledgements The authors are grateful to the editor and two anonymous referees for their comments.
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Appendix A. Sectors in the European input–output tables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Agriculture, hunting and forestry Fishing, operation of fish hatcheries and fish farms; service activities incidental to fishing Mining of non metal and extraction crude Mining of metal ores, other mining and quarrying Food, beverages and tobacco manufacturing Textile manufacturing Manufacture of wearing apparel; dressing; dyeing of fur Tanning, dressing of leather and luggage manufacturing Manufacturing of wood and products of wood and cork, except furniture; manufacturing of articles of straw and plaiting materials Pulp, paper and paper products Publishing, printing, reproduction of recorded media Coke, refined petroleum products and nuclear fuel Chemicals and chemical products Rubber and plastic products Other non-metallic mineral products Basic metals Metal products, except machinery and equipment Machinery and equipment n.e.c. Office machinery, computers, communications, medical, precision Motor vehicles, trailers and semi-trailers Other transport equipment Furniture; manufacturing n.e.c. And recycling Electricity, gas, steam and hot water supply Collection, purification and distribution of water Construction Trade Hotels and restaurants Land transport and transport through pipelines Water transport Air transport Auxiliary transport activities and activities of travel agencies Post and telecommunications Financial intermediation, except insurance and pension funding Insurance and pension funding, except compulsory social security Activities auxiliary to financial intermediation Business Public administration and defence; compulsory social security Education Health and social work Other social and cultural services Private households with employed persons
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