Prospects and Drivers of Future European Resource Requirements Evidence from a Multi-National Macroeconomic Simulation Study *

Transcription

1 Prospects and Drivers of Future European Resource Requirements Evidence from a Multi-National Macroeconomic Simulation Study * Paper prepared for the final WIOD Conference Groningen, April 2012 by Martin Distelkamp, Mark Meyer ** and Bernd Meyer GWS mbh Osnabrueck Abstract Material Flow Accounting gave significant distinction to environmental research activities over the past ten years. The cumulative findings of widespread analyses of the interrelationships between global materials use and economic development in this regard seem to caution about multiple future scarcities which doubt the long-run sustainability of historical resource extraction patterns (see, e.g., Krausmann et al as a reference). Correspondingly, policy makers seem to acknowledge the needs for environmental policy measures which should satisfy an absolute decoupling of resource extraction from economic growth (see, e.g., the resource-efficient Europe flagship initiative of the Europe 2020 Strategy in this regard). This situation demands for the development of appropriate quantitative assessment frameworks which enable policy makers to identify global drivers of future resource requirements and to assess the prospects of national policy actions by means of simulation analyses. * ** This paper summarises selected findings of recent work on behalf of DG Environment at the European Commission. The authors acknowledge financial support from the project Macroeconomic modelling of sustainable development and the links between the economy and the environment (MACMOD, ENV.F.1/ETU/2010/0033) in this regard. Nevertheless, all views expressed in this paper are the sole responsibility of the authors and should not be interpreted as representing those of the European Commission. Interested readers find the full project report at Corresponding author s address: Gesellschaft fuer Wirtschaftliche Strukturforschung mbh Heinrichstr. 30 D Osnabrueck m.meyer[at]gws-os.de phone: +49 (541)

2 But even if numerous ecological economists focussed on the development of multi-region input-output models over the past few years, one has to be aware that most of these applications have been laid out as static case studies (see also Wiedmann 2009 for a recent review), i.e.: Usually, only isolated national developments are subject to the analysis, the period of analysis stays restricted to the past and long run shifts in global trade, production and consumption patterns are not endogenously determined. Our paper is intended to contribute a brick to bridge this gap. We discuss recent simulation results from the multi-national GINFORS (Global INterindustryFORecastingSystem) model which illustrate the methodological feasibility of modelling and forecasting sectoral Total Material Requirements (TMR, see Bringezu et al in this regard) on EU-Member States level by means of a dynamic simulation model whose backbone is given by 30 national inputoutput models linked by bilateral trade matrices. Keywords: Environmental policy, Global resource use, Multi-region input-output analysis, Material flow analysis, Total material requirements, World trade model, Embodied environmental impacts. JEL Classification C53, C54, C63, C67, Q01, Q56, F17, F18

3 Distelkamp_Meyer_Meyer_Groningen_2012.doc INTRODUCTION Material Flow Analysis (MFA) by means of input output models represents a prominent field of industrial ecology. Accordingly a comprehensive literature evolved over the last years and a handbook on this topic was made available only a couple of years ago (Suh 2009). Recent research indicates that 20 th century growth rates of global resource extractions cannot be maintained over the next decades. See, e.g., Behrens et al. (2007) or Krausmann et al. (2009) for analyses coping with historical growth patterns in global resource consumption. In any case, policy makers seem to approve this challenge. See, e.g., the European Unions flagship initiative for a resource-efficient Europe under the Europe 2020 growth strategy, 2 the German resource efficiency program ProgRess, 3 the recently initiated PolRess 4 or the recently terminated MaRess 5 project of the German Environment Ministry and the Federal Environment Agency in this regard. However, whereas the MFA literature accrued significantly over the last years (see, i.a., also Wood et al. 2009, Gierlinger and Krausmann 2011 or Krausmann et al for recent country studies), all of the just mentioned analyses suffer from the fact that they trace past developments only. Unfortunately, they can thus provide only limited guidance to policy makers which have to tackle the challenges of future global resource demand (see Pirgmaier et al for a recent risk analysis concerning future resource demand). Their predominant finding that historical increases in resource efficiency only achieved relative decoupling of economic growth from resource consumption is rather continuously utilised by critics of ongoing resource-efficiency-initiatives (e.g. Jackson 2009). We therefore perceive basic needs for (further) development and intensified applications of adequate ex ante modeling tools. Yet, our paper is not intended to provide an exhaustive literature review to this topic (we refer to Wiedmann et al and Wiedmann 2009 in this regard). Instead, we rather explore the opportunities for ex ante MFA simulation studies from a practitioner s viewpoint by establishing most recent simulation results from the multi-national GINFORS (Global InterindustryFORecastingSystem) model in this area of research. Focusing on the Total Material 2 The European Commission adopted a "Roadmap for a resource-efficient Europe". This framework sets out a vision for the structural and technological change needed up to 2050, with milestones to be reached by The Roadmap proposes ways to increase resource productivity and to decouple economic growth from resource use and its environmental impacts. See also

4 Distelkamp_Meyer_Meyer_Groningen_2012.doc Requirements (TMR, see Bringezu et al. 2004) indicator we present our latest methodological advancements in multi-national resource demand forecasting and illustrate the resultant model capabilities by means of a simulation study which highlights the prospects of future European resource demand until the year However, we also discuss the weak spots of this state of the art approach and point to the most significant potentials for future improvements. The paper is structured as follows: Section 2 presents our empirical approach for estimating historical annual TMR time series for each European Member State. Section 3 introduces the global multi region input output model GINFORS as a tool for integrated material flow projections and presents our approach for integrating TMR projections. Section 4 shortly summarises our simulation results, Section 5 discusses our overall findings and Section 6 concludes. 2 CALCULATION OF NATIONAL TMR SERIES ON EU MEMBER STATES LEVELS This subsection presents our approach for estimating historical material flow data for European Member States. These model refinements were intended to trace shifting environmental burdens within a globalised economy. Thus, apart from modelling Direct Material Inputs (DMI), we also took first steps to project national Total Material Requirement (TMR) indicators on a Member States level. 6 The following annotations generally focus on the TMR indicator only. However, readers should be aware that whereas EUROSTAT s Material Flow Account (MFA) dataset reports annual direct material inputs (domestic extraction used and imports) of a multitude of materials (see Table 1 for aggregated historical summary information), official statistics do not provide data for unused domestic extraction and materials hidden in imported goods with a degree of differentiation which facilitates the calculation and modelling of TMR data. Our approach for modelling TMR data therefore combines official information from EURO- STAT s MFA dataset with preliminary work by Acosta and Schütz (2011a, 2011b) who provided ground-breaking historical TMR data. The following subsections discuss how we processed their datasets for own calculations of corresponding historical EU27 Member States TMR-series. 6 See Eurostat (2001) for methodological details of the DMI/TMR concepts.

5 Distelkamp_Meyer_Meyer_Groningen_2012.doc per capita in tons (2007) Germany ,63 France ,22 Spain ,97 Italy ,02 Great Britain ,17 Poland ,08 Netherlands ,82 Romania ,89 Belgium ,58 Sweden ,03 Czech Rep ,67 Portugal ,41 Finland ,01 Ireland ,61 Austria ,21 Greece ,55 Denmark ,94 Hungary ,29 Bulgaria ,95 Slovakia ,77 Slovenia ,06 Lithuania ,73 Latvia ,67 Estonia ,05 Cyprus ,40 Luxemburg ,39 Malta ,99 Table 1: Time series of DMI in millions of tons for EU-27 countries. Data sources: Eurostat MFA and Population data; own calculations. 2.1 THE INITIAL ACOSTA AND SCHÜTZ DATASET Based on the framework of Environmentally Extended Input-Output Analysis (EE-IOA), Acosta and Schütz provided initial TMR datasets for three major European countries (France, Germany and Italy) for reporting years 1995, 2000 and 2005 in compliance with the following classification: metals iron/steel non-ferrous metals other minerals construction (aggregates) industrial other

6 Distelkamp_Meyer_Meyer_Groningen_2012.doc biomass wood agricultural other fossil fuels. As accounting components, used domestic extraction, unused domestic extraction, imports and hidden flows associated to imports were calculated. Further erosion and GLU A (global agricultural land use) was part of the delivery. 2.2 CALCULATING DOMESTIC EXTRACTION USED AND IMPORTS FOR EU27 MEMBER STATES Our complementary calculations initiated from official EUROSTAT datasets. Due to space constraints we will only outline corresponding computations referring to the categories used domestic extraction, imports, unused domestic extraction and hidden flows associated to imports on the following pages. First of all, we had to derive a mapping of these material categories to the Acosta and Schütz classification. 7 See Table 2 for a condensed overview which associates EUROSTAT s MFA-categories (MF1 MF6) to their respective TMR categories. Biomass (MF1) Metal ores (MF2) Non-metallic minerals (MF3) MF4 MF5 + MF6 Table 2: Domestic extraction used Agriculture Wood other iron non-ferrous metals others construction minerals industrial minerals others fossil energy materials/carriers Imports agriculture wood other products mainly from biomass iron and products mainly from iron/steel non-ferrous metals and products mainly from nonferrous metals other metals and products mainly from metals construction minerals industrial minerals other products mainly non-metallic minerals products fossil energy materials/carriers others and waste Material categories for domestic extraction used and imports in the TMR module of GINFORS. 7 For example the MFA-data accounts for non-metallic minerals like chalk and dolomite, slate and chemical and fertilizer minerals etc. whilst the Acosta-Fernández dataset (and the TMR module) differs between construction minerals and industrial minerals.

7 Distelkamp_Meyer_Meyer_Groningen_2012.doc CALCULATING UNUSED DOMESTIC EXTRACTION FOR EU27 MEMBER STATES Material flow category Material intensity (rucksack-factor) in kg per kg Unused domestic Domestic France Germany Italy extraction extraction used Biomass: Biomass: Agriculture Agriculture 0,20 0,20 0,21 0,13 0,14 0,14 0,13 0,14 0,14 Biomass: Erosion Biomass: Agriculture 0,70 0,62 0,68 0,62 0,55 0,57 0,50 0,52 0,47 Biomass: Wood Biomass: Wood 0,45 0,45 0,45 0,45 0,45 0,45 0,15 0,15 0,15 Biomass: Other Biomass: Other 0,33 0,33 0,33 0,16 0,13 0,17 Metal ores: Iron Metal ores: Iron 0,59 0,59 0,59 0,59 0,59 Metal ores: nonferrous metals ferrous metals Metal ores: non- 2,84 3,62 4,45 0,11 2,10 0,11 Non-met. min.: Construction minerals Non-met. min.: Construction minerals 0,15 0,15 0,15 0,16 0,16 0,16 0,04 0,04 0,03 Non-met. min.: Industrial minerals Fossil energy materials/carriers Table 3: Non-met. min.: Industrial minerals Fossil energy materials/carriers 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,11 1,75 1,62 0,21 7,11 7,38 7,78 0,09 0,04 0,04 Rucksack-factors of domestic extraction. Data sources: Eurostat MFA data, Wuppertal Institute TMR data; own calculations. For France, Germany and Italy the Acosta and Schütz dataset facilitates the calculation of ratios of unused domestic extractions to respective direct material flows (see Table 3 in this regard). At a first glance, distinct time trends do not figure out significantly from this crosscountry comparison. Furthermore, the individual rucksack-factors do not seem to differ substantially between Member States. Most striking national characteristics are observable for the unused domestic extraction of fossil energy materials/carriers. 8 A supplemental regression analysis revealed that this observation seems to be induced by national variations in the corresponding energy-shares of coal. We therefore decided to rate national annual TMR figures of individual EU Member States for the sample according to the following rucksack factor assumptions: 9 - Fossil energy materials/carriers: o In all countries the parameters of the just mentioned regression analysis have been applied. 8 9 For being precise, we should annotate that some Italian observations might also raise readers concerns. See the Italian observations for categories Biomass: wood / Metal ores: non-ferrous metals / Non-metallic minerals: construction minerals in this regard. However, these categories do not contribute significantly to overall Italian TMR dynamics. Thus, we omitted further explorative analyses in this regard. Due to space constraints, we omit a detailed discussion of TMR calculations for disaggregated material flows, like, e.g., excavation and dredging, which has been directly linked to the economic activity of the construction sector. However, supplemental information are of course available upon request by the authors.

8 Distelkamp_Meyer_Meyer_Groningen_2012.doc per capita in tons (2007) Germany ,41 Poland ,09 Greece ,07 Czech Rep ,78 France ,32 Spain ,74 Great Britain ,23 Bulgaria ,74 Romania ,59 Italy ,71 Estonia ,51 Finland ,62 Ireland ,39 Netherlands ,73 Sweden ,67 Belgium ,08 Austria ,36 Hungary ,28 Portugal ,34 Denmark ,87 Slovenia ,70 Slovakia ,92 Lithuania ,33 Latvia ,95 Luxemburg ,16 Cyprus ,36 Malta ,56 Table 4: Time series of unused domestic extraction in millions of tons for EU-27 countries. Data sources: own calculations. - For the rucksack-factors of the remaining material categories: o For France, Germany and Italy the years 2001 to 2004 have been interpolated. o In case of the remaining 24 countries, rucksack-factors have been calculated as average values of the Table 3 observations. Due to missing observations, national rucksack-factors have to be assumed to remain constant from 2006 onwards. The resulting findings are illustrated within Table 4. The results show that the unused domestic extraction has increased in 22 countries. Only in five countries (Germany, Poland, Czech Republic, Italy and Hungary) we observe a fall. The other very interesting result is that there are very big differences among the Member States with regard to the per capita values. The range reaches from less than 2 tons (Malta) up to more than 100 tons (Estonia).

9 Distelkamp_Meyer_Meyer_Groningen_2012.doc Whereas we cannot provide a final conclusion with regards to the underlying causes for these differences we would like to remind our readers that a high per capita value might result from one or several influences: - a high per capita value of domestic extraction used, - a high relevance of materials with a relative high rucksack-factor (metal ores; biomass agriculture) within the domestic extraction used, - a high share of coal within the extraction of fossil energy materials/carriers. 2.4 CALCULATING HIDDEN FLOWS ASSOCIATED TO IMPORTS FOR EU27 MEMBER STATES The procedure outlined above can be straightforwardly carried over to the task of estimating hidden flows associated to imports. The relation between the later and the corresponding direct material flows (imports) was again observable for France, Germany and Italy for the years 1995, 2000 and See Table 5 for the implied rucksack-factors. In light of these observations, we decided to calculate national time series of rucksack-factors in line with the following assumptions: - For France, Germany and Italy the 2001 to 2004 figures have been interpolated. - For the 24 remaining countries the rucksack-factors have been calculated as averages of the available nation observations.

10 Distelkamp_Meyer_Meyer_Groningen_2012.doc Material intensity (rucksack-factor) in kg per kg Material flow category France Germany Italy Biomass: Agriculture 4,56 4,42 2,75 6,99 6,63 8,94 8,09 6,45 Biomass: Wood 0,20 0,19 0,17 0,19 0,21 0,36 0,32 0,28 Biomass: other products 8,87 8,97 8,33 8,77 9,08 8,65 9,38 10,50 mainly from biomass Metal ores: iron and products mainly from 3,97 5,47 5,17 4,45 4,80 4,93 5,47 5,99 iron/steel Metal ores: non-ferrous metals and products mainly 50,02 45,16 44,68 53,54 50,54 58,15 78,76 65,37 from non-ferrous metals Metal ores: other metals and products mainly from 7,61 7,86 9,04 7,31 7,13 20,14 10,83 9,39 metals Non-met. min.: 0,60 0,59 0,59 0,60 0,64 0,86 0,80 0,73 construction minerals Non-met. min.: Industrial 1,06 0,87 0,50 0,47 0,50 0,63 0,88 0,74 minerals Non-met. min.: other products mainly nonmetallic 0,68 0,79 0,94 1,43 1,42 1,65 0,99 1,42 minerals products Fossil energy 0,58 0,63 0,82 0,96 1,14 0,99 0,95 1,09 materials/carriers Others and waste 9,34 10,8 10,84 6,73 6,74 8,70 10,98 9,05 Table 5: Rucksack-factors of imports. Data sources: Eurostat MFA data, Wuppertal Institute TMR data; own calculations. Due to missing observations, national rucksack-factors have to be assumed to remain constant from 2006 onwards. The resulting findings are illustrated within Table 6. Apparently, hidden flows associated to imports seem to have increased in all 27 countries. But again we can infer significant differences among Member States per capita values: The range reaches from 10 tons (Romania) up to nearly 150 tons (Luxembourg). These distinct national characteristics will presumably be effected by - high per capita value of imports, - predominant import-shares of materials with relative high rucksack-factors (metal ores; other products mainly from biomass; others and waste).

11 Distelkamp_Meyer_Meyer_Groningen_2012.doc per capita in tons (2007) Germany ,55 Italy ,51 France ,46 Netherlands ,70 Spain ,86 Great Britain ,77 Belgium ,39 Poland ,93 Austria ,02 Sweden ,44 Ireland ,16 Czech Rep ,66 Greece ,44 Finland ,99 Denmark ,41 Portugal ,15 Romania ,14 Slovakia ,66 Hungary ,02 Bulgaria ,59 Slovenia ,76 Luxemburg ,17 Lithuania ,94 Estonia ,40 Latvia ,69 Cyprus ,26 Malta ,62 Table 6: Time series of hidden flows associated to the imports in millions of tons for EU-27 countries. Data sources: own calculations

12 Distelkamp_Meyer_Meyer_Groningen_2012.doc CALCULATING TMR SERIES FOR EU27 MEMBER STATES per capita in tons (2007) Germany ,58 France ,99 Poland ,10 Spain ,57 Italy ,24 Great Britain ,17 Netherlands ,26 Belgium ,05 Czech Rep ,11 Greece ,06 Romania ,61 Sweden ,13 Austria ,60 Ireland ,16 Finland ,62 Portugal ,90 Bulgaria ,28 Denmark ,22 Hungary ,59 Slovakia ,35 Slovenia ,52 Estonia ,96 Lithuania ,00 Latvia ,31 Luxemburg ,72 Cyprus ,01 Malta ,17 Table 7: Time series of TMR in millions of tons for EU-27 countries. Data sources: own calculations. Last but not least we can add all material flow categories to the TMR. Results have been summarized in the table above. Except for Italy, we observe an increase in TMR for all Member States. It can also be observed that big differences in the per capita values do not only apply with regards to individual subcategories but also with reference to the overall TMR. In addition Figure 1 illustrates the differences with respect to the composition of TMR between the Member States.

13 Distelkamp_Meyer_Meyer_Groningen_2012.doc Figure 1: Composition of TMR in the EU27-countries in the year % 20% 40% 60% 80% 100% Germany France Poland Italy Spain Great Britain Netherlands Belgium Czech Republic Greece Romania Sweden Austria Ireland Finland Portugal Bulgaria Denmark Hungary Slovakia Slovenia Estonia Lithuania Latvia Luxemburg Cyprus Malta Domestic extraction used Unused domestic extraction Imports Hidden flows associated to the imports In the majority of the countries the imports and their rucksacks represent more than 50% of TMR. In Malta and Luxembourg domestic activities contribute less than 10% to TMR. But on the other hand, there remain a couple of countries (Poland, Greece, Estonia) whose domestic activities contribute to about 80% to TMR. Up to now the presented results distinguished only between the four main TMR-categories (domestic extraction used, unused domestic extraction, imports, hidden flows associated to the imports). But within these categories the calculations also distinguish different material flows. Altogether the TMR module calculates for each Member State material flows for 40 different material categories (see Appendix A for a detailed overview on the year 2005 figures). The next table highlights the most important ones for any Member State. These calculations refer to the year Table 8 shows, that for some countries one single of the 40 different material flows is responsible for more than 50% of TMR.

14 Distelkamp_Meyer_Meyer_Groningen_2012.doc material category share of TMR material category Germany Fossil Energy Materials/Carriers 28,6% France Non metallic minerals: Construction minerals 14,9% share of TMR material category share of TMR 10,5% Non metallic minerals: Construction minerals 9,7% 10,4% Non metallic minerals: Excavation and Dredging 9,5% Poland Fossil Energy Materials/Carriers 52,0% Non metallic minerals: Construction minerals 6,3% Fossil Energy Materials/Carriers 5,8% Italy Spain Non metallic minerals: Construction minerals 20,1% 16,7% Non metallic minerals: Construction minerals 16,4% iron/steel 10,0% 14,5% Non metallic minerals: Excavation and Dredging 7,9% Great Britain Non metallic minerals: Construction minerals 11,9% Biomass: Other products mainly from biomass 10,8% Fossil Energy Materials/Carriers 8,1% Netherlands Biomass: Other products mainly from biomass 16,2% Biomass: Agriculture 10,0% Belgium 15,1% Biomass: Other products mainly from biomass 14,5% iron/steel Czech Republic Fossil Energy Materials/Carriers 50,5% Non metallic minerals: Construction minerals 7,4% iron/steel Greece Fossil Energy Materials/Carriers 58,8% Fossil Energy Materials/Carriers 6,5% Non metallic minerals: Construction minerals 4,5% Romania Non metallic minerals: Construction minerals 25,7% 14,1% Fossil Energy Materials/Carriers 12,9% Sweden Non metallic minerals: Construction minerals 13,9% 11,0% Biomass: Wood 8,9% Austria 13,4% Non metallic minerals: Construction minerals 12,6% iron/steel 10,0% Ireland 30,2% Non metallic minerals: Industrial minerals 13,1% Non metallic minerals: Construction minerals 8,8% Finland Non metallic minerals: Construction minerals 18,3% Fossil Energy Materials/Carriers 14,4% 12,6% Portugal Non metallic minerals: Construction minerals 27,0% Biomass: Other products mainly from biomass 11,4% Biomass: Agriculture 8,3% Bulgaria Fossil Energy Materials/Carriers 49,0% 9,9% 8,8% 5,7% 10,6% Non metallic minerals: Construction minerals 8,2% Denmark Non metallic minerals: Construction minerals 17,4% Biomass: Other products mainly from biomass 8,2% Biomass: Agriculture 7,8% Hungary Non metallic minerals: Construction minerals 23,5% Slovakia 14,3% iron/steel Slovenia Fossil Energy Materials/Carriers 19,8% 7,6% Fossil Energy Materials/Carriers 7,3% 14,2% Non metallic minerals: Construction minerals 10,7% 18,7% Non metallic minerals: Construction minerals 14,1% Estonia Fossil Energy Materials/Carriers 63,4% Fossil Energy Materials/Carriers 7,0% Non metallic minerals: Construction minerals 5,7% Lithuania Non metallic minerals: Construction minerals 13,4% Fossil Energy Materials/Carriers 9,9% Biomass: Agriculture 9,8% Latvia Biomass: Wood 25,3% Non metallic minerals: Construction minerals 12,9% Biomass: Wood 8,9% Luxemburg Most important material flow Second most important material flow Third most important material flow iron/steel 26,2% 13,9% Others 9,2% Cyprus Non metallic minerals: Construction minerals 32,1% Biomass: Other products mainly from biomass 9,9% Biomass: Agriculture 8,2% Malta Biomass: Other products mainly from biomass 18,9% Biomass: Agriculture 15,6% Others 10,7% Table 8: Legend: = Domestic extraction used = Unused domestic extraction = Imports = Hidden flows associated to the imports The most important material flows within TMR for EU-27 countries Another finding of these calculations is that there are a few material flows that rank among the most important ones in many of the Member States. This applies to the domestic extraction used of construction minerals and to the hidden flows associated to the imports of non-ferrous metals and products mainly from non-ferrous metals. The third astonishing result of Table 8 is that in none of the countries a single material flow of imports counts to the three most important material flows. Background of this result is that for every import material flow (except biomass: wood and non-metallic minerals) the rucksack-factor is bigger than one (compare Table 5). The following figure presents the values for Total material productivity (GDP per TMR) in 2005 and the change in these observations between 2000 and 2007.

15 Distelkamp_Meyer_Meyer_Groningen_2012.doc Figure 2: Level and development of Total material productivity (TMP) for the EU27-countries TMP in 2005 Average: 0,36 0,00 0,20 0,40 0,60 0,80 Great Britain France Italy Malta Denmark Sweden Austria Germany Luxemburg Spain Portugal Cyprus Netherlands Finland Ireland Hungary Belgium Greece Lithuania Slovakia Slovenia Latvia Romania Czech Republic Poland Estonia Bulgaria Change of TMP from 2000 to 2007 in % Average: + 2,8% -20,0% -10,0% 0,0% 10,0% 20,0% 30,0% Luxemburg Latvia Poland Czech Republic Greece Estonia Bulgaria Great Britain Hungary Italy Ireland France Finland Belgium Spain Germany Sweden Cyprus Romania Denmark Netherlands Slovakia Portugal Lithuania Slovenia Austria Malta Our last excercise within the description of historical TMR developments apply to the TMR without fossil energy materials/carriers. Incentive for this analysis is on the one hand that in many countries the fossil energy materials/carriers play a quite dominant role within TMR, so that developments within the other material categories are covered. On the other hand fossil energy materials/carriers are not only subject of resource efficiency policy but also a topic of climate change policy. We call the indicator Total material requirement without fossil energy materials/carriers TMR NE. The following table shows the results for this indicator:

16 Distelkamp_Meyer_Meyer_Groningen_2012.doc per capita in tons (2007) Germany ,07 France ,83 Spain ,88 Italy ,50 Great Britain ,94 Netherlands ,78 Poland ,47 Belgium ,82 Romania ,35 Austria ,63 Sweden ,76 Ireland ,13 Czech Rep ,92 Portugal ,09 Finland ,97 Denmark ,44 Greece ,62 Hungary ,07 Slovakia ,73 Bulgaria ,50 Slovenia ,51 Lithuania ,26 Latvia ,06 Luxemburg ,87 Estonia ,21 Cyprus ,24 Malta ,16 Table 9: Time series of TMR NE in millions of tons for EU-27 countries. Data sources: own calculations. The results show that not only TMR but also TMR NE has increased in all Member States except Italy. But we can also observe that the differences in the per capita values are smaller than in the overall TMR. In analogy to Figure 1 the following figure illustrates the differences with respect to the composition of TMR NE between the Member States.

17 Distelkamp_Meyer_Meyer_Groningen_2012.doc Figure 3: Composition of TMR NE in the EU27-countries in the year % 20% 40% 60% 80% 100% Germany France Italy Spain Great Britain Belgium Netherlands Poland Romania Sweden Austria Ireland Czech Republic Finland Portugal Denmark Hungary Greece Slovakia Bulgaria Slovenia Lithuania Latvia Luxemburg Estonia Cyprus Malta Domestic extraction used Unused domestic extraction Imports Hidden flows associated to the imports Again for the majority of countries imports and their implied rucksacks represent more than 50% of TMR NE. But now the maximum contribution of domestic material flows to TMR NE appear very much smaller (64% in Latvia) than with regard to overall TMR. The next table gives an insight which of these material flows has been the most important ones within the TMR NE of every single country in the year If we compare these results with the ones for overall TMR (Table 8), we see that single material flows achieve a predominant role less frequently. Another difference is that now some biomass material flow categories attract notice. But there are also some similarities. Again the domestic extraction used of construction minerals and hidden flows associated to the imports of metal ores rank among the three most important material flows in quite a lot of countries.

18 Distelkamp_Meyer_Meyer_Groningen_2012.doc material category share of share of share of material category material category TMR NE TMR NE TMR NE Germany 18,3% Non metallic minerals: Construction minerals 16,9% Biomass: Other products mainly from biomass 11,8% France Non metallic minerals: Construction minerals 16,9% 11,8% Non metallic minerals: Excavation and Dredging 10,8% Italy 20,1% Non metallic minerals: Construction minerals 19,8% iron/steel 12,0% Spain Non metallic minerals: Construction minerals 24,6% 17,7% Non metallic minerals: Excavation and Dredging 9,6% Great Britain Non metallic minerals: Construction minerals 15,1% Biomass: Other products mainly from biomass 13,8% Non metallic minerals: Excavation and Dredging 10,3% Belgium 18,2% Biomass: Other products mainly from biomass 17,5% iron/steel iron/steel Netherlands Biomass: Other products mainly from biomass 21,3% Biomass: Agriculture 13,1% Poland Non metallic minerals: Construction minerals 15,9% Biomass: Agriculture 13,0% Romania Non metallic minerals: Construction minerals 33,7% 18,5% Biomass: Agriculture 9,6% Sweden Non metallic minerals: Construction minerals 15,8% 12,6% Biomass: Wood 10,1% Austria 15,1% Non metallic minerals: Construction minerals 14,3% iron/steel 11,3% Ireland 34,0% Non metallic minerals: Industrial minerals 14,8% Non metallic minerals: Construction minerals 9,9% Czech Republic Non metallic minerals: Construction minerals 18,7% iron/steel Finland Non metallic minerals: Construction minerals 24,1% 14,4% 16,7% iron/steel Portugal Non metallic minerals: Construction minerals 30,7% Biomass: Other products mainly from biomass 13,0% Biomass: Agriculture 9,4% Denmark Non metallic minerals: Construction minerals 21,6% Biomass: Other products mainly from biomass 10,1% Biomass: Agriculture 9,7% Hungary Non metallic minerals: Construction minerals 29,3% 9,5% Biomass: Agriculture 8,9% Greece Non metallic minerals: Construction minerals 15,5% 10,8% Biomass: Other products mainly from biomass 9,6% Slovakia 17,0% iron/steel 16,9% Non metallic minerals: Construction minerals 12,8% Bulgaria 27,3% Non metallic minerals: Construction minerals 21,1% iron/steel 9,0% Slovenia 25,6% Non metallic minerals: Construction minerals 19,2% Biomass: Other products mainly from biomass 15,0% Lithuania Non metallic minerals: Construction minerals 17,0% Biomass: Agriculture Metal ores: Other metals and products mainly 12,4% from metals 10,8% Latvia Biomass: Wood 27,6% Non metallic minerals: Construction minerals 14,1% Biomass: Wood 9,7% Luxemburg Most important material flow Second most important material flow Third most important material flow iron/steel 27,8% 10,6% 13,0% 12,3% 10,8% 7,8% 14,7% Others 9,8% Metal ores: Other metals and products mainly Estonia Non metallic minerals: Construction minerals 21,1% Biomass: Other products mainly from biomass 10,0% from metals Cyprus Non metallic minerals: Construction minerals 37,1% Biomass: Other products mainly from biomass 11,4% Biomass: Agriculture 9,5% Malta Biomass: Other products mainly from biomass 22,0% Biomass: Agriculture 18,1% Others 12,4% 8,5% Table 10: Legend: = Domestic extraction used = Unused domestic extraction = Imports = Hidden flows associated to the imports The most important material flows within TMR NE for EU-27 countries Data sources: own calculations. The following figure presents the values for Total material productivity (GDP per TMR NE ) in 2005 and the change in these observations between 2000 and 2007.

19 Distelkamp_Meyer_Meyer_Groningen_2012.doc Figure 4: Level and development of Total material productivity (TMP NE ) for the EU27-countries TMP NE in 2005 Average: 0,52 0,00 0,20 0,40 0,60 0,80 1,00 Great Britain France Germany Italy Greece Malta Denmark Sweden Austria Netherlands Spain Cyprus Luxemburg Portugal Finland Ireland Hungary Belgium Czech Republic Poland Estonia Lithuania Slovenia Slovakia Latvia Romania Bulgaria Change of TMP NE from 2000 to 2007 in % Average: - 0,2% -30,0% -20,0% -10,0% 0,0% 10,0% 20,0% 30,0% Luxemburg Latvia Italy Great Britain Finland France Ireland Belgium Greece Germany Poland Cyprus Spain Sweden Hungary Denmark Netherlands Romania Portugal Estonia Czech Republic Austria Slovakia Lithuania Bulgaria Malta Slovenia 3 INTEGRATED MATERIAL FLOW PROJECTIONS WITH GINFORS Trying to keep the manuscript as clearly arranged as possible, the following subsection only present a rather rough sketch of basic GINFORS modeling principles supplemented by a short outlook on the chosen specifications for TMR modeling. Readers being interested in complementary technical details are referred to Barker et al. (2011), Lutz et al. (2010) as well as Lutz and Meyer (2009) and the references therein. 3.1 GINFORS: BASIC MODELING PRINCIPLES GINFORS constitutes a multi-region input-output model which facilitates global policy analyses of prospective economic developments and their associated impacts on the environment. According to Wiedmann et al. (2007), GINFORS represents one of only six major modeling frameworks known from the literature which sufficiently employ multisector, multi-region input output analysis for estimating the environmental impacts embodied

20 Distelkamp_Meyer_Meyer_Groningen_2012.doc in international trade. In any case, recent international applications, i.a. within the 5th and 6 th framework program (MOSUS project, see Giljum et al. 2008; ALARM project, see Stocker et al. 2012) as well as for the Anglo- German Foundation (petre project, see Lutz 2010 or Lutz and Meyer 2010) verified its powerful simulation capabilities. The GINFORS model maps international trade flows in accordance with structural economic statistics provided by OECD (Bilateral Trade Statistics, Structural Analysis Database, Input- Output database). The applied modelling philosophy rests on econometric grounding, i.e.: as far as possible, all model parameters should be subjected to statistical falsification tests. 10 This modeling framework evolved from the COMPASS model (Meyer and Uno 1999, see also Uno 2002 for a related literature review). Rooted in the well-defined macroeconomic system of national accounts GINFORS thus merges econometric-statistical methods with inputoutput analysis to forecast international trade developments resulting from global competition on a soundly disaggregated level (see Meyer et al for more information in this regard). GINFORS overall modeling philosophy is therefore closely related to the INFORUM approach (see Almon 1991). Energy-emission simulations, tracing energy consumption by major energy carriers, are available for all countries and regions with national CO2 emissions being linked to fossil energy consumption by constant carbon relations. However, most recent model refinements focussed on the endogenisation of EU Member States material consumption as outlined within the following subsections. 3.2 INTEGRATING MATERIAL FLOW PROJECTIONS The basic procedure for integrating material flow projections into the GINFORS simulation system rests on the model s economic input-output backbone. Thus, the following annotations might be best understood with regards to Figure 5, which illustrates the model s multinational feedback interrelationships initiating from bilateral trade projections. As a demonstrative exposition, it is not intended to capture all model dynamics. Furthermore, we abstain from trying to illustrate GINFORS overall country coverage. 10 See also Barker and Scrieciu (2010) for basic arguments in favour of the econometric time series modelling approach as opposed to CGE modelling.

21 Distelkamp_Meyer_Meyer_Groningen_2012.doc Figure 5: GINFORS model - stylized architecture of the economic backbone Starting from the top, the national Input Output (IO) GINFORS-submodels of three arbitrary national economies have been clockwise arranged (depicted by rectangles), with the fourth remaining rectangle (labelled ) being intended to recall the reader that overall, 53 national economies together with 2 world regions (OPEC and Rest of World) are modelled within GINFORS. Each of these dynamic input output models represents the national interrelationships between gross production, prices, employment, wages and investment per sector as well as the individual growth patterns of intermediate and final demand for 48 industries. The central part of this diagram represents the multi-national trade model which is based on bilateral trade matrices for the complete set of OECD countries, any EU-27 Member State and sixteen further major economies. Based on historical multi-national trade patterns of 25 commodities and one service good, our simulation approach thus allows global economic developments to spill-over to national quantity and price dynamics and vice versa. We may therefore get out of these annotations that GINFORS assembles national economic submodels towards a closed and dynamic multi-national input output simulation system. Focussing on real economic activities, a comprehensive set of economic variables is projected by each simulation run.

22 Distelkamp_Meyer_Meyer_Groningen_2012.doc For an arbitrary GINFORS IO-submodel, let X denote this overall set of endogenously determined national economic variables. Furthermore, let Z denote a vector of national TMR series with subscript i indicating the individual accounting categories (Domestic Extraction Used, Unused Domestic Extraction, Imports, Hidden Flows associated to imports). Then, the general task of integrating material flow projections within the GINFORS model might straightforwardly be implemented by the introduction of coherent links between macroeconomic developments and their induced material flows. More formally, for any Member State s TMR accounting category, we had to find reasonable specifications for the interdependencies of Zi and X, i.e., we had to specify Zi = Zi ( X ), i = 1,...,4. i Lacking sufficient bottom-up information we decided to tackle this task by means of a selfevident statistical approach: Let us assume that, for a given national TMR series z Z, the specification of z t focuses on one intrinsic time series of x t X, i.e., z t = z t ( x t ). Historical material intensities γ t might then be gauged as zt γ t =. x A statistical analysis of the underlying long-run trends inγ t p projected future developmentsγ t. Projections of x t, denoted by t might then be applied to derive p x t, are already provided by the GINFORS IO-models. Accordingly the projection of z t might then be computed as t i z p t = γ x. p t p t For the analysis presented within this paper we relied on long run material intensity forecasts p γ t provided by Giljum and Lugschitz (2011). Due to space constraints we omit a detailed replication of their findings and proceed with some additional details referring to our chosen associations between individual material flows and their related economic drivers. On the domestic side national material intensities were defined as relation between the material flows Domestic Extraction and gross output in constant prices of corresponding extracting sectors. See Table 11 in this regard. The corresponding historical calculations were based on the MFA data of Eurostat. The gross output at basic prices in current prices was also taken from the Eurostat database and combined with historical price indices from the EU-KLEMS database.

23 Distelkamp_Meyer_Meyer_Groningen_2012.doc Material Flow γ t defined as material flow domestic extraction divided by output of sector Biomass: Agriculture Biomass: Wood Agriculture, hunting, forestry and fishing Biomass: Other Metal ores: Iron Metal ores: Non-ferrous metals Mining and quarrying (non-energy) Non-metallic minerals: Construction minerals Non-metallic minerals: Industrial minerals Fossil energy materials/carriers Mining and quarrying (energy) Table 11: Definition of material intensities domestic extraction. Imported materials are also provided as soundly disaggregated time series by the EUROSTAT database. However, compared to the case of Domestic Extraction, the identification of driving economic variables for individual material imports is far from obvious. But fortunately, the initial dataset provided by Acosta and Schütz combined imported material flows with individual CPA codes which identify the imported products. Imports in current prices are given in deep sectoral disaggregation as time series in the STAN dataset of OECD. The calculation of time series in constant prices was possible by using the GINFORS import price indices. Thus, we were able to calculate material intensities for imported materials according to the mapping given by Table 12.

24 Distelkamp_Meyer_Meyer_Groningen_2012.doc Material Flow Biomass: Agriculture Biomass: Wood Biomass: Other products mainly from biomass Metal ores: Iron + products mainly from iron/steel Metal ores: Other metals and products mainly from metals Non-metallic minerals: Construction minerals Non metallic minerals: Industrial minerals Non metallic minerals: Other products mainly non-metallic mineral products Fossil Energy Materials/Carriers Others Table 12: γ t Definition of material intensities imports. defined as material flow imports divided by imports of Agriculture, hunting, forestry and fishing Food products, beverages and tobacco Wood and products of wood and cork Pulp, paper, paper products, printing and publishing Mining and quarrying (non-energy) Iron & Steel Mining and quarrying (non-energy) Non-ferrous metals Fabricated metal products, except machinery & equipment Machinery & equipment, nec Office, accounting & computing machinery Motor vehicles, trailers & semi-trailers Building & repairing of ships & boats Aircraft & spacecraft Railroad equipment & transport equip nec. Mining and quarrying (non-energy) Other non-metallic mineral products Mining and quarrying (energy) Coke, refined petroleum products and nuclear fuel Textiles, textile products, leather and footwear Pulp, paper, paper products, printing and publishing Chemicals excluding pharmaceuticals Pharmaceuticals Rubber & plastics products Electrical machinery & apparatus, nec Radio, television & communication equipment Medical, precision & optical instruments Manufacturing nec; recycling (include Furniture)

25 Distelkamp_Meyer_Meyer_Groningen_2012.doc SIMULATION RESULTS Total material productivity in 2030 (GDP in constant prices in Euro / TMR in kg) UK France Sweden Italy Malta Germany Austria Cyprus Spain Denmark Luxembourg Ireland Netherlands Greece Hungary Finland Portugal Belgium Lithuania Romania Latvia Poland Slovenia Slovakia Czech Rep. Estonia Bulgaria Latvia Slovakia Hungary Cyprus Luxembourg Portugal Lithuania Slovenia Finland Malta Belgium Denmark Italy Ireland Sweden UK Poland Spain Estonia Czech Rep. Netherlands Bulgaria France Austria Germany Greece Romania Average annual growth rate of TMR between 2010 and 2030 (in %) -2.0% -1.0% 0.0% 1.0% 2.0% 3.0% Figure 6: Differences in Total material productivity (2030) and in growth of TMR among the EU27- countries; GINFORS simulation results. This Section summarises the findings of an exemplary GINFORS simulation experiment. For this, we calibrated main economic aggregates (namely national GDPs) in order to meet the PRIMES growth rates projections until These result in a 1.8% average growth rate for accumulated EU27 real GDP over the period and, respectively, of 2.2% for Since the unadjusted baseline dynamics of the GINFORS model were not far off the PRIMES paths, it was possible to meet more or less for every country the PRIMES developments. Exogenous raw material prices were assumed to maintain their individual cross-correlations with historical oil price dynamics. In relation to its 2010 levels we assume the nominal oil price to increase by 123% until Accordingly, the price for wheat is assumed to increase by 20% until 2030, prices for copper and iron ore by 58% and, respectively, 62%. Due to space constraints the following pages focus on the resulting TMR projections only Supplemental information are of course available upon request by the authors.