1. Total Material Resource Use – The Coefficient Approach (CA)
Dr. Helmut Schütz José Acosta Fernández
Material Flows and Resource Management – RG 3
Wuppertal Institute
Presentation
Workshop „Material Resource Use embodied in imports and exports“ April 2012
Copenhagen

2. The presentation Intro Brief description/explanation of approach,
Coverage (time and geography),
Main assumptions made, incl. strengths and limitations of the approach,
Data requirements (detailed listing or table),
Workload required (in man-month or k€ to produce estimates of material extractions embedded in traded products to EU for one year),
Experiences with results
Further development – Outlook
Special issues

3. Material Input Indicators3

4. Material flows and derived indicators
Material Input
Material Consumption
Material Output

5. 1. Brief description/explanation of approach
Indirect flows (IF) coefficients (multipliers) are used to account for total upstream indirect Material Resource Use, i.e. without differentiation between used and unused material resource extraction
IF coefficients are LCI-type data from cradle to gate, largely from Material Intensity (MI) studies at WI, that means, separation of used and unused materials is not possible without full access to the data and also per se not foreseen; only in case of own coefficients the distinction could be done
In some cases, IF data are available at the level of country of origin (raw materials), in other cases region is as specified by MI-values (e.g. World, Europe, Germany) or own data (often for Germany)
IF data are always differentiated by abiotic material, biotic material, soil erosion
Linkage to external trade data is at HS6 level (ca commodities - but not all covered)
Auxiliary data are used to determine specific properties of commodities – metal content in concentrates from statistics or literature
Precious metals and diamonds are identified by value/mass relation 5

6. 2. Coverage (time and geography),
Time: - as LCI (MI) data are specified - seldom unambiguously specific by year - rarely in time series - exception are few copper data from WRI/US-BOM - and own data derived from annual statistics - what could be annualised (and regionalised) is data for energy use
Geography: - in principle any import by country of origin - in reality the CA refers to country of origin: -- for some raw materials, in particular metals -- and agricultural raw materials to link with FAO yields to account for land use and estimate soil erosion 6

7. 3. Main assumptions made, incl
3. Main assumptions made, incl. strengths and limitations of the approach
Main assumptions: - IF doesn’t change much over time - IF is useful for estimates across all countries -
Strenghts: - relatively quick and easy estimate of total global material resource use - a range of LCI-type IF coefficients (MI) readily available at WI - simple coefficients can be relatively easily derived from statistics (e.g. feed and food) -
Weaknesses: - low specificity over time - low specificity by geo - real LCI-type data are limited (MI-values) - range of products not covered (especially complex ones); services not covered - potentially high IF products not covered or uncertain – precious stones resp. precious metals - uncertainty about secondary materials - mostly same IF for imports and exports - 7

8. 4. Data requirements (detailed listing or table),
Import and export data (direct) at HS6 ~ 7,000 commodities
IF data: - MI-values from WI database - additional MI from WI studies and/or personal communications - Statistical data for own work on coefficients - FAO yields (land use primary crops) - Erosion intensities (own database) - Special literature (e.g. metal mining) - Personal communications (e.g. US-BOM) 8

9. 5. Workload required (in man-month or k€ to produce estimates of material extractions embedded in traded products to EU for one year),
Extended approach or Simplified?
Extended: - imports specific by country - adjust afap for time specific = more elaborated: 5-6 months first compilation, specific IF study may add up to 6-12 months (3-4 months for update)
Simplified: - average coefficients derived from previous work - use constant coefficients over time = quick and dirty: 3-5 days if data structure is there 9

10. 6. Experiences with results
Own application for some countries and EU27, including ETC/SCP working paper 3/2011: Key messages on material resource use and efficiency in Europe
Some NSI – e.g. lately by IFEN, France, with own follow-up study to derive IF more specific for French situation
Applied on global scale on basis UN COMTRADE (M. Dittrich) - Dittrich et al. 2012* - OECD report resource productivity**
Precious metals/minerals are critical and may possibly influence results strongly, they are thus often disregarded to avoid potential flaws
* Dittrich. M., S. Bringezu and H. Schütz (2012, in print): The Physical Dimension of International Trade, Part 2: Indirect Global Resource Flows between 1962 and 2005, Ecological Economics.
** OECD 2011: Resource Productivity in the G8 and the OECD. A Report in the Framework of the Kobe 3R Action Plan. 10

11. 7. Further development – Outlook
Work within CREEA e.g. UDE, Land use , LUC
Requirements are high, therefore find way to get infos with less effort but sufficient precision 11

12. Sensitivity analysis for IF of imported metals to Germany 1991 - 2004
Probability distributions of abiotic IF of imported metals were based on expert judgement of the range of IF coefficients for metals, and done by Monte Carlo simulation over the probability distributions of the IF coefficients (1000 samples) from which the total IF was calculated.
The figure presents the reference value and different percentiles.
With 90% probability the TMR for metals lies in between ± 15% of the reference value.
Source: Saurat, M., Schütz, H., Bringezu, S. (WI)

13. Sensitivity analysis for TMR of Germany 1991 – 2004
Based on the above probability distributions of abiotic IF of imported metals, the result for the entire TMR of Germany was calculated.
The figure presents the reference value and different percentiles.
With 90% probability the TMR of Germany lies in between ± 2% of the reference value.
Source: Saurat, M., Schütz, H., Bringezu, S. (WI)

14. Questions Resources (inputside): Materials Water Air Land
(1) How efficient (productive) uses an economy natural resources (economy-wide to single enterprises)?
 Relation of GDP (or GVA) to Resource indicator(s)
(2) How sustainable is resource consumption of an economy?
 „Safe-Operating-Space“ of resource consumption and fair global distribution (Values for orientation are absolute consumption per capita)
Resources (inputside):
Materials
Water
Air
Land
Categories in each case:
Domestic/foreign
Used/unused resp. intensive/extensive use
regenerative/non-regenerative
References:
domestic production (incl. for Exports)
Domestic consumption
Exports 14

15. Which indicator to choose depends on target question
Where to set the system boundary?
SOCIO-ECONOMY
ENVIRONMENT ?!
sold
„used"
Extraction
Mining / Agriculture / Forestry /
Fishing
DMI-RME
Total Input
Waste "unused" Extraction
TMR
Ratio of Unused / Used
indicates Resource efficiency of primary sectors
Source: Bringezu 2011 15

16. ProgRess – German Resource Efficiency Programme
Unused extraction needs be included
Important step towards TMC
German government aims at ensuring data acquisition in EU and will support international initiatives
If NSI can provide data in sufficient quality and continuously the indicator DMCRME can be extended towards TMC
German government will then take up TMC in reporting
Rucksack data will be checked in regular intervals
Source: BMU (2012): Deutsches Ressourceneffizienzprogramm (ProgRess). Programm zur nachhaltigen Nutzung und zum Schutz der natürlichen Ressourcen. Beschluss des Bundeskabinetts vom 16

17. EW-MFA indicators and policy questions
Table 1: Economy-wide input and consumption indicators: derivation and related policy questions
Type of indicator
Name
Derived by
Policy questions
Input
Direct Material Input (DMI)
Domestic extraction used + imports
How much material is directly used in domestic production and consumption?
Raw Material Input (RMI)
DMI + raw material equivalents of imports
How much raw material is required in domestic production and consumption?
Total Material Requirement (TMR)
RMI + unused domestic extraction + resource requirements of imports
How much primary material is required globally by domestic production and consumption?
Consumption
Domestic Material Consumption (DMC)
DMI - exports
How much material is directly used for domestic consumption?
Raw Material Consumption (RMC)
RMI – exports (incl. ecological rucksacks)
How much raw material is required for domestic consumption?
Total Material Consumption (TMC)
TMR – exports – indirect flows associated with exports
How much of the global primary material requirement is associated with domestic consumption?
Source: Based on Bringezu et al. 2009 17

18. EW-RP indicators and policy questions
Table 2: Economy-wide resource productivity indicators and their related policy questions
Name
Derived by
Policy questions
Direct Material Productivity
GDP / DMI
Is there a decoupling of material use from economic growth over time?
Total Resource Productivity
GDP / TMR
Is there a decoupling of total material resource requirements from economic growth over time?
Material productivity (proxy resource productivity)
GDP / DMC
Is there a decoupling of material consumption from economic growth over time?
Resource productivity
GDP / TMC
Is there a decoupling of total resource consumption from economic growth over time?
Source: Based on Bringezu et al. 2009 18

19. Basket of 16 headline indicators
Table 3: Basket of 16 “headline” indicators
Resource use-oriented indicators
Env. Impact-oriented indicators
Short-term
(1 year)
Medium-term
(2-5 years)
Material use
Domestic Material Consumption (DMC)
Raw Material Consumption (RMC)
Environmentally-Weighted Material Consumption (EMC)
Life-Cycle Resource Indicator* (by JRC)
Energy use and climate
Gross inland energy consumption
Actual primary energy consumption (incl. energy flows “embodied” in trade)
Territorial GHG emissions (UNFCCC/Kyoto)
Carbon Footprint (incl. GHG emissions “embodied” in trade)
Water use
Water abstraction (only blue water)
Water Footprint (blue and green water)
Water Exploitation Index (only blue water; territorial)
Global Water Consumption Index (blue and green; incl. “embodied” water)
Land use
Domestic Land Demand
Actual Land Demand (incl. land use “embodied” in trade)
Human Appropriation of Net Primary Production (HANPP)
eHANPP, LEAC and other indicators on ecosystem quality
Source: BIO Intelligence Service et al
*The Life-Cycle Resource indicator is developed by JRC and not only covers material use, but also provides information on the life-cycle wide
environmental impacts of other resource categories, which are not covered by the suggested set of indicators, such as water pollution.
With regard to the impacts related to traded goods, it is, however, based on a rather limited selection of products (JRC project). 19

20. Key indicators 20 Table 4: Suggestions and availability
Territory or national perspective
Global supply chain or international perspective
Materials
Domestic extraction (used and unused), DMI, DMC*
Available for all EU Member States
TMR and TMC
Available for the EU-27 (aggregated) and some Member States
Land
Artificial land or built-up area (km²)
Available with restrictions in time series
Direct and Indirect land use / "embodied" land for consumption of biomass-based products focussing on cropland (ha)
Available for the EU-27 (aggregated)**
Water
Water exploitation index*** (WEI, %)
Available with restrictions on completeness of data and regional / temporal resolution (river basin / intra-annual variations)
Water footprint or
"Embodied" water
In need of improvement; In need of development
GHG Emissions
GHG emissions (t)
Available
Direct and indirect GHG emissions (both carbon and non-carbon emissions)
Available for selected Member States through statistical offices and for all countries from scientific sources
Table 4: Suggestions and availability
of key indicators from the national and
global perspectives for
materials, land, water and
GHG emissions
Source: O’Brien et al. 2012; Resource efficiency
in European industry. Study for the European
Parliament.
*DMI and DMC do not apply the territory
principle, but account for the nationality of
actors
** See Bringezu et al. (2012)
*** This indicator has limitations; e.g. it
aggregates different water resources,
it does not take into account the nature of the
water use after abstraction, the commonly
used threshold values are under discussion.
The Commission is exploring alternatives,
which are however not yet fully available.
Awaiting improvements, the WEI will be
further used. 20

21. Availability of TMR
COUNTRY
USA
Japan
China
Brazil
EU27
Austria
Czech Republic
Denmark
Finland
France
Germany
Hungary
Netherlands
Italy
Poland
Portugal
Spain
Sweden
Switzerland UK
Venezuela
Extended listing by H. Schütz in O‘Brien et al. 2012 21

22. Many thanks for your attention ! helmut.schuetz@wupperinst.org