Calculation of material flow indicators with the use of combined IOT-LCA model: a Eurostat approach Karl Schoer1, Jan Kovanda3, Jürgen Giegrich2, Christoph Lauwigi2, Axel Liebich2, Jan Weinzettel3, Stephan Moll4 Sustainable Solutions Germany (SSG)1, Institute for Energy and Environmental Research (IFEU)2, Charles University Environmental Center (CUEC)3, Eurostat4 EEA RME workshop, 26-27 April, Copenhagen, Denmark

Background Eurostat projects „Conceptual framework for measuring the environmental impact of the use of natural resources and products“, „Assistance in the development and maintenance of Raw Material Equivalents conversion factors and calculation of RMC time series“ Major goal: further development of material flow indicators used by Eurostat for monitoring of EU Sustainable Development Strategy Domestic Material Consumption indicator (DMC): domestic extraction of raw material and biomass (DE) plus imports (IM) minus exports (EX) Two assymetries related to DMC: DE vs. IM/EX, IM vs. EX Solution: IM and EX in terms of raw materials needed for their production – Raw material equivalents (RME) of imports (RMEIM) and exports (RMEEX) → Raw Material Consumption (RMC) Additional goals: establishing an ecological link of RMC (detailed disaggregtion plus pressure profiles) and an economic link (relating RMC to final demand of products)

RMEIM/RMEEX = F. (I – A)-1. IM/EX Approach (1) Starting point: standard IO product-by-product model... q = (I – A)-1. y A = S . (diag(q-im))-1 Where q is the total product supply, I is the identity matrix, A is the technology coefficients matrix, y is the total final demand, S is the IO matrix for intermediates and im is the vector of total imports. …with environmental extension e = F. (I – A)-1. y F = F_r . (diag(q-im))−1 Where e is the induced material flows and F_r is a matrix of DE by sectors in absolute values (in 1000 tons). This would allow for calculation of RMEIM, RMEEX and RMC: RMEIM/RMEEX = F. (I – A)-1. IM/EX RMC = F. (I – A)-1. domestic final demand = DE + RMEIM – RMEEX

Approach (2) Above model assumes that the imported commodities are produced abroad using the same production technology as the identical commodities at home → this may introduce severe distortions Possible solutions: Building a multi-regional input output model, which uses country specific input-output tables (EXIOPOL, PetrE, GTAP projects) Integration of life cycle inventory (LCI) data into the model for commodities, for which the assumption of the same production technology does not hold Eurostat project: 2nd approach – LCI data for crude oil, natural gas, metal ores, basic metals (sc. LCA products) Imported LCA products are treated as being produced by domestic economy: cumulated material requirements embodied in these products are incorporated into the matrix of DE and there are changes made in IOT (imports of LCA products are set to zero, rows and columns are added for LCA products).

Approach (3): Adjusted IOT and F_r P_LCA P1 P2=P_LCA P3 Y TU=q IM IM_adjusted ic=0 ic=im y=0 ∑ic + y im im=0 ic y P-product,ic-intermediary consump., Y,y-final demand, TU-total product use, q-total product supply, IM,im-import A = S . (diag(q-im_adjusted))-1 S_LCA S1 S2 S3 S4 S5 S6 RM1 imf*LCI de RM2 RM3 RM-raw material, S-homogeneous sector, imf-import in tons, LCI-life cycle inventiory coefficient, de-domestic extrac. F = F_r_adjusted . (diag(q-im_adjusted))−1

Data Expanded hybrid input output tables (expHIOT166) for EU 27 of the size 166 product groups x 166 homogeneous branches Based on standard 60 x 60 MIOT from Eurostat, which were: 1) Expended into 166 format by disaggregating environmentally important branches: agriculture, extraction industries, primary processing, manufacturing. Mostly based on German IOT and EU data from COMEXT, the structural business statistics (SBS) and agricultural statistics. 2) Hybridized into HIOT. Based on data from energy balance, COMEXT, EW-MFA, etc. Expanded vectors for domestic extraction of raw materials by 166 homogeneous branches and 52 raw material categories Cumulated material requirements embodied in imported LCA products (LCI data from Ecoinvent database, specific LCI data for metals based on country and mine specific information)

Specific calculation issues Treatment of scrap and secondary metal RMEIM and RMEEX of scrap and secondary metal were assumed to be zero The import and export of basic metal products which are made from secondary metal have also to be neglected. For estimating the proportion of secondary metal the world average for each metal product was applied. External trade data for gold Measured in RME, the imports and exports of gold account for roughly 20 to 30 % of all metal imports or exports Trade statistics in weight units appear to be extremely erratic, but figures in Euro seem to be much more plausible Monetary values were taken and converted into tonnes of metal content with the use of annual gold price

Calculation set-up Calculation was carried out for EU 27 for benchmark years of 2000 and 2005, which resulted in: RMEIM, RMEEX and RMC in absolute values Coefficients showing RME for particular raw materials per unit of imported/exported commodity Coefficients were further extrapolated for the period of 2000-2010 using a simple linear extrapolation A method was developed for maintaining/updating of RME coefficients and derived indicators 7 updating methods were tested including the use of constant RME coefficients and/or Lentief inverse over time or the use of less disaggregated tables An approach was selected which uses expHIOT166, its compilation is however simplified compared to project approach

* Excludes data for Estonia, Greece, Malta and Finland

Strengths and limitations of the approach Takes into account upstream flows of all product groups and services in their whole production cycle Addresses the issue of domestic technology assumption for the most striking cases for which this assumption does not hold Due to high level of disaggreagtion the approach allows for studying the environmental and economic links of the indicators (attribution of pressure profiles to particular raw materials; relating the demand for particular raw materials to the final use of particular products) Lower data and compilation time requirements compared to MRIOT approach Allows for a calculation in excel sheets Average technology of production for LCA products and domestic technology assumption for other products Regionalization…

EW-MF balance, Czech Republic, 2008 (Mt) Source: CUEC; Note: IFIM estimated with the use of coefficient approach

EW-MF balance, Czech Republic, 2008 (Mt) Source: CUEC; Note: IFIM estimated with the use of combined IOT/LCA approach

Thank you for your attention jan.kovanda@czp.cuni.cz