EIPOT Final Project Report - Stockholm Environment Institute

ERA-NET SKEP Project EIPOT (www.eipot.eu)
“Development of a methodology for the assessment of global environmental impacts of traded goods and services”
One use of LCA data in a MRIO, for instance, is for the disaggregation of environmental extensions
such as energy use. This approach was used in the EU-FP6 Project EXIOPOL,where for
environmental extension data on energy use, the IEA energy balances were used as the main data
source. These balances comprise around 60 energy commodities which are too broad for EXIOPOL
requirements. Hence, these 60 commodities had to be allocated to detailed industries. The general
approach here was to combine LCA-data on energy use for detailed products/industries with the data
on physical output of the respective detailed products/industries, to calculate the share of industry in
the total energy use of an aggregated group of industries. The main problems in this process were
obtaining physical output data. Alternatively, auxiliary data such as employment numbers were used
(Lutter et al., forthcoming).
5.2 MRIO-specific data requirements
The next step in compiling an MRIO framework, after collecting all the data, is the construction of the
multi-region input-output table. There are several ways to do this, for example by starting from the
trade data or from the input-output data (see Bouwmeester and Oosterhaven (2007) for a suggested
methodology). In constructing the MRIO table, several issues play a role like valuation, disaggregation
of data, balancing and so on. These issues are discussed in this section.
5.2.1 Currency conversion
With respect to currency conversion, Peters (2007) writes: "[Country-specific IO] data eventually
needs to be converted to a common currency [in an MRIO model]. The GTAP solves these problems
simultaneously by scaling the values using GDP data in US$ converted with Market Exchange Rates
(MERs). The trade data is also converted in MERs. In effect this process accounts for inflation and
currency differences. In terms of inflation this process assumes that all sectors have the same inflation
rate. Given the immense size of the database, this is probably the most realistic approach. In terms of
currency conversion several issues arise. For MRIO modelling there has been some discussion on
whether to use Purchasing Power Parity (PPP) or MERs for currency conversions… PPPs are better
for cross-country comparisons of GDP and MERs are better for trade data. In MRIO modelling it might
be best to use some weighting of the two or use other hybrid techniques to help reflect additional
problems due to product and quality differentiation, inflation, and so on. Ideally physical data should be
used where possible, such as for electricity flows. Consistent conversion of data from a range of
countries to a uniform currency and year (via inflation) is an area that needs further investigation,
particularly in regards to the correct use of MERs and PPPs."
5.2.2 Disaggregating data
In most cases, environmental data come in a different, often more aggregated, sectoral breakdown
than IO data. With certain limitations and assumptions, the data can still be used by disaggregating
and/or making further adjustments.
In the absence of better information, emissions and other pressure and impact data can be broken
down proportionally to total industry output. For example, emissions of sector e j can be broken down
into two sub-sectors e j1 and e j2 given available information on total industry output g j1 and g j2 by
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