300
2.2 User Perspective: Using Electricity
Life Cycle Inventory Data
LCA practitioners, especially in industry, frequently rely on generic LCI data for
electricity generation and supply from commercial LCA background databases
such as ecoinvent,
1 GaBi,
2 or ELCD
3 in their daily LCA business. Therefore, the
quality of these data is often crucial for the quality of LCA applications (e.g.
certifi cation, Eco-design, decision making) and users are confronted with the data
issues discussed above.
Usually, use of generic electricity from the grid, i.e. electricity supply mixes, and
from the generic electricity market, respectively, is suffi cient in the industrial LCM
context, if companies do not have own power plants or specifi c contracts for power
supply.
However, today’s industrial value chains are for many global products, i.e. industrial production, manufacturing, sales and research activities are distributed around
the globe. In such cases, LCI data for electricity supply in different countries need
to be used for LCM applications.
Background electricity LCI data need to represent actual local or regional/
national power supply as close as possible in order to be useful for LCA practitioners. “As close as possible” means that the background data need to represent specifi c supply regions and markets, respectively, and need to be up-to-date in terms of
technology market shares, technology performance as well as fuel and infrastructure supply chains. They also need to be available in a modifi able way on the unit
process level in order to allow users to adapt the data to their specifi c needs in case
they have more specifi c information concerning their electricity supply. This
requirement calls for a comprehensive and transparent documentation on the unit
process level as well.
Today, most LCAs in the LCM context are attributional (Zamagni et al. 2012 ;
Masanet et al. 2013 ). However, taking into account goal and scope and changeoriented questions to be answered by LCA carried out in industry, consequential
LCA should be performed more frequently. Ideally, such CLCA will be based on
consequential background data as well, also for electricity supply. Unfortunately, the
availability of consequential background data is currently limited to the ecoinvent
database and even these data need to be used cautiously (Treyer and Bauer 2014 ).
Change-oriented and prospective LCA also calls for background data taking into
account technology development, i.e. representing future technologies and economies. However, such “future background data” are almost unavailable, at least in a
consistently implemented way. The only known attempt to create consistent LCI
data for future electricity production was carried out in the European research project “NEEDS” and LCI data are available online.
4
1 www.ecoinvent.org
2 http://www.gabi-software.com/international/databases/gabi-databases/
3 http://eplca.jrc.ec.europa.eu/ELCD3/
4 http://www.needs-project.org/needswebdb/search.php
M.F. Astudillo et al.
2.2 User Perspective: Using Electricity
Life Cycle Inventory Data
LCA practitioners, especially in industry, frequently rely on generic LCI data for
electricity generation and supply from commercial LCA background databases
such as ecoinvent,
1 GaBi,
2 or ELCD
3 in their daily LCA business. Therefore, the
quality of these data is often crucial for the quality of LCA applications (e.g.
certifi cation, Eco-design, decision making) and users are confronted with the data
issues discussed above.
Usually, use of generic electricity from the grid, i.e. electricity supply mixes, and
from the generic electricity market, respectively, is suffi cient in the industrial LCM
context, if companies do not have own power plants or specifi c contracts for power
supply.
However, today’s industrial value chains are for many global products, i.e. industrial production, manufacturing, sales and research activities are distributed around
the globe. In such cases, LCI data for electricity supply in different countries need
to be used for LCM applications.
Background electricity LCI data need to represent actual local or regional/
national power supply as close as possible in order to be useful for LCA practitioners. “As close as possible” means that the background data need to represent specifi c supply regions and markets, respectively, and need to be up-to-date in terms of
technology market shares, technology performance as well as fuel and infrastructure supply chains. They also need to be available in a modifi able way on the unit
process level in order to allow users to adapt the data to their specifi c needs in case
they have more specifi c information concerning their electricity supply. This
requirement calls for a comprehensive and transparent documentation on the unit
process level as well.
Today, most LCAs in the LCM context are attributional (Zamagni et al. 2012 ;
Masanet et al. 2013 ). However, taking into account goal and scope and changeoriented questions to be answered by LCA carried out in industry, consequential
LCA should be performed more frequently. Ideally, such CLCA will be based on
consequential background data as well, also for electricity supply. Unfortunately, the
availability of consequential background data is currently limited to the ecoinvent
database and even these data need to be used cautiously (Treyer and Bauer 2014 ).
Change-oriented and prospective LCA also calls for background data taking into
account technology development, i.e. representing future technologies and economies. However, such “future background data” are almost unavailable, at least in a
consistently implemented way. The only known attempt to create consistent LCI
data for future electricity production was carried out in the European research project “NEEDS” and LCI data are available online.
4
1 www.ecoinvent.org
2 http://www.gabi-software.com/international/databases/gabi-databases/
3 http://eplca.jrc.ec.europa.eu/ELCD3/
4 http://www.needs-project.org/needswebdb/search.php
M.F. Astudillo et al.