297
geographical scope of inventory data and using country-specifi c statistics. The life
cycle inventory database ecoinvent version 3 covers nearly 85 % of global electricity production in 2008 (Treyer and Bauer 2013 ) with country (or even region-specifi c) LCI data showing substantial differences in LCI data between specifi c
countries and regions.
Despite the geographical coverage increase, gaps in LCI data keep existing and
are in general more pronounced in non-OECD countries, where often extrapolations
are unavoidable, increasing uncertainty (Treyer and Bauer 2013 ; Schmidt et al.
2011 ; Laurent and Espinosa 2015 ).
2.1.1.2 Grid Mix Boundaries: From Production Mixes to Supply Mixes
Once available in the grid, it is not possible to know where the electricity is coming
from ( Dones et al. 1998 ; Itten et al. 2014 ; Weber et al. 2010 ). This tracking issue
becomes even more challenging as electricity grids are increasingly getting interconnected, and hence makes selecting a grid mix boundary a complicated task for
the practitioner.
The common approach is to use national electricity mixes and accounting for
imports from the neighboring jurisdictions. The underlying justifi cation is that
neighboring countries have either physical connections or administrative contracts
to trade (Treyer and Bauer 2014 ). However, the boundaries selection is to some
extent arbitrary and raises equity issues. As an example, if we take the NorthAmerican electricity grid, different resolutions are available: national, interconnect,
Jurisdiction-average production and consumption mixes (US countries, Canadian
provinces, etc.), ISO/RTO, EPA’s eGrid subregions, and EIA region (Weber et al.
2010 ).
On top of that, congestion can effectively limit electricity transmission within a
national boundary (an administrative barrier), which even makes the common
approach selection (i.e. using the national energy mixes) unrealistic. A recent study
developed an approach creating clusters of data according to the congestion status
and its location within the Ontario (Canada) grid-mix. As an example, the avoided
greenhouse gas emissions varied, for uncongested (i.e. using as a common approach
selection the production energy mix: Ontario mix) and congested hours, between
280 and 390 kg/MWh. Even if these empirical estimates cannot be generalized to
other contexts, the study underscored the importance of congestion in defi ning the
grid mix boundary (Amor et al. 2014a ).
2.1.2 Temporal Aspects of Electricity
Our capacity to store electricity is very limited, and in practice demand is dynamically (hour by hour) matched with a range of production technologies. Obtaining
past yearly-averaged country supply mixes to be used in attributional LCA (ALCA)
is relatively straightforward by using national statics. Typically organizations such
21 Exploring Challenges and Opportunities of Life Cycle Management…
geographical scope of inventory data and using country-specifi c statistics. The life
cycle inventory database ecoinvent version 3 covers nearly 85 % of global electricity production in 2008 (Treyer and Bauer 2013 ) with country (or even region-specifi c) LCI data showing substantial differences in LCI data between specifi c
countries and regions.
Despite the geographical coverage increase, gaps in LCI data keep existing and
are in general more pronounced in non-OECD countries, where often extrapolations
are unavoidable, increasing uncertainty (Treyer and Bauer 2013 ; Schmidt et al.
2011 ; Laurent and Espinosa 2015 ).
2.1.1.2 Grid Mix Boundaries: From Production Mixes to Supply Mixes
Once available in the grid, it is not possible to know where the electricity is coming
from ( Dones et al. 1998 ; Itten et al. 2014 ; Weber et al. 2010 ). This tracking issue
becomes even more challenging as electricity grids are increasingly getting interconnected, and hence makes selecting a grid mix boundary a complicated task for
the practitioner.
The common approach is to use national electricity mixes and accounting for
imports from the neighboring jurisdictions. The underlying justifi cation is that
neighboring countries have either physical connections or administrative contracts
to trade (Treyer and Bauer 2014 ). However, the boundaries selection is to some
extent arbitrary and raises equity issues. As an example, if we take the NorthAmerican electricity grid, different resolutions are available: national, interconnect,
Jurisdiction-average production and consumption mixes (US countries, Canadian
provinces, etc.), ISO/RTO, EPA’s eGrid subregions, and EIA region (Weber et al.
2010 ).
On top of that, congestion can effectively limit electricity transmission within a
national boundary (an administrative barrier), which even makes the common
approach selection (i.e. using the national energy mixes) unrealistic. A recent study
developed an approach creating clusters of data according to the congestion status
and its location within the Ontario (Canada) grid-mix. As an example, the avoided
greenhouse gas emissions varied, for uncongested (i.e. using as a common approach
selection the production energy mix: Ontario mix) and congested hours, between
280 and 390 kg/MWh. Even if these empirical estimates cannot be generalized to
other contexts, the study underscored the importance of congestion in defi ning the
grid mix boundary (Amor et al. 2014a ).
2.1.2 Temporal Aspects of Electricity
Our capacity to store electricity is very limited, and in practice demand is dynamically (hour by hour) matched with a range of production technologies. Obtaining
past yearly-averaged country supply mixes to be used in attributional LCA (ALCA)
is relatively straightforward by using national statics. Typically organizations such
21 Exploring Challenges and Opportunities of Life Cycle Management…