303
3.2.2 Best Practices
In cases where it is important to accurately estimate the environmental impacts
associated with electricity use with high temporal resolution, using a methodology
where the electricity dynamics is considered (as developed by Amor et al. 2014b ) is
recommended. Such approaches are particularly relevant in assessing the implication of choosing different electricity supply modeling approaches during decisionmaking, e.g. in estimating the avoided environmental impacts as a consequence of
renewable distributed generation.
In other cases, one must keep in mind the often-limited resources of LCA practitioners; generating specifi c LCI data for electricity supply at a high level of detail
can be arduous, and often relying on generic background data from databases can
be suffi cient. However, these generic data should be adapted through modifi cation
of key parameters in order to represent specifi c electricity supply more closely.
In any case, knowing the implications associated with electricity supply modeling choices, it is highly recommended that practitioners exercise caution and sensitivity analyses should be systematically conducted using different electricity supply
scenarios in order to take into account the complexity of electricity systems.
Finally, interpretation of LCA results of electricity generation technologies
deserves attention, especially if used for decision support. Often, results show substantial variations without transparently documented and easily traceable underlying reasons. In this context, the harmonization approach by NREL (Brandão et al.
2012 ) should be considered.
4 Conclusions
This chapter summarizes key challenges and opportunities of LCM in the electricity
sector, with focus at inventory level. Despite the advances, the challenges are
numerous and span from gaps in geographical and technological coverage, uncertainties over emission factors, to complexities on the identifi cation of marginal
technologies.
There are many opportunities to improve the inventories that are being currently
explored and some are described here: Economic models are being used to identify
marginal technologies and assess the effect of policies, statistical techniques such as
regression analysis are useful to fi ll inventory data gaps and experience curves can
be used to assess novel technologies. As any methodological development, one
should however, be very clear about their limitations, provide transparent documentation and uncertainty estimates together with the results.
Some of these developments are already available for practitioners, including
consequential inventories, greater capabilities in uncertainty modeling and parameterized datasets. Other developments are not yet available in databases and would
require that data providers take methodological decisions to maintain consistent
inventories. These decisions could include questions where interpretation of ISO
21 Exploring Challenges and Opportunities of Life Cycle Management…
3.2.2 Best Practices
In cases where it is important to accurately estimate the environmental impacts
associated with electricity use with high temporal resolution, using a methodology
where the electricity dynamics is considered (as developed by Amor et al. 2014b ) is
recommended. Such approaches are particularly relevant in assessing the implication of choosing different electricity supply modeling approaches during decisionmaking, e.g. in estimating the avoided environmental impacts as a consequence of
renewable distributed generation.
In other cases, one must keep in mind the often-limited resources of LCA practitioners; generating specifi c LCI data for electricity supply at a high level of detail
can be arduous, and often relying on generic background data from databases can
be suffi cient. However, these generic data should be adapted through modifi cation
of key parameters in order to represent specifi c electricity supply more closely.
In any case, knowing the implications associated with electricity supply modeling choices, it is highly recommended that practitioners exercise caution and sensitivity analyses should be systematically conducted using different electricity supply
scenarios in order to take into account the complexity of electricity systems.
Finally, interpretation of LCA results of electricity generation technologies
deserves attention, especially if used for decision support. Often, results show substantial variations without transparently documented and easily traceable underlying reasons. In this context, the harmonization approach by NREL (Brandão et al.
2012 ) should be considered.
4 Conclusions
This chapter summarizes key challenges and opportunities of LCM in the electricity
sector, with focus at inventory level. Despite the advances, the challenges are
numerous and span from gaps in geographical and technological coverage, uncertainties over emission factors, to complexities on the identifi cation of marginal
technologies.
There are many opportunities to improve the inventories that are being currently
explored and some are described here: Economic models are being used to identify
marginal technologies and assess the effect of policies, statistical techniques such as
regression analysis are useful to fi ll inventory data gaps and experience curves can
be used to assess novel technologies. As any methodological development, one
should however, be very clear about their limitations, provide transparent documentation and uncertainty estimates together with the results.
Some of these developments are already available for practitioners, including
consequential inventories, greater capabilities in uncertainty modeling and parameterized datasets. Other developments are not yet available in databases and would
require that data providers take methodological decisions to maintain consistent
inventories. These decisions could include questions where interpretation of ISO
21 Exploring Challenges and Opportunities of Life Cycle Management…