299
2014 ). Marginal changes in the electricity sector are likely to affect a range of technologies (Pehnt et al. 2008 ; Mathiesen et al. 2009 ) and it is not straightforward task
to consistently identify them with a heuristic approach (Zamagni et al. 2012 ; Earles
and Halog 2011 ; Menten et al. 2015 ). Energy system models such as TIMES or
LEAP can help to overcome such diffi culties.
2.1.3 Technology Coverage
The main challenges in technology data coverage concerns currently used
technologies and those, which will be installed in the future and are not yet commercially available (e.g. on a pilot plant level).
2.1.3.1 Actual Technologies
There is a wide variation among generation stations in terms of emissions and
inputs per unit generation across and even within fuel types. Such variation
becomes even more challenging with the differences among statistics sources (e.g.
Eurostat and EIA) treating the same technology using a given fuel type at a given
geographic location (e.g. jurisdiction). Specifi cation of the time frame of LCI data
can also be challenging: statistic sources often refer to different years and the
availability of up- to-date data is not always given, depending on the type of environmental exchanges. In addition to that, there is considerable uncertainty over
certain emission factors, even for mature technologies, such as hydropower or coal
(Hertwich 2013 ; Henriksson et al. 2014 ). Moreover, not only LCI data for power
plants as such can substantially vary, also specifi c fuel supply as well as infrastructure manufacturing chains can have important effects on LCA results (Bouman
et al. 2015 ; Yue et al. 2014 ).
2.1.3.2 Prospective Technologies
Prospective LCA studies often rely on LCI data of current electricity generation,
even if technology performance of current power generation chains is likely to
improve in the future and new technologies will emerge (IEA 2014 ). Modeling
how technology performance will change over time is particularly diffi cult for
nascent technologies (Curran et al. 2005 ) such as organic photovoltaic panels or
carbon capture and storage (Volkart et al. 2013 ). Moreover, disruptive technologies can bring improvements in effi ciency, but also have implied changes in
infrastructure and user behavior, which are more diffi cult to predict (Miller and
Keoleian 2015 ).
21 Exploring Challenges and Opportunities of Life Cycle Management…
2014 ). Marginal changes in the electricity sector are likely to affect a range of technologies (Pehnt et al. 2008 ; Mathiesen et al. 2009 ) and it is not straightforward task
to consistently identify them with a heuristic approach (Zamagni et al. 2012 ; Earles
and Halog 2011 ; Menten et al. 2015 ). Energy system models such as TIMES or
LEAP can help to overcome such diffi culties.
2.1.3 Technology Coverage
The main challenges in technology data coverage concerns currently used
technologies and those, which will be installed in the future and are not yet commercially available (e.g. on a pilot plant level).
2.1.3.1 Actual Technologies
There is a wide variation among generation stations in terms of emissions and
inputs per unit generation across and even within fuel types. Such variation
becomes even more challenging with the differences among statistics sources (e.g.
Eurostat and EIA) treating the same technology using a given fuel type at a given
geographic location (e.g. jurisdiction). Specifi cation of the time frame of LCI data
can also be challenging: statistic sources often refer to different years and the
availability of up- to-date data is not always given, depending on the type of environmental exchanges. In addition to that, there is considerable uncertainty over
certain emission factors, even for mature technologies, such as hydropower or coal
(Hertwich 2013 ; Henriksson et al. 2014 ). Moreover, not only LCI data for power
plants as such can substantially vary, also specifi c fuel supply as well as infrastructure manufacturing chains can have important effects on LCA results (Bouman
et al. 2015 ; Yue et al. 2014 ).
2.1.3.2 Prospective Technologies
Prospective LCA studies often rely on LCI data of current electricity generation,
even if technology performance of current power generation chains is likely to
improve in the future and new technologies will emerge (IEA 2014 ). Modeling
how technology performance will change over time is particularly diffi cult for
nascent technologies (Curran et al. 2005 ) such as organic photovoltaic panels or
carbon capture and storage (Volkart et al. 2013 ). Moreover, disruptive technologies can bring improvements in effi ciency, but also have implied changes in
infrastructure and user behavior, which are more diffi cult to predict (Miller and
Keoleian 2015 ).
21 Exploring Challenges and Opportunities of Life Cycle Management…