302
2014b ). Statistical interpolation methods such as kriging have also been tested
and appear to be superior to regression analysis (Moreau et al. 2012 ) but their
application in LCI need to be extended. The same observation remains regarding the effects of technological change to be accounted for in prospective
LCA. Experience curves have been used to predict improvements in effi ciency
and effect of investment ( Sandén and Karlström 2007 ) but more work is needed
on the treatment of related uncertainties (Yeh and Rubin 2012 ; Miller and
Keoleian 2015 ).
3.1.2 Uncertainty Management
Recent advances in the uncertainty management can be used to improve the models
of the electricity sector. At the inventory level, data inaccuracy and lack of representative data are typically addressed using the semi-quantitative approach of the pedigree matrix, assuming log-normal distributions. New advances in LCI implementation
now allow to model parameter uncertainty with different types of probability distributions, allowing the modeler to use the distribution that best fi ts the data (Muller
et al. 2014 ).
In addition, it is recommended to distinguish between uncertainty and variability,
as just uncertainty can be reduced with better data. Recent studies suggest that the
spread in emissions of power plants can be dominated by uncertainty (Henriksson
et al. 2014 ) or variability (Steinmann et al. 2014a ) depending on the region under
study. To support the modeling, novel methodological developments propose ways
of tackling this issue (Steinmann et al. 2014a ).
3.2 Opportunities: The User Perspective
3.2.1 Greater Use of Consequential Analysis
Most LCAs performed to date on electric power and systems can be classifi ed as
attributional. However, beyond the coming methodological improvement, ALCAs
are not designed to assess the total environmental implications (i.e. consequences)
of decisions. Alternatively, CLCAs are particularly useful for estimating the energy
and emissions implications of policies such as a Renewable Portfolio Standard or
those of policies that encourage carbon taxes (Amor et al. 2010 ; Masanet et al.
2013 ). Thus, increasing CLCA use help in understanding and improving the effects
of a given energy policy.
There is an urgent need to introduce large improvements in the electricity sector, which will entail major economic and environmental consequences. The analysis of future scenarios will especially suit CLCA, which should receive more
attention to overcome the challenges and profi t from the opportunities outlined in
this chapter.
M.F. Astudillo et al.
2014b ). Statistical interpolation methods such as kriging have also been tested
and appear to be superior to regression analysis (Moreau et al. 2012 ) but their
application in LCI need to be extended. The same observation remains regarding the effects of technological change to be accounted for in prospective
LCA. Experience curves have been used to predict improvements in effi ciency
and effect of investment ( Sandén and Karlström 2007 ) but more work is needed
on the treatment of related uncertainties (Yeh and Rubin 2012 ; Miller and
Keoleian 2015 ).
3.1.2 Uncertainty Management
Recent advances in the uncertainty management can be used to improve the models
of the electricity sector. At the inventory level, data inaccuracy and lack of representative data are typically addressed using the semi-quantitative approach of the pedigree matrix, assuming log-normal distributions. New advances in LCI implementation
now allow to model parameter uncertainty with different types of probability distributions, allowing the modeler to use the distribution that best fi ts the data (Muller
et al. 2014 ).
In addition, it is recommended to distinguish between uncertainty and variability,
as just uncertainty can be reduced with better data. Recent studies suggest that the
spread in emissions of power plants can be dominated by uncertainty (Henriksson
et al. 2014 ) or variability (Steinmann et al. 2014a ) depending on the region under
study. To support the modeling, novel methodological developments propose ways
of tackling this issue (Steinmann et al. 2014a ).
3.2 Opportunities: The User Perspective
3.2.1 Greater Use of Consequential Analysis
Most LCAs performed to date on electric power and systems can be classifi ed as
attributional. However, beyond the coming methodological improvement, ALCAs
are not designed to assess the total environmental implications (i.e. consequences)
of decisions. Alternatively, CLCAs are particularly useful for estimating the energy
and emissions implications of policies such as a Renewable Portfolio Standard or
those of policies that encourage carbon taxes (Amor et al. 2010 ; Masanet et al.
2013 ). Thus, increasing CLCA use help in understanding and improving the effects
of a given energy policy.
There is an urgent need to introduce large improvements in the electricity sector, which will entail major economic and environmental consequences. The analysis of future scenarios will especially suit CLCA, which should receive more
attention to overcome the challenges and profi t from the opportunities outlined in
this chapter.
M.F. Astudillo et al.