296
spot in LCA results for a majority of product life cycles (Curran et al. 2005 ; Treyer
and Bauer 2013 ). It has also been shown, from the LCA perspective, that electricity
sector as such is an important originator of environmental burdens worldwide and
that energy policies can result in burden-shifting (Laurent and Espinosa 2015 ). As
LCAs are being conducted frequently, it is key that suitable life cycle inventory
(LCI) data become in general more readily available (Soimakallio et al. 2011 ).
This book chapter aims to highlight issues on electricity supply modeling, methodological choices and data set selections. Attributional and consequential perspectives as well as systemic aspects of the electricity sector are also refl ected. Finally,
this chapter summarizes the key challenges and opportunities and provides suggestions on how to deal with such problems.
2 Identifying the Issues: Major Methodological Challenges
2.1 Data Issues: Generating Electricity
Life Cycle Inventory Datasets
According to ISO 14044, electricity inventories shall take into account electricity
mixes, fuel effi ciencies, as well as transmission and distribution losses. Given the
heterogeneity of electricity LCI data, representativeness is an important aspect when
conducting an LCA. Referring to ISO 14044 (ISO 14044: 2006 ), data representativeness is the qualitative assessment of the degree to which the data set refl ects the
true population of interest for a specifi c application: geographical coverage, timerelated coverage, and technology coverage. Other quality indicators are such as
completeness, consistency and uncertainty are also addressed. Aspects covering
special challenges in the electricity sector are highlighted in the following sections.
2.1.1 Geographic Coverage
The geographical coverage is the geographical area from which data for unit processes should be collected to satisfy the goal of the study. In the context of electricity process, LCA practitioner can face two main challenges: the fi rst one refers to
the situation where no regionalized electricity data is available (Sect. 2.1.1.1 ) and
the second one refers to the grid delimitation (Sect. 2.1.1.2 ).
2.1.1.1 Extending the Geographical Coverage: Improving
Production Data Accuracy
Regionalization of inventory data is recognized as an important need to increase
the accuracy of LCA results, even if it is disputed down to which level the regionalization should go. Recent efforts have been undertaken to increase the
M.F. Astudillo et al.
spot in LCA results for a majority of product life cycles (Curran et al. 2005 ; Treyer
and Bauer 2013 ). It has also been shown, from the LCA perspective, that electricity
sector as such is an important originator of environmental burdens worldwide and
that energy policies can result in burden-shifting (Laurent and Espinosa 2015 ). As
LCAs are being conducted frequently, it is key that suitable life cycle inventory
(LCI) data become in general more readily available (Soimakallio et al. 2011 ).
This book chapter aims to highlight issues on electricity supply modeling, methodological choices and data set selections. Attributional and consequential perspectives as well as systemic aspects of the electricity sector are also refl ected. Finally,
this chapter summarizes the key challenges and opportunities and provides suggestions on how to deal with such problems.
2 Identifying the Issues: Major Methodological Challenges
2.1 Data Issues: Generating Electricity
Life Cycle Inventory Datasets
According to ISO 14044, electricity inventories shall take into account electricity
mixes, fuel effi ciencies, as well as transmission and distribution losses. Given the
heterogeneity of electricity LCI data, representativeness is an important aspect when
conducting an LCA. Referring to ISO 14044 (ISO 14044: 2006 ), data representativeness is the qualitative assessment of the degree to which the data set refl ects the
true population of interest for a specifi c application: geographical coverage, timerelated coverage, and technology coverage. Other quality indicators are such as
completeness, consistency and uncertainty are also addressed. Aspects covering
special challenges in the electricity sector are highlighted in the following sections.
2.1.1 Geographic Coverage
The geographical coverage is the geographical area from which data for unit processes should be collected to satisfy the goal of the study. In the context of electricity process, LCA practitioner can face two main challenges: the fi rst one refers to
the situation where no regionalized electricity data is available (Sect. 2.1.1.1 ) and
the second one refers to the grid delimitation (Sect. 2.1.1.2 ).
2.1.1.1 Extending the Geographical Coverage: Improving
Production Data Accuracy
Regionalization of inventory data is recognized as an important need to increase
the accuracy of LCA results, even if it is disputed down to which level the regionalization should go. Recent efforts have been undertaken to increase the
M.F. Astudillo et al.