scope to part of the system may miss important changes outside the chosen system
boundaries. The potential lack of completeness needs to be considered in the definition of the goal and scope. The truncation of the system boundary is especially
unwarranted for consequential studies, where limiting the scope to specific sectors
would result in an incomplete inventory.
TIMES and LCA often have overlapping representations of the supply chain,
and modellers have to choose which data prevail, substituting parts of the TIMES
representation by LCA counterparts. Direct substitution entails the risk of missing
important changes across the supply chain. Therefore, the need for systematic
approaches to prioritise which LCI data should be updated. The ordering of processes and application of a cut-off based on a criterion such as CO 2 eq emissions
can help to both identify relevant changes and reduce the number of processes to be
mapped.
Consistent linking requires also updating parameters such as efficiency and
emission factors. Integration efforts outside the LCA field suggested already in
1996 to use common measurement points, unambiguous measurement points where
the two models should yield identical results [30]. The formalisation of these points
implies a harmonisation of parameters, and the assessment of the extent by which
both models measure the same phenomena and the same future [30]. The author
also pointed out the need to share a common formalised language between models
[30]. The specification of such conceptualisation is called ontology, a field of
growing interest in industrial ecology and recently discussed in the LCM conference [31]. The need for more traceable and transparent workflows that go beyond
the common reporting on scientific articles was also stressed [31]. We agree, as
articles often don’t offer sufficient explanation to understand the details of how the
linking was done.
The process of linking models goes beyond solving the implementation problem
[30]. It is also an opportunity to learn about the system and the implications of
different perspectives, which are essential to interpret results. LCA modellers
should consequently keep in mind the underlying assumptions and values of
ESOM.
References
1. Smil V, World history and energy. Encycl Energy 2004;6:549–61.
2. IEA, Energy, climate change and environment, 2016 insights 2016. http://www.iea.org/
publications/freepublications/publication/ECCE2016.pdf (accessed September 23, 2017).
3. García-Gusano D, Iribarren D, Martín-Gamboa M, Dufour J, Espegren K, Lind A, Integration
of life-cycle indicators into energy optimisation models: The case study of power generation
in Norway. J Clean Prod 2016;112:2693–6.
4. Choi J-K, Friley P, Alfstad T, Implications of energy policy on a product system’s dynamic
life-cycle environmental impact: Survey and model. Renew Sustain Energy Rev 2012;16:
4744–52.
Integrating Energy System Models in Life Cycle Management
257
boundaries. The potential lack of completeness needs to be considered in the definition of the goal and scope. The truncation of the system boundary is especially
unwarranted for consequential studies, where limiting the scope to specific sectors
would result in an incomplete inventory.
TIMES and LCA often have overlapping representations of the supply chain,
and modellers have to choose which data prevail, substituting parts of the TIMES
representation by LCA counterparts. Direct substitution entails the risk of missing
important changes across the supply chain. Therefore, the need for systematic
approaches to prioritise which LCI data should be updated. The ordering of processes and application of a cut-off based on a criterion such as CO 2 eq emissions
can help to both identify relevant changes and reduce the number of processes to be
mapped.
Consistent linking requires also updating parameters such as efficiency and
emission factors. Integration efforts outside the LCA field suggested already in
1996 to use common measurement points, unambiguous measurement points where
the two models should yield identical results [30]. The formalisation of these points
implies a harmonisation of parameters, and the assessment of the extent by which
both models measure the same phenomena and the same future [30]. The author
also pointed out the need to share a common formalised language between models
[30]. The specification of such conceptualisation is called ontology, a field of
growing interest in industrial ecology and recently discussed in the LCM conference [31]. The need for more traceable and transparent workflows that go beyond
the common reporting on scientific articles was also stressed [31]. We agree, as
articles often don’t offer sufficient explanation to understand the details of how the
linking was done.
The process of linking models goes beyond solving the implementation problem
[30]. It is also an opportunity to learn about the system and the implications of
different perspectives, which are essential to interpret results. LCA modellers
should consequently keep in mind the underlying assumptions and values of
ESOM.
References
1. Smil V, World history and energy. Encycl Energy 2004;6:549–61.
2. IEA, Energy, climate change and environment, 2016 insights 2016. http://www.iea.org/
publications/freepublications/publication/ECCE2016.pdf (accessed September 23, 2017).
3. García-Gusano D, Iribarren D, Martín-Gamboa M, Dufour J, Espegren K, Lind A, Integration
of life-cycle indicators into energy optimisation models: The case study of power generation
in Norway. J Clean Prod 2016;112:2693–6.
4. Choi J-K, Friley P, Alfstad T, Implications of energy policy on a product system’s dynamic
life-cycle environmental impact: Survey and model. Renew Sustain Energy Rev 2012;16:
4744–52.
Integrating Energy System Models in Life Cycle Management
257