The integration efforts reviewed used multisector TIMES models, but most of
the studies limit their scope to attributional studies of the electricity sector (Table 1)
and do a partial integration. Several studies use market mixes from TIMES for
prospective assessments, improving temporal representativeness [3, 4, 12], while
others integrate life cycle emissions in TIMES models [7–11]. Limiting the
boundaries to a particular sector reduces the number of technologies that need to be
mapped, but it could result in a loss of completeness, questioning the suitability of
the system boundary. For example, Ref. [4] used multisector MARKAL model to
update a prospective electricity mix in the US for different scenarios such as cap
and trade or CO 2 taxes. However, such policies affect more than just the electricity
sector, and induced changes in other sectors of the model should be considered to
make a fair comparison of different scenarios. Refs. [7–11] added life cycle
emissions to several processes of a MARKAL/TIMES model to internalise environmental emissions. It was a first step towards the integration of externalities, but
incomplete mapping may be problematic as it may induce a bias against the mapped
technologies (see Sect. 3.3). Reference [5] improved completeness, assigning
life-cycle impact scores to all end-uses of all sectors. In this case, there is a wider
range of processes that deliver the energy services (heat, transportation, etc.),
raising the number of equivalent processes required (Table 1). The study used
end-use technology mixes and energy demands from TIMES. However, the technology mixes and efficiencies for “non-end-use” processes (such as electricity
generation) were selectively updated. The authors recommended using a more
consistent approach in future research [5]. Indeed, upstream processes may also
change over time or between scenarios (e.g. switch from conventional to unconventional gas or feedstock from biofuels). These changes should be identified in a
systematic manner.
The approach of limiting the boundaries to a particular sector is more difficult to
justify in consequential studies since all the processes that are expected to change
should be included [19, 24]. Changes can be induced by market or policy effects.
Our experiments using a TIMES model of a relatively small region (Quebec,
Canada) indicate that a large proportion of the processes change their output to
some extent [24]. The best example found of a consequential study using TIMES
and LCA was an analysis of the effects of introducing biodiesel from biomass in
France [6]. In this case, all technologies in the TIMES model were mapped (192).
This approach produced a complete mapping, but it may be unfeasible with larger
TIMES models, which can easily contain thousands of technologies.
We have recently proposed to use a cut-off criterion, that is, to exclude a percentage of the material and energy flows based on their contribution to an indicator
measured in TIMES (e.g. CO 2 eq emissions) [24]. The cut-off can help to discern
the most relevant changes, reducing exponentially the number of processes that
need to be mapped [24]. Nonetheless, it introduces some other problems, such as
the possibility of omitting processes with high impacts in other areas of concern but
low CO 2 eq emissions.
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the studies limit their scope to attributional studies of the electricity sector (Table 1)
and do a partial integration. Several studies use market mixes from TIMES for
prospective assessments, improving temporal representativeness [3, 4, 12], while
others integrate life cycle emissions in TIMES models [7–11]. Limiting the
boundaries to a particular sector reduces the number of technologies that need to be
mapped, but it could result in a loss of completeness, questioning the suitability of
the system boundary. For example, Ref. [4] used multisector MARKAL model to
update a prospective electricity mix in the US for different scenarios such as cap
and trade or CO 2 taxes. However, such policies affect more than just the electricity
sector, and induced changes in other sectors of the model should be considered to
make a fair comparison of different scenarios. Refs. [7–11] added life cycle
emissions to several processes of a MARKAL/TIMES model to internalise environmental emissions. It was a first step towards the integration of externalities, but
incomplete mapping may be problematic as it may induce a bias against the mapped
technologies (see Sect. 3.3). Reference [5] improved completeness, assigning
life-cycle impact scores to all end-uses of all sectors. In this case, there is a wider
range of processes that deliver the energy services (heat, transportation, etc.),
raising the number of equivalent processes required (Table 1). The study used
end-use technology mixes and energy demands from TIMES. However, the technology mixes and efficiencies for “non-end-use” processes (such as electricity
generation) were selectively updated. The authors recommended using a more
consistent approach in future research [5]. Indeed, upstream processes may also
change over time or between scenarios (e.g. switch from conventional to unconventional gas or feedstock from biofuels). These changes should be identified in a
systematic manner.
The approach of limiting the boundaries to a particular sector is more difficult to
justify in consequential studies since all the processes that are expected to change
should be included [19, 24]. Changes can be induced by market or policy effects.
Our experiments using a TIMES model of a relatively small region (Quebec,
Canada) indicate that a large proportion of the processes change their output to
some extent [24]. The best example found of a consequential study using TIMES
and LCA was an analysis of the effects of introducing biodiesel from biomass in
France [6]. In this case, all technologies in the TIMES model were mapped (192).
This approach produced a complete mapping, but it may be unfeasible with larger
TIMES models, which can easily contain thousands of technologies.
We have recently proposed to use a cut-off criterion, that is, to exclude a percentage of the material and energy flows based on their contribution to an indicator
measured in TIMES (e.g. CO 2 eq emissions) [24]. The cut-off can help to discern
the most relevant changes, reducing exponentially the number of processes that
need to be mapped [24]. Nonetheless, it introduces some other problems, such as
the possibility of omitting processes with high impacts in other areas of concern but
low CO 2 eq emissions.
254
M. F. Astudillo et al.