14
F. Scrucca et al.
et al. [58] encourages to report both time horizons, mostly due to the much higher
CO2e emissions of CH4 over 20 than 100 years.
4.4.2 Dynamic Carbon Footprint
The variable time in the CF or LCA analysis plays a fundamental role because it
can be considered at different levels, leading to a wrong estimation of the impacts.
In particular, two areas can be considered priority: the Life Cycle Inventory (LCI),
by clearly assuming the temporal profile of emissions, and the Life Cycle Impact
Assessment, by using time-dependent characterization factors for GHG emissions
taking into account the exact instant when the emissions occur [9].
In the conventional approach, steady-state (static) conditions are assumed and
inventory data are aggregated directly without considering their temporal differences
and disregarding their potential variation over time. The limits of this approach are
amplified when biogenic carbon and long life cycles are analyzed [35]. Pignè et al.
[64] identified two different approaches to include temporal aspects in the LCI. In
the first one, the practitioner does not built any dynamic model but defines several
scenarios with different LCIs, happening at different times of the life cycle. In this
way, each scenario is built when substantial changes occur in the mass and energy
flows and it is related to a given time period. This approach allows to take into
account changes in foreground processes while it is much more difficult to consider
modifications in background processes. The second approach aims to allocate the
processes, flows, and LCI of a given system over time, on the basis of the evidence that
the linked processes of the life cycle are time-deferred. In particular, Pignè et al. [64]
developed a temporal database in order to include full temporalization of background
system, highlighting that temporal differentiation of the LCI, and especially of the
background processes, can significantly change the overall results.
This issue is particularly significant for the buildings which are characterized
by long life cycles (usually 40–70 years) and are characterized by time-dependent
parameters [54]. Negishi et al. [53] identified the main time-dependent characteristics
of a building system related to the building technology level (performance degradation over time, replacement and use of new technologies, inclusion of biogenic
carbon), end-user level (occupancy behaviour), and external system level (energy
mix, regulations).
In a fully dynamic CF, it is necessary to consider also dynamic characterization
factors of global warming. In a static analysis, a unit emission released today is
assumed to have the same impact of a unit emission released decades later. However,
the radiative forcing of a unit mass pulse emission differs considerably over time
[43]. In the last years, different metrics have been proposed to take into account
time effect of GHG emissions which also allow to count CO 2 uptake and biogenic
emissions. Kendall [36] proposed a new metric, named Time-Adjusted Warming
Potential (TAWP), which considers the difference in global warming effect over a
specific time between an emission occurring in the future and an emission released
today. Levasseur et al. [43] calculated dynamic characterization factors for 1-year
F. Scrucca et al.
et al. [58] encourages to report both time horizons, mostly due to the much higher
CO2e emissions of CH4 over 20 than 100 years.
4.4.2 Dynamic Carbon Footprint
The variable time in the CF or LCA analysis plays a fundamental role because it
can be considered at different levels, leading to a wrong estimation of the impacts.
In particular, two areas can be considered priority: the Life Cycle Inventory (LCI),
by clearly assuming the temporal profile of emissions, and the Life Cycle Impact
Assessment, by using time-dependent characterization factors for GHG emissions
taking into account the exact instant when the emissions occur [9].
In the conventional approach, steady-state (static) conditions are assumed and
inventory data are aggregated directly without considering their temporal differences
and disregarding their potential variation over time. The limits of this approach are
amplified when biogenic carbon and long life cycles are analyzed [35]. Pignè et al.
[64] identified two different approaches to include temporal aspects in the LCI. In
the first one, the practitioner does not built any dynamic model but defines several
scenarios with different LCIs, happening at different times of the life cycle. In this
way, each scenario is built when substantial changes occur in the mass and energy
flows and it is related to a given time period. This approach allows to take into
account changes in foreground processes while it is much more difficult to consider
modifications in background processes. The second approach aims to allocate the
processes, flows, and LCI of a given system over time, on the basis of the evidence that
the linked processes of the life cycle are time-deferred. In particular, Pignè et al. [64]
developed a temporal database in order to include full temporalization of background
system, highlighting that temporal differentiation of the LCI, and especially of the
background processes, can significantly change the overall results.
This issue is particularly significant for the buildings which are characterized
by long life cycles (usually 40–70 years) and are characterized by time-dependent
parameters [54]. Negishi et al. [53] identified the main time-dependent characteristics
of a building system related to the building technology level (performance degradation over time, replacement and use of new technologies, inclusion of biogenic
carbon), end-user level (occupancy behaviour), and external system level (energy
mix, regulations).
In a fully dynamic CF, it is necessary to consider also dynamic characterization
factors of global warming. In a static analysis, a unit emission released today is
assumed to have the same impact of a unit emission released decades later. However,
the radiative forcing of a unit mass pulse emission differs considerably over time
[43]. In the last years, different metrics have been proposed to take into account
time effect of GHG emissions which also allow to count CO 2 uptake and biogenic
emissions. Kendall [36] proposed a new metric, named Time-Adjusted Warming
Potential (TAWP), which considers the difference in global warming effect over a
specific time between an emission occurring in the future and an emission released
today. Levasseur et al. [43] calculated dynamic characterization factors for 1-year