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Including CO2-emission equivalence of changes in land surface albedo in life cycle assessment. Methodology and case study on greenhouse agriculture



Purpose Climate change impacts in life cycle assessment (LCA) are usually assessed as the emissions of greenhouse gases expressed with the global warming potential (GWP). However, changes in surface albedo caused by land use change can also contribute to change the Earth’s energy budget. In this paper we present a methodology for including in LCA the climatic impacts of land surface albedo changes, measured as CO2-eq. emissions or emission offsets. Methods A review of studies calculating radiative forcings and CO2-equivalence of changes in surface albedo is carried out. A methodology is proposed, and some methodological issues arising from its application are discussed. The methodology is applied in a practical example dealing with greenhouse agriculture in Southern Spain. Results The results of the case study show that the increase in surface albedo due to the reflective plastic cover of greenhouses involves an important CO2-eq. emission offset, which reduces the net GWP-100 of tomato production from 303 to 168 kg CO2-eq. per ton tomato when a 50-year service time is considered for the agricultural activity. This example shows that albedo effects can be very important in a product system when land use plays an important role, and substantial changes in surface albedo are involved. Conclusions Although the method presented in this work can be improved concerning the calculation of radiative forcing, it constitutes a first operative approach which can be used to develop regionalized characterization factors and provide a more complete evaluation of impacts on the climate change impact category.
Including CO
-emission equivalence of changes in land
surface albedo in life cycle assessment. Methodology and case
study on greenhouse agriculture
Ivan Muñoz &Pablo Campra &
Amadeo R. Fernández-Alba
Received: 3 November 2009 / Accepted: 1 June 2010 / Published online: 17 June 2010
#Springer-Verlag 2010
Purpose Climate change impacts in life cycle assessment
(LCA) are usually assessed as the emissions of greenhouse
gases expressed with the global warming potential (GWP).
However, changes in surface albedo caused by land use
change can also contribute to change the Earths energy
budget. In this paper we present a methodology for including
in LCA the climatic impacts of land surface albedo changes,
measured as CO
-eq. emissions or emission offsets.
Methods A review of studies calculating radiative forcings
and CO
-equivalence of changes in surface albedo is carried
out. A methodology is proposed, and some methodological
issues arising from its application are discussed. The
methodology is applied in a practical example dealing with
greenhouse agriculture in Southern Spain.
Results The results of the case study show that the increase
in surface albedo due to the reflective plastic cover of
greenhouses involves an important CO
-eq. emission offset,
which reduces the net GWP-100 of tomato production from
303 to 168 kg CO
-eq. per ton tomato when a 50-year
service time is considered for the agricultural activity. This
example shows that albedo effects can be very important in
a product system when land use plays an important role,
and substantial changes in surface albedo are involved.
Conclusions Although the method presented in this work
can be improved concerning the calculation of radiative
forcing, it constitutes a first operative approach which can
be used to develop regionalized characterization factors and
provide a more complete evaluation of impacts on the
climate change impact category.
Keywords Climate change .Global warming potential
(GWP) .Greenhouse agriculture .Land transformation .
Land use change .Life cycle impact assessment (LCIA) .
Radiative forcing
1 Introduction
Climate change is among the most established impact
categories in life cycle assessment (LCA) (Udo de Haes et
al. 1999). Currently, impacts of products and services on
the global climate can be measured by LCA practitioners
with several approaches, such as the global warming
potential (GWP; Forster et al. 2007), which measures the
radiative forcing per unit of emission of different green-
house gases. GWP is probably the most generally used
method, although other approaches exist which go further
in the effects chain, measuring potential consequences on
humans and ecosystems (De Schryver et al. 2009; Steen
Responsible editor: Llorenc Milà i Canals.
Electronic supplementary material The online version of this article
(doi:10.1007/s11367-010-0202-5) contains supplementary material,
which is available to authorized users.
I. Muñoz (*):P. Campra :A. R. Fernández-Alba
Department of Hydrogeology and Analytical Chemistry,
University of Almería,
Ctra. de Sacramento s/n, La Cañada de San Urbano,
04120 Almería, Spain
A. R. Fernández-Alba
Instituto Madrileño de Estudios Avanzados, IMDEA Agua,
C/Punto Net 4, Edificio ZYE,
Parque Científico Tecnológico de la U. de Alcalá,
Alcalá de Henares 28805 Madrid, Spain
Present Address:
I. Muñoz
Safety & Environmental Assurance Centre, Unilever,
Colworth Park,
Sharnbrook, Bedfordshire MK44 1LQ, UK
Int J Life Cycle Assess (2010) 15:672681
DOI 10.1007/s11367-010-0202-5
1999a,b). A common feature of all approaches is the fact
that they only focus on emissions of greenhouse gases.
However, anthropogenic changes to the land cover can
affect surface albedo and exert a radiative forcing by
perturbing the shortwave radiation budget (Ramaswamy et
al. 2001). According to the Intergovernmental Panel on
Climate Change, in the 17502005 period global land cover
changesespecially deforestationhave increased the ter-
restrial albedo, resulting in a radiative forcing (RF) of
0.2 W m
(Forster et al. 2007). Even though this
influence might appear small at the global level (radiative
forcing from long-lived greenhouse gases is +2.63 W m
in recent years the implications of surface albedo changes
have been gaining attention, especially as a climate change
mitigation strategy (Hamwey 2007; Ridgwell et al. 2009).
For example, a global increase of albedo in urban areas, by
means of using reflective building materials, has been
estimated to have a cooling effect equivalent to offsetting
44 Gt CO
(Akbari et al. 2009). On the other hand, Betts
(2000) found that reforestation in high latitudes could be
detrimental in terms of climate change mitigation, since the
positive forcing induced by forest albedo can offset the
negative forcing expected from carbon sequestration.
Inclusion of land use impacts in LCA is also gaining
attention, since land as a resource can be especially
important in agricultural, forestry, and mining products.
Actually, this rising interest can be easily illustrated by the
establishment in this journal of a specific Land use
subject (Milà i Canals 2007). To date, research on land use
impacts has been mainly focused on biodiversity and soil
quality indicators Milà i Canals et al. (2006), whereas the
only link made between land use and climate change
corresponds to the alteration of carbon stocks, by such
processes as converting forest to agricultural land
(Cherubini et al. 2009; Silalertruksa et al. 2009), but no
attempt has been made so far to tackle the subject of albedo
change in the context of LCA. In this paper we present an
approach for LCA to include albedo changes from land
cover in the climate change impact category, measuring
them as CO
-eq. emissions. The method presented is tested
in a practical example on greenhouse agriculture.
2 Fundamentals and review of existing methods
2.1 Relationship between surface albedo
and top-of-atmosphere radiative forcing
The amount of shortwave energy reaching the top of the
atmosphere (TOA), averaged over the entire planet, has been
estimated as 341 W m
by Trenberth et al. (2009;Fig.1). Of
this amount, 79 W m
is reflected back to space due to
clouds and aerosols, and 78 W m
is absorbed by the
atmosphere. The remaining 184 W m
reaches the Earths
surface, where 23 W m
is reflected to space and
161 W m
are absorbed. Therefore, the average surface
albedo is 23/184=0.13, whereas the TOA albedo is 102/341=
0.3. According to Le Treut et al. (2007), changing the
atmospheric and/or the surface albedo constitutes one of the
fundamental ways to disturb the radiation balance of the Earth.
Ramaswamy et al. (2001) defined RF as the change in
net (down minus up) irradiance (solar plus longwave; in
watts per square meter) at the tropopause. In broad terms, it
describes any imbalance in the planets radiation budget
caused by human interventions. Once RF is applied, the
climate system tends to adjust to recover equilibrium,
usually by means of changes in temperature (Forster et al.
2007). For most shortwave forcing agents, the instanta-
neous RF at the TOA is linked to surface temperature
change and can be used instead of the stratospheric-
adjusted RF at the tropopause (Forster et al. 2007). The
instantaneous TOA RF (in watts per square meter) is given
by Eq. 1:
where R
is downward solar radiation at the TOA and Δa
is a variation in planetary albedo. R
is basically a
function of latitude (see Electronic Supplementary Material).
Concerning Δa
, according to Lenton and Vaughan (2009),
changes in surface albedo can be linearly related to changes
in a
(Eq. 2):
where Δa
is a variation in surface albedo and f
is a
parameter accounting for absorption and reflection of solar
radiation throughout the atmosphere. Under clear-sky con-
ditions, f
has an approximate global mean value 0.73 (Chen
and Ohring, 1985), representative of regions with very low
cloud cover, like deserts. On the other hand, lower f
Incoming solar
radiation at the top of
the atmosphere (TOA)
79 78
Absorbed by
Reflected by
clouds and
TOA reflection
by surface
Absorbed by
Fig. 1 Global mean shortwave energy flows in watts per square
meter. Absorbed incoming shortwave radiation is balanced by
releasing the same amount of outgoing longwave radiation. Source:
Trenberth et al. (2009)
Int J Life Cycle Assess (2010) 15:672681 673
are representative of cloudy skies. Lenton and Vaughan
for cloudy skies with Eq. 3:
where R
is downward solar radiation at the Earthssurface
(in watts per square meter) and T
is an atmospheric
transmittance factor expressing the fraction of the radiation
reflected from the surface that reaches the TOA. Using a
global value of T
=0.854 and the global incident radiation at
TOA at the Earthssurface,LentonandVaughanobtaina
global f
value of 0.48. However, Eq. 3canbeusedto
calculate site-specific f
values where R
is known. As a
consequence, combining Eqs. 1,2,and3, we can obtain the
following expressions to estimate RF
as a function of
surface albedo changes:
The method we present in this paper is based on RF
estimation using Eq. 4. Nevertheless, in the Electronic
Supplementary Material, we show that RF
can also be
estimated with Eqs. 1and 2.
2.2 CO
-equivalence of changes in surface albedo
The concept of RF allows us to compare modifications of
the Earths energy budget exerted by greenhouse gases,
with those caused by alterations due to changes in albedo.
However, policies like the Kyoto Protocol address climate
change mitigation targets in terms of greenhouse gas
emission reductions. For this reason, several authors
interested in the implications of changes in surface albedo
have developed calculation methods to express those
changes as CO
-eq. emissions or emission offsettings.
Betts (2000) developed a methodology aiming to express
the forcings from forest sequestration and albedo. Specifically,
he aimed at determining the change in terrestrial carbon stock
that would be equivalent to a change in surface albedo
resulting from a transition from agricultural land to forest land
in several regions. He simulated changes in albedo and
associated RF and then calculated the change in atmospheric
concentration (ΔC) which would give the same forcing,
by means of Eq. 5, taken from Myhre et al. (1998):
RF ¼5:35 ln 1 þ
=ðÞ ð5Þ
where C
is the 1997 global CO
concentration. ΔCis
converted to a terrestrial carbon stock change ΔC
by means
of Eq. 6:
where M
and M
are the molecular masses of carbon and dry
air and m
is the mass of the atmosphere. The factor of 2
accounts for an average airborne fraction of 0.5, taken from
Schimel et al. (1995). Including the airborne fraction is
necessary in the calculations since a fraction of emitted CO
does not remain in the atmosphere but dissolves in ocean
water and reacts with CaCO
in the sea floor, among other
processes. ΔC
was called by Betts emissions equivalent of
the shortwave forcingand can be easily converted to CO
emissions multiplying it by the molecular weight ratio of CO
to Cof 44/12=3.67.
Akbari et al. (2009) carried out an assessment of the
cooling potential at the planetary scale of increasing albedo
by 0.1 in urban areas. They estimated CO
-emission offsets
by first calculating with Eq. 5the RF of a marginal increase
of 0.128 ppmv in atmospheric CO
, resulting in a forcing of
+0.91 W kg CO
. They calculated that a 0.01 increase in
the Earths surface albedo exerts a mean global forcing of -
1.27 W m
. With these data, they concluded that
increasing albedo by 0.01 is equivalent to offsetting 1.27/
0.91=1.4 kg CO
. However, this figure refers to
changes in atmospheric CO
. Similarly to Betts (2000),
they consider an average CO
airborne fraction of 0.55
(Denman et al. 2007); thus, the emission offset is 1.4/0.55 =
2.55 kg CO
when surface albedo is increased by 0.01.
Bird et al. (2008) attempted to model the climate change
effects of afforestation/reforestation projects, by comparing
the RF due to carbon sequestration and to changes in land
use from grasslands to forest in various locations and forest
types in Canada. Both effects were expressed as CO
emissions. They developed a set of equations describing
changes in TOA albedo, radiative forcing, and CO
equivalence of albedo change. This method differs from
that by Akbari et al. (2009) in the fact that local incident
radiation is used instead of global values, thus discriminat-
ing the CO
equivalence of albedo change in different
locations. Another difference of this approach with regard
to Akbari et al. (2009)andBetts(2000) is the conversion of
atmospheric CO
to emitted CO
, by the so-called airborne
fraction. As we have seen, Akbari et al. (2009) and Betts
(2000) deal with this by taking into account an average
airborne fraction of 0.55 and 0.5, respectively. These figures
are based on the observed constant relationship between
global CO
emissions and atmospheric concentration since
1958 (Schimel et al. 1995;Denmanetal.2007). On the other
hand, Bird et al. (2008) do not take into account in their
model a fixed airborne fraction, but a time-dependent
relationship. This is justified by the fact that the airborne
fraction of an instantaneous release of CO
decays over time.
For relatively small perturbations, it can be approximated
from the Bern carbon cycle model (Joos et al. 2001):
fðtÞ¼0:217 þ0:259e
674 Int J Life Cycle Assess (2010) 15:672681
According to this model, after 10 years 66% of the initial
emission remains in the atmosphere, while only 36%
remains after 100 years. As a consequence, the choice of
a time horizon affects the magnitude of the CO
emissions. For a time horizon of 100 years, usually used
in the calculation of GWP, the average airborne fraction,
calculated as the integral of Eq. 7from year 0 to 99, is 0.48,
quite close to those used by Betts (2000) and Akbari et al.
3 Determination of the radiative parameters
3.1 Downward solar radiation at the Earths surface
For a particular site, average annual R
can be either
experimentally measured with a pyranometer for a repre-
sentative period of time or calculated from available
statistics from the closest meteorological station. It is also
possible to take advantage of existing tools and databases
which have been developed to determine this parameter for
the assessment of solar energy potential in different regions.
An example of this is the Photovoltaic Geographical
Information System (EC 2008).
3.2 Surface albedo
We can distinguish between empirical and modeling
approaches for the determination of a
. Empirical
approaches include remote sensing and field measurements.
Concerning remote sensing, data from the Moderate
Resolution Imaging Spectrometer (MODIS) are particularly
useful. MODIS is a radiometer operated aboard the NASA
Earth Observing System Terra and Aqua spacecrafts. It
collects data over a broad spectral range from the visible to
longwave infrared (Xiong et al. 2009). MODIS provides
measurements of instantaneous land surface reflectivity,
and daily mean and annual averages must be estimated
from representative data series. However, MODIS data
have a resolution of 500 m; hence, when the focus is on
small land parcels, field measurements are preferred. The
latter can be made by means of an albedometer, which
essentially consists of a combination of two pyranometers,
one facing upward and one facing downward.
Changes in surface albedo can also be estimated by
means of modeling techniques. Yin (1998) proposed a
model for the analysis and projection of albedo in vegetated
land surfaces. Models for simulation of albedo in urban
areas have also been developed, such as that by Chimklai et
al. (2004), taking into account the building height distribu-
tion, solar positions, the effects of multiple reflections and
shading. Comprehensive overviews of albedo for various
vegetation types, land covers, and materials were published
by Kondratyev (1969,1972), Iqbal (1983), Gates (1980),
and Breuer et al. (2003).
4 A framework for considering surface albedo changes
in LCA
A special feature of LCA as an environmental assessment
tool is the fact that it focuses on product systems, the
environmental burdens of which are allocated to so-called
functional units, representing a quantitative measure of the
function delivered by the product system. As a conse-
quence, CO
-eq. emissions from surface albedo changes
need to be attributed to a product system and functional
unit. Figure 2shows a simplified representation of albedo
changes in land cover albedo for two product systems P
and P
with two land use types, LU
and LU
, which have
surface albedo values a
and a
, respectively, where
. For simplicity, we assume albedo to be
constant in each type of use. It is important to highlight at
this point the difference between land occupation and land
transformation: Land occupation refers to using a land area
during a certain amount of time, assuming no transforma-
tion of the land properties during this use (Lindeijer et al.
2002; Milà i Canals et al. 2007a); land occupation is
measured as the product of surface and time (square meter
year). In Fig. 1land occupation for P
starts at t
finishes at t
. On the other hand, land transformation
implies changing the properties of a land area according to
the requirements of a given new type of use (Lindeijer et al.
2002; Milà i Canals et al. 2007a); land transformation is
measured in surface units (square meter). In Fig. 2there is a
land transformation process when P
starts and a new one
when P
starts. Radiative forcings exerted by changes in
albedo are related to land transformation rather than to land
occupation. In Fig. 2when LU
is changed to LU
, albedo
t0 t2
s LU1
s LU2
P1 P2
RFLT < 0
CO2-eqLT < 0
RFLT > 0
CO2-eqLT > 0
Surface albedo ( s)
Fig. 2 Conceptual representation of surface albedo change in two
product systems. LU land use, LT land transformation, RF radiative
forcing, Pproduct system, CO
-eq. carbon dioxide equivalent
Int J Life Cycle Assess (2010) 15:672681 675
increases, inducing a negative RF that can be expressed as a
-eq. emission offset. It must be stressed that this offset
is a result of changing the albedo, regardless of the duration
of LU
. Subsequently, when activity P
finishes and P
starts we have again a land transformation, from LU
, inducing a positive RF that can be expressed as a
-eq. emission. Assuming that P
and P
use the same
amount of land, then the CO
-eq. offset by the start of P
and the CO
-eq. emission by the start of P
each other.
This example raises at least two methodological ques-
tions concerning how to allocate CO
-eq. from changes in
surface albedo in LCA studies: (1) allocation to a given
product system and (2) allocation to a functional unit.
4.1 Allocation to a product system
As we have seen from the example in Fig. 2,P
transforming the land to a more reflective type of land
cover, and this can be expressed as a CO
-eq. emission
saving, analogous to a carbon sequestration. However,
when the activity of P
finishes, land is changed again by
to its original state before P
so that the environmental
achievement by P
is canceled. The question here is if P
should then be allocated a CO
-eq. offset. If we analyze
Fig. 2with a focus on land as a whole system in the t
period, the net environmental benefit is zero. However,
LCA deals with product systems; thus, there is a need to
allocate transformation interventions in Fig. 2to P
and P
Using causality as guiding principle, we suggest that land
transformed by a use of the land for new purposes should
be attributed to this future new use. In such a case, P
should receive an environmental credit due to the increase
in surface albedo from a
s LU1
to a
, whereas P
receive an environmental burden caused by decreasing
surface albedo from a
to a
. This allocation
principle is in accordance with current practice in the LCI
database ecoinvent (Frischknecht and Jungbluth 2007).
4.2 Allocation to a functional unit
The second question is how to allocate a CO
-eq. emission
or emission offset within a product system to a functional
unit. Changes in albedo are attributed to land transforma-
tion; hence, they constitute one-time interventions, just as
manufacturing capital equipment (machinery, buildings) or
clearing a forest before an agricultural activity. The only
way of allocating one-time interventions to a functional unit
is to assume an expected lifetime for the affected activity,
which can be uncertain. In economics, for instance, this is
dealt with by means of the depreciation concept. The
subject of one-time interventions or preparatory processes
in the context of land use in LCA has already been debated
in the past (see Udo de Haes 2006; Milà i Canals et al.
2007b). The problem of allocating climate burdens from
land transformation to a functional unit is not different
from, for instance, allocating the manufacture of a tractor to
an agricultural product. As Milà i Canals et al. (2007b)
point out, there is no scientific way to predict the future of
markets, and a clearallocation of preparative interven-
tions to the future years of the created structure has to be
based on societal agreements to avoid arbitrariness. As they
also point out, an exclusion of preparatory processes from
LCA, due to their less direct allocability to the product or
service, would seriously jeopardize the usability of LCA as
a decision tool. The latter is supported by Frischknecht et
al. (2007) who assessed the influence of capital goods in
the environmental profile of hundreds of datasets from the
ecoinvent database. This does not necessarily mean that
changes in surface albedo have the same influence in LCA
studies than capital goods, but we want to stress the fact
that they do not involve a particularly special type of
allocation problem. Some examples of how land use change
can be dealt with are provided by the carbon footprinting
methodology according to the British PAS 2050 standard
(BSI 2008) and by the European Directive on energy from
renewable sources (European Union 2009), in which
emissions from land use change must be taken into account,
distributing them over the functional unit during the first
20 years after land was changed.
4.3 Mathematical expression for CO
of changes in surface albedo and characterization factors
The derivation of a general expression for CO
emissions from surface albedo changes is based on previous
work by Bird et al. (2008). The reader is referred to that
work for further details. Based on Betts (2000), Bird et al.
(2008) express CO
-eq. emissions (in grams) from surface
albedo changes as
CO2eq:¼ARFTOA ln 2pCo2;ref MCO2mair
F2X Mair AF ð8Þ
where Ais the area affected by the change in surface albedo
(in square meter), RF
is measured in watts per square
meter, pCO
is a reference partial CO
pressure in the
atmosphere (383 ppmv), M
is the molecular weight of
(44.01 g mol
), m
is 5.148×10
Mg, A
is the
area of the Earth (5.1×10
), ΔF
is the radiative
forcing resulting from a doubling of current CO
tration in the atmosphere (+3.7 W m
), M
is the
molecular weight of dry air (28.95 g mol
), and AF is
the average CO
airborne fraction. All the variables taking
constant values in Eq. 8can be grouped in a single
parameter, the value of which is 1,101 W g CO
. The
676 Int J Life Cycle Assess (2010) 15:672681
inverse of this constant is the marginal RF of CO
emissions at the current atmospheric concentration, which
we will express in kilograms (0.908 W kg CO
). Substi-
tuting in Eq. 8, we obtain Eq. 9:
In this equation, CO
-eq. emissions are expressed in
kilograms. Now we call Aas the land transformation per
functional unit (LT
) and substitute RF
by means of
Eq. 4:
CO2eq:¼LTFU RsTa
Substituting Δa
by an initial and final surface albedo
values, a
and a
, we finally obtain Eq. 11:
CO2eq:¼LTFU RsTaasLU1 asLU2
The average AF is a function of time, since the airborne
fraction of an instantaneous release of CO
decays over
time. Given a time horizon of nyears, AF is calculated with
Eq. 12, where f(t) is the Bern carbon cycle model (Eq. 7):
AF ¼
Equation 12 gives AF values of 0.69, 0.48, and 0.32 for
the usual time horizons considered in GWP of 20, 100, and
500 years, respectively.
From Eq. 11 we can derive characterization factors (ChF,
in kilograms CO
-eq. per square meter) for the initial and
final land uses, respectively:
ChFLU1 ¼þRsTaasLU1
ChFLU2 ¼RsTaasLU2
Positive and negative signs in Eqs. 11,13, and 14 are
used in such a way that negative CO
-eq. emissions are
obtained when albedo is increased, and vice versa. As it can
be seen, characterization factors, besides being albedo-
dependent, allow the user to define specific values for
locations receiving different solar radiation levels, as well
as for different time horizons. It must also be highlighted
that this approach can be used either for land trans-
formations taking place in the foreground system as well
as for those related to the background system, provided
that the background data include land transformation
4.4 Uncertainty
The uncertainty in these calculations depend on the respective
uncertainties of the user-defined data (LT
) and of the default values for T
approximate error of ±30% is associated with T
, based on the
range of values suggested by Lenton and Vaughan (2009),
whereas for RF
Akbari et al. (2009) suggest a ±10% error.
Concerning AF, the error is less than ±15% (Forster et al.
-eq. emissions
around ±35% should be expected, excluding the contribution
from user-defined parameters. The uncertainty of LT
be associated with that of the inventory data used but also
with the definition of a service life for the activity under
study, as discussed in Section 4.2. On the other hand, R
be quantified with a low level of error, either with measure-
ments or with models. With regard to initial and final albedo,
substantial uncertainty can be expected if the values used do
not come from measurements; otherwise, they should be
substantially lower. In the following section, the overall
uncertainty of the CO
-eq. emissions calculation is provided
for plastic greenhouses in Almería.
5 Case study: horticultural production in Almería
As an example of application, a cradle-to-gate LCA of
intensive tomato production in the province of Almería
(southeastern Spain) is carried out. This region has
experienced from the 1970s a rapid development of
greenhouse horticulture. According to Sanjuan (2007),
almost 26,000 ha of land were covered by plastic green-
houses in 2007, and they currently increase at an average
rate of 500 ha year
. The study focuses only on the climate
change impact category, using GWP-100 as characteriza-
tion model, taking into account carbon from both biogenic
and fossil sources. Nevertheless, values for GWP-20 and
GWP-500 are also calculated as a sensitivity analysis.
5.1 Inventory for the farming activity
Unfortunately, there are no published LCA studies for tomato
production in Almería. As a consequence, most of the data
used corresponds to the same process in Barcelona (Antón et
al. 2005), with a similar climate to that of Almería. It is
assumed for this example that the amount of inputs to the
farm in Barcelona is comparable to those in Almería.
Nevertheless, some processes related to soil preparation,
greenhouse maintenance, and water pumping are taken from
a study in this region (Muñoz et al. 2009). The following
processes are included in the inventory: change in biomass
carbon stocks due to clearing of land prior to the agricultural
activity, greenhouse infrastructure production, maintenance
Int J Life Cycle Assess (2010) 15:672681 677
and disposal, soil preparation, carbon fixation by the crop,
fertilizers production and N
O emissions from their applica-
tion, water pumping, transport, and treatment of green waste.
Soil CO
emissions due to changes in soil organic matter
during the farming period are not included due to lack of
data. This can be considered as an important limitation of
this case study, since these emissions have been found to be
of the outmost importance in agricultural systems (Koerber
et al. 2009; Brandão et al. 2010). As background data, the
ecoinvent 2.0 database has been used (Swiss Centre for Life
Cycle Inventories 2008). Production of pesticides has been
excluded from the study, since their contribution outside
toxicity-related impact categories is very low (Antón et al.
2005). The detailed inventory data for the farming activity
are shown in the Electronic Supplementary Material.
5.2 Calculation of CO
-eq. emissions from surface albedo
The calculation is made using the following data: The
tomato yield considered is 12 kg m
(Antón 2004);
therefore, land occupation is 83 m
year t
. As already
discussed, the CO
equivalence of albedo change is related
to land transformation. As a consequence, a time span for
the farming activity has to be defined. For this example we
choose a period of 50 years, resulting in a LT
1.67 m
. The annual mean incident solar radiation (R
in this area is 196 W m
for the 20012005 period,
according to Campra et al. (2008). Concerning the surface
albedo values, a mean annual value of 0.19±0.02 was
observed for the replaced grassland and 0.4±0.06 for an
area fully covered by greenhouses (Campra et al. 2008). If
we use Eq. 15 with AF for 100 years, the resulting GWP-
100 is 134 kg CO
-eq. per ton tomato. The corresponding
results for GWP-20 and GWP-500 can be also calculated as
in Eq. 11, replacing 0.48 by 0.69 and 0.32, respectively.
CO2eq:¼1:67 196 0:854 0:19 0:40ðÞ
0:908 0:48
¼134 kg CO2eq:ton1ð15Þ
The uncertainty involved in this calculation, excluding
the contribution from LT
, is up to ±45%. The contribu-
tion of R
to this uncertainty is not included either, although
it is considered to be very small, given that the value used
comes from field measurements with a pyranometer
(Campra et al. 2008).
6 Results and discussion
In Table 1the CO
-eq. emissions associated to the cradle-
to-gate farming activities are summarized. As it can be
seen, the GWP-100 is 303 kg CO
-eq. per ton tomato,
which is reduced to 168 kg CO
-eq. per ton tomato if the
change in surface albedo is taken into account. The choice
of time horizon in the GWP affects the magnitude of the
albedo effect, being increased with longer time horizons
such as 500 years. These results show that the local
radiative forcing caused by the land cover change has a
remarkable offset effect on the overall greenhouse gas
balance of this particular product system, equivalent to 44%
of its emissions when GWP-100 is considered. Campra
et al. (2008) showed the first empirical evidence to support
that changes in surface albedo caused by the highly
reflective plastic cover in this area have led to a cooling
trend in surface temperature. However, the magnitude of
this effect, when measured as CO
-eq. emissions per unit
product, depends on the choice of a service lifetime, which
in this example was taken as 50 years. The emission offset
increases for shorter lifetimes, for example it increases from
134 to 269 kg CO
-eq. per ton tomato when a 25-year
lifetime is considered but decreases to 67 kg CO
-eq. per
ton tomato when it is expanded to 100 years. Therefore,
when the implications of changes in albedo are of such high
magnitude as in the system under study, the choice of this
lifetime is of the utmost importance. Nevertheless, it should
not always be expected that changes in albedo have such a
high influence in LCA studies. The example shown is a
very particular case in which a sharp increase in surface
albedo is caused by white greenhouses. Another particular
case where albedo could have an important influence in the
-eq. emission balance is in the context of forestry or
any other system involving land use in high latitudes,
where long-lasting snow cover is affected in some way
(Betts 2000) or anywhere where the reflectance of land
cover materials is changed on purpose, as is the case of
buildings and urban areas (Akbari et al. 2009). These are
examples of product systems where land use plays a
significant role; in product systems where land use is only
a background issue, the influence of changes in surface
albedo is expected to be less important than that from
emissions of greenhouse gases.
Nonetheless, the presented method can be used to assess
both interventions in the foreground and the background
systems. In the latter case, a set of characterization factors
should be developed for different land use classes, although
a certain level of regionalization is needed due to several
reasons: The first is that characterization factors depend on
the local intensity of solar radiation, and the second is the
effect of snow: Even though a coniferous forest may have a
similar summer albedo in different locations, the presence
or absence of snow in winter would make a substantial
difference in the annual mean albedo for locations in, for
instance, southern and northern Europe. Regionalized
impact assessment methods have already been developed
678 Int J Life Cycle Assess (2010) 15:672681
for other well-established impact categories like acidifica-
tion and eutrophication (Huijbregts et al. 2000), but this is
the first time that such a need is identified for climate
change, an impact category with site-independent charac-
terization factors. While greenhouse gases are assumed to
be well mixed and distributed in the atmosphere, regardless
of where they are emitted, changes in albedo involve effects
on the climate at a regionallocal scale, in the area where
solar energy budget is changed.
This method constitutes a simple analytical approach to
assess the climate burdens from changes in land surface
albedo. Besides the allocation problems discussed in
Section 3, it has other limitations, such as the uncertainty
involved. In the presented case study, the calculations are
estimated to have an uncertainty of up to ±45%. Among the
most important factors contributing to this uncertainty is the
simple modeling of atmospheric transmittance (T
). Further
refinement of this parameter would require a more
sophisticated modeling, which should include local data
on cloud cover, as provided for example by the Interna-
tional Satellite Cloud Climatology Project (ISCCPD2).
Another limitation is the need to obtain local data on
surface albedos, either by means of field measurements or
remote sensing. Although it might be tempting to use
literature albedo values, it must be stressed that the CO
emissions are very sensitive to small changes in albedo, and
general albedo values in the literature are sometimes given
as ranges with rather broad limits. For instance, according
to Taha et al. (1988) albedo for crops is in the 0.150.25
range, whereas for urban areas, it can be anything from 0.1
to 0.35. Finally, it is also important to consider that RF and
hence the GWP metric itself have their limitations (see
Forster et al. 2007, pp. 210211), especially when the focus
is on climate impacts from land use change. For example,
deforestation in the tropics decreases evapotranspiration
rates and increases sensible heat fluxes, resulting in
regionally decreased precipitation and increased surface
temperature (Bala et al. 2007). These kinds of effects
cannot be quantified in terms of radiative forcing nor,
therefore, as GWP either. Some authors have proposed new
metrics to quantify land use disturbances on the climate,
such as the Regional Climate Change Potential by Pielke et
al. (2002). Nevertheless, direct comparison of land cover
change effects with greenhouse gas emissions remains a
7 Conclusions
A method has been introduced to include in LCA studies
the radiative forcing exerted by changes in land surface
albedo, expressed as CO
-eq. emissions. This method uses
a simple analytical approach, based on previous work in the
field of climate geoengineering. Besides enabling the
assessment of foreground interventions on land, it can also
be used to assess background interventions, although this
will require the development of regionalized characteriza-
tion factors. A practical example of intensive tomato
cultivation under reflective plastic greenhouses in southern
Spain has shown that the effect of surface albedo changes
can have a very important influence in the climate change
impact category, provided that (1) land use plays an
important role in the system (such as in agriculture, forestry
and mining, buildings, and urban areas) and (2) substantial
changes in surface albedo are expected in the product
This method raises some methodological problems in the
context of LCA, which have also been discussed. They are
related to the fact that the CO
-eq. emissions associated to
changes in surface albedo are a consequence of land
Process GWP-20 GWP-100 GWP-500
Change in biomass carbon stock
22 2
Carbon fixation by crop
190 190 190
Greenhouse infrastructure 283 226 204
Soil preparation 6 6 6
Greenhouse maintenance <1 <1 <1
Fertilizers 94 93 65
O emissions 55 57 29
Greenhouse disposal 6 3 2
Water pumping 24 22 21
Green waste treatment
83 84 83
Overall emissions (a) 365 303 223
Change in surface albedo (b)93 134 202
Net with albedo change (c=a+b) 272 168 21
Ratio (c)to(a) 75% 56% 9%
Table 1 GWP (kilogram
-eq.) for growing 1,000 kg
Biogenic CO
Mostly biogenic CO
Int J Life Cycle Assess (2010) 15:672681 679
transformation, not of land occupation. As a consequence,
these emissions are one-time interventions which have to be
allocated to the functional unit by means of an expected
service lifetime. Another problem arising from the land
transformation dependency is the fact that impacts are
reversible: If in the same product system albedo is changed
but returned to its final state at the end of the service
lifetime, the net albedo change, and thus, the CO
emissions are zero. These methodological problems are
analogous to those from accounting changes in carbon
stocks in LCA.
Although the method presented can be improved
concerning the calculation of radiative forcing, it constitutes
a first operative approach for LCA to go beyond green-
house gas accounting and provide a more complete
evaluation of human contributions to climate change.
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... Bird et al., 2008) and empirical parameterisations that neglect the effect of surface albedo on radiative transfer (e.g. Muñoz et al., 2010). Bright and O'Halloran (2019) provide a quantitative evaluation of different methods. ...
... Kirschbaum et al. (2013) assessed climate impacts due to albedo change and GHG fluxes associated with land use changes between forestry and agriculture. Agricultural LCA studies that have quantified albedo effects evaluated bioenergy crops (Cai et al., 2016;Caiazzo et al., 2014;Jørgensen et al., 2014), biochar (Meyer et al., 2012), cover crops (Guardia et al., 2019) and greenhouse agriculture (Muñoz et al., 2010). Forestry LCA studies have included albedo in the context of forestation (Schwaiger & Bird, 2010) and forest management including biofuels (Bright et al., 2011;Cherubini et al., 2012;Holtsmark, 2015). ...
... Other approaches interpret albedo change as a consequence of land transformation, i.e. a one-time intervention with lasting effect (Muñoz et al., 2010). Thus, albedo RF is treated as a one-off pulse with infinite lifetime that lasts until it is cut off at the TH, regardless of the study period. ...
Full-text available
Agricultural systems for production of food, energy and materials are a major driver of climate change, due to land use and greenhouse gas (GHG) emissions along the supply chain. Crop cultivation also affects the climate by changing land surface albedo, i.e. the fraction of solar radiation reflected back from the ground. Increased albedo could counteract the radiative forcing and warming effect of emitted GHGs. This thesis examined how individual crops and cultivation practices in Sweden influence albedo, and thus the climate. Field measurements and satellite data were used to analyse differences between crops, management practices and environmental conditions. Methods for assessing climate impacts due to albedo change and for comparing these impacts with those of GHGs were developed. Time-dependent life cycle assessment (LCA) was performed to obtain a perspective on the importance of albedo change for the climate impact of crop and bioenergy production, relative to life-cycle GHG emissions and carbon sequestration. The results showed higher albedo for soil kept covered year-round, e.g. by perennial crops or winter varieties and by straw left in the field, combined with delayed or reduced tillage. In case studies, albedo increased by 31% under willow and 6-11% under different food or feed crops relative to unused land. This albedo increase countered the effect of GHG emissions from manufacture of inputs and fuel consumption, by 20-60% when measured as GWP100 and by 60-200% as GWP20. Impacts assessed as global mean temperature change (ΔT) over time were dominated by albedo-induced cooling on short time scales and by the effects of emitted GHGs and carbon sequestration on longer time scales. The local, immediate effect of increased albedo could be exploited in strategies to dampen warming locally and alleviate heat stress in summer.
... -Quelles améliorations dans la gestion agronomique de ces pratiques séquestrantes permettraient d'obtenir des synergies entre ces effets biogéophysiques et CT (« conventional tillage », travail du sol), NT (« no-till », pas de travail du sol), NTM (« notill + mulch », pas de travail du sol avec mulch). La comparaison des effets biogéochimiques et biogéophysiques n'est pas simple, même si des méthodes sont en cours de développement pour convertir les effets biogéophysiques en équivalent CO2 (Bright, 2015;Bright et al., 2016;Bright and Lund, 2021;Muñoz et al., 2010). Les effets biogéophysiques sont plus souvent exprimés en W m -2 . ...
... Les effets biogéophysiques sont plus souvent exprimés en W m -2 . Le forçage radiatif lié à l'albédo (RFαi en W m -2 ) peut être calculé grâce à l'équation suivante (Muñoz et al., 2010) : RFαi = -SWINi × Tai × Δαi où SWINi est le rayonnement solaire incident journalier du jour i qui peut être mesuré à la surface de la parcelle (W m -2 ), Tai est la transmittance atmosphérique moyenne journalière et Δαi est la différence d'albédo entre le traitement étudié et le traitement de référence, également au jour i. ...
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Mémoire présenté publiquement le 16 juin 2023 pour l’obtention de l’Habilitation à Diriger des Recherches de l'Université de Montpellier, école doctorale GAIA (Biodiversité, Agriculture, Alimentation, Environnement, Terre, Eau).
... In the LCA, climate impacts due to albedo change, soil C and soil N balance were included, considering regional conditions. Effects of land use are often omitted in LCAs, or quantified using generic literature data and emission factors such as IPCC Tier 1 methods (Goglio et al., 2015;Henryson et al., 2020;Muñoz et al., 2010). These methods are attractive because they are transparent and simple to use, with high availability of input data and comparability of LCA results across studies. ...
... Other methods account for impacts of land transformation and/or delayed regeneration and attribute hypothetical fluxes to land use, e.g. IPCC Tier 1-2, and methods proposed by Müller-Wenk and Brandão (2010), Schmidinger and Stehfest (2012), Muñoz et al. (2010) and Bright et al. (2012). These methods operate with amortisation periods to allocate the effects of one-time interventions over time and/or simplified assumptions about regeneration over time. ...
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Agricultural land use and management practices affect the global climate due to greenhouse gas (GHG) fluxes and changes in land surface properties. Increased albedo has the potential to counteract the radiative forcing and warming effect of emitted GHGs. Thus considering albedo could be important to evaluate and improve agricultural systems in light of climate change, but the albedo of individual practices is usually not known. This study quantified the albedo of individual crops under regional conditions, and evaluated the importance of albedo change for the climate impact of current crop production using life cycle assessment (LCA). Seven major crops in southern Sweden were assessed relative to a land reference without cultivation, represented by semi-natural grassland. Crop-specific albedo data were obtained from a MODIS product (MCD43A1 v6), by combining its spatial response pattern with geodata on agricultural land use 2011–2020. Fluxes of GHGs were estimated using regional data and models, including production of inputs, field operations, and soil nitrogen and carbon balances. Ten-year mean albedo was 6–11% higher under the different crops than under the reference. Crop-specific albedo varied between years due to weather fluctuations, but differences between crops were largely consistent. Increased albedo countered the GHG impact from production of inputs and field operations by 17–47% measured in GWP100, and the total climate impact was warming. Using a time-dependent metric, all crops had a net cooling impact on global mean surface temperature on shorter timescales due to albedo (3–12 years under different crops), but a net warming impact on longer timescales due to GHG emissions. The methods and data presented in this study could support increasingly comprehensive assessments of agricultural systems. Further research is needed to integrate climatic effects of land use on different spatial and temporal scales, and direct and indirect consequences from a systems perspective.
... local time, A is the area for which the hypothesized albedo change occurred (here normalized to 1 m 2 ), AF(t) is the CO2 airborne fraction that remains in the atmosphere at time (t) following a single pulse emission, rfCO₂ is the marginal RF for CO2 emissions at a given atmospheric concentration, and TH represents the time horizon of global warming. The parameter AF(t) is modeled with an exponential function through a multi-model impulse response function analysis (for more details, see Equation A (6) in Appendix A) [40], while rfCO₂ is kept constant at 0.908 W kg CO2 −1 [13,41,42] and TH is fixed at 100 years (i.e., the number of time steps the GWI is then divided by) [43,44]. With Equation (4), we calculate the equivalent RFΔα that a unit area of A would have at the global scale. ...
... where SWTOA is the incident shortwave radiation at the top-of-atmosphere and Δαp is the change in planetary albedo. Changes in planetary albedo (Δαp) are linearly related to changes in surface albedo (Δαs) as follows [37,42,83,84]: ...
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Land surface albedo is a significant regulator of climate. Changes in land use worldwide have greatly reshaped landscapes in the recent decades. Deforestation, agricultural development, and urban expansion alter land surface albedo, each with unique influences on shortwave radiative forcing and global warming impact (GWI). Here, we characterize the changes in landscape albedo-induced GWI (GWIΔα) at multiple temporal scales, with a special focus on the seasonal and monthly GWIΔα over a 19-year period for different land cover types in five ecoregions within a watershed in the upper Midwest USA. The results show that land cover changes from the original forest exhibited a net cooling effect, with contributions of annual GWIΔα varying by cover type and ecoregion. Seasonal and monthly variations of the GWIΔα showed unique trends over the 19-year period and contributed differently to the total GWIΔα. Cropland contributed most to cooling the local climate, with seasonal and monthly offsets of 18% and 83%, respectively, of the annual greenhouse gas emissions of maize fields in the same area. Urban areas exhibited both cooling and warming effects. Cropland and urban areas showed significantly different seasonal GWIΔα at some ecoregions. The landscape composition of the five ecoregions could cause different net landscape GWIΔα.
... This simplifies the assessment of the radiative forcing of different GHGs (e.g., methane and water vapor) and their mixtures. The different emissions are converted to CO2-equivalents (CO2-eq) by multiplying them with their respective global warming potentials (GWPs) for a given time horizon [11]. The default timeframe of 100 years, also applied to the parameters in this study, was adopted during the UNFCC and used in the Kyoto protocol [12]. ...
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Harvested wood products (HWP) can play an important role in climate-smart bioeconomic transformation. They contribute to climate change mitigation through two main mechanisms: carbon storage and substitution. Norway has ambitions to strengthen the contribution of its forest sector in climate change mitigation. Ideally, the future production and use of HWPs would increasingly shift towards products with high carbon storage and substitution benefits. We collected data from the literature and, when necessary, supplemented it with our own calculations, on carbon storage and substitution factors of HWPs that seemed relevant in evaluating the climate change mitigation potential in the context of the Norwegian forest sector. There are many uncertainties in the parameters. We identified and examined in more detail some uses of wood for industrial products that offer clear substitution benefits and, in some cases, long-term carbon storage. Wood-based construction materials, textile fibres, and insulation materials are examples of such products that could have high potential in the bioeconomy transformation in Norway.
... Campra et al. [9] is given as interpreted and cited by Akbari, Matthews & Seto [6]. Values from Muñoz & Campra [10] were computed by the author using exclusively parameters available in the published paper 1 . The five studies estimate GWP A values ranging from À2.55 kgCO 2 /m 2 to À7.50 kgCO 2 /m 2 when integrating over a 100-year time horizon. ...
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A global warming potential of albedo (GWPA) is proposed, that represents the carbon dioxide emissions equivalent to a 0.01 increase in albedo over 1 m² of horizontal surface. A survey of prior literature suggests GWPA ≈ −4.2 kgCO2/m². Taking Los Angeles, CA as a test site for urban global warming mitigation actions, a residential “cool roof” project offers approximately seven times as much radiative forcing benefit from albedo change as from GHG reduction of energy efficiency; and a citywide increase to commercial building roof albedo offers radiative forcing benefit equivalent to the first 6½ years of all commercial sector GHG emission reductions proposed in the City of Los Angeles climate action plan. Discussion explores pathways and challenges to making albedo increases fungible with GHG reductions in GHG markets.
... The possibility exists, therefore, that peatland restoration may not only provide climate mitigation by means of additional carbon sequestration, but may directly modify, and cool the local climate. In the literature that has discussed the influence of land cover influence on the energy balance at the land surface, there have been many studies on deforestation (Rautiainen et al., 2011) and cropland conversion (Muñoz et al., 2010), but few studies of peatlands (see review ;Luyssaert et al., 2014). Over the wetlands of the Sacremento River delta, California, Hemes et al. (2018) found that air temperature over the wetlands were lower than the air temperature over the surrounding drained agricultural land. ...
We hypothesize that peatlands represent a cool humid island in their landscape context and that this cool humid island effect could be brought about by successful peatland restoration. This study used 20 years of Earth observation data for land surface temperature (day‐ and night‐ time LST), albedo (near infra‐red white sky albedo) and vegetation indices measured for 42 one km ² grid squares across two peatlands and their surrounding arable fields. The peatlands have undergone restoration (re‐vegetation and raising of water tables) since 2004. The results show that over the restored peatlands: Day time temperatures over the peatlands cooled relative to the surrounding arable‐land by up to 1.1 K (°C), but there was no significant change in night time temperatures. Over the peatlands the average amplitude of the diurnal temperature cycle decreased by up to 2.4 K (°C) over the period of the restoration. Comparison to vegetation indices and albedo shows the cooling effect of increasing albedo was smaller than warming effect of changes aerodynamic resistance brought about by development of shrubby vegetation. The presence of an overall cooling effect, despite a warming effect due to vegetation development, means that a rising water table led to a lowering of the Bowen ratio. Peatlands revegetated to, or dominated by, moss carpets rather than shrubby vegetation will maximise the potential cooling effect, whereas shrub development across peatlands without a rise in water table would lead to warming.
Natural gravel-recycled aggregate concrete (NG-RAC) has favorable prospects in rural highway pavement due to instant acceptance and tremendous need. This paper studies the material properties (including mechanical properties and frost resistance durability) and carries out a life cycle assessment (LCA) based on an actual engineering case. It can be found that, although the overall performance of NG-RAC is somewhat lower than those of natural aggregate concrete (NAC) or crushed stone-recycled aggregate concrete (CS-RAC), it meets the requirements for low-grade pavements. Compared with the pavement constructed with CS-NAC and CS-RAC, the results show that, carbon emissions are reduced by about 48.7% and 40.5% for the practical NG-RAC pavement, respectively. Besides, the sensitivity analysis of transport distance was conducted to estimate the limit transportation distance ratios of RCA/NCA and NG/CS. By quantitative analysis, it can be concluded that rural highway pavement built with NG-RAC will produce considerable environmental and ecological benefits, including energy-saving, carbon emission reduction, environmental cost lowering and land conservation.
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Aims and Scope. Land use by agriculture, forestry, mining, house-building or industry leads to substantial impacts, particularly on biodiversity and on soil quality as a supplier of life support functions. Unfortunately there is no widely accepted assessment method so far for land use impacts. This paper presents an attempt, within the UNEP-SETAC Life Cycle Initiative, to provide a framework for the Life Cycle Impact Assessment (LCIA) of land use. Main Features. This framework builds from previous documents, particularly the SETAC book on LCIA (Lindeijer et al. 2002), developing essential issues such as the reference for occupation impacts; the impact pathways to be included in the analysis; the units of measure in the impact mechanism (land use interventions to impacts); the ways to deal with impacts in the future; and bio-geographical differentiation. Results. The paper describes the selected impact pathways, linking the land use elementary flows (occupation; transformation) and parameters (intensity) registered in the inventory (LCI) to the midpoint impact indicators and to the relevant damage categories (natural environment and natural resources). An impact occurs when the land properties are modified (transformation) and also when the current man-made properties are maintained (occupation). Discussion. The size of impact is the difference between the effect on land quality from the studied case of land use and a suitable reference land use on the same area (dynamic reference situation). The impact depends not only on the type of land use (including coverage and intensity) but is also heavily influenced by the bio-geographical conditions of the area. The time lag between the land use intervention and the impact may be large; thus land use impacts should be calculated over a reasonable time period after the actual land use finishes, at least until a new steady state in land quality is reached. Conclusion. Guidance is provided on the definition of the dynamic reference situation and on methods and time frame to assess the impacts occurring after the actual land use. Including the occupation impacts acknowledges that humans are not the sole users of land. Recommendations and Perspectives. The main damages affected by land use that should be considered by any method to assess land use impacts in LCIA are: biodiversity (existence value); biotic production potential (including soil fertility and use value of biodiversity); ecological soil quality (including life support functions of soil other than biotic production potential). Biogeographical differentiation is required for land use impacts, because the same intervention may have different consequences depending on the sensitivity and inherent land quality of the environment where it occurs. For the moment, an indication of how such task could be done and likely bio-geographical parameters to be considered are suggested. The recommendation of indicators for the suggested impact categories is a matter of future research.
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An update is provided on the Earth's global annual mean energy budget in the light of new observations and analyses. In 1997, Kiehl and Trenberth provided a review of past estimates and performed a number of radiative computations to better establish the role of clouds and various greenhouse gases in the overall radiative energy flows, with top-of-atmosphere(TOA) values constrained by Earth Radiation Budget Experiment values from 1985 to 1989, when the TOA values were approximately in balance. The Clouds and the Earth's Radiant Energy System (CERES) measurements from March 2000 to May 2004 are used at TOA but adjusted to an estimated imbalance from the enhanced greenhouse effect of 0.9 W m(-2). Revised estimates of surface turbulent fluxes are made based on various sources. The partitioning of solar radiation in the atmosphere is based in part on the International Satellite Cloud Climatology Project (ISCCP) FD computations that utilize the global ISCCP cloud data every 3 h, and also accounts for increased atmospheric absorption by water vapor and aerosols. Surface upward longwave radiation is adjusted to account for spatial and temporal variability. A lack of closure in the energy balance at the surface is accommodated by making modest changes to surface fluxes, with the downward longwave radiation as the main residual to ensure a balance. Values are also presented for the land and ocean domains that include a net transport of energy from ocean to land of 2.2 petawatts (PW) of which 3.2 PW is from moisture (latent energy) transport, while net dry static energy transport is from land to ocean. Evaluations of atmospheric re-analyses reveal substantial biases.
The objective of this book is to make analytical methods available to students of ecology. The text deals with concepts of energy exchange, gas exchange, and chemical kinetics involving the interactions of plants and animals with their environments. The first four chapters are designed to show the applications of biophysical ecology in a preliminary, sim­ plified manner. Chapters 5-10, treating the topics of radiation, convec­ tion, conduction, and evaporation, are concerned with the physical environment. The spectral properties of radiation and matter are thoroughly described, as well as the geometrical, instantaneous, daily, and annual amounts of both shortwave and longwave radiation. Later chapters give the more elaborate analytical methods necessary for the study of photosynthesis in plants and energy budgets in animals. The final chapter describes the temperature responses of plants and animals. The discipline of biophysical ecology is rapidly growing, and some important topics and references are not included due to limitations of space, cost, and time. The methodology of some aspects of ecology is illustrated by the subject matter of this book. It is hoped that future students of the subject will carry it far beyond its present status. Ideas for advancing the subject matter of biophysical ecology exceed individual capacities for effort, and even today, many investigators in ecology are studying subjects for which they are inadequately prepared. The potential of modern science, in the minds and hands of skilled investigators, to of the interactions of organisms with their advance our understanding environment is enormous.
Goal, Scope and Background. On June 12–13 June 2006 in Guildford (UK) an international workshop was held to address indicators to incorporate land use impacts in LCA. It provided an interdisciplinary forum where soil scientists and biologists met with LCA experts and users to discuss the challenges of including land use impacts in LCA and potential approaches to addressing these challenges. The discussion used as starting point the definitions framed in the past work on land use impacts within the UNEP/SETAC Life Cycle Initiative (Milà i Canals et al. 2006). However, the presence of soil quality and biodiversity experts allowed for a more in-depth consideration of the nature of land use impacts. Main Features. The discussions were focused on three main themes: general methodological issues to be addressed in including land use impacts in LCA; recommendations for soil quality indicators; and recommendations for biodiversity indicators. Results and Discussion. There is a conflict between the levels of detail at which LCA should assess land use impacts: a coarse assessment may allow the detection of hotspots from a life cycle perspective, whereas a more detailed assessment might allow the distinction between land management modes (e.g. organic vs. conventional agriculture). Different land use processes need to be modelled in consequential and attributional LCA. Land use effects on biodiversity and soil quality are non-linear and also depend on the scale of land use, which is difficult to address in LCA. Soil is multi-functional and many threats affect its quality, which results in a case-specific selection of the most adequate indicator. In the case of biodiversity, two main options for defining indicators were identified at species and ecosystem levels. The main advantage of the former is data availability, but the election of a particular taxon may be arbitrary. Ecosystem level indicators include a higher degree of subjectivity but may be more relevant than species level ones. Conclusions. Land use impacts need to be considered in LCA for all life cycle stages in all types of products. An urgent need for LCA is to incorporate land use impacts particularly in comparisons of systems which differ substantially in terms of land use impacts. The main differences between consequential and attributional LCA are the need for the consideration of off-site effects and marginal vs. average land uses in consequential LCA. In order to define the marginal effects of land use a similar approach to the description of the electricity grid and its marginal technology may be followed. ‘Dose-response’ functions need to be defined for land use interventions and their effects. The main soil degradation processes (considering soil’s vulnerability to different threats) should be captured in a spatial-dependent way in LCA. Criteria and examples to select biodiversity indicators at species and ecosystem levels were proposed in the workshop. Recommendations and Perspectives. The conduction of LCA case studies for relevant systems (especially fossil energy compared to bio-energy systems involving different eco-regions to account for potential international trade) may provide a good platform to further develop the workshop suggestions.
The Need for a New Subject Area 'Land use in LCA' Land use by humans is the primary direct cause of many impacts from production systems. There is wide consensus that land use is the main cause of biodiversity degradation, and inappropriate land management is a main driver for the reduction in the biological production capacity of soil. Emissions caused by land use changes in the last two centuries have caused a change in the Earth's radiative forcing of the same order of magnitude as emissions from burning fossil fuels. It is thus not surprising that ways to account for land use impacts in LCA have been explored since the early methodological guides for this tool. However, there is to date no consensus as to how land use impacts may be incorporated in LCA. As noted in the synthesis of a recent workshop on land use impacts in LCA [1], "accounting for land use in LCA is inherently problematic [because] land represents a scarce resource, yet it is not simply consumed like mineral or fossil energy reserves, in the sense that it is not extracted and dissipated". Accounting for the use of the flow 'land' by adding up the m 2 year used in different stages of the life cycle is a good first step, but not enough. It is indeed the change in land quality that needs to be assessed, and this change obviously depends on how land is managed. There exist many good tools for the assessment of land use impacts in specific life cycle stages or sectors, which use appropriate and detailed indicator sets. However, there is a conflict between the level of detail and the life cycle coverage that can be attained. In general, it may be noted that LCA is not an appropriate tool to guide land management: it will be most valuable in comparing systems which differ quite substantially (e.g. bio-based/fossil-based products), but it should also be able to highlight substantive differences in land quality measures caused by different management systems (e.g. organic vs. conventional agriculture) [1].
In simple energy balance climate models all physical and dynamical processes are parameterized in terms of the single unknown variable: the surface temperature. To simulate the ice-albedo feedback, the surface albedo is usually assumed to be a function of surface temperature. But to compute the absorbed solar radiation in such models one requires the top-of-the atmosphere albedo: the planetary albedo. In the present study, a simple linear relationship is derived between planetary albedo and surface albedo for the case of clear skies. The relationship is based upon a regression equation derived from simulations and has a standard error of estimate of 0.028. The estimation of planetary albedo from surface albedo is checked by comparing zonally averaged clear-sky planetary albedos, estimated from zonally averaged surface albedos, to satellite determinations of zonally averaged minimum albedos for monthly mean conditions. The minimum albedos are assumed to be representative of the clear-sky planetary albedos. The results show root-mean square differences of 0.05 between the estimated clear-sky planetary albedos and the minimum albedos. More accurate relationships can be obtained if one uses an additional parameter - the solar zenith angle. In this case, the standard errors of estimate are reduced to 0.017 for a zenith angle of 0°, 0.018 for a zenith angle of 60° and 0.021 for a zenith angle of 85°.