Waste Management 27 (2007) 989–996
www.elsevier.com/locate/wasman
What life-cycle assessment does and does not do in assessments
of waste management
Tomas Ekvall a,*, Getachew Assefa b, Anna Björklund c,
Ola Eriksson d, Göran Finnveden c
a
c
IVL Swedish Environmental Research Institute, P.O. Box 5302, SE-400 14 Göteborg, Sweden
b
Industrial Ecology, Royal Institute of Technology (KTH), SE-100 44 Stockholm, Sweden
Environmental Strategies Research – FMS, Royal Institute of Technology (KTH), SE-100 44 Stockholm, Sweden
d
Technology and Built Environment, University of Gävle, SE-801 76 Gävle, Sweden
Accepted 16 February 2007
Available online 16 April 2007
Abstract
In assessments of the environmental impacts of waste management, life-cycle assessment (LCA) helps expanding the perspective
beyond the waste management system. This is important, since the indirect environmental impacts caused by surrounding systems, such
as energy and material production, often override the direct impacts of the waste management system itself. However, the applicability of
LCA for waste management planning and policy-making is restricted by certain limitations, some of which are characteristics inherent to
LCA methodology as such, and some of which are relevant specifically in the context of waste management. Several of them are relevant
also for other types of systems analysis. We have identified and discussed such characteristics with regard to how they may restrict the
applicability of LCA in the context of waste management. Efforts to improve LCA with regard to these aspects are also described. We
also identify what other tools are available for investigating issues that cannot be adequately dealt with by traditional LCA models, and
discuss whether LCA methodology should be expanded rather than complemented by other tools to increase its scope and applicability.
Ó 2007 Elsevier Ltd. All rights reserved.
1. Introduction
1.1. Background
Waste management is a complex phenomenon with a
range of consequences for the involved stakeholders and
the society. One of the many parameters to evaluate is
the environmental impact of different treatment options
or technical solutions. There are many tools for assessment of environmental impact, but one of the most commonly used is life-cycle assessment (LCA). It helps
expanding the perspective beyond the waste management
system. This is important since the environmental consequences of waste management often depend more on
*
Corresponding author. Tel.: +46 31 725 62 81; fax: +46 31 725 62 90.
E-mail address: tomas.ekvall@ivl.se (T. Ekvall).
0956-053X/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.wasman.2007.02.015
the impacts on surrounding systems than on the emissions
from the waste management system itself (Ekvall, 1999).
In particular, the broad perspective of LCA makes it possible to take into account the significant environmental
benefits that can be obtained through different waste management processes:
waste incineration with energy recovery reduces the need
for other energy sources,
material from recycling processes replaces production of
virgin material,
biological treatment may reduce the need for production
of artificial fertilisers and vehicle fuel1,
1
It may also help improving the quality of soils, but this is difficult to
take into account in LCA.
990
T. Ekvall et al. / Waste Management 27 (2007) 989–996
residues from waste incineration may replace gravel at
road constructions (Birgisdottir, 2004), etc.
The broad system perspective makes LCA a powerful
tool for environmental comparison of different options
for waste management of a specific product, a material,
or a complex waste flow. Because of this, LCA has gained
in acceptance as a tool for waste management planning and
policy-making. It is now being used in various contexts,
ranging from local planning to policy making at national
and international levels. An example of this is the recent
thematic strategy on waste management presented by the
European Commission.
An international standard for LCA has been developed,
and handbooks are available (e.g., Guinée, 2002), as well as
scientific reviews of recent developments (Rebitzer et al.,
2004; Pennington et al., 2004). Separate publications
describe how to apply the method on waste management
systems (Finnveden, 1999; Clift et al., 2000). However, to
be able to make sustainable use of LCA in the waste management, it is important to be aware of the limitations of
the methodology and to understand that the environmental
information it generates is neither complete, nor absolutely
objective or accurate. The international standardisation
process helps to reduce what can appear to be arbitrariness
of the methodology, but important methodological choices
still remain free to be made in each separate study. The
LCA results therefore depend on methodological decisions,
for example:
1.2. Aim of the paper
In order to contribute to the awareness of the limitations
of LCA, the aim of this paper is to discuss the restrictions
in the applicability of LCA as a decision-support tool in
waste management planning and policy-making. We do
this by identifying certain characteristics of LCA, discuss
how these may restrict the applicability of LCA, efforts
made to improve LCA methodology with regard to these
characteristics, and what other tools are available that
cover issues currently not adequately dealt with in LCA.
We also discuss whether LCA methodology should be
expanded rather than complemented by other tools to
increase its scope and applicability. Most of the discussion
is valid also for LCA applied outside the waste management sector, and to a large extent it is also valid for other
tools for environmental systems analysis.
The advantages and disadvantages of LCA applied to
waste management can be discussed at three conceptual
levels. The discussion can focus on the characteristics of
LCA as a scientific method, on methodological applications of LCA in computer models or methodological guidelines, or on the practical applications of LCA in actual case
studies. Our discussion aims at the most general level. The
purpose is to shed light on the characteristics of LCA as a
scientific method. However, we use examples of methodological applications as well as practical applications as
illustrations.
2. Functional unit and system dynamics
choice of time perspective (Finnveden et al., 1995; Obersteiner et al., 2007),
assumptions made in the study,
sources of input data,
allocation of environmental burdens to different life
cycles (Ekvall and Tillman, 1997; Winkler, 2007), and
modelling of environmental impacts.
These methodological choices may be influenced by
the values and perspectives of the LCA practitioner and
the LCA commissioner. This means that an LCA typically does not yield objective answers. The methodology
also suffers from large uncertainties (Huijbregts,
1998a,b). As indicated by the references above, the subjective and uncertain aspects of the answers given by
LCA have been thoroughly discussed elsewhere. These
limitations are also not unique to LCA. Several methods
for environmental systems analysis have been developed
to support different types of decisions (Wrisberg et al.,
2002; Finnveden and Moberg, 2005). Similar problems
occur in most of them.
A limitation that has not been much discussed, however,
is the fact that a traditional LCA model has several inherent characteristics that prohibit it from giving adequate
answers to many significant questions. This is the focus
of our paper.
2.1. Restrictions in applicability
LCA models of waste management often calculate the
environmental burdens per kg or tonne of waste generated.
It implies that the quantity of waste is unaffected by the
management measures investigated. Having identical
amounts of waste treated in different scenarios makes it
possible to simplify comparative analyses by neglecting
the production and use of the materials (Finnveden,
1999). This simplification is sometimes called the ‘‘zero
burden assumption’’, suggesting that the waste carriers
none of the upstream burdens into the waste-management
system.
LCA models that calculate the environmental burdens
per kg or tonne of waste generated allow for environmental
comparisons of different options for dealing with this
waste, but not for analyses of changes in the quantities of
waste generated. They are inadequate for the identification
and assessment of waste prevention strategies. They also
fail to account for the serious challenges posed by a continuation of the short-term and long-term trends of increasing
waste flows, and consequently do not give information on
how large capacity for waste treatment is required.
Traditional LCA models are also static. In the context
of waste management, this implies that they cannot give
T. Ekvall et al. / Waste Management 27 (2007) 989–996
information about the appropriate time for investments in
waste management plants.
Perhaps more seriously, the system structure and the
input data in a traditional LCA both reflect the recent past.
This means that, at the best, traditional LCA provides
basis for identifying what waste management strategies
are best served to solve the needs of the current society.
But waste management plants are large investments that
will be used for several decades, and the surrounding society can change significantly during this time. A technology
that is appropriate today might be incompatible with the
long-term sustainability of the society.
2.2. Amendments
A first step towards amending the restrictions imposed
by a static assumption with regard to the waste quantity,
is to relate the study not only to the composition of the
waste but also to the waste quantity (Coleman et al.,
2003). This can be made by changing the functional unit
to the annual quantity of waste generated in a geographical
area. As an example, Xará (2004) used the annual quantity
of waste in the city of Porto as the functional unit. Matsui
(2004) presented a comparative LCA including different
waste management options, as well as waste prevention.
The functional unit was a ton of waste generated for the
waste management options, and a ton of waste prevented
for the waste prevention. Olofsson et al. (2004) also compared waste prevention to different waste management
strategies, using the annual quantity of waste in Sweden
as the functional unit. The quantity of waste varied
between the scenarios because the analysis accounted for
the reduction in waste quantity resulting for potential
waste prevention measures.
Adjusting the functional unit is obviously a measure to
facilitate the assessment of waste prevention. This measure
may appear simple enough, but if different scenarios
include different waste quantities, the zero burdens assumption is no longer valid. It is reasonable to demand that such
studies include the environmental burdens associated with
the production of all the materials that eventually become
waste. This makes the assessment more complicated.
To be able to plan for changes in waste flows, and to
decide on the size of investments in waste-treatment technologies, decision-makers require futures studies of the
waste flows. Futures studies include forecasting through,
for example, extrapolation and dynamic modelling
(Börjeson et al., 2006). Dynamic modelling can also be
used for identifying and assessing the efficiency of different strategies for waste prevention. Futures studies also
include backcasting, which can be effective for finding
routes to a desired, future waste-management system.
The different methods for futures studies can be used
for defining future waste scenarios that specify the future
waste quantities and technologies for waste treatment.
LCA is then applied to assess the environmental impact
of these scenarios.
991
However, methods for futures studies can also be integrated in the LCA methodology (Weidema et al., 2004).
An example is Olofsson et al. (2004), who made a forecast
of the Swedish waste quantity in 2008–2012 as a base case
scenario to which the waste prevention was compared.
Trisyanti (2004) who performed a systems analysis of solid
waste management in Jakarta, also considered the difference
in the quantity of waste between 2003 and a forecast for
2015. Björklund and Finnveden (2007) used extrapolated
values of Swedish waste quantities to assess the expected
effectiveness of a proposed waste incineration tax. When
methods for futures studies are integrated in the LCA, the
methodology not only assesses scenarios but also assists in
developing the scenarios that are to be assessed.
To provide information on the appropriate time for
investments, the futures studies probably need to include
dynamic models spanning over multiple years. Such models
have, to our knowledge, not yet been used in LCA of waste
management.
3. Spatial information and information on specific pollutants
3.1. LCA characteristics and restrictions in applicability
Traditional LCA includes emissions and fuel demand of
transports: it takes transport distances into account. However it does not differentiate between emissions occurring
at different locations. Instead, all emissions of each specific
pollutant are summarised, with complete loss of spatial
information as a consequence. The environmental impacts
of several pollutants may depend heavily on where and
when they are emitted. As an example, the sensitivity for
SO2 emissions can be more than a thousand times higher
in Sweden than in Greece (Hauschild and Potting, 2004),
depending also on how the impact is defined. When geographical information is not included, the impacts of these
emissions may not be accurately described. Because of its
inability to handle spatial information, the typical LCA
model also does not give information that is adequate for
deciding where a waste-management facility should be sited.
Pollution involves a very large number of chemical substances. Society handles literally thousands of chemicals,
many of them with largely unknown characteristics. Since
these chemicals are used in different products, a very large
number of chemicals will end up in the waste management
system. The fate of these chemicals in different treatment
processes is in practise impossible to model and include
in an LCA. Furthermore, an LCA typically aggregates substances of the same type into sum parameters such as
polyaromatic hydrocarbons (PAH), volatile organic compounds (VOC), and total organic compounds (TOC). Most
probably, this is for practical reasons, since emissions are
often reported in this manner in environmental monitoring.
However, the environmental impacts may vary greatly
between different substances within these sum parameters.
Therefore, such aggregate measures reduce the ability of
LCA to accurately model actual environmental impacts.
992
T. Ekvall et al. / Waste Management 27 (2007) 989–996
3.2. Amendments
The LCA can include a range of impact factors for each
pollutant, corresponding to the spatial variability of the
impact of the pollutant. With this approach, the LCA
results will accurately reflect the uncertainty in actual environmental impact of a pollutant due to spatial variation;
however, the approach will not reduce the potentially large
uncertainty.
Approaches to reduce the uncertainty by taking geographical aspects into account have been presented for
the assessment of several environmental impacts. It is useful to distinguish between site-dependent and site-specific
modelling of the impacts (Hauschild and Potting, 2004).
Site-dependent modelling takes into account the environmental conditions and sensitivity of the country or region
where the pollutant is emitted. Site-dependent approaches
have been developed for, e.g., acidification, terrestrial
eutrophication, and tropospheric ozone formation (Potting
et al., 1998a,b; Huijbregts, 1999; Krewitt et al., 2001; Hauschild and Potting, 2004). Site-dependent approaches are
also integrated in recent LCA tools such as the EDIP
2003 (Hauschild and Potting, 2004). In the context of waste
management, a site-dependent approach to acidification
and human health was implemented by Finnveden and
Nilsson (2005), to investigate whether a site-dependent
approach would suggest a geographically differentiated
national waste management strategy in Sweden. Nilsson
et al. (2005) applied the site-dependent approach in an
environmental assessment of a waste incineration tax in
Sweden. They found that the level of impacts varied
between different parts of the country, but this did not
affect the ranking between the different waste management
options.
Some site-dependent approaches tend to give a greater
weight to pollutants that are emitted in regions where the
level of pollution is already high. This encourages relocating activities that burden the environment to regions with
lower levels of pollution. Hence, there is a risk that unreflective use of LCAs with a site-dependent approach results
in the loss of relatively unpolluted areas.
Site-dependent approaches reduce the uncertainty of the
environmental impacts caused by pollutants, but they do
not include enough spatial detail to decide in what part
of a region a waste-management plant should be located.
The latter requires site-specific modelling, which takes into
account the local conditions. Site-specific approaches have
been developed for, e.g., the leaching of heavy metals from
landfills (Hellweg et al., 2005) and for the impact of airborne emissions from, e.g., waste incineration on human
health (Sonnemann, 2002). These approaches are so far
rarely used in LCA case studies.
Alternative ways to obtain site-specific knowledge on
the environmental impacts is by means of an environmental impact assessment (EIA) or risk assessment. These tools
can take local aspects into account, and can be used for
deciding what site for a waste management plant is best
for the environment. An LCA can be included as part of
an EIA, but the EIA also includes qualitative statements
that can take into account for instance the specific value
of unpolluted natural areas.
To increase the accuracy of the description of environmental impacts, some guidelines on LCA recommend that
sum parameters should be avoided, and that data on emissions of specific substances should be used whenever possible. Guinée (2002) and other comprehensive guidelines also
present characterisation factors for a great number of specific substances. A problem, in this context, is that emission
measurements are often made using sum parameters. In
these cases, data on emissions of specific substances do
not exist. Because of the shear number of chemicals used
in society we expect that there will always be data gaps
for many chemicals that are potentially relevant in environmental assessments.
4. Non-linear relationships
4.1. LCA characteristics and restrictions in applicability
An LCA facilitates environmental comparisons of welldefined alternatives, such as recycling, landfilling and incineration of specific waste fractions. However, LCA models
are typically linear steady-state models of physical flows
(Guinée, 2002). The LCA results can indicate what
waste-management option contributes the least to different
environmental impacts. This is illustrated in Fig. 1a, which
is a schematic representation of the weighted environmental burdens associated with the production and use of a
hypothetical material. In this case, the results indicate that
recycling is the environmentally preferable option because
it reduces the total environmental impact.
In reality, the environmental burdens of collection and
recycling are likely to be a non-linear function of the collection rate (see Fig. 1b). There will be initial activities and
environmental burdens when a collection system is established. At very high recycling rates, the required extra
transports and processing of materials may increase fuel
consumption and emissions greatly for each additional
tonne of material that is collected. The environmental optimal collection rate will be somewhere in between. However, since LCA results are linear, they cannot be used
for identifying the optimum mix of waste-management
options: recycling, landfilling and incineration. This means
that typical LCA models cannot be used for identifying
optimal reuse and recycling rates.
4.2. Amendments
Linear-programming (LP) models are linear models that
account for boundary conditions. In waste management,
limitations in achievable recycling rates of a bring system
would be one such boundary condition. Very high recycling rates might require a switch to curbside collection,
with higher economic costs and possibly more environmen-
T. Ekvall et al. / Waste Management 27 (2007) 989–996
a
Environmental burdens (B)
Collection and
recycling
Primary materials
production
Total
0
100
Collection rate (%)
b
Environmental burdens (B)
Collection and
recycling
Total
5.1. LCA characteristics and restrictions in applicability
100
Collection rate (%)
Environmental burdens (B)
c
0
in an LCA. The ORWARE model and MIMES/waste are
examples of LP model that integrate the life-cycle perspective and, hence, also are tools for LCA (Eriksson et al.,
2003). A recent example that focuses on paper recycling
was presented by Schenk et al. (2004).
Comparing Fig. 1b and c, it is easy to draw the conclusion that an LP model is not a very precise representation
of the real system. Non-linear programming is required to
account for the more complex, non-linear relations in the
real system. However, as the complexity of the model
increases, so does the requirement for data. High quality
data for an LP model can be difficult to obtain. It is, for
example, difficult to estimate the maximum collection rate
that can be achieved through bring systems. The problem
with data availability and data quality increases for a
non-linear model.
5. Effects on background systems
Primary materials
production
0
993
100
Collection rate (%)
Fig. 1. (a) An LCA model typically describes environmental burdens of
materials production as a linear function of the collection rate. (b) The
environmental burdens of real collection and recycling schemes can be
expected to be a non-linear function of the collection rate. (c) A linearprogramming model can describe the system as a partially linear function
of the collection rate.
tal burdens. As a result, the environmental burdens can be
described as a partially linear function of the collection
rate. As illustrated in Fig. 1c, such a function makes it possible to identify an optimal recycling rate. Optimising LP
models of the waste management system can be integrated
Many LCAs use average data to model the background
systems, i.e., the systems indirectly affected by the actual
system under study. In LCAs of waste management,
important background systems include for instance the
energy system and the production of materials and fertilisers, all of which may be significantly affected by decisions
concerning waste management. The use of average data
to model these systems may be relevant if the aim is to perform an attributional LCA (Tillman, 2000; Ekvall et al.,
2004).
However, if the aim is to model the consequences of a
decision, the use of average data may be misleading. The
use of average data means that the LCA model is inaccurate in describing how the background systems are affected
by changes in the waste management system, because
changes in the waste management system will not affect
all parts of a background system equally. For instance,
changes in electricity use or generation in the waste management system will affect the electricity production system
at the margin. In general, all actions in the waste management system can be expected to have marginal effects on
the production of bulk materials (e.g., steel, aluminium,
and polyethylene), energy carriers (e.g., electricity, fuel
oil, and petrol), and/or fertilisers. Marginal effects are the
consequences of infinitesimal or small changes in the quantity produced of a good or service (Ekvall and Weidema,
2004).
5.2. Amendments
Marginal effects should, ideally, be modelled using marginal data. These reflect, by definition, the environmental
burdens of the technology affected by a marginal change
(Weidema, 1993). If we account for the fact that a change
in electricity use can affect investments in new power plants
and the closing of old power plants, accurate identification
994
T. Ekvall et al. / Waste Management 27 (2007) 989–996
of the marginal electricity production becomes difficult.
The marginal electricity can be dominated by extended
use of old coal-power plants, by the postponed closing of
Swedish and German nuclear reactors, or by the construction of new CHP plants for natural gas, etc. Such effects
are, in the context of LCA denoted as long-term marginal
effects (Weidema et al., 1999).
The marginal technologies are often identified using static models of the electricity system, but they can also be analysed using dynamic optimising models. The latter approach
gives a more complete description of the consequences of
using or delivering electricity, because it takes into account
effects on the utilisation of existing production facilities, as
well as effects on investments in new production facilities.
Mattsson et al. (2001) investigated how a dynamic optimising model of the production of electricity and district heat in
the Nordic countries reacts to a change in the Nordic electricity demand or the Swedish nuclear power production.
The results demonstrate that the marginal electricity production in the Nordic countries is complex in the sense that
it involves several different technologies. The mix of technologies is uncertain because it depends heavily on assumptions
regarding uncertain boundary conditions, future fuel prices
etc. Scenarios from the study by Mattsson et al. (2001) were
later used in an LCA on waste and competing fuels for
Swedish production of district heat (Eriksson et al., 2007).
The LCA methodology can be further expanded to take
more causal relationships into account and, hence, describe
the consequences of a decision more accurately. Possible
expansions include the integration of economic partial
and general equilibrium models, experience curves, etc.
(Ekvall and Weidema, 2004; Ekvall et al., 2004). A partial
equilibrium model of scrap material markets has been presented by Ekvall (2000) and applied, for example, to model
the consequences of cardboard recycling in an LCA of
cheese (Berlin, 2002). This model takes into account the
fact that the recycling of material from a specific product
or a geographical area may affect not only the use of recycled material but also the collection for recycling of other
products and in other geographical areas.
6. Non-environmental impacts
6.1. LCA characteristics and restrictions in applicability
The results of LCAs are limited to environmental
impacts of waste management. Addressing the long-term
sustainability of a waste management system requires
knowledge of the financial costs and social impacts of
available waste management options. Apparently, traditional LCA can only provide part of the necessary basis
for a well-informed decision.
6.2. Amendments
It is possible to obtain a more comprehensive basis for
decisions either by making separate analyses of financial
costs (Thorneloe et al., 2007) and relevant social aspects
or by expanding the LCA methodology to include these
additional aspects. A study that includes financial costs
as well as monetised environmental burdens, described
through an LCA, is often called a cost-benefit analysis
(Leach et al., 1997; Radetzki, 1999; Ekvall and Bäckman,
2001; Strömberg and Ringström, 2004). It has also been
denoted as life cycle costing (Carlson Reich, 2005) or
technology assessment (Assefa et al., 2005). These studies
can also include other aspects such as the time required
for source separation in households, the space required
for the multiple dustbins used for the source separation,
etc.
In these studies, the emissions and other environmental
burdens are typically aggregated into one figure representing the environmental cost of each investigated option for
waste management. This is made to be able to compare
the environmental costs to the economic costs. The drawbacks are that a lot of information is lost in the aggregation
and that the scientific basis for monetisation of environmental burdens has limitations (Stirling, 1997). The first
problem can be partially overcome by not only presenting
the aggregated results but also the disaggregated results
from the life cycle inventory analysis and possible characterisation. The second problem can be partly amended by
using several methods for monetisation in parallel.
7. Discussion
At first glance, the message of this paper may seem to be
that LCA is quite insufficient as a decision-support tool in
waste management. Our intention is, however, much more
constructive. We believe that identifying its restricting
characteristics, understanding the implications of these,
and finding complementary tools, will lead to better use
of LCA in waste management, either by actually finding
ways of improving the models, or by simply being more
realistic about their capacity.
Since different tools for environmental systems analysis
are developed to focus on different aspects of reality (Wrisberg et al., 2002; Finnveden and Moberg, 2005), a combination of tools can provide a more holistic picture.
Several of the limitations that are discussed in this paper
are, however, general and relevant also for other tools for
environmental systems analysis and, indeed, for science in
general.
An LCA, just like systems analysis in general, entails a
drastic simplification of the complex reality. The description can be more complete and detailed by adding methodological aspects: economic analysis, dynamic linear and
non-linear modelling, site-dependent modelling of environmental impacts, etc. As more aspects are added to the analysis, the complexity of the study increases. More data are
required, which increases the cost of the study. In order
to provide the most comprehensive information possible
about the consequences of possible actions – within the
budget and/or time constraints given – the study should
T. Ekvall et al. / Waste Management 27 (2007) 989–996
focus on the parts of the technological system that are
expected to be most affected by such actions.
Several of the possible additions to LCA methodology
require different types of economic data in addition to technological and environmental data. This means that economists ought to be involved in the study. Otherwise, the risk
for mistakes increases. The data required to model the
additional aspects are also often associated with a high
degree of uncertainty. As a result, the uncertainty in the
results of the study increases. It can be argued that, if an
aspect of the reality is relevant to the study, it is better to
describe it by using uncertain data than to ignore it completely. This implies that the boundaries of the study
should ideally be defined at the point where the uncertainties and risk for mistakes become so large that further
expansion of the study will yield no information that is significant for any realistic decision. Good judgement is
required to identify this point in each case.
However, in our experience the audience or target group
of a study tends to focus on the results of the study and disregard the significance of the uncertainties, even when they
are clearly reported. If the study describes part of the reality with highly uncertain quantitative data, there is a risk
that it will convey a false sense of security. This risk might
be lower if highly uncertain parts of the reality are excluded
from the study, provided that this limitation is clear from
the report.
When the study grows increasingly complex, it also
becomes more difficult to understand. This makes it more
difficult for the target group to assess the credibility and
relevance of the study. The transparency of the study can
increase if different aspects are separately analysed - if the
economic analysis, for example, is kept separate from the
environmental assessment as far as possible. On the other
hand, presenting them together makes it easier to find the
most cost/eco-efficient solution. As a compromise between
the difficulty of comprehending complex results and the
need to ‘‘tell the whole story’’, information about uncertainties can be included on a demand-driven basis depending on the potential interest of the end users.
As evidenced by the suggested thematic strategy on
waste by the European Commission (EC, 2005), there are
great expectations on LCA and life-cycle thinking. Indeed,
LCAs have been shown to provide policy relevant and consistent results (Finnveden and Ekvall, 1998; Björklund and
Finnveden, 2005). However, it is also clear that the studies
will always be open for criticism. Assumptions can be challenged and it may be difficult to generalise from case studies to policies (Finnveden, 2000). This suggests that there
will continue to be a role for decision-makers in the policy
process. If we have to wait for clear-cut and indisputable
results from science, we may have to wait forever. If decisions are going to be made, they need to be made on a less
than perfect basis. LCA and other tools for environmental
systems analysis can contribute to the basis for such decisions, not by making it complete but by making it more
comprehensive.
995
Acknowledgements
The writing of this paper was funded by the Swedish Energy Administration through the programme for general
energy systems studies. Constructive comments were received from the International Expert Group for Life Cycle
Assessment for Integrated Waste Management.
References
Assefa, G., Eriksson, O., Frostell, B., 2005. Technology assessment of
thermal treatment technologies using ORWARE. Energy Conversion
and Management 46, 797–819.
Berlin, J., 2002. Environmental life cycle assessment (LCA) of Swedish
semi-hard cheese. International Dairy Journal 12, 939–953.
Birgisdottir, H., 2004. Life cycle assessment of MSWI residues: recycling
in road construction and landfilling. In: Integrated Waste Management & Life Cycle Assessment Workshop and Conference, 13–16th
April 2004, Prague, Czech Republic.
Björklund, A., Finnveden, G., 2005. Recycling revisited – life cycle
comparisons of waste management strategies. Resources, Conservation and Recycling 44, 309–317.
Björklund, A., Finnveden, G., 2007. Life cycle assessment of a national
policy proposal – the case of a Swedish waste incineration tax. Waste
Management 27 (7), 974–986.
Börjeson, L., Höjer, M., Dreborg, K.-H., Ekvall, T., Finnveden, G., 2006.
Scenario types and techniques – towards a user’s guide. Futures 36,
723–739.
Carlson Reich, M., 2005. Economic assessment of municipal waste
management systems – case studies using a combination of life cycle
assessment (LCA) and life cycle costing (LCC). Journal of Cleaner
Production 13, 253–263.
Clift, R., Doig, A., Finnveden, G., 2000. The application of life cycle
assessment to integrated solid waste management, Part I – methodology. Transactions IchemE 78 (part B), 279–287.
Coleman, T., Masoni, P., Dryer, A., McDougall, F., 2003. International
expert group on life cycle assessment for integrated waste management. International Journal of Life Cycle Assessment 8, 175–178.
Ekvall, T., 1999. Key methodological issues for life cycle inventory
analysis of paper recycling. Journal of Cleaner Production 7, 281–294.
Ekvall, T., 2000. A market-based approach to allocation at open-loop
recycling. Resources, Conservation and Recycling 29, 93–111.
Ekvall, T., and Bäckman, P., 2002. Assessing external and indirect costs
and benefits of recycling. In: Proceedings of the Workshop of System
Studies of Integrated Solid Waste Management, Stockholm 2–3 April
2001. Report B1490, Swedish Environmental Research Institute,
Stockholm, Sweden, pp. 99–106.
Ekvall, T., Tillman, A.-M., 1997. Open-loop recycling: criteria for
allocation procedures. International Journal of Life Cycle Assessment
2, 155–162.
Ekvall, T., Weidema, B., 2004. System boundaries and input data in
consequential life cycle inventory analysis. International Journal of
Life Cycle Assessment 9, 161–171.
Ekvall, T., Ciroth, A., Hofstetter, P., Norris, G., 2004. Evaluation of
attributional and consequential life cycle assessment. Working paper
distributed at 14th SETAC-Europe Annual Meeting, 18–22 April 2004,
Prague.
Eriksson, O., Olofsson, M., Ekvall, T., 2003. How model-based systems
analysis can be improved for waste management planning. Waste
Management & Research 21, 488–500.
Eriksson, O., Finnveden, G., Ekvall, T., Björklund, A., 2007. Life cycle
assessment of fuels for district heating. Energy Policy. 35 (2), 1346–
1362.
Finnveden, G., 1999. Methodological aspects of life cycle assessment of
integrated solid waste management systems. Resources, Conservation
and Recycling 26, 173–187.
996
T. Ekvall et al. / Waste Management 27 (2007) 989–996
Finnveden, G., 2000. On the limitations of life cycle assessment and
environmental systems analysis tools in general. International Journal
of Life Cycle Assessment 5, 229–238.
Finnveden, G., Ekvall, T., 1998. Life cycle assessment as a decisionsupport tool – the case of recycling vs incineration of paper. Resources,
Conservation and Recycling 24, 235–256.
Finnveden, G., Moberg, Å., 2005. Environmental systems analysis tools –
an overview. Journal of Cleaner Production 13, 1165–1173.
Finnveden, G., Nilsson, M., 2005. Site-dependent life cycle impact
assessment in Sweden. International Journal of Life Cycle Assessment
10, 235–239.
Finnveden, G., Albertsson, A.C., Berendson, J., Eriksson, E., Höglund,
L.O., Karlsson, S., Sundqvist, J.-O., 1995. Solid waste treatment
within the framework of life-cycle assessment. Journal of Cleaner
Production 3, 189–199.
Guinée, J.B. (Ed.), 2002. Handbook on Life Cycle Assessment –
Operational Guide to the ISO Standards. Kluwer Academic Publishers., Dordrecht, The Netherlands.
Hauschild. M., Potting, J., 2004. Spatial differentiation in characterisation
modelling – what difference does it make? Presentation at the 14th
SETAC-Europe Annual Meeting, 18–22 April 2004, Prague.
Hellweg, S., Fischer, U., Hofstetter, T.B., Hungerbühler, K., 2005. Sitedependent fate assessment in LCA: transport of heavy metals in soil.
Journal of Cleaner Production 13, 341–361.
Huijbregts, M., 1998a. Application of uncertainty and variability in LCA.
Part 1. International Journal of Life Cycle Assessment 3, 273–280.
Huijbregts, M., 1998b. Application of uncertainty and variability in LCA.
Part 2. International Journal of Life Cycle Assessment 3, 343–351.
Huijbregts, M., 1999. Life cycle impact assessment of acidifying and
eutrophying air pollutants – calculation of equivalency factors with
RAINS-LCA. Interfaculty Department of Environmental Science,
University of Amsterdam, The Netherlands.
Krewitt, W., Trukenmüller, A., Bachmann, T.M., Heck, T., 2001.
Country-specific damage factors for air pollutants – a step towards
site dependent life cycle impact assessment. International Journal of
Life Cycle Assessment 6, 199–210.
Leach, M., Bauen, A., Lucas, N., 1997. A systems approach to materials
flow in sustainable cities – a case study of paper. Journal of
Environmental Planning and Management 40, 705–723.
Matsui, Y., 2004. Oral presentation at 13th meeting of the international
expert group for life cycle assessment for integrated waste management, 27–28 September 2004, Porto, Portugal.
Mattsson, N., Unger, T., and Ekvall, T., 2001. Marginal effects in a
dynamic system – the case of the Nordic power system. Presented to
the International Workshop on Electricity Data for Life Cycle
Inventories, Cincinnati, 10, pp. 23–25.
Nilsson, M., Björklund, A., Finnveden, G., Johansson, J., 2005. Testing
an SEA methodology for the energy sector – a waste incineration tax
proposal. Environmental Impact Assessment Review 25, 1–32.
Obersteiner, G., Binner, E., Mostbauer, P., Salhofer, S., 2007. Landfill
modelling in LCA – A contribution based on empirical date. Waste
Management 27 (7), 939–955.
Olofsson, M., Ekvall, T., and Sundberg, J., 2004. Impacts of Swedish
waste prevention and the scrap market equilibrium on greenhouse gas
emissions. In: M. Olofsson. Improving Model-Based Systems Analysis
of Waste Management. PhD thesis, Department of Energy Technology, Chalmers University of Technology, Gothenburg, Sweden.
Pennington, D.W., Potting, J., Finnveden, G., Lindeijer, E., Jolliet, O.,
Rydberg, T., Rebitzer, G., 2004. Life cycle assessment Part 2:
current impact assessment practice. Environment International 30,
721–739.
Potting, J., Schöpp, W., Blok, K., Hauschild, M., 1998a. Comparison of
the acidifying impact from emissions with different regional origin in
life-cycle assessment. Journal of Hazardous Materials 61, 155–162.
Potting, J., Schöpp, W., Blok, K., Hauschild, M., 1998b. Site-dependent
life-cycle assessment of acidification. Journal of Industrial Ecology 2,
63–87.
Radetzki, M., 1999. Recycling without profit. Ds 1999:66, Expertgruppen
för studier i offentlig ekonomi, Ministry of Finance, Stockholm,
Sweden (in Swedish).
Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G.,
Rydberg, T., Schmidt, W.-P., Suh, S., Weidema, B.P., Pennington,
D.W., 2004. Life cycle assessment – part 1: framework, goal & scope
definition, inventory analysis, and applications. Environment International 30, 701–720.
Schenk, N.J., Moll, H.C., Potting, J., 2004. The nonlinear relationship
between paper recycling and primary pulp requirements. Journal of
Industrial Ecology 8, 141–162.
Sonnemann, G., 2002. Environmental damage estimations in industrial
process chains. Ph.D. thesis. Universitat Rovira i Virgili, Tarragona,
Spain.
Stirling, A., 1997. Limits to the value of external costs. Energy Policy 25,
517–540.
Strömberg, K., and Ringström, E., 2004. Cost benefit analysis (CBA) –
broader basis for decisions in integrated waste management. In:
Integrated Waste Management & Life Cycle Assessment Workshop
and Conference, 13–16th April 2004, Prague, Czech Republic.
Thorneloe, S.A., Weitz, K.A., Jambeck, J., 2007. Moving from solid waste
disposal to materials management in the United States. Waste
Management 27 (7), 871–885.
Tillman, A.-M., 2000. Significance of decision-making for LCA methodology. Environmental Impact Assessment Review 20, 113–123.
Trisyanti, D., 2004. Solid waste management of Jakarta – Indonesia: an
environmental systems perspective. Master of Science Thesis, Division
of Industrial Ecology. Department of Chemical Engineering and
Technology, Royal Institute of Technology. TRITA-KET-IM 2004:6.
Weidema, B.P., 1993. Market aspects in product life cycle inventory
methodology. Journal of Cleaner Production 1, 161–166.
Weidema, B.P., Frees, N., Nielsen, P., 1999. Marginal production
technologies for life cycle inventories. International Journal of Life
Cycle Assessment 4, 48–56.
Weidema, B.P., Ekvall, T., Pesonen, H.-L., Rebitzer, G., Sonnemann,
G.W., and Spielmann, M., 2004. Scenarios in life cycle assessment.
Society of Environmental Toxicology and Chemistry, Brussels,
Belgium.
Winkler, J., 2007. Comparative evaluation of life cycle assessment models
for solid waste management. Waste Management 27 (7), 921–931.
Wrisberg, N., Udo de Haes, H.A., Triebswetter, U., Eder, P., Clift, R.,
2002. Analytical Tools for Environmental Design and Management in
a Systems Perspective. Kluwer Academic Publishers., Dordrecht.
Xará, S., 2004. LCA case study of IWM options in Porto. In: Oral
Presentation at 13th Meeting of the International Expert Group for
Life Cycle Assessment for Integrated Waste Management, 27–28
September 2004, Porto, Portugal.