Executive Summary
Greenhouse gas emissions are a major concern in society today, and waste
managers need to be aware of the impact that municipal solid waste management
practices have on greenhouse gas emissions. Life cycle assessment is a tool that is able to
analyze a products emissions profile from its transport, treatment, and other ancillary
processes.
Specifically in reference to the University of Alberta, the green house gas
emissions due to waste management can be calculated using the Waste Reduction Model
from the EPA. The current waste management practices on campus result in 556 metric
tonnes carbon equivalent. The proposed targets of 80% recycling and 25% composting
will lead to a net sequestration of 260 metric tonnes carbon equivalent.
Alternative management strategies should be examined to provide managers with
good background information to make reasonable decisions regarding waste
management. Alternative scenarios were considered and calculated with the WARM
model. 80% recycling and 50% composting resulted in sequestration of 492 MTCE.
Combustion of all waste leads to sequestration of 312 MTCE, while landfilling with gas
collection for energy recovery sequesters 584 MTCE. Source reduction of 35% yields
greenhouse gas emissions of 315 MTCE.
The model must be easy to use and applicable to the facility that it is being used
at. The Waste Reduction Model is simple and efficient to use, and is applicable to the
University of Alberta, although it is not with out its limitations. Limitations of the model
are the handling of the current mix of recycling against 100% virgin inputs and the
management of the source reduction.
1
Civ E 499 Final Report: Greenhouse Gas at University of Alberta
Matthew Scott, 1055898
May 7, 2007
1. Introduction
Solid waste is generated in all aspects of life, be it municipal, commercial,
institutional or industrial. This waste needs to be collected and managed efficiently, to
prevent excess greenhouse gas (GHG) emissions. Tanskanen (2000) suggest that
“Integrated municipal solid waste management (MSWM) can be defined as the
selection and application of suitable techniques, technologies and management programs
to achieve waste management objectives and goals”
Life cycle assessment (LCA) is one such strategy. LCA follows a product or
service throughout its lifetime, from primary production through to consumption, and
disposal. (Ekvall and Finnveden, 2000) This can be applied to MSW to determine the
environmental burden caused by MSW and the MSWM practices. LCA follows the waste
from cradle to grave (ECS, 1997) in addition to examining ancillary processes associated
with handling waste.
This report will examine the Environmental Protection Agency’s (EPA) Waste
Reduction and Management (WARM) model. The WARM model is a spreadsheet-based
model used to calculate GHG emissions from common waste management practices.
(EPA, 2007) The WARM model will be applied to current, expected and possible
MSWM at the University of Alberta (UA). The UA GHG emissions will be computed
using waste collection audit numbers from Waste Management (WM), and using the
WARM model.
2
The WARM model is able to estimate emissions from a baseline management
option and an alternative management scenario. This report will examine the GHG
emissions generated from only landfilling of the UA waste compared to the current UA
waste management practices. The current UA practices are then compared to the
proposed targets of 80% recycling and 25% composting the UA has set out. To check the
validity of the WARM GHG emission values, they will be compared to emissions
calculated using emission factors from ICF Consulting’s GHG emissions report. (ICF,
2005)
Alternative MSWM strategies will also be examined using the WARM model. A
strategy where the amount of composting is increased to 50% will be analyzed in addition
to an alternative that which focuses on waste combustion. An option that collects LFG
and uses it for energy will be examined. Another alternative will look at the emissions if
source reduction is applied. These strategies are examined to estimate the GHG of
potential MSWM options, and provide insight as to MSWM options that could lower
GHG emissions, and if the UA should consider these alternatives.
The EPA’s WARM model level of user friendliness will be analyzed. The level of
expertise needed to use the model will be examined; in addition the advantages of using
the WARM model will be weighted against the negatives. Limitations of the model will
also be explored.
The utility of the WARM model for the UA will be studied, based on the types
and amounts of waste produced. Also, due to the available MSWM alternatives the
WARM model may not be appropriate for the UA. The advantages and disadvantages of
the model will be compared to the MSWM needs of the UA.
3
2. Methodology
Waste Management’s (WM) waste audit numbers and characterizations (Tables 11, 1-2, Appendix 1) were used as inputs for the WARM model. The corresponding
assumptions are found in Table 1-3 of Appendix 1. These inputs, and the assumptions
made during each model trial, give the input parameters for that particular trial.
A baseline management scenario (usually the current waste recovery) is compared
to a theoretical alternative. The WARM model outputs both scenarios GHG emissions, as
well as the difference between them, and emission for each category. The ICF numbers
are generated using the WM data (Tables 1-1, 1-2), and is shown in Tables 3-1 and 3-2.
Further assumptions are stated in each section as they arise, or within the corresponding
appendix.
3. Greenhouse Gas Emission
Anthropogenic GHG emissions are a large reason for the challenges waste
managers currently face. (ICF, 2005) Decisions must be made to promote MSWM
practices that lower emissions. The WARM model uses waste generation amounts and
emission factors based on different technologies to estimates the GHG emissions for
MSWM practices.
A baseline scenario is completed and compared to an alternative management.
Using WM audit numbers for different waste categories, and the WARM model,
emission amounts were found for a scenario where all waste is landfilled, and compared
to the current UA MSWM practices.
4
Assumptions are made when using the WARM model and are explained in the
following paragraph. An assumption of using the current mix of recycled/virgin material
was made. This changes the emissions values as fewer GHG are emitted when recycled
material is used in place of virgin materials. (Kaplan et al., 2004) It was assumed that the
waste was landfilled with no landfill gas (LFG) recovery (McCartney, 2007), and that a
distance of 32km was traveled to any management option location. Table 1-3 of
Appendix 1 shows the conversions used from the WM audit data to the WARM input
categories.
A WARM scenario was inputted comparing a landfill only practice to the current
recycling and landfilling amounts at the UA. When landfilling is the only technology
used for UA MSWM, 1643 metric tonnes-carbon equivalent (MTCE) are produced,
where-as landfilling and current recycling lead to 556 MTCE. This is a reduction of 1087
MTCE of GHG emissions, or the same effectiveness as 863 passenger cars from the road
every year. This alternative is known as Trial 1 this information can be found in Table 21 of Appendix 2.
Another trial of WARM was ran, comparing the current landfilling and recycling
at the UA to the targeted 80% recycling and 25% compost. As previously stated the
current UA practices generate 556 MTCE. The proposed recovery targets would generate
a net sequestering of emissions of 260 MTCE. The proposed MSWM practices would
produce a negative value for GHG emissions, actually taking in GHG instead of emitting
it. If the proposed recovery targets were met there would be a net GHG emission
reduction of 816 MTCE. This alternative is known as Trial 2 can be found in Table 2-2 of
Appendix 2.
5
Using the ICF report’s (ICF, 2005) emissions factors and WM waste generation
numbers, GHG emissions were calculated and compared to emissions calculated using
the WARM model. It was assumed that landfill carbon sequestration occurs. The
difference between the GHG generation amounts for landfill only waste management is
shown in Figure 1. This information can be found in Tables 3-1 and 3-2. The emissions
generated for the proposed recovery targets of 80% recycling and 25% composting were
calculated and compared to the WARM model and shown in Figure 2. The negative
values indicate net sequestration of emissions. Considering the differing assumptions and
waste categorizations between the models, the values are similar enough for a baseline
guide. The models are still useful for comparing emissions between MSWM options, but
should only be compared using the same models.
GHG Emissions (MTCE)
Landfill Only Emissions
2000
1500
1000
500
0
Model Used
WARM
1643
ICF
1110
Figure 1: Landfill Only Emissions
6
GHG Emissions (MTCE)
Proposed Recovery Emissions
0
-200
-400
-600
-800
-1000
Model Used
WARM
-260
ICF
-943
Figure 2: Proposed Recovery Emissions
4. Alternative Waste Management Scenarios
It is important to examine possible alternative scenarios to enable waste managers
to make informed decisions regarding the emissions of their MSWM practices.
Alternatives should be weighted against emissions, cost, and public opinion. Alternatives
will be examined using the WARM model with differing assumptions as to which
MSWM strategies are implemented. All alternatives will be compared against a baseline
of the UA current MSWM practices.
The first alternative considered consists of recovering 80% of recyclables and
50% compostables. This alternative is known as Trial 3, and is shown in Table 2-3 of
Appendix 2. The increase in compost percentage is reasonable as most of the
compostable material is food waste, which could be separately collected. This would lead
to net GHG sequestration of 492 MTCE, a reduction of 1048 MTCE. The net emissions
are shown in Figure 3.
7
The next alternative examines if all the waste were to be combusted without
energy recovery, and this results in GHG sequestration of 312 MTCE. This correlates to a
net negative change of 868 MTCE. The emission comparison is shown in Figure 3. This
alternative is known as Trial 4, and is shown in Table 2-4 of Appendix 2.
Landfilling with a LFG recovery efficiency of 85%, and burning the gas for
energy (Trial 5) was calculated next, and is shown in Table 2-5 of Appendix 2. A
difference of -1140 MTCE results when compared to the current UA waste practices, this
leads to GHG sequestration of –584 MTCE for the LFG recovery and energy conversion.
The comparison is shown in Figure 3.
Source reducing 35% of the waste (Trial 6), that is able to be source reduced
within the constraints of the model, leads to 315 MTCE of emissions a reduction of 241
MTCE. This is also shown in Figure 3, and is shown in Table 2-6 of Appendix 2.
GHG Alternative Emissions
GHG Emissions (MTCE)
800
600
400
200
0
-200
-400
-600
-800
Alternative
Current
Trial 3
Trial 4
Trial 5
Trial 6
556
-492
-312
-584
315
Figure 3: GHG Alternative Emissions
8
5. Ease of Use
As a tool for waste managers to make informed decisions regarding GHG
emissions, it is important that the WARM model maintain a simplicity and quickness,
while still being accurate enough to provide a reasonable estimation of emissions. The
WARM model is easy to use, as it requires limited spreadsheet data input. Waste is
separated into a number of common waste categories, and all calculations are carried out
in the background. It is possible to have not read the background methodology (Freed et
al., 1998) and still use the model efficiently. Comparing the resulting GHG emission
from the WARM model to those found using the ICF report (ICF, 2005) shows how
differing assumptions can affect the calculated GHG emissions. Although the emissions
differ by approximately 30%, the assumptions make this a reasonable difference. A user
can easily change the reporting method (MTCE, or metric tonnes CO2 equivalent) using a
selection on the spreadsheet. Most of the options related to the model are either buttons,
or numerical input fields. Any changes are applied immediately. The WARM model is
very simple and efficient.
A major limitation of the WARM model is the manned in which the difference in
which the current mix of recyclables against 100% virgin inputs is handled. This input
parameter seems to have no affect on emissions with regards to increased recycling,
which it should. (Denison, 1996) The current mix/100% virgin parameter only effects
emissions when source reduction is applied, and does not reduce emissions by enough.
Source reduction is the most effective means of lowering emissions and this is not
reflected enough in the model.
9
Although the data input is simple but the data must be properly formatted optimal
usage. Waste masses must be available, in addition to waste categorization in order to
efficiently use this model. A more time consuming part of the model is deciding which
categories fit into the WARM categories if they are not the same. If the data is not
available initially it is easily obtainable by taking samples from various waste containers.
While doing so may require man-hours, the cost benefits may be worthwhile due to the
price of emission credits.
The WARM model provides a simple and quick estimation of GHG emissions.
For more in depth analysis it is necessary to read the methodology related to the model,
and examine closely if considering the effects of source reduction.
6. Utility of WARM for U of A
The WARM model is a simplified general model with many waste category
inputs making it plausible for use as a decision support tool. Many of the assumptions
associated with the WARM model are averages based on MSWM from the United States.
While some of the assumptions may correlate and match practices at the UA, it is
recommended to explore these assumptions to maintain a greater level of accuracy.
Since it can be assumed that the UA will continue its trend of growth, the amount
of source reduction will likely not be of great concern. Source reduction is also more
difficult to implement due to the requirement to change, and educate the public. The low
importance of source reduction applies, as the limitation of the model to accurately
estimate the impact of source reduction is not as important.
The simple data input and instant GHG emission results make the WARM model
desirable to use provided categorized waste amounts are available. WM provided the UA
10
with their audit numbers and these are close to the type used by the WARM model. For
quickly comparing alternative MSWM strategies, the WARM model does have use for
UA waste managers, assuming no comparisons are made between a current mix/100%
virgin and source reduction is not considered.
7. Conclusion
Life cycle assessment is a useful for determining the environmental burden of
municipal solid waste management system. It follows the waste throughout its lifetime,
examining emissions from transport, treatment, and other ancillary processes. The Waste
Reduction Model (WARM) quantifies these emissions relating them to the waste
generation amounts and the treatment option used.
The WARM model was applied to the current University of Alberta (UA) waste
management practices and an estimated 556 metric tonnes carbon equivalent (MTCE) of
greenhouse gas emissions were produced. The proposed UA targets of 80% recycling and
25% composting result in net sequestration of 260 MTCE.
Alternative scenarios were also considered and calculated with the WARM
model. 80% recycling and 50% composting resulted in sequestration of 492 MTCE.
Combustion of all waste leads to sequestration of 312 MTCE, while landfilling with gas
collection for energy recovery sequesters 584 MTCE. Source reduction of 35% yields
greenhouse gas emissions of 315 MTCE.
The WARM model is simple, efficient and provides a good estimate of
greenhouse gas emissions from municipal solid waste management practices. The model
is easy to use provided waste amounts and categories are assigned. Limitations of the
model are the handling of the current mix of recycling against 100% virgin inputs and the
11
management of the source reduction. The WARM model is useful for rough estimation of
the UA waste management.
Word Count: 2315
References
Denison, R.A. 1996. Environmental life-cycle comparisons of recycling, landfilling, and
incineration: a review of recent studies. Annual Review of Energy and the Environment,
21: 191-237.
Ekvall, T., and Finnveden, G. 2000. Application of life cycle assessment to integrated
solid waste management. Part 2 - perspectives on energy and material recovery from
paper. Process Safety and Environmental Protection: Transactions of the Institution of
Chemical Engineers, Part B, 78: 288-294.
EPA. 2007. WAste Reduction Model (WARM) [online]. Available from
http://www.epa.gov/climatechange/wycd/waste/calculators/Warm_home.html [cited 06
may 2007].
European Committee for Standardization (ECS). 1997. Environmental Management, Life
cycle assessment, Principles and framework, International Standard ISO 14040, Brussels,
Belgium
12
Freed, R., Driscoll, W., and Stafford, S. 1998. Greenhouse Gas Emissions from
Management of Selected Materials in Municipal Solid Waste. Office of Solid Waste and
Emergency Response, Report EPA/530/R-98/013, Environmental Protection Agency,
Washington, DC.
ICF Consulting (ICF). 2005. Determination of the Impact of Waste Management
Activities on Greenhouse Gas: 2005 Update. Draft Report Contract No. K2216-04-006,
Environment Canada and Natural Resources Canada, Toronto, Ontario.
Kaplan, P. O., Barlaz, M. A., and Ranjithan, S. R. 2004. A procedure for life-cycle-based
solid waste management with consideration of uncertainty. Journal of Industrial Ecology,
8: 155-172.
McCartney, D. 2007. Various dates, personal correspondence.
Tanskanen, J.H. 2000. Strategic planning of municipal solid waste management:
Resources, Conservation and Recycling, 30: 111-133.
Appendices Guide
Appendix 1: Waste Categorization
Appendix 2: WARM Spreadsheets
Appendix 3: ICF Spreadsheets
An electronic copy of the appendices, recommended for Appendix 2, is available at
http://www.ualberta.ca/~mrscott/host/hw/CivE499final/
13