A Review of Waste Management Decision Support Tools and Their Ability to Assess Circular Biowaste Management Systems

Abstract

The circular economy concept offers a number of solutions to increasing amounts of biowaste and resource scarcity by valorising biowaste. However, it is necessary to consistently address the environmental benefits and impacts of circular biowaste management systems (CBWMS). Various decision support tools (DST) for environmental assessment of waste management systems (WMS) exist. This study provides a review of life cycle assessment based WMS-DSTs. Twenty-five WMS-DSTs were identified and analysed through a shortlisting procedure. Eight tools were shortlisted for the assessment of their applicability to deliver sustainability assessment of CBWMS. It was found that six tools model key properties that are necessary for assessing the environmental sustainability of CBWMSs, including waste-specific modelling of gaseous emissions, biogas generation or bioproduct composition. However, only two tools consider both waste-specific heavy metals content in bioproducts and the associated implications when applied on soil. Most of the shortlisted tools are flexible to simulate new technologies involved in CBWMS. Nevertheless, only two tools allow importing directly new background data, which is important when modelling substitution of new bioproducts developed in emerging biowaste refineries.

Full text

sustainability
Review
A Review of Waste Management Decision Support
Tools and Their Ability to Assess Circular Biowaste
Management Systems
Eldbjørg Blikra Vea 1, Veronica Martinez-Sanchez 2and Marianne Thomsen 1 ,*
1Department of Environmental Science, Aarhus University, 40000 Roskilde, Denmark; ebv@envs.au.dk
2FundacióENT, Carrer Josep Llanza, 08800 Vilanova i la Geltrú, Spain; vmartinezs@ent.cat
*Correspondence: mth@envs.au.dk; Tel.: +45-8715-8602
Received: 31 August 2018; Accepted: 11 October 2018; Published: 16 October 2018


Abstract:
The circular economy concept offers a number of solutions to increasing amounts of
biowaste and resource scarcity by valorising biowaste. However, it is necessary to consistently address
the environmental benefits and impacts of circular biowaste management systems (CBWMS). Various
decision support tools (DST) for environmental assessment of waste management systems (WMS)
exist. This study provides a review of life cycle assessment based WMS-DSTs. Twenty-five WMS-DSTs
were identified and analysed through a shortlisting procedure. Eight tools were shortlisted for the
assessment of their applicability to deliver sustainability assessment of CBWMS. It was found that
six tools model key properties that are necessary for assessing the environmental sustainability of
CBWMSs, including waste-specific modelling of gaseous emissions, biogas generation or bioproduct
composition. However, only two tools consider both waste-specific heavy metals content in
bioproducts and the associated implications when applied on soil. Most of the shortlisted tools
are flexible to simulate new technologies involved in CBWMS. Nevertheless, only two tools allow
importing directly new background data, which is important when modelling substitution of new
bioproducts developed in emerging biowaste refineries.
Keywords:
decision support tools; biowaste; waste management systems; biowaste characteristics;
circular biowaste management; circular economy; bioproducts; biorefinery
1. Introduction
Circular economy has gained attention as a key solution for mitigating the increasing generation
of solid waste and resource scarcity. As opposed to the linear economy, the concept describes how to
develop closed-loop technical and biological cycles by either recycling materials indefinitely with no
degradation of their properties (the technical cycle) or returning materials to the natural ecosystem
with no harm to the environment (the biological cycle) [
1
]. Although circular economy practices (such
as material recycling) are widely embraced as a sustainability strategy, it is important to consistently
assess their net environmental benefits and possible drawbacks [
2
] and develop methods and indicators
that are suitable for assessing circular economy concepts [
3
]. The term “circular economy” is frequently
applied to suggest increased sustainability. However, it tends to focus on an increased quantity of
reused and recycled resources and overlook the quality of resource flows re-entering to the product
cycle [
4
]. This can pose a risk of augmenting unwanted recirculation of micro-pollutants [
5
–
8
],
if disregarding the material quality, particularly in the transition period from linear to circular systems.
In 2015, 241 million tonnes of municipal solid waste were generated in the EU [
9
]. Of this
waste, 40–60% was organic waste [
10
], representing a great challenge in terms of its management.
However, at the same time, organic waste also constitutes a valuable resource as a component in the
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Sustainability 2018,10, 3720 2 of 27
circular bioeconomy [
11
,
12
]. Biowaste-based biorefineries, producing high value products such as
enzymes, bioplastic and biofertilizer from the organic fraction of municipal solid waste, is an emerging
technology field whose environmental performance should be addressed to ensure a beneficial
implementation [
12
]. This study refers to such circular economy systems related to management
of municipal biowaste as circular biowaste management systems (CBWMS).
Several decision support tools (DSTs) based on life cycle assessment (LCA) are currently available
to assess the sustainability of waste management systems (WMS). These WMS-DSTs are specifically
developed to analyse the performance of integrated WMSs from collection, treatment and final
disposal. Winkler and Bilitewski [
13
] and Jain et al. [
14
] showed large discrepancies in the results
obtained when modelling specific scenarios across different WMS-DSTs. Gentil et al. [
15
] analysed the
technical assumptions that caused the difference in the results obtained with various WMS-DST; e.g.,
time horizon for landfill emissions and calculation of long-term carbon balance when applying biowaste
derived compost on soil [15].
However, there is still a need to identify, analyse and challenge the technical assumptions included
in the tools to strengthen waste LCA modelling [
15
]. Moreover, a recent in-depth review of WMS-DSTs
does not exist and how they are applicable for assessing CBWMS is not yet studied. For example, what
types of biological treatment technologies are included in existing DSTs and if they are adaptable to
represent emerging advanced treatment set-ups in biorefineries transforming biowaste into biobased
products. The applicability to assess CBWMS would also imply that the tools are able to assess the
fate of impurities present in the biowaste and model the nutrient capture when applying biobased
products such as compost on soil.
The overall aim of this study was to review and analyse existing LCA-based WMS-DSTs and
their applicability in sustainability assessment of CBWMSs, focusing on municipal biowaste. To reach
this aim, this study fulfilled the following objectives: (1) identify, compare, evaluate and shortlist
existing WMS-DSTs based on their general characteristics; (2) analyse the applicability of the shortlisted
tools to assess CBWMS, based on default datasets and flexibility; and (3) discuss how to increase
the applicability of existing WMS-DSTs to assess CBWMS. This review is performed to provide a
state-of-the-art overview of existing WMS-DST to support the development of a decision support tool
for CBWMS within the project DECISIVE [16].
2. Methodology
2.1. Waste Management Decision Support Tools
A LCA WMS-DST often has two main modules: life cycle inventories (LCI) and impact assessment
modules (Figure 1). The LCI module includes all information needed to model foreground and
background process data. Foreground processes account for direct consumptions, emissions or outputs
in the collection and treatment phases, while background processes account for the production of
material and energy items consumed in waste collection and treatment. The LCI module can encompass
two sub-modules: waste collection chain processes and waste treatment processes. Both include type
of collection system or treatment process, foreground data inventories, technical assumptions and
calculations algorithms and they are connected with background processes. Calculation algorithms
typically connect the modules, linking the data inventory module to the impact assessment module by
computing, e.g., mass flow balances of the system of study. Technical assumptions are for example
type of substituted energy sources and the assumptions applied in the calculations, e.g., the time
horizon for carbon storage. The impact assessment module translates inventory data to indicators (e.g.,
climate change and human toxicity) commonly applying life cycle impact assessment (LCIA) methods.

Sustainability 2018,10, 3720 3 of 27
Sustainability 2018, 10, x FOR PEER REVIEW 3 of 28
considering macro-impurities (non-biowaste fractions such as paper, plastic and metals that can be
mechanically removed) and micro-impurities (chemical contamination that cannot be mechanically
removed) is normally included in a separate waste characterization process. For a full definition of
macro- and micro-impurities refer to [17]. The waste treatment processes module covers technologies
related to both main treatment (e.g., composting) and final treatment of refuse derived from the main
treatment (e.g., incineration and landfill)
Figure 1. Main features included in a typical LCA WMS-DST. The focus of the review is on waste
treatment processes and waste characteristics (in bold).
The focus of this paper is on waste characteristics and waste treatment processes as it is assumed
that these are most affected by the change towards circular economy (Figure 1). Waste collection is
addressed indirectly by considering how the tools quantify the content of macro- and micro-
impurities in the collected waste. The impact assessment part is only addressed to the level that at
least one environmental impact indicator must be included for the tools to be considered in this
review (see Step 1 in Section 2.2).
2.2. Review Methodology
The WMS-DST review methodology comprised six steps as visualized in Figure 2. Steps 1–4 refer
to the shortlisting procedure (Table 1) and Steps 5 and 6 consist of a detailed evaluation of the
shortlisted tools (Table 2). The evaluation and shortlisting of the tools was mainly based on tool
documentation and publications of the tool version available during the elaboration of the present
paper. Hence, future planned updates of the tools are excluded in the main body of the paper due to
the lack of documentation to validate them. Future planned updates can be found in Table A1. For the
evaluation in Steps 5 and 6, some information was retrieved directly from tool developers or inside
the interface of the tool itself. The feasibility to access information to conduct this review was
considered when assessing the transparency in Step 6.
In Step 1, tools were identified through the search engine Scopus and LIFE and Cordis project
databases according to the first three criteria in Table 1 and Figure 2. Combinations of the keywords
“decision support tool”, “waste”, “municipal solid waste”, “biowaste”, LCA” and “biomass” were
applied. This review is limited to tools designed to assess the environmental sustainability of WMS.
The identified tools should model as a minimum one impact assessment indicator quantifying
environmental performance of municipal solid waste treatment technologies and be able to model
reference flows with different waste compositions (per cent of, e.g., plastic, glass and biowaste). Tools
designed for agricultural waste streams were excluded, since the focus of this paper is on municipal
solid waste.
Table 1 lists the shortlisting parameters and criteria applied in each step of the methodology
shown in Figure 2. In Step 2, tools were shortlisted based on the availability of documentation and the
last updates. WMS-DSTs with no documentation available in English were excluded as well as the
tools not updated after 2000. In Step 3, tools were shortlisted according to general life cycle modelling
characteristics (i.e., assessment type and co-products modelling). The tools not based on life cycle
Waste collection chain process Waste treatm ent process
Impact asses sment
Collection system type
Distances
Foreground data inventories
Calculation algorithm
Technology Type
Technical assumptions
Foreground data inventories
Calculation algorithm
Calculation
method
Assessment
indicators
Background data
Life cycle inventory
Waste
character istics
Figure 1.
Main features included in a typical LCA WMS-DST. The focus of the review is on waste
treatment processes and waste characteristics (in bold).
In the waste collection chain processes, type of collection system, transportation and distances are
typically included. In addition, data on amounts and composition of the collected waste considering
macro-impurities (non-biowaste fractions such as paper, plastic and metals that can be mechanically
removed) and micro-impurities (chemical contamination that cannot be mechanically removed) is
normally included in a separate waste characterization process. For a full definition of macro- and
micro-impurities refer to [
17
]. The waste treatment processes module covers technologies related to
both main treatment (e.g., composting) and final treatment of refuse derived from the main treatment
(e.g., incineration and landfill)
The focus of this paper is on waste characteristics and waste treatment processes as it is assumed
that these are most affected by the change towards circular economy (Figure 1). Waste collection is
addressed indirectly by considering how the tools quantify the content of macro- and micro- impurities
in the collected waste. The impact assessment part is only addressed to the level that at least one
environmental impact indicator must be included for the tools to be considered in this review (see Step
1 in Section 2.2).
2.2. Review Methodology
The WMS-DST review methodology comprised six steps as visualized in Figure 2. Steps 1–4
refer to the shortlisting procedure (Table 1) and Steps 5 and 6 consist of a detailed evaluation of the
shortlisted tools (Table 2). The evaluation and shortlisting of the tools was mainly based on tool
documentation and publications of the tool version available during the elaboration of the present
paper. Hence, future planned updates of the tools are excluded in the main body of the paper due to
the lack of documentation to validate them. Future planned updates can be found in Table A1. For the
evaluation in Steps 5 and 6, some information was retrieved directly from tool developers or inside the
interface of the tool itself. The feasibility to access information to conduct this review was considered
when assessing the transparency in Step 6.
In Step 1, tools were identified through the search engine Scopus and LIFE and Cordis project
databases according to the first three criteria in Table 1and Figure 2. Combinations of the keywords
“decision support tool”, “waste”, “municipal solid waste”, “biowaste”, LCA” and “biomass” were
applied. This review is limited to tools designed to assess the environmental sustainability of
WMS. The identified tools should model as a minimum one impact assessment indicator quantifying
environmental performance of municipal solid waste treatment technologies and be able to model
reference flows with different waste compositions (per cent of, e.g., plastic, glass and biowaste).
Tools designed for agricultural waste streams were excluded, since the focus of this paper is on
municipal solid waste.
Table 1lists the shortlisting parameters and criteria applied in each step of the methodology
shown in Figure 2. In Step 2, tools were shortlisted based on the availability of documentation and the

Sustainability 2018,10, 3720 4 of 27
last updates. WMS-DSTs with no documentation available in English were excluded as well as the
tools not updated after 2000. In Step 3, tools were shortlisted according to general life cycle modelling
characteristics (i.e., assessment type and co-products modelling). The tools not based on life cycle
approach, i.e., considering the whole chain of the WMS were excluded. Co-products generated must
be considered, e.g., by substituting energy and primary resources.
Sustainability 2018, 10, x FOR PEER REVIEW 4 of 28
approach, i.e., considering the whole chain of the WMS were excluded. Co-products generated must
be considered, e.g., by substituting energy and primary resources.
Figure 2. Methodological approach adopted for review of WMS-DSTs focusing on the applicability to
assess CBWMSs.
In Step 4, tools were shortlisted based on: (1) type of biowaste treatment technologies included as
default in the tools; and (2) material-specific properties. These two aspects are considered minimum
criteria for existing WMS-DSTs to be supportive for assessing CBWMS. As a minimum, one biowaste
treatment technology should be available in the default technology portfolio of the tool (i.e., current
dataset available in the tool). Treatment technologies not particularly dedicated to biowaste such as
landfilling, incineration and pyrolysis, were not mapped, as they are not considered as a part of
CBWMS. Consequently, the treatment of refuse is not covered in this paper. Material-specific
properties of the waste include chemical properties such as methane generation potential and
elemental composition (e.g., %VS, %VFA %P, %N, and %Cd). This needs to be included to enable
modelling of waste-specific features such as direct gaseous emission of, e.g., CO2 and CH4, occurring
during composting.
Figure 2.
Methodological approach adopted for review of WMS-DSTs focusing on the applicability to
assess CBWMSs.
In Step 4, tools were shortlisted based on: (1) type of biowaste treatment technologies included as
default in the tools; and (2) material-specific properties. These two aspects are considered minimum
criteria for existing WMS-DSTs to be supportive for assessing CBWMS. As a minimum, one biowaste
treatment technology should be available in the default technology portfolio of the tool (i.e., current
dataset available in the tool). Treatment technologies not particularly dedicated to biowaste such
as landfilling, incineration and pyrolysis, were not mapped, as they are not considered as a part
of CBWMS. Consequently, the treatment of refuse is not covered in this paper. Material-specific

Sustainability 2018,10, 3720 5 of 27
properties of the waste include chemical properties such as methane generation potential and
elemental composition (e.g., %VS, %VFA %P, %N, and %Cd). This needs to be included to enable
modelling of waste-specific features such as direct gaseous emission of, e.g., CO
2
and CH
4
, occurring
during composting.
Table 1.
Shortlisting steps (Steps 1–4) of the WMS-DST review method including scope of the
identified tools (Step 1) and evaluation parameters and associated shortlisting criteria for Step 2
(general characteristics), Step 3 (general modelling characteristics) and Step 4 (minimum criteria for
current applicability to assess CBWMS).
Step Parameter Shortlisting Criteria
1 Environmental assessment Assess sustainability (as a minimum one impact
assessment indicator quantifying environmental
performance) of WMS
1 Waste type Must evaluate municipal solid waste fractions
1 Fractional waste composition Must be able to work with various waste fractions
2 Documentation Must be included
2 Last update Must be updated after year 2000
3 Assessment type Life cycle-based thinking assessing a complete WMS
3 Co-production modelling Must be included
4Biowaste treatment
technologies included Minimum one
4 Waste-specific properties Must be included (e.g., VFA content and methane
generation potential)
A detailed analysis of the applicability to model CBWMSs was conducted in Step 5 (Table 2and
Figure 2). This involved an analysis of calculation algorithms and technology assumptions applied
when modelling biowaste treatment, waste-specific features and bioproduct application. Waste-specific
features relate to how waste composition and characteristics influence the performance of different
waste treatment technologies, e.g., if the composition of output bioproduct depends on the composition
of the input waste. Modelling of application of waste derived bioproducts (e.g., compost) on soil was
evaluated according to whether the tools account for resource consumption, substitution, emissions
occurring after application and how and if carbon sequestration is modelled. Lastly, in Step 5, it was
assessed how the functional unit of the system is defined. In LCA, the functional unit describes the
quantified performance of a product or a system [18].
Table 2.
Evaluation steps (Steps 5–6) of the WMS-DST review method including detailed evaluation
of current applicability to assess CBWMS (Step 5) and future flexibility and applicability to assess
CBWMS (Step 6).
Step Parameter
5 Waste-specific emissions
5 Waste-specific biogas generation
5 Waste-specific bioproducts
5 Waste-specific resource consumptions
5 Modelling of bioproduct application
5 Functional unit *
6 (i) Flexibility: feasibility to analyse new conditions
6 (ii) Flexibility: feasibility to add and modify existing processes and to directly import
background processes from databases
6 (iii) Accessibility and transparency
* The functional units of the reviewed WMS-DSTs were classified in four major classes according to [
19
]: (1) unitary
(a unitary measure, e.g., management of 1 ton of waste); (2) generation-based (waste generation in a delimited
region for a specified period of time); (3) input-based (waste amounts entering a given facility); and (4) output-based
(waste by-products, e.g., amounts of recovered energy or recycled material).

Sustainability 2018,10, 3720 6 of 27
The feasibility to simulate new conditions was analysed looking at how the tools allow modifying
default parameters related to site-specific conditions (including waste composition, material properties
and energy mix) and the possibility to import additional material fractions (Step 6 (i)). Such parameters
must be adjustable to reflect various waste collection designs, associated variations in the feedstock
quality, etc. In Step 6 (ii), the possibility for the user to increase the treatment technology portfolio
to assess novel options, such as the biorefinery concepts, was considered by analysing whether the
tools allow creating new processes and changing default parameters related to existing treatment
processes. This includes substitution ratios, transfer coefficients, energy consumption and methane
potential. In addition, the feasibility to directly import background processes from different databases
(e.g., Ecoinvent, Eurostat, national energy statistics and waste statistics) was analysed.
Finally, the accessibility of the shortlisted tools (Step 6 (iii)) was analysed considering if the
tool is currently available and the mode of access (e.g., as freeware). Transparency was evaluated
as low, medium or high based on the available documentation including the tool specification (i.e.,
algorithm behind the tool). The level of transparency was classified as: “High” if all information
needed to do a full evaluation was available in documents. The transparency was evaluated as
“Medium” if information was partially retrieved in accessible documentation and partially by direct
contact with developers. If complete tool specification is available, the transparency was classified as
“Medium/high”. For tools with insufficient information to conduct the full evaluation, the transparency
was classified as “Low”.
3. Results
3.1. General Tool Characteristics and Modelling Assumptions (Steps 1–3)
Twenty-five WMS-DST assessing the environmental performance of treatment of municipal solid
waste were identified and considered within the scope of this review (Step 1) (Figure 3).
Sustainability 2018, 10, x FOR PEER REVIEW 6 of 28
The feasibility to simulate new conditions was analysed looking at how the tools allow
modifying default parameters related to site-specific conditions (including waste composition,
material properties and energy mix) and the possibility to import additional material fractions (Step 6
(i)). Such parameters must be adjustable to reflect various waste collection designs, associated
variations in the feedstock quality, etc. In Step 6 (ii), the possibility for the user to increase the
treatment technology portfolio to assess novel options, such as the biorefinery concepts, was
considered by analysing whether the tools allow creating new processes and changing default
parameters related to existing treatment processes. This includes substitution ratios, transfer
coefficients, energy consumption and methane potential. In addition, the feasibility to directly import
background processes from different databases (e.g., Ecoinvent, Eurostat, national energy statistics
and waste statistics) was analysed.
Finally, the accessibility of the shortlisted tools (Step 6 (iii)) was analysed considering if the tool is
currently available and the mode of access (e.g., as freeware). Transparency was evaluated as low,
medium or high based on the available documentation including the tool specification (i.e., algorithm
behind the tool). The level of transparency was classified as: “High” if all information needed to do a
full evaluation was available in documents. The transparency was evaluated as “Medium” if
information was partially retrieved in accessible documentation and partially by direct contact with
developers. If complete tool specification is available, the transparency was classified as
“Medium/high”. For tools with insufficient information to conduct the full evaluation, the
transparency was classified as “Low”.
3. Results
3.1. General Tool Characteristics and Modelling Assumptions (Steps 1–3)
Twenty-five WMS-DST assessing the environmental performance of treatment of municipal solid
waste were identified and considered within the scope of this review (Step 1) (Figure 3).
Figure 3. Number of tools included and tools that were excluded in the shortlisting Steps 1–3.
25 tools
Step 1: Literature search
and identification of waste
managment DST
 Assess environmental performance of
WMS?
 Evaluate municipal solid waste fraction?
 Include fractional waste composition?
Step 2: Evaluation of general
characteristics
 Documentation available?
 Updated after 2000?
Tools
excluded
Wasptool, HOLIWAST,
SSWMS, WISARD, LCA-
land, MSWI, ARES, IFEU
Step 3: Evaluation of general
modelling characteristics
 Based on life cycle thinking?
 Modelling of co-products?
Tools
excluded
17 tools
None
17 tools
Step 4
Figure 3. Number of tools included and tools that were excluded in the shortlisting Steps 1–3.
Several DST tools were not included in the review because they focus on non-biowaste municipal
solid waste fractions as AgroLCAmanager [
20
] that exclusively considers agricultural waste. General

Sustainability 2018,10, 3720 7 of 27
LCA tools such as Simapro, OpenLCA and Gabi were also excluded as they do not enable modelling
of reference flows with different fractional composition (per cent of, e.g., plastic, glass and biowaste),
unless the user creates “add-on” models [
21
]. These more generic LCA tools do not include biowaste
characterization data and are often based on average reference flows with average emission data for a
particular waste treatment technology, neglecting any effects from the waste composition [
22
]. Table A2
summarizes the identified tools describing (if available): (1) the reference for documentation; (2) the
aim and concept; (3) tool type; (4) environmental impact indicators; (5) number of waste fractions;
(6) year of last update; and (7) whether the documentation is available.
All identified tools evaluate the environmental performance of WMS as presented in the “aim and
concept” column of Table A2. However, environmental indicators considered could not be identified
for six tools. For the remaining nineteen tools, the environmental indicators covered range from carbon
footprint only (CO2ZW waste management tool) and several environmental indicators applying
standardized life cycle impact assessment (LCIA) methods such as CML (LCA-IWM, SIMUR and
WRATE) to the option to choose between several LCIA methods and indicators (EASETECH). Table A2
reports the environmental indicators used in each tool. Eight tools also consider costs. Some tools have
very detailed waste composition such as EASETECH (80 waste fractions and additional 43 energy
fractions (i.e., energy crops such as wheat and straw)), WRATE (15 waste fractions and up to 52
sub-waste fractions), SWOLF (41 fractions) and MSW-DST (26 fractions). However, five of the reviewed
tools include less than 10 fractions (IWM (EPIC/CSR), CO2ZW, IWM-PL, IWM2-UK and WASTED).
In Step 2, eight tools were excluded mainly due to poor or inaccessible documentation
(HOLIWASTE, SSWMSS, WISARD, MSWI, ARES and IFEU) or not being updated after 2000
(LCA-LAND) (Figure 3). In the following, seventeen tools were analysed based on their general
modelling characteristics (Step 3). The seventeen tools model co-products by substituting energy or
primary resources (Table A3). All tools are based on life cycle thinking including an interconnected
waste management system with activities from waste generation or collection to waste treatment
processes and final disposal. There are differences in the starting point of the WMS chain. Indeed,
some tools include waste generation while others start at the point of collection. For example, WARM
considers generation by including source reduction factors and LCA-IWM starts with temporary
storage prior collection. No tools were excluded in Step 3, hence seventeen tools were further analysed
according to their current applicability to model CBWMS (Step 4) (Figure 3).
3.2. Minimum Criteria to Model CBWMS (Step 4)
Table 3and the next subsections describe the outcomes of Step 4 mapping types of bioconversion
technologies and to which extent material-specific properties are included.

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Table 3.
Evaluation of minimum criteria for the current modelling of biowaste management
technologies (Step 4).
ToolName Bioconversion Technologies Material-Specific
Properties a
Anaerobic Digestion (AD) Compost
Balkwaste 1 (type NA) 1 (type NA) None
SIMUR - 1 (windrow) None
EASETECH 1 (one stage, wet thermophilic) 4 (enclosed channel, enclosed windrow,
open-air windrow and decentralised) High
SWOLF 1 (wet single-stage
mesophilic digester) 4 (windrow, aerated static piles, gore
cover system, and in-vessel systems) Medium
WARM_v14 2 (two single-stage and
mesophilic; wet and dry) 1 (windrow) Medium
IWM (EPIC/CSR) 2 (wet/dry) 2 (windrow and in-vessel) None
IWM-PL - 1 (type NA) NA
IWM2, UK 2 (wet and dry) 1 (type NA) None
LCA-IWM 1 (dry thermophilic) 1 (two-step windrow) Medium/high b
MSW-DST - 4 (high and low quality windrow and
yard waste and one static pile design) Medium
ORWARE 1 (continuous single stage
mixed reactor) 4 (home, windrow, reactor
and membrane) High
WRATE
4 (2 large dry, 1 medium thermal
1 small low solid) 10 (4 enclosed, 3 in vessel, 1 open
windrow and 2 home composting) High
CO2ZW - 1 (type NA) None
WAMPS
1 (continuous single stage mixed
reactor, thermophilic
or mesophilic) c
4 (home composting, open windrow,
close windrow and reactor) NA
Wasted 1 (type NA) 2 (windrow and vessel) None
VMR 1 (wet) - High
KISS model 1 (two-stage, mesophilic) - None
a
High (>10 elements/properties), medium (4–10 elements/properties), low (1–3 elements/properties), none
(0 elements/properties);
b
The exact number of properties included is not available (NA);
c
ORWARE AD module is
used in the tool, refer to ORWARE [23] for information regarding AD process.
3.2.1. Biowaste Treatment Technologies and Types
Most of the tools contain a generic anaerobic digestion (AD) process in their technology portfolio,
except SIMUR, IWM-PL, MSW-DST and CO2ZW, that do not include any AD technology (Table 3).
WRATE has the highest number of default AD technologies included (four), followed by WARM,
IWM (EPIC/CSR) and IWM2, which all contain two default AD technologies. Balkwaste, EASETECH,
SWOLF, LCA-IWM, ORWARE, WAMPS, WASTE, VMR and KISS all include one default AD technology
(Table 3).
Fifteen of the seventeen tools consider composting while KISS and VMR exclusively include AD
(Table 3). WRATE contains ten and EASETECH, SWOLF, MSW-DST, WAMPS and ORWARE four
default composting technologies. IWM (EPIC/CSR) and Wasted include two and Balkwaste, SIMUR,
WARM, IWM-PL, IWM2, LCA-IWM and CO2ZW one default composting technology (Table 3).
3.2.2. Material-Specific Properties
Table 3shows to which extent material-specific properties (i.e., chemical properties and the
elemental composition) are considered. Balkwaste, Simur, IWM, IWM2_UK, CO2ZW, Wasted and
KISS do not include any material-specific properties while EASETECH, SWOLF, WARM, LCA-IWM,
MSW-DST, WRATE, ORWARE and VMR have four or more material-specific properties included.
For example, EASETECH works with 53 physical properties and chemical elements characterizing
each waste fraction and additionally 12 elements only characterized for biomass (e.g., sucrose and lignin
content in sugar cane and soy meal). ORWARE works with a vector of 43 elements characterizing waste
fractions including chemical composition (content of fat, proteins, cellulose, hemicellulose, lignin and

Sustainability 2018,10, 3720 9 of 27
rapidly degradable carbohydrates), elemental composition (e.g., N, P, C, Cd, Cu, and Zn), parameters of
environmental relevance (e.g., NOx, SO
2
, HCl, PCB and dioxins) and process performance parameters
(e.g., H
2
O, VS, and energy). In VMR, the waste composition is defined at three levels: (1) the waste or
product flow (e.g., household waste or biogas); (2) the components including macro-impurities (e.g.,
content of glass and metal) and molecules (e.g., CH
4
and CO
2
); and (3) the elemental composition (e.g.,
C, H, O, Al, and Fe).
3.2.3. Shortlisting (Step 4)
Nine tools were excluded as they did not include material-specific properties or the documentation
available was insufficient to retrieve this information (for IWM-PL and WAMPS) (Table 3). Hence,
eight tools (EASETECH, SWOLF, WARM, LCA-IWM, MSW-DST, ORWARE, WRATE and VMR) were
shortlisted for a further evaluation in Steps 5 and 6 (Figure 4). Note that a screening of the parameters
of Step 5 of the 17 tools of Step 4 is available in Table A4. However, a detailed evaluation was conducted
for the shortlisted eight tools only (Section 3.3).
Sustainability 2018, 10, x FOR PEER REVIEW 9 of 28
For example, EASETECH works with 53 physical properties and chemical elements
characterizing each waste fraction and additionally 12 elements only characterized for biomass (e.g.,
sucrose and lignin content in sugar cane and soy meal). ORWARE works with a vector of 43 elements
characterizing waste fractions including chemical composition (content of fat, proteins, cellulose,
hemicellulose, lignin and rapidly degradable carbohydrates), elemental composition (e.g., N, P, C, Cd,
Cu, and Zn), parameters of environmental relevance (e.g., NOx, SO2, HCl, PCB and dioxins) and
process performance parameters (e.g., H2O, VS, and energy). In VMR, the waste composition is
defined at three levels: (1) the waste or product flow (e.g., household waste or biogas); (2) the
components including macro-impurities (e.g., content of glass and metal) and molecules (e.g., CH4
and CO2); and (3) the elemental composition (e.g., C, H, O, Al, and Fe).
3.2.3. Shortlisting (Step 4)
Nine tools were excluded as they did not include material-specific properties or the
documentation available was insufficient to retrieve this information (for IWM-PL and WAMPS)
(Table 3). Hence, eight tools (EASETECH, SWOLF, WARM, LCA-IWM, MSW-DST, ORWARE,
WRATE and VMR) were shortlisted for a further evaluation in Steps 5 and 6 (Figure 4). Note that a
screening of the parameters of Step 5 of the 17 tools of Step 4 is available in Table A4. However, a
detailed evaluation was conducted for the shortlisted eight tools only (Section 3.3).
Figure 4. Number of tools included and tools that were excluded in the evaluation and shortlisting of
Step 4.
3.3. Detailed Evaluation of Current Applicability to Model CBWMS (Step 5)
The shortlisted tools apply waste properties to estimate up to four aspects: gaseous emissions,
biogas generation, solid outputs and resource consumption. Table 4 provides an overview of whether
waste-specific features are considered in the modelling of each of these aspects as well as whether the
application of bio-based products is included in the tool.
 Include biowaste management
technologies?
 Include material specific properties?
Tools excluded
Step 4: Current applicability
to assess CBWMS –
minimum criteria
17 tools
8 tools
Balkwaste, SIMUR, IWM
(EPIC/CSR), IWM, IWM2,
CO2ZW, WAMPS, Wasted,
Kiss
Step 5 and Step 6 (i-iii)
(Evaluation only)
Figure 4.
Number of tools included and tools that were excluded in the evaluation and shortlisting of
Step 4.
3.3. Detailed Evaluation of Current Applicability to Model CBWMS (Step 5)
The shortlisted tools apply waste properties to estimate up to four aspects: gaseous emissions,
biogas generation, solid outputs and resource consumption. Table 4provides an overview of whether
waste-specific features are considered in the modelling of each of these aspects as well as whether the
application of bio-based products is included in the tool.

Sustainability 2018,10, 3720 10 of 27
Table 4.
Overview of tools shortlisted in Steps 1–4 including tool version and whether gaseous
emissions, biogas generation and compost and digestate composition are modelled considering the
input waste composition and if they include application of bio-treated material.
Tools
Waste-Specific
Gaseous
Emissions
Waste-Specific
Biogas
Generation
Waste-Specific
Bioproducts
Waste-Specific
Resource
Consumption
Bio-Treated
Material
Application
EASETECH Yes Yes Yes Partially Yes
SWOLF Yes Yes Yes Partially Yes
WARM_v14 Yes Yes Yes No Yes
LCA-IWM Yes Yes Yes No Yes
MSW-DST Yes No No Partially No
ORWARE Yes Yes Yes Partially Yes
WRATE Yes Yes No No Yes
VMR - aYes Yes Partially - a
a
VMR is at its early stage of development, hence composting, curing of digestate or application of these products is
currently not implemented.
3.3.1. Waste-Specific Gaseous Emissions
In seven tools (EASETECH, SWOLF, WARM_14, LCA-IWM, MSW-DST, ORWARE and WRATE),
direct gaseous emissions during composting or curing of digestate are modelled considering the
composition of the input waste (Table 5).
Table 5.
Strategies adopted for modelling of direct emissions during composting or curing of digestate.
Where applicable, data from default composting technologies are provided.
Tools
Modelling Principles
for C- and N-
Containing Emissions
Example of Emissions Factors for C- and N- Containing Emissions
during Composting a
Other
Emissions
CO2CH4N2O NH3N2VOCs
EASETECH a*Portion of degraded C
or N (%) 99.8 0.2 1.4 98.5 0.1 -
SWOLF * Portion of degraded C
or N (%) 98.3 1.7 0.4 4 95.6 VOC (0.238 kg/ton VS)
WARM_v14
Emission factors for
various waste fractions
e.g., for food waste and
green waste (tCO2-eq/t
wet weight)
-0.0055
and
0.0139
0.0396
and
0.0609 ---
LCA-IWM * Portion of degraded C
or N (%) - NA NA NA NA NA
MSW-DST Functions considering
the content of paper,
yard and food waste
Equation
(A1) b- - Equation
(A2) b-Equation (A3) b
ORWARE *c
C-emissions:
decomposition of
organic matter
contained in input
waste e.g., lignin.
N-emissions: fraction of
N-loss d(%)
30% of C 0.35% of
CO22 96 2 74% of input VOC
WRATE Fraction of input C
and N NA NA NA NA - VOC (% of wastein), Cl
(% of Cl)
VMR - - - - - - -
* Tools including a biofilter reducing emissions to air;
a
Example of dataset from generic enclosed windrow
composting facility, USA [
24
]. Note that in the example dataset, 95% of CH
4
is further oxidized to CO
2
and 99% of
the NH
3
is oxidized;
b
Calculated according to Equations (A1)–(A3) in Appendix B;
c
Includes emissions of CHX,
AOX, Phenols, PCB and dioxins, which are modelled as % of input VOC [
25
];
d
Calculated according to Equation (1).
EASETECH, SWOLF, LCA-IWM and WRATE model gaseous emissions during aerobic digestion
(composting or curing of digestate) in similar ways. They consider degradation of C- and N-containing

Sustainability 2018,10, 3720 11 of 27
matter for different waste fractions and apply emission factors describing the distribution of initial or
degraded C and N- containing matter into different C- and N-containing gasses (Table 5). For example,
in EAETECH, a dataset for a generic enclosed windrow composting facility (USA) is provided. In this
dataset, degradation of organic carbon is proportional to volatile solids (VS) degradation, e.g., 74.56%
for vegetable food waste and 26.23% for branches (garden waste). Degradation of N bound in the
organic matter is the same for all fractions (65% as default). The type waste-specific direct emissions
considered vary between the tools (Table 5). For example, SWOLF considers emissions of CO
2
, CH
4
,
N
2
, NH
3
and N
2
O and emissions of volatile organic compounds (VOCs) as a function of VS content
(Table 5). WRATE include emission of CO
2
, CH
4
, NH
3
, N
2
O, and VOC and is the only tool including
waste-specific chloride emissions. However, the direct VOC emissions are not waste-specific but
depend on mass of total feedstock (i.e., kg VOCair/kg wastein).
WARM includes waste-specific emission factors for CH
4
and N
2
O considering the different
organic waste fractions present in the model (food waste, yard trimmings, grass, leaves, branches and
mixed organics). For example, the emission factors for methane and nitrous oxide are 0.0055 tCO
2-eq
and 0.0396 tCO
2-eq
per ton wet weight food waste composted, respectively (Table 5). WARM currently
assumes that the biogenic CO
2
emissions occurring during composting are climate neutral and are
therefore excluded. MSW-DST estimates CO2, NH3, and VOC emissions during composting and it is
assumed that no CH
4
is emitted during the composting process. Functions estimating emissions per
dry ton of waste with various compositions can be seen in Equations (A1)–(A3) of Appendix B.
In ORWARE, emission of CO
2
and CH
4
is modelled considering different degrees of
decomposition for what concerns the organic compounds contained in the input waste. For example,
30% of carbon in lignin, 90% in cellulose, 80% in sugars and 65% in proteins turns into CO
2
gas
and the remaining into humus. Considering that some anaerobic spots in the compost are present,
methane emissions for composting is calculated as 0.35% of the CO
2
produced. For proteins, emission
of N-compounds (NH3, N2O and N2O) is also considered and is calculated based on Equation (1):
Nloss (% of input)=0.55903 – 0.01108 ×(C/N)(1)
The N—loss is distributed as displayed in Table 5. ORWARE also considers transfer of other
compounds (VOC, CHX, AOX, Phenols, PCB and dioxins) for division into mature compost, gaseous
emissions and degraded in the process (as per cent of input). For example, of the VOC present in the
input waste, 1% goes to compost, 74% to gaseous emissions and 25% is degraded (refer to [
25
] for
transfer coefficients for the other compounds). ORWARE is the only tool with waste-specific modelling
of the degradation of such complex elements, while the other tools solely consider emissions per
amount of treated waste.
SWOLF, EASETECH, LCA-IWM and ORWARE all include biofilters reducing the gaseous
emissions. For example, in ORWARE the biofilter is modelled simulating the return of material
captured by the filter into the compost (80% for N-NH
3
and N-N
2
O and 50% CH
4
). The CH
4
that is
captured by the filter and stays in the compost is oxidized to CO
2
. Half of the N-containing compounds
passing the biofilter are denitrified and pass the filter as N
2
(i.e., 10% of the initial N-NH
3
and N-N
2
O).
3.3.2. Waste-Specific Biogas Generation
The amount and composition of the generated biogas (CO
2
and CH
4
) is waste-specific in six of
the shortlisted tools (Table 6).

Sustainability 2018,10, 3720 12 of 27
Table 6.
The approach used for modelling biogas generation during AD and examples of energy
conversion and emissions from default biogas combustion technologies available in the tools.
Tools Biogas Generation Biogas Combustion
Modelling Principles Energy Conversion Emissions
EASETECH
Biogas yield as a portion of
anaerobically degradable carbon for
each fraction (default 70%) and user
defined CH4content in biogas (%)
Default energy
coefficients—51% CH4is
transferred to heat and 39%
to electricity and 10% lost
Emission factors afor
stationary engine e.g.,
0.0077 kg NOx/m3CH4
and biogas leakage (2%)
SWOLF
Methane potential for each fraction
and obtained yield e.g.,
86.64 m3/ton wet weight and
obtained yield of 91% for vegetable
food waste
Energy conversion factor
9 CH4MJ/kWh
Emission factors bfor
gas turbine e.g.,
0.0204 kg NOx/m3CH4
and biogas leakage (3%)
WARM_v14
Methane potential for each fraction
and obtained yield e.g., 369 m3/ton
dw and 90% methane yield reached
for food waste
Electricity produced per
waste fraction. For example,
201.4 kWh/ton food waste
and 69.6 kWh/ton yard
trimmings (for wet and
dry AD)
CH4leakage (2%)
LCA-IWM
Methane potential in biowaste (user
defined). Default NA
Energy generated is linked
to the amount and quality of
the biogas NA
MSW-DST c- - -
ORWARE
Proportional to degraded organic
matter calculated according to
Equation (2), considering maximum
degradation ratio, the first-order
rate constant and the hydraulic
retention time.
Energy content in the
methane gas and the heat
and electricity efficiency
(60% and 30% respectively in
default gas engine)
NA
WRATE Energy produced is linearly
correlated to the quantity of
biogenic carbon
Energy produced is linearly
correlated to the quantity of
biogenic carbon NA
VMR
Conversion of microbiological
reactions considering hydrolysis
rates, degradable fraction, VS,
particle size of the waste and
retention time
- -
a
Emission factors for 27 compounds incl. NMVOC, PM
10
, PM
2.5
, CO, N
2
O, NH
3
;
b
Emission factors for biogenic
CO
2
, CH
4
, particulates, NOx, NMVOCs, SOx, NH
3
CO and H
2
S are available;
c
Note that the current version of
MSW-DST does not include any AD process module.
SWOLF, WARM and LCA-IWM model biogas conversion by considering the theoretical methane
potential for specific biowaste fractions and yield coefficient (Table 6). In ORWARE, the biogas
generated is proportional to the amount of degraded organic matter, which is modelled considering
the degradation potential of the organic compounds contained in the waste (fat, carbohydrates, protein,
etc.) and the retention time in the digester tank. For a continuous single stage mixed tank reactor in
steady-state, the degradation ratio (D) is calculated according to Equation (2):
D=D0/(1+1/(k×R)) (2)
where D
0
is the maximum degradation ratio, kis the first-order rate constant and Rthe hydraulic
retention time (in days). The total gas production is further calculated from the quantity of organic
compounds, the degradation ratio, and the proportion of methane in the biogas.
EASETECH includes a user-defined coefficient for biogas yield as a proportion of anaerobically
degradable carbon specific for each waste fraction. Default values for biogas yield of 70% is given,
but this value should be specified for each waste fraction. The user can apply theoretical ratios for CH
4
in biogas (%) (default values for a thermophilic generic AD plant is, e.g., 83.72% for yard waste and

Sustainability 2018,10, 3720 13 of 27
54.45% for vegetable food waste) or specify measured CH
4
in biogas. The rest of the biogas consists
of biogenic CO
2
. Partitioning of CO
2
between gas and liquid phase is also accounted for (default
2.906% to the liquid phase) to keep track of the total biogas produced. There is a user-defined leaking
coefficient (default 2%) representing leaks from pipes, valves, etc.
In WRATE, the amount of biogas-based electricity produced is linearly correlated to the quantity
of biogenic carbon in the waste and it is not possible to see the quantity of methane produced. The basis
for biogas modelling in VMR is microbiological reactions for anaerobic digestion considering the VS,
the non-hydrolysable fraction of VS, the hydrolysis constant and the retention time. The hydrolysis
constant is calculated considering the diameter and particle density of the biowaste feedstock
(depending on pre-treatment). Finally, the biogas flow and composition are estimated assuming
a complete reaction of the hydrolysable fraction involving C, H, N and S elements in the biowaste.
Constant parameters for each biowaste fraction and equations are available in the documentation
presented by Tanguay et al. [26].
The tools apply similar approaches when modelling combustion of biogas for energy production.
As default in EASETECH, biogas can be combusted in a stationary engine producing both heat and
electricity where 51% of the energy in methane is transferred to heat, 39% to electricity and 10% lost.
In SWOLF, the collected biogas is either flared or combusted for energy recovery in a gas turbine or
internal combustion engine. EASETECH and SWOLF include default technology-specific emission
factors considering the amount of methane gas combusted (e.g., 3.93 kg biogenic CO
2
/m
3
CH
4
combusted in SWOLF) (Table 7). In WARM, methane yield is combined with energy or electricity
conversion factors for each waste fraction (e.g., 201.4 kWh/ton food waste and 69.6 kWh/ton yard
trimmings). In ORWARE, a range of alternatives for biogas conversion is available either in gas engines
or by upgrading to vehicle fuel. The energy produced using biogas in a stationary engine is calculated
considering the energy content in the methane gas and the resulting heat and electricity conversion
efficiency (60% and 30%, respectively, and 10% loss). In LCA-IWM, the energy generated is linked to
the amount and quality of the biogas, while air emissions are only related to type of technology not
considering characteristics of the treated waste. The type of air emissions considered is not available.
Table 7.
Summary of the strategies considered for modelling of the content of nutrients and
micro-impurities in the solid output bioproducts (compost or digestate).
Tools Modelling Principles Nutrients Considered * Micro Impurities
EASETECH Mass balance principles C, N, P and K Heavy metals remain in TS
SWOLF Mass balance principles C, N, P and K No
WARM_v14 Mass balance principles N and C No
LCA-IWM Mass balance principles C and N Heavy metal content is unchanged
and distributed to the solid output
MSW-DST Not waste-specific - -
ORWARE Mass balance principles C, N
P and K Heavy metal content is unchanged
and distributed to the solid output
WRATE Not waste-specific - -
VMR Mass balance principles C and N Heavy metal content is unchanged
and distributed to the solid output
* For the decomposition modelling of N and C containing organic matter, refer to Section 3.3.1 on waste-specific
emissions and Section 3.3.2 on waste-specific biogas modelling.
3.3.3. Waste-Specific Bioproducts
Six tools (EASETECH, SWOLF, WARM, LCA-IWM, ORWARE and VMR) apply mass balance
principles based on transfer coefficients to model the distribution of elements or input waste fractions
between air emissions and solid output (i.e., compost and digestate) (Table 7). For example, in
EASETECH, the non-degraded dry matter from each input waste fraction is distributed between solid
output fractions (e.g., compost and rejects) according to user defined transfer coefficients. Default
transfer coefficients are available. For example, 95% of the non-degraded dry matter of vegetable food

Sustainability 2018,10, 3720 14 of 27
waste goes to compost while 5% goes with the reject, and if present in the feedstock to composting,
85% rubber goes to compost and 15% to landfill [
24
]. The degraded dry matter is emitted to air as
described in Section 3.3.1.
In SWOLF, ORWARE and LCA-IWM, transfer coefficient per element is applied. For example,
in the composting process of ORWARE, the major part of N remaining in the compost after
decomposition (Section 3.3.1) is organically bound in humid substances produced during composting.
The rest is present in the final compost either as inorganic plant available NH
4+
(1% of the total N
remaining) and NO
3−
(6% of the total N remaining). In VMR, the non-hydrolysable fraction and
fractions not involving C, H, N and S elements (e.g., heavy metals), which are not transformed to
biogas, will remain in the digestate. EASETECH, LCA-IWM, ORWARE and VMR are the only tools
including distribution of heavy metals contained in the waste based on the same assumption that all
heavy metals present in the waste are unchanged and distributed to the solid outputs.
The bioproduct composition is not waste-specific in MSW-DST or WRATE. However, MSW-DST
includes screening efficiency ratios for different waste fractions to estimate the amount and composition
of the rejected materials. They also include dry matter reduction as a function of biowaste-specific
CO
2
emissions to determine the fraction of raw solid waste that will end up as finished compost.
The moisture content and densities were also used in these calculations. Hence, it would be possible
to estimate the fractional composition of the final compost, but this is not a standard function in the
current interface of the tool. In WRATE, the composition of the material outputs (compost or digestate)
is predefined (not waste-specific) where four grades of biotreated materials can be selected. However,
the moisture content and calorific value depend on the characteristics of the input waste.
3.3.4. Waste-Specific Resource Consumption
Five tools include some features estimating resource consumption considering feedstock specific
characteristics (EASETECH, SWOLF, MSW-DST, ORWARE and VMR) (Table 7). In EASETECH,
the default resource consumptions are set on mass. However, it is possible for the user to implement
resource consumptions related to the waste-specific features. In SWOLF, different materials will have
different transfer coefficients and energy and emissions are allocated appropriately. For example,
in the AD model, most of the branches remain in the overs, and therefore skip the reactor and go
directly to aerobic curing piles. Hence, the electricity use for post-composting screening depends
on how much the mass of each material was reduced during composting. Resource consumptions
in MSW-DST are mostly independent of the waste quality. However, there are some waste-specific
consumptions such as the power required for the hammer mill, which depends on whether the waste
is pre-sorted. ORWARE also includes some waste-specific resource consumptions e.g., electricity
consumed in an AD plant is approximately 5% of the energy in the produced biogas. VMR proposes a
methodology to model the energy consumptions based on different operating settings and properties
of the waste. For example, the efficiency of the trommel is based on the particle size of the waste and
energy consumption during pre-treatment (shredding, classifying and recycling) is determined as a
function of mass flow and moisture content of the waste.
3.3.5. Bioproduct Use
Six of the eight shortlisted tools include land-application modelling of treated biowaste,
considering substitution of commercial fertilizer, emissions after application, carbon sequestration or
resource consumption (Table 8).

Sustainability 2018,10, 3720 15 of 27
Table 8. Modelling of land application of treated biowaste.
Tools Substitution of Fertilizer Emissions Included Carbon Sequestration Resource Consumption
EASETECH 40% for N and 100% for P,
K
CO2, CH4, NH3, N2O,
NO3−, N2, PO3and
heavy metals
Fraction of C
stored—depends on
application frequency
Yes (l Diesel/kg wet
weight compost)
SWOLF 40% for N NH3, N2O, NO3−Fraction of C applied
(0.29) Yes (Diesel/kg wet weigh
compost/digestate)
WARM_v14
40% for N and 100% for P
aN2O and CH40.08 ton CO2, eq/tons
food waste treated Yes
LCA-IWM 100% substitution for N,
P2O5and K2ONo No No
MSW-DST No 22 compounds in total
including NO3−, Na,
K, and Cd. No No
ORWARE
100% for P, K and mineral
N, 30% during the first
year and 30% for organic
N
NH3, N2O, NO3−, N2
and heavy metals Yes Yes (MJ/ha)
WRATE 100% of commercial
fertilizer CO2, N, P, K, Cl−,
heavy metals Fraction of C applied
(2%) No
VMR - - - -
aOnly for anaerobic digested biowaste and it excludes the substitution of compost (aerobic digested biowaste).
Substitution of commercial fertilizers is generally modelled based on utilization ratios describing
crop-uptake of nutrients in biowaste-derived fertilizers compared to commercial fertilizers considering
the accumulated effect over time. In EASETECH, two types of utilization rates can be considered: one is
based on simulations from the Daisy model (agro-ecosystem model) based on local soil conditions, and
the other is based on legislations. For example, in Danish regulations for nutrients in organic fertilizers,
the substitution ratio for N is 40% for anaerobically digested and 20% for composted municipal solid
waste. For P and K, the rate is set to 100% as default. SWOLF and WARM apply the same substitution
factor for N fertilizer (40%), but unlike EASETECH, SWOLF do not distinguish between compost
derived from anaerobic or aerobic digestion and WARM does not account for substitution of compost
derived after aerobic digestion at all. SWOLF also considers substitution of peat (applying a 1:1
substitution ratio). ORWARE uses P, K and mineral N substitution factor of 100%. For organic N, 30%
utilization is assumed during the first year after application and 30% is utilized in the following years
in addition to the nitrogen pool in the soil. The nitrogen pool is the difference between nitrogen input,
plant uptake and losses during the first year. In WRATE, commercial mineral fertilizer is substituted
100% by the waste-derived compost. LCA-IWM considers full substitution of commercial fertilizers
(N, P2O5and K2O).
Emissions after application of compost or digestate are included in the tools to varying degrees
(Table 8). As default option in EASETECH, the user can apply fate factors for C and N simulated by
the Daisy model based on input of substances and avoided NPK substances. For example, nitrate loss
to ground- and surface water, and as N
2
O, NH
3
and N
2
to air, is defined as a fraction of the nitrogen
present in the treated biowaste and depends on the soil type (33%, 2.47%, 7.5% and 4.32%, respectively).
Similarly, a fraction of input C is emitted as CO
2
and CH
4
and input P as PO
3
. Ammonium ions
transferred to surface water are also accounted for. SWOLF only considers emissions of nitrogen to
air and water (as NH
3
, N
2
O, and NO
3−
) and WARM includes details on N
2
O and CH
4
emissions.
ORWARE models the additional emissions resulting from use of organic nitrogen instead of mineral
nitrogen based on a detailed land-use model considering local conditions. N
2
is calculated as a fraction
of N lost though leachate, e.g., 30% for application on sandy loam under moderate drainage conditions
and emissions of N
2
O are 1.25% of the fertilizer-N added. NH
3
emissions are quantified as a fraction
of ammonia-N in the compost and vary between 0.03 and 0.5 (typical value around 0.10) for Swedish
conditions (according to the time of spreading, spreading technique and dry matter content of the

Sustainability 2018,10, 3720 16 of 27
waste). In WRATE and MSW-DST, the composition of the output products is not waste-specific. Hence,
in WRATE, the emissions to groundwater (N, P, K, Cl
−
, and heavy metals) and air (CO
2
) only depend
on type of compost substituted. In MSW-DST, the emissions from 22 compounds are included and
expressed per ton produced compost. The specific emission factors adopted are reported in [
27
].
LCA-IWM does not include emissions after application of compost or digestate. Only EASETECH,
MSW-DST, ORWARE and WRATE consider emissions of heavy metals present in the compost and
digestate to the soil compartment.
Carbon storage in soil is modelled in four of the shortlisted tools (EASETECH, SWOLF, ORWARE
and WRATE) (Table 8). Ratios and the soil storage of C are estimated based on Daisy and local soil
conditions in EASETECH. For example, in the case of application of anaerobically digested municipal
biowaste on sandy loam soil with average DK Crop rotation, the C stored in soil is 13.2% of the applied
C in a 100-year time horizon. SWOLF and WARM consider both direct storage and storage due to
hummus formation (e.g., 0.10 kg C per kg C present in the applied compost and 0.19 kg C stored per
kg C input in SWOLF). In WARM, data compiled in the soil application simulation model “Century”
is used, e.g., 0.08 ton CO
2, eq
/ton food waste and 0.22 ton CO
2, eq
/ton mixed organics digested in a
dry digester with no curing of digestate. Different ratios are provided for digestate with and without
curing and for wet and dry AD. In WRATE, it is assumed that 2% of the carbon applied stays in the
soil and in ORWARE a default value for storage is set to 9% for organic carbon from AD and 15%
for organic carbon from composting. Four tools model the resources consumed during compost or
digestate application on soil (EASETECH, SWOLF, WARM and ORWARE). For example, ORWARE
applies a rate for energy used in MJ/ha specified for three types of spreaders.
3.3.6. Functional Unit
In all eight shortlisted tools, the functional unit can be classified as generation-based, i.e.,
an amount of waste generated in a specific study area for a specific period of time, accounting
for the local characteristics of the generated waste (Table A3). However, the definition of the functional
units is case-specific and the results derived from the tools can be adjusted to fit the functional unit
defined in a specific study. For example, a generation-based functional unit can be converted to a
unitary one if starting the assessment after the collection part, i.e., by looking at only the technology
and not the entire WMS. In EASETECH, the functional unit can also be adjusted to be output-based
(e.g., production of 1 m3biogas from biowaste).
3.4. Future Applicability to Assess CBWMS (Step 6)
3.4.1. Feasibility to Analyse New Conditions (Step 6 (i))
EASETECH, SWOLF, MSW-DST, ORWARE and VMR allow changing default parameters
including waste composition, material properties and energy mix and importing additional waste
fractions (Table 9). This renders the tools flexible to represent local conditions other than those
represented by the default data. WARM and WRATE display less flexibility as they do not allow
modifying the material properties or adding additional waste fractions.

Sustainability 2018,10, 3720 17 of 27
Table 9. Comparison of the flexibility of the tools to analyse new conditions.
Tools Waste
Composition
Material
Properties
Import of Additional
Fractions Energy Mix
EASETECH Yes Yes Yes Yes
SWOLF Yes Yes Partially aYes
WARM_v14 Yes No No Partially b
LCA-IWM NA NA NA NA
MSW-DST Yes Yes Partially cYes
ORWARE Yes Yes Yes Yes
WRATE Yes No No Partially d
VMR Yes Yes Yes No
a
The tool includes a limited number of blank materials for which custom properties can be added (19 slots);
b
The
user can select a default energy mix for the US national average or for a specific US state, but cannot specify a
unique energy mix;
c
The tool includes a limited number of blank materials for which custom properties can be
added (five “other” categories for paper, five for plastic, two for aluminium and a few additional slots);
d
Only the
expert version of WRATE (V4) allows modifying the energy mix.
3.4.2. Feasibility to Modify and Create Processes (Step 6 (ii))
In four of the eight shortlisted tools (EASETECH, SWOLF, ORWARE and VMR), it is possible
to modify and create new processes (Table 10). For example, the default AD plant in SWOLF (a
single-stage, wet, mesophilic digester) can be altered and various combinations of dry, two-stage,
and thermophilic digesters can be modelled by changing input parameters. EASETECH also includes
empty processes templates that can be used as a basis for creating new processes. As such, emerging
biowaste refinery technologies could be modelled in these tools. Direct import of background data is
only possible in EASETECH and WRATE V4.
Table 10. Feasibility to modify existing processes and add new processes.
Tools Substitution
Ratios
Transfer
Coefficients
Energy
Consumptions
Methane
Potential
Add New
Processes
Direct Import
of Background
Data
EASETECH Yes Yes Yes Yes aYes Ecoinvent and
ELCD
SWOLF Yes Yes Yes Yes Yes No
WARM_v14 NA NA NA No No NA
LCA-IWM bNR NR NR NR NR NR
MSW-DST No No No No No No
ORWARE Yes Yes Yes Yes Yes No
WRATE Partially cPartially cPartially cNo Partially cEcoinvent c
VMR Yes Yes Yes Yes Yes No
aThis parameter is not used in the tool; however, the fraction of anaerobically degradable carbon can be modified
to consider variation in methane generation potential;
b
LCA-IWM is not available, so the feasibility to modify
processes is not relevant (NR); cNot possible in the academic or the freeware version, only in V4.
3.4.3. Accessibility and Transparency (Step 6 (iii))
Six of the shortlisted tools are currently available while two tools (LCA-IWM and VMR) are
not (Table 11). LCA-IWM is no longer in use and VMR is under development and still not released.
However, when the latter will be released, the background code will be open source. WARM and
MSW-DST are freeware while SWOLF can be obtained upon request. Similarly, the academic version
of WRATE is free of charge for education purposes. EASETECH is free of charge for academic use;
however, the attendance to an EASETECH course is required.
None of the shortlisted tools were considered to have a high transparency (Table 5) as all
information needed for a full evaluation of Steps 1–6 was not possible to retrieve in publicly available
material. Three of the shortlisted tools (EASETECH, ORWARE and VMR) were evaluated as displaying
medium transparency as the full evaluation was achieved though available documentation or personal

Sustainability 2018,10, 3720 18 of 27
communication with developers. This also applies for SWOLF, WARM and MSW-DST. However,
these three tools also have a complete tool specification and were classified to have a medium/high
transparency. LCA-IWM and WRATE were considered to have low transparency, as it was not possible
to achieve complete evaluation. Details related to the transparency evaluation can be found in Table A5.
Table 11.
Accessibility and means of access for the shortlisted tools and transparency evaluated as low,
medium or high. For details of the evaluation of the transparency, refer to Table A5.
Tools Available? Means of Access Transparency
EASETECH Yes Freeware for academic use (requires
attendance in course), one-time fee for
commercial use Medium
SWOLF Yes Final interface is being developed, but a
prototype can be obtained upon request Medium/high
WARM_v14 Yes Freeware Medium/high
LCA-IWM No Not available Low
MSW-DST Yes Freeware Medium/high
ORWARE Yes By request but is difficult to use with no
preliminary knowledge as no user manual
is available Medium
WRATE Yes
By request for academic institutions. Expert
version is only available upon payment Low
VMR No Interface is not yet developed Medium
a
VMR is under development and still not released. However, when it will be released the background code will be
open source [28].
4. Discussion
4.1. Modelling of Elements Crucial for the Assessment of CBWMS—Waste-Specific Features
All shortlisted tools model the composition of the biowaste-based bioproduct considering the
composition of the input waste except MSW-DST and WRATE and four tools consider waste-specific
content of heavy metals in the final products (EASETECH, LCA-IWM, ORWARE and VMR).
Application of the bio-treated material on soil is modelled in detail in some DSTs (EASETECH,
SWOLF, WARM, ORWARE and WRATE) while some only include substitution of mineral fertilisers
(LCA-IWM) and other exclusively includes emissions to surface water (MSW-DST). Only EASETECH
and ORWARE consider both waste-specific heavy metal content in compost and the consequences it
has when applied on soil.
These waste specific features are crucial for the tools to be applicable for the evaluation of
CBWMS and for elaboration of legislations for the circular bioeconomy field. For example, modelling
waste-specific content of micro-pollutants such as heavy metals in waste-derived compost is important
to avoid accumulation in soil that can cause externalities in terms of adverse health impacts [
6
].
An average increase in zinc concentration of >45% from 1998 to 2014 is observed due to application
of pig manure in Danish agricultural soils [
29
]. This will reach critical levels if no measures are
implemented [
29
,
30
]. To avoid similar long-term challenges for CBWMS, it is crucial to design future
DSTs capable of supporting a safe application of waste-derived compost. They must be able to compute
waste-specific compost composition including micro-pollutants and include differential use of compost
depending on various compost qualities and options for upgrading the quality of the compost by
reducing heavy metal content (e.g., [31,32]).
4.2. Flexibility to Model New Emerging Technologies
Five of the eight shortlisted tools (EASETECH, SWOLF, MSW-DST, ORWARE and VMR) allow
changing default parameters, rendering these tools flexible to represent site-specific conditions others
than those represented by the default data. It is possible to modify and create new processes in four of

Sustainability 2018,10, 3720 19 of 27
the shortlisted tools (EASETECH, SWOLF, ORWARE and VMR). Direct import of background data is
only possible in EASETECH and WRATE V4.
The flexibility for the tools to model new conditions and processes is highly important in order to
consider the new technologies and associated products involved in the emerging biorefinery field such
as bioplastics and enzymes. Hence, the feasibility to modify co-product substitution factors and import
of new background processes data are crucial to model substitution of new bioproducts developed in
biowaste refineries.
4.3. Further Developments to Improve the Assessment of CBWMS
Six of the eight shortlisted tools (EASETECH, SWOLF, WARM, LCA-IWM, ORWARE and VMR)
include waste-specific modelling of gaseous emissions, biogas generation and bioproduct composition
or use of bioproducts, which are key properties that are necessary for assessing CBWMS. However,
certain features should be further developed to achieve a complete assessment of CBWMS. Specifically,
the waste-specific composition of bioproduct should include micro-pollutants (such as in EASETECH
and ORWARE) and not be limited to nutrients. Moreover, features similar to the modelling principles
in VMR, should be developed. Here, calculation of complex chemical and physical interaction of each
step of the waste treatment technology allows for estimation of emissions and resource consumptions
rather than redesigning these interactions for each case study. This flexible modelling approach would
render it possible to assess the sustainability of new process set-ups and technologies. Likewise,
it would enable the tools to assess emerging technologies in the design phase responding to the newest
technological developments within the bioeconomy sector and test new operational settings prior to
invest and implement new technologies.
Most of the tools consider substitution of mineral fertilizers by applying waste-derived compost on
soil. However, data on unquantified benefits of compost such as reducing the need for harmful or toxic
pesticides and fungicides should also be implemented in the tools. For example, the use of compost
may substitute or reduce the need for soil fumigation with methyl bromide (an ozone-depleting
substance) to kill plant pests and pathogen. However, research is needed to analyse and quantify these
benefits to include them in the LCIA part of the tools [
33
]. This requires the tools to be flexible to
include data quantifying such benefits, i.e., to accommodate future developments in the LCIA methods.
5. Conclusions
This study identified and analysed 25 existing WMS-DSTs. In a comprehensive shortlisting
procedure, eight tools were excluded due to lacking documentation and nine tools were excluded,
since they did not include any material-specific property. Eight tools were shortlisted and analysed
in detail in light of their potential applicability to model the environmental performance of CBWMS
(EASETECH, SWOLF, WARM, LCA-IWM, MSW-DST, ORWARE, WRATE and VMR). It was found
that the modelling approach and flexibility vary between the tools, influencing their suitability for
assessing CBWMS.
Based on the findings in this study, it is recommended to use EASETECH, SWOLF, ORWARE or
VMR (when available) when aiming to assess the sustainability of CBWMS. These tools are able to
model waste-specific features, which are crucial for a proper assessment of CBWMS. Moreover, these
tools: (1) are available or under development at the time of carrying out the present study (LCA-IWM
is not); (2) allow modifying material properties or import additional waste fractions (WARM and
WRATE do not); and (3) include AD process or can create one (MSW-DSTs does not include it and
does not allow to create a new process). However, note that only EASETECH and ORWARE consider
impacts when applying the waste-derived compost on soil. Hence, an external assessment of this
should be conducted if selecting SWOLF or VMR.
A novel analysis of existing WMS-DSTs in the light of circular economy is provided in this study.
Of twenty-five identified WMS-DSTs, only four tools are considered applicable for the environmental
sustainability assessment of CBWMS. To further increase the understanding of how to improve the

Sustainability 2018,10, 3720 20 of 27
sustainability assessment of CBWMS and improve WMS-DSTs for this purpose, the performance of
selected WMS-DSTs should be evaluated by applying them on a case study.
Author Contributions:
Conceptualization, E.B.V., V.M.-S. and M.T.; Methodology, E.B.V.; Investigation, E.B.V.;
Data curation, E.B.V.; Writing—Original Draft Preparation, E.B.V.; Writing—Review and Editing, E.B.V., M.T. and
V.M.-S.; Visualization, E.B.V.; Supervision, M.T., V.M.-S.; and Funding Acquisition, M.T.
Funding:
This project received funding from the European Union’s Horizon 2020 research and innovation
program under grant agreement No 689229.
Acknowledgments:
Efforts have been taken to ensure factual accuracy for the description of the shortlisted tools
by consulting the developers of the tools. The authors are grateful to the developers that responded to our requests
and provided valuable answers and feedback: A. Damgaard and A. Boldrin (EASETECH), J. Levis (SWOLF), K.
Weitz (MSW-DST), O. Eriksson (ORWARE) and F. Tanguay-Rioux (VMR).
Conflicts of Interest:
The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
Appendix A
Table A1. Future planned updates of the eight shortlisted tools.
Tools Future planned updates
EASETECH
A complex structure calculating chemical and physical interaction of each step of the
waste treatment technology is under development. This will allow for estimation of
emissions and resource consumptions at each step rather than redesigning these
interactions for each case study
SWOLF No known updates
WARM_v14 No known updates
LCA-IWM No known updates
MSW-DST
Step 4: Note that the forthcoming (end of 2018/early 2019) next version of the
MSW-DST will include AD as well. It will use the same process models as SWOLF.
Step 6: Adding new processes and direct import of background process (will be
possible in future version of the tool)
Next generation of the MSW-DST is currently under development with the US EPA
and a test version is expected to be completed end of 2018/early 2019. The next
generation MSW-DST is a version of SWOLF. It is expected that adding new processes
and direct import of background process will be possible in future version of the tool.
ORWARE A second AD process is under development (Solid State Anaerobic Digestion)
WRATE No known updates
VMR Currently at its early stage of development. A future version will be combined with a
LCA module and it will be possible to change energy mix

Sustainability 2018,10, 3720 21 of 27
Table A2. Identified WMS-DST for assessing environmental sustainability of municipal solid waste.
Name Reference Aim and Concept Tool Type Environmental Impact Indicator Number of Waste
Fractions Last Update Documentation?
Balkwaste [34]
Aims to support the decision maker throughout
the various steps of municipal solid waste
management planning Stand alone
Greenhouse gas effect, emission
to air, conventional fuel savings,
water consumption, hazardous
waste
13 2011 Yes
Wasptool [35]Examines and evaluates the effectiveness of
possible waste prevention strategies Web-tool Generated waste, waste
reduction, diversion from landfill
11 2017 No
HOLIWAST [36]
Comparisons of up to five stakeholder views or
waste management policies. Data only available
for “best available technologies” and user
cannot modify the technology data.
Web-tool NA NA 2007 No
SIMUR [37]Models different waste collection and treatment
options and evaluated the environmental
impacts of different scenarios Stand alone LCIA impact
methodology—CML Version 3,
august 2007 * 16 2011 Yes
EASETECH [21,38,39]Comprehensive waste LCA and LCC tool
developed at the Technical University of
Denmark
Stand alone on
Windows The user can choose various
LCIA methods e.g., ILCD * 80 2018 Yes
SWOLF
[40,41]
AD and
composting:
[42,43]
Optimizable dynamic life-cycle assessment
framework considering future changes in policy
requirements, waste composition and energy
system.
Stand alone Landfill diversion, and GHG
emissions * (41) 2014 Yes
WARM_v14 [34,44,45]
Created by the U.S. EPA to help solid waste
planners and organizations estimate GHG
emission from different waste management
practices.
Stand alone GHG emissions and energy
reduction 54 2016 Yes
IWM
(EPIC/CSR) [46]
Aims to evaluate the environmental and
economic performance of the various elements
of their existing or proposed waste management
systems
Stand alone
Global warming, acidification,
urban smog, health risks water
quality impairment and land use
disruption *
8 2004 Yes
IWM - PL [47,48]LCA and cost analysis quantifying potential
environmental impacts and economic aspects of
municipal waste management systems NA Ecopoints 6 2011 Yes
IWM2, UK [49] User-friendly model for waste managers Stand alone Fuels, Final Solid Waste, Air
Emissions (GWP) and Water
Emissions * 9 2001 Yes
LCA-IWM [50]
Supports the decision making in the planning of
urban waste management systems by allowing
comparison of different scenarios. Stand alone LCIA method CML 2001 10 2006 Yes
MSW-DST [27,51]Aims to calculate life-cycle environmental
trade-offs and full costs of different waste
management or materials recovery programs. Stand alone Energy consumption, and
emissions for 32 pollutants * 26 2012 Yes

Sustainability 2018,10, 3720 22 of 27
Table A2. Cont.
Name Reference Aim and Concept Tool Type Environmental Impact Indicator Number of Waste
Fractions Last Update Documentation?
ORWARE
[23,52],
AD [53],
composting
[25]
Designed for strategic long-term planning of
recycling and waste management. It is originally
developed for environmental assessment of
biodegradable liquid and organic waste, but can
also handle treatment of mixed waste.
Stand alone
GWP, acidification,
eutrophication, photo oxidants,
primary energy carriers,
non-renewable energy carriers *
12 Applied in a
study in 2017
[54]Yes
SSWMSS,
Japan NA NA NA NA NA 2009 No
WISARD NA Aims to assist decision makes when evaluating
alternative waste management scenarios Stand alone NA NA 2000, still in
use till 2009 No
WRATE [55]Environmental assessment of waste
management systems Stand alone LCIA method CML 2001 52 2017 Yes
CO2ZW [56]Calculates GHG emissions emanating from the
waste operations of European municipalities. Stand alone Carbon footprint 6 2013 Yes
LCA-LAND [57] Landfill model Stand alone No impact indicators—only
emissions NA 1999 Yes
MSWI NA NA NA NA NA 2003 No
ARES NA NA NA NA NA 2004 No
WAMPS [58]
[59]
AD [23]
Screening LCA model applied to find optimal
waste management solutions and alternative
waste treatment technologies. The focus is on
environmental aspects but also evaluates
economic consequences
Stand alone GWP, eutrophication,
acidification,
photo-oxidant formation * 11 2009 Yes
IFEU project NA
German model for environmental assessment of
waste systems based on the software
UMBERETO. The detailed biological treatment
module is not included in the official library of
the UMBRETO software, but in the IFEU
project specifically.
Stand alone NA NA 2002 No
WASTED [60]Provides a comprehensive view of the
environmental impacts of municipal solid waste
management systems. Stand alone No impact
indicators—only emissions 6 2005 Yes
VMR [26]
Aims to be an open-source waste management
tool with a detailed representation of the effects
of operating conditions and input
stream characteristics.
NA Not implemented at this stage 19 Under
development Yes
KISS [61]
Calculates carbon footprint of different sorting
and treatment systems in waste management.
The model has been developed as part of the
TOPWASTE project (The Optimal Treatment
of Waste)
Stand alone Carbon footprint, resource
recovery and loss 13 2015 Yes
* The estimation of the costs is also included.

Sustainability 2018,10, 3720 23 of 27
Table A3. Step 3: Evaluation of general modelling specifications.
Tools Assessment Type System Boundaries of the Interconnected
Waste Management System
Functional Unit
Type Substitution
Balkwaste MFA and energy
balance Generation, collection (including transport)
and treatment Generation-based Yes
SIMUR Life cycle thinking Generation, collection (including transport),
treatment, final disposal Generation-based Yes
EASETECH LCA and LCC Generation (waste separation), collection,
transport, processing technologies,
disposal and application of output products
Flexible (unitary,
generation, input
and output-based) Yes
SWOLF LCA and LCC Collection, treatment, final disposal,
land application, or remanufacturing Generation-based Yes
WARM_v14 Carbon footprint,
LCA based
Generation (incl. source reduction),
collection (including transport), treatment,
final disposal (land application or landfill) Generation-based Yes
IWM (EPIC/CSR) LCA and LCC Collection (including transport), treatment,
final disposal Unitary Yes
IWM-PL LCA and LCC Collection (incl. transport), treatment and
final disposal Unitary yes
IWM2, UK LCA and economic
assessment Generation, collection, treatment,
final treatment Generation-based Yes
LCA-IWM, EU LCA, economic
and social Generation, temporary storage, collection,
transport, treatment/final disposal Generation-based Yes
MSW-DST LCA + LCC Generation, source reduction, collection,
transport, treatment Generation-based Yes
ORWARE LCA Generation, collection (incl. transport),
treatment and utilisation of products from
waste treatment Generation-based Yes
WRATE, UK LCA Collection, sorting, treatment Generation-based Yes
CO2ZW Carbon footprint Generation, collection (including transport),
sorting and treatment Generation-based Yes
WAMPS LCA Collection (incl. transport), treatment,
final disposal Input-based Yes
Wasted LCA Generation (predicted amounts), collection,
material recovery, final disposal (landfill) Input-based Yes
VMR
The module will
potentially be
combined with
LCA module
Generation (waste separation), collection,
transport, processing technologies, disposal
and application of output products Generation based Yes
KISS model LCA based carbon
footprint model Generation, collection (incl. transport),
treatment, final disposal Generation-based Yes
Table A4. Screening of parameters of Step 4 for the 17 tools shortlisted after Step 2.
Tools Waste-Specific
Emissions
Waste-Specific
Biogas
Generation
Waste-Specific
Outputs
Waste-Specific
Consumptions
Application of Treated
Biowaste On Land
Balkwaste No NA No No No
SIMUR No NR No No Yes
EASETECH Yes Yes Yes Partially Yes
SWOLF Yes Yes No Partially Yes
WARM_v14 Yes Yes Yes No Yes
IWM
(EPIC/CSR) Partially NA No No No
IWM-PL NA NR NA NA Yes
IWM2, UK No NA No No Yes
LCA-IWM, EU Yes Yes Yes No Yes
MSW-DST Yes NR No Partially Yes
ORWARE Yes Yes Yes Partially Yes
WRATE Partially Yes No No Partially
CO2ZW Partially NR No No No
WAMPS NA Yes NA3NA Yes
Wasted No NA No No No
VMR No Yes Yes Partially No
KISS model No No No No Yes

Sustainability 2018,10, 3720 24 of 27
Table A5.
Evaluation of transparency of the shortlisted WMS-DSTs considering where the information
needed to enable an evaluation of the six steps was retrieved. The level of transparency was classified as:
“High” if all information was available in documents, “Medium” if information was partially retrieved
by direct contact with developers and partially by accessible documentation and “Low” if the full
evaluation was not possible due to lacking documentation and lacking information from developers.
Tools Step 1–3 Step 4 Step 5 Step 6 (i) Step 6 (ii) Tool Specification Overall
Evaluation
EASETECH [21] [21]
Inside tool
[24] and
confirmed
[62]
[14] and
confirmed
[62]
[14] and
developer
[62]
Not complete—but some
information is available in
[21] and user manual [39]
and process documentation
inside the tool [24]
Medium
SWOLF [42,43] [42,43] [42,43][14] and
confirmed
[63]
[14] and
developer
[63]
Yes—detailed process model
documentations (e.g., [
43
,
44
])
Medium/high
WARM [33,44,45] [33,44,45] [33,44,45] [14][14] and
NA Yes [33,44,45] Medium/high
LCA-IWM [50] [50] [50] or NA NA NR Not available Low
MSW-DST [27,51] [27,51] [27,51][14] and
developers
[64]
[14] and
developers
[64]
Yes (https://mswdst.rti.org/
resources.htm)Medium/high
ORWARE [23,25,52,
53]
[23,54],
AD [25,53]
and
developer
[65]
[23,25,52,
53] and
developer
[65]
Developer
[65]Developer
[65]
Not complete—but
documentation of most
process models are available
(e.g., [25,53])
Medium
WRATE [55][55] and
inside tool
[66]
[15,55] or
NA [14] NA or [14]Not complete—but process
documentation is available
inside the tool [66]Low
VMR [26] [26] [26]Developer
[28]Developer
[28]Not completeaMedium
Appendix B. Equations Estimating Waste-Specific Gaseous Emission in MSW-DST
CO2 =217.4 ×FP+237.3 ×FY+370.5 ×FF(A1)
NH3 =1.29(±1.38)FP+5.15(+1.37)FY+37.6(+1.56)FFB68.9(+23.4)FP×FF(A2)
VOC =4, 162(±1701)FP+831(±1890)FY+458(±2340)FF−7, 558(±17, 662 )FP×FY−6, 006(±28, 770)FP×FF(A3)
where F
P
, F
Y
and F
F
are the dry fraction of paper, yard and food waste, respectively, ranging from 0 to
1 and with FP+ FY+ FFalways equal to 1.
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