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.
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 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.
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 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.
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.
A detailed analysis of the applicability to model CBWMSs was conducted in Step 5 (Table 2
and 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
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”.