Table 4.

Main methodological characteristics in the reviewed sample

AspectMain options/ sharesNotesReferences (examples)
Goal of the studyComparative analysis (87%), hotspot analysis (11%), other (2%)LCAs mainly compare alternative treatment optionsAleisa and Alsaleh (2024); Nhubu et al. (2020) 
Functional unit (FU)Mass-based (84%, mostly 1 tonne of waste), energy-based (5%), output based (e.g. compost, biochar) (11%)Mass-based FUs dominate (predominantly input-based)Adhikari et al. (2024); Castellani et al. (2024) 
System boundaries (SBs)Not explicitly defined (83%), cradle-to-grave (5%), cradle-to-gate (5%), others (7%)Although implicitly, waste-to-grave SBs are mainly adoptedCastellani et al. (2024); Liu et al. (2017) 
Multi-functionality approachSubstitution/ avoided burden (66%), allocation (10%), system expansion (3%), not specified (21%)Substitution preferred despite ISO’s hierarchyAleisa and Alsaleh (2024) 
Inventory dataSecondary data only (48%), mixed (45%), primary only (3%)Ecoinvent most used database; limited use of primary dataNyitrai et al. (2023) 
LCA softwareSimaPro (31%), EASETECH (13%), LCA for Experts (11%), OpenLCA (2%), unspecified (43%)Reflects database accessibility and regional preferencesArfelli et al. (2023) 
Impact assessment methodsReCiPe (25%), CML (21%), IPCC (17%), other (37%)ReCiPe and CML dominate for comprehensive midpoint coverage; IPCC is prefereed for climate change analyses
Impact categoriesClimate change (96%), acidification (47%), eutrophication (42%), human toxicity (43%), photochemical ozone (41%), energy demand (17%)GHG emissions dominate assessment focusAlsaleh and Aleisa (2023); Ni and Zhang (2024) 
Sensitivity analysisPerformed in 51% of studiesMainly on energy and transport parametersNi and Zhang (2024) 
Uncertainty analysisPerformed in 17% of studiesMonte Carlo simulation most commonLewerenz et al. (2023) 

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