Reducing greenhouse gas (GHG) emissions in municipal solid waste (MSW) systems is a major part of climate mitigation—yet identifying cost-effective and robust mitigation options remains difficult. Waste systems are full of uncertainties: household participation in recycling, changing waste composition, market prices for recovered materials, landfill gas capture rates, and fluctuating investment costs.

This new study applies Simulation Decomposition (SimDec) to a real MSW system in the Golbaşı municipality (Türkiye) to quantify these uncertainties and identify which waste-management strategies remain cost-effective under different future conditions.

The results demonstrate that while several solutions can reduce emissions, their performance varies substantially depending on uncertain parameters—making robustness more important than nominal cost alone.

The four alternative waste-management scenarios

The study evaluates four improvements to the current landfill-based system (BAU):

• A1: Door-to-door collection of packaging waste
• A2: Door-to-door collection of biowaste → electricity production
• A3: Door-to-door collection of biowaste → upgraded biogas (vehicle fuel)
• A4: Mechanical–biological treatment (MBT) with RDF production

A system diagram in Fig. 1 (page 3) shows how each scenario shifts material and energy flows, from recycling streams to anaerobic digestion and RDF substitution.

LCA and LCC show different emissions and cost profiles

According to Fig. 4 (page 6):

• A4 (MBT + RDF) gives the largest GHG reduction, mainly due to replacing coal in cement kilns.
• A1 and A3 have the lowest total system costs (55 and 54 €/ton MSW).
• Abatement costs range from 32 €/tCO₂-eq (A1) to well above 100 €/tCO₂-eq (A2 and A4).
  

But these nominal results are only part of the story—because the uncertainty ranges of all alternatives heavily overlap.

Why SimDec was used

Traditional LCA uncertainty analysis often uses Monte Carlo independently from sensitivity analysis. SimDec, by contrast:

• combines Monte Carlo with global sensitivity analysis
• identifies the most influential parameters
• decomposes the output distribution into scenario-specific segments
• reveals interactions that affect robustness
  (see the methodological comparison in Fig. 3, page 5).
  

This makes it ideal for understanding which MSW strategies perform reliably under uncertain future conditions.

What drives uncertainty in abatement costs?

Across the four scenarios, nine parameters were identified as most influential (Table 1, page 7). The strongest drivers include:

• Biowaste capture rate
• Landfill gas (LFG) collection efficiency
• Paper and plastic capture rates (A1)
• CBG price (A3)
• MBT capital cost (A4)

These parameters directly shape GWP outcomes and system costs.

Decomposed uncertainty reveals which scenarios are robust

A2: High-risk despite potential low cost

The histogram for A2 (Fig. 6, page 8) shows extreme sensitivity to:

• Biowaste capture rate
• LFG collection efficiency

Under high LFG capture efficiency, A2 can fail to achieve any emission reduction at all—because BAU already captures significant methane. Some sub-scenarios even yield negative emission reductions.

A2 therefore has low robustness, despite possible low abatement costs when biowaste capture is maximized.

A1: Most stable, smallest uncertainty range

A1’s uncertainty range is narrow, driven mostly by paper and plastic capture rates. Even under pessimistic assumptions, A1 remains relatively inexpensive and consistently produces emission reductions.

A3: Potentially low-cost and safe

A3 performs well when biowaste capture is high and CBG price is favourable. Importantly, unlike A2, A3 does not risk failing to reduce emissions because LFG effects are minor.

A4: Robust but higher-cost

A4’s abatement cost is dominated by MBT capex. It is consistently above zero, making it stable but not the cheapest option.

What this means for municipalities

The findings show that:

• Nominal cost rankings are misleading without uncertainty analysis.
• A1 (packaging collection) is the most robust low-cost solution.
• A3 (biowaste → vehicle fuel) offers strong potential but depends on capture rates and fuel prices.
• A2 (biowaste → electricity) is unreliable, as it may fail to reduce emissions under plausible conditions.
• A4 (MBT + RDF) is robust but more expensive.

More broadly, the study shows how modern hybrid uncertainty–sensitivity approaches can support waste-management decisions, especially when investment risks and behavioural uncertainties are high.

Reference

Zaikova, A., Kozlova, M., Şenaydın, O., Havukainen, J., Fruergaard Astrup, T., & Horttanainen, M. (2025). Decomposing uncertainty of greenhouse gas emission reduction costs in MSW management: A case study. Waste Management, 205, 115025. https://doi.org/10.1016/j.wasman.2025.115025

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