Uncertainty quantification for renewable energy generation from landfill methane

Renewable energy recovery from landfill methane is vital for the environment and has a significant impact on global warming mitigation; however, its estimation is associated with multiple layers of uncertainty. This study quantified the uncertainties associated with landfill energy recovery by integrating field measurements, laboratory analyses, and uncertainty assessment approaches, including Bayesian inference for parameter estimation and Markov Chain Monte Carlo (MCMC) for simulations. Biochemical methane potential (BMP) assays were conducted to obtain methane generation potential (L 0 ),and stochastic method used to estimate site specific methane decay rate (k field =0.26 yr⁻¹), applied in all field estimations. Applying methane generation parameters and accounting for measured seasonal capture efficiency, methane oxidation in the final cover (quantified by δ¹³C stable-isotope tracing and IRMS), and plant performance factors (LHV, heat rate, parasitic load, availability), net electricity generation was estimated at ≈ 3.8 GWh in a representative year at five years of waste age, with a cumulative 10-year production (2026–2035) of ≈ 34.9 GWh, 95% credible interval 27.4–41.7 GWh. Results are presented as probability distributions and credible intervals for annual and cumulative energy, allowing assessment of project viability under realistic operational variability. Notably, stable-isotope measurements quantified methane oxidation efficiency within the final cover at 46%, exceeding default inventory assumptions and illustrating a critical, yet often neglected, mitigation pathway. The integrated methodology demonstrates that site-specific measurements combined with Bayesian uncertainty quantification substantially improve the reliability of landfill waste-to-energy assessments and support more informed planning, monitoring, and decision-making for renewable energy recovery from municipal solid waste.