Advancing Uncertainty Decomposition in Environmental Assessment: The LMDI Method in Life Cycle Analysis of Bioethanol

Understanding the environmental impact of products such as bioethanol requires comprehensive tools, and life cycle assessment (LCA) plays a central role in this. However, LCAs often rely on numerous assumptions and data sources, which introduce uncertainties that complicate decision making. This study presents a practical way to break down and better understand these uncertainties using a combination of Monte Carlo simulation (MCS) and the logarithmic mean Divisia index (LMDI) method. Focusing on bioethanol production from broken rice in India, the research applied a cradle-to-gate LCA using the ReCiPe 2016 method across seven key environmental impact categories. By running 1,000 MCS iterations, the study simulated the range of possible outcomes based on data variability. The LMDI method was then used to trace how much each input—such as electricity, steam, or additives—contributed to the overall uncertainty in the results. The findings show that electricity and polydimethylsiloxane are the biggest sources of uncertainty, although the latter contributes little to the actual environmental impact. This highlights a critical insight: while insignificant, some minor ingredients can introduce large uncertainty levels due to poor data quality or lack of reliable emission factors. Overall, this approach makes it easier to see where improvements in data or methods can significantly boost the confidence in LCA results. The method supports smarter environmental choices, more reliable assessments, and better-informed sustainability planning by making the uncertainty more transparent and easier to interpret.