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Today, life cycle assessment (LCA) is the most widely used approach to model and calculate the environmental impacts of products and processes. The results of LCAs are often said to be deterministic, even though the real-life applications are uncertain and vague. The uncertainty, which may be simply ignored, is one of the key factors influencing the reliability of LCA outcomes. Numerous sources of uncertainty in LCA are classified in various ways, such as parameter and model uncertainty, choices, spatial variability, temporal variability, variability between sources and objects, etc. Through a scoping review, the present study aims to identify and assess the frequency with which LCA studies reflect the uncertainty and what are the tools to cope with the uncertainty to map the knowledge gaps in the field to reveal the challenges and opportunities to have a robust LCA model. It is also investigated which database, methodology, software, etc., have been used in the life cycle assessment process. The results indicate that the most significant sources of uncertainty were in the model and process parameters, data variability, and the use of different methodologies and databases. The probabilistic approach or stochastic modeling, using numerical methods such as Monte Carlo simulation, was the dominating tool to cope with the uncertainty. There were four dominant LCA methodologies: CML, ReCiPe, IMPACT 2002+, and TRACI. The most commonly used LCA software and databases were SimaPro® and Ecoinvent®, respectively.

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In recent years, computer-based simulations have been used to enhance production processes, and sustainable industrial strategies are increasingly being considered in the manufacturing industry. In order to evaluate the performance of a gasification process, the Life Cycle Thinking (LCT) technique gathers relevant impact assessment tools to offer quantitative indications across different domains. Following the PRISMA guidelines, the present paper undertakes a scoping review of gasification processes’ environmental, economic, and social impacts to reveal how LCT approaches coping with sustainability. This report categorizes the examined studies on the gasification process (from 2017 to 2022) through the lens of LCT, discussing the challenges and opportunities. These studies have investigated a variety of biomass feedstock, assessment strategies and tools, geographical span, bioproducts, and databases. The results show that among LCT approaches, by far, the highest interest belonged to life cycle assessment (LCA), followed by life cycle cost (LCC). Only a few studies have addressed exergetic life cycle assessment (ELCA), life cycle energy assessment (LCEA), social impact assessment (SIA), consequential life cycle assessment (CLCA), and water footprint (WLCA). SimaPro® (PRé Consultants, Netherlands), GaBi® (sphere, USA), and OpenLCA (GreenDelta, Germany) demonstrated the greatest contribution. Uncertainty analysis (Monte Carlo approach and sensitivity analysis) was conducted in almost half of the investigations. Most importantly, the results confirm that it is challenging or impossible to compare the environmental impacts of the gasification process with other alternatives since the results may differ based on the methodology, criteria, or presumptions. While gasification performed well in mitigating negative environmental consequences, it is not always the greatest solution compared to other technologies.

Researchers have long been interested in developing new economic assessment methods to provide credible information and facilitate the sustainable development of new technologies and products. The techno-economic analysis (TEA) and the life cycle cost analysis (LCCA) are the most widely used approaches for modeling and calculating processes’ economic impacts. A simulation-based TEA is a cost-benefit analysis that simultaneously considers technical and economic factors. In addition, the method facilitates the development of the entire project and provides a systematic approach for examining the interrelationships between economic and technological aspects. When it comes to economic studies, it is intimately bonded with uncertainty. There are numerous uncertainty sources, classified in various ways. The uncertainty reflects “an inability to determine the precise value of one or more parameters affecting a system.” The variability refers to the different values a given parameter may take. This implies that a probability density function (PDF), for instance, can be employed to estimate and quantify the variability of a given parameter. The bias refers to “assumptions that skew an analysis in a certain direction while ignoring other legitimate alternatives, factors, or data.” The present study identifies the frequency with which TEA/LCCA studies address uncertainty and gaps within the selected papers through a scoping review. The results indicate that the uncertainty associated with economic factors and model uncertainties were the main sources of uncertainty in TEA and LCCA. Moreover, possibilistic approaches such as the Monte Carlo methodology were the most frequently used tool to cope with the uncertainties associated with LCCA and TEA.

A growing awareness of global climate change has led to an increased interest in investigating renewable energy sources, such as the anaerobic digestion of biomass. This process utilizes a wide range of microbial communities to degrade biodegradable material in feedstock through a complex series of biochemical interactions. Anaerobic digestion exhibits nonlinear dynamics due to the complex and interacting biochemical processes involved. Due to its dynamic and nonlinear behavior, uncertain feedstock quality, and sensitivity to the process’s environmental conditions, anaerobic digestion is highly susceptible to instabilities. Therefore, in order to model and operate a biogas production unit effectively, it is necessary to understand which parameters are most influential on the model outputs. This also reduces the amount of estimation required. Through a scoping review, the present study analyzes the studies on the application of sensitivity analysis in anaerobic digestion modeling. Both local and global sensitivity analysis approaches were carried out using different mathematical models. The results indicate that anaerobic digestion model no.1 (ADM1) was the most commonly used model for analyzing sensitivity. Both local and global sensitivity analyses are widely employed to investigate the influence of key model parameters such as kinetic, stoichiometric, and mass transfer parameters on model outputs such as biogas production, methane concentration, pH, or economic viability of the plant.

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