- NTNU is leading Task 3.1, which involves a deep review of prospective LCA approaches applied to bio-based and fossil-based technologies. Could you explain how NTNU plans to conduct this
We performed a literature review of studies applying future-oriented LCA to understand their different definitions and approaches to perform environmental assessments of emerging bio-based technologies. More than 120 scientific articles were reviewed and classified. Three main challenges that hinder the comparability and applicability of future-oriented LCA to assess the environmental impacts of emerging bio-based technologies were identified: I) an unclear differentiation of technology maturity (e.g., technology readiness level) from temporal position (e.g., position in time, present or future); II) varying methods to scale up technology maturity and to modify the future background economy; and III) lack of harmonized application of Integrated Assessment models (IAMs) to project background data, that needs to improve its consistency, transparency, and reproducibility. NTNU is now working together with the other partners in Task 3.1 to identify key strategies to overcome such challenges. For example, the harmonization of ex-ante and prospective LCA calls for an international initiative gathering multiple practitioners and stakeholders, thereby contributing to a better comparability among technologies, processes, and studies.
- NTNU collaborates with other partners to develop a framework for generating life cycle inventory (LCI) data of up-scaled bio-based technologies for prospective LCA. How does NTNU plan to harmonize different techniques for technology upscaling and tailor them to low-TRL bio-based technologies? Could you elaborate on the approach NTNU will take to adapt scale-up methodologies to degrees of knowledge about foreground process units?
Emerging bio-based technologies are at very early stages and a proper assessment of their environmental impacts is challenging, as they don’t provide representative data of a commercial performance. This is mostly because they rely on lab or pilot scale data that are not optimized for an industrial scale yet. We, at NTNU, developed a combined framework https://www.sciencedirect.com/science/article/pii/S2352550923002543 that integrates ex-ante and prospective LCA to deal with the scaling-up of technology maturity (ex-ante) by means of process-based calculations in close communication with the technology developers. This combined framework introduces a stepwise approach that identifies the main potential environmental hotspots and can be easily applied by LCA practitioners as there is no need of detailed process simulation and it can be tailored to several bio-based processes. This is one of the possible ways to deal with low technology maturity level and we are looking forward to discussing with Carlos Robles (INSA), that leads this Task.
- NTNU is responsible for coupling outputs from IAMs with background inventory data for future-oriented LCA studies. How does NTNU intend to embed IAM-derived projections of changes in technologies and socio-economic conditions within background inventory data? What role will the Shared Socio-economic Pathways (SSPs) play in representing alternative future developments, and how will NTNU ensure compatibility with other developments?
There are several possibilities of technological changes based on multiple IAMs, SSPs, climate policy, years, thus a large range of prospective data can be generated. Processing this data requires significant computational power. Marcos Watanabe and Konstantin Stadler from NTNU are developing a user-friendly interface that connects PREMISE and Brightway. This interface will allow users to run a wide range of sensitivity and uncertainty analyses. The focus on a user-friendly interface ensures that people with varying levels of LCA and programming skills can work with the prospective data. The goal is to make it easy for users to incorporate IAM-based projections into LCA studies, promoting consistent, transparent, and reproducible transformations of background data.
- improving uncertainty analysis in prospective LCA, with NTNU collaborating with other partners to develop a practical framework for defining and accounting for uncertainty in the assessment of bio-based technologies. Could you discuss the importance of an uncertainty-first paradigm in prospective LCA and how NTNU plans to provide guidance on defining, quantifying, and propagating uncertainties in the modeling process?
Targeting the best bio-based technologies is key for a faster transition from a linear and fossil economy to a circular and bio-based one. This cannot be done is a far future, decision must be made during the early research phases. However, assessing the environmental impacts of emerging bio-based technologies involves considerable uncertainty. Even when in close communication with the technology developers, it’s difficult to predict when these technologies will be market-ready, leading to many assumptions in our assessments. Massimo Pizzol from AAU is leading this task considering an uncertainty-first approach, by focusing on a range of possible outcomes rather than relying on a single point result. This approach considers a broader scope of uncertainties to better understand the range of potential environmental impacts.