Missing Data in Life Cycle Assessment
A Review of Inventory Modeling Methods for Missing Data in Life Cycle Assessment
Published in the Journal of Industrial Ecology
Authors:
Shiva Zargar, Ph.D. Candidate at the Sustainable Bioeconomy Research Group, The University of British Columbia, Vancouver, Canada
Professor Yuan Yao at the Center for Industrial Ecology, Yale School of the Environment, Yale University, New Haven, Connecticut, USA
Professor Qingshi Tu at the Sustainable Bioeconomy Research Group, The University of British Columbia, Vancouver, Canada
Understanding the environmental impact of everything we create and use is crucial. This is where Life Cycle Assessment (LCA) comes into play. LCA helps us track the journey of materials from extraction to disposal, shedding light on the impacts they have along the way.
Tackling Missing Data in LCA
One of the biggest hurdles in LCA is dealing with missing data. Collecting this missing data can be incredibly costly and sometimes outright impossible. In my recent paper, "A Review of Inventory Modeling Methods for Missing Data in Life Cycle Assessment," published during my PhD at The University of British Columbia, I explored this challenge head-on.
Conducted in collaboration with Professor Yuan Yao from Yale School of the Environment and my PhD supervisor, Professor Qingshi Tu, our research delved into two main strategies to address missing data: proxy selection and data creation. Proxy selection involves using surrogate datasets to represent missing data, while data creation uses empirical equations or mechanistic models to generate the needed information.
Key Approaches and Methods
We identified three common approaches within these strategies:
Data-driven Modeling
Mechanistic Modeling
Future Inventory Modeling (e.g., projecting to 2050)
Our paper critically reviews 12 widely-used methods within these approaches. We evaluated them based on several practical criteria:
Domain Knowledge Requirement: Both for developers and users.
Post-treatment Requirement
Challenge in Assessing Data Quality Uncertainty
Challenge in Generalizability
Challenge in Automation
Practical Insights for LCA Practitioners
These criteria provide a framework for LCA practitioners to select the most suitable methods to bridge data gaps, tailored to the specific goals and scope of their studies. By understanding the strengths and limitations of each method, practitioners can make more informed decisions and improve the reliability of their assessments.
Future Directions
Our research also points out areas for future improvement, aiming to refine these methods further and make LCA a more robust tool for environmental assessment.
Conclusion
By addressing the issue of missing data in LCA, we can better understand and mitigate the environmental impacts of the materials and processes that drive our economy. This research is a crucial step towards more sustainable industrial practices.
For more details, you can access the full paper here.
Contact Information:
For further inquiries or advising services related to life cycle assessment and sustainable bioeconomy, please contact hello@buildneutral.ca