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Temporalis, a generic method and tool for dynamic Life Cycle Assessment
Giuseppe Cardellini1, Christopher L Mutel2, Estelle Vial3
1University of Leuven (KU Leuven), Division Forest, Nature and Landscape, Celestijnenlaan 200E, Box 2411, 3001 Leuven, Belgium; Université Libre de Bruxelles (ULB), Institute for Environmental Management and Land Use Planning (IGEAT), Avenue Franklin D. Roosevelt 50 CP 130/02, 1050 Brussels, Belgium; Technical University of Munich (TUM), Chair of Wood Science, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany.
The Science of the Total Environment
|July 21, 2018
Summary
This study introduces a new dynamic Life Cycle Assessment (LCA) methodology to capture temporal data, improving accuracy for environmental impact assessments of products like glued laminated timber (glulam). Dynamic LCA reveals significant differences compared to static methods.
Area of Science:
- Environmental Science
- Industrial Ecology
- Sustainability Assessment
Background:
- Static Life Cycle Assessment (LCA) methodologies aggregate data, leading to a loss of crucial temporal information.
- Existing dynamic LCA approaches aim to incorporate temporal dynamics but often lack a generic and operational framework.
- Accurate environmental impact assessment requires accounting for the time-dependent nature of industrial processes and resource flows.
Purpose of the Study:
- To present a novel, generic, and operational methodology for dynamic Life Cycle Assessment (LCA).
- To integrate temporal information into both the Life Cycle Inventory (LCI) and Life Cycle Impact Assessment (LCIA) phases.
- To demonstrate the application and sensitivity of the dynamic LCA approach using a glued laminated timber (glulam) product.
Main Methods:
- Development of a dynamic LCA methodology utilizing graph traversal and convolution for temporally differentiated inventory calculation.
- Application of the method to a cradle-to-grave dynamic LCA of a glued laminated timber (glulam) product.
- Sensitivity analysis of global warming results based on temporally explicit LCI data.
- Implementation of the framework in the free and open-source software Temporalis.
Main Results:
- The dynamic LCA methodology successfully incorporates temporal information in both LCI and LCIA phases.
- Application to glulam products shows considerable differences in outcomes between static and dynamic LCA approaches.
- Sensitivity analysis highlights the impact of temporal LCI data on global warming potential results.
Conclusions:
- The proposed dynamic LCA methodology offers a significant advancement over static approaches by preserving temporal information.
- The Temporalis software provides a fully operational and accessible tool for implementing dynamic LCA with existing databases.
- Dynamic LCA is essential for a more accurate and nuanced understanding of a product's environmental footprint over time.
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