Balancing data requirement and modelling quality in neighbourhood life cycle assessments

Abstract

Introduction: When modelling complex systems such as cities, a quality-complexity compromise is to subdivide them into smaller cells. Life Cycle Assessment can help to comprehensively handle urban intricacies but is a data-intensive technique. Balancing data requirement and collection feasibility while acknowledging uncertainty become key. Methods: This research explored top-down and bottom-up approaches to generate information input for environmental modelling at neighbourhood scale and to identify strategies to improve modelling while balancing data collection needs. SimaPro v.9 supported the assessments. Results: Influence of elements like interior finishings and wall partitions is not captured by the top-down approach, but should not be neglected, for their impacts are substantial. Modelling can be improved by application of cut-off rules to limit data requirements and cluster sampling techniques to derive a minimum range of archetypes to adequately describe the studied area. Finally, an evolutive hybrid approach is suggested to gradually improve both background archetypes and foreground bottom-up objects.