Diagnosis of uncertainty treatment in neighbourhood life cycle assessments

SUSTAINABLE BUILT ENVIRONMENT D-A-CH CONFERENCE 2019

生态环境科学

Zara, O.O.C.^1 ; Guimaraes, G.D.^1 ; Gomes, V.^1

School of Civil Engineering Architecture and Urban Design, Department of Architecture and Construction, University of Campinas, Brazil^1

关键词: Degree of uncertainty;    Environmental assessment;    Global sensitivity analysis;    Life Cycle Assessment (LCA);    Probabilistic distribution;    Systematic literature review;    Uncertainty assessment;    Uncertainty treatment;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/323/1/012060/pdf
DOI  :  10.1088/1755-1315/323/1/012060

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Urban areas are complex, multifunctional, long-lasting dynamic systems responsible for impressive resource consumption and environmental impacts. Assessments at the neighbourhood scale offers an important complexity compromise. This paper scrutinizes approaches for handling uncertainty analysis (UA) and sensitivity analysis (SA) in LCAs at the neighbourhood scale, aiming at identifying inconsistencies, limitations and challenges, and supporting the development of assessment guidelines. A systematic literature review was performed. Results from the final 35-paper sample show that only one-third of the papers actually performed some calculation. Two of the most recent ones used Monte Carlo (MC) simulations, whilst SA was mainly carried out through scenarios. Despite no clear trend is shown, this may indicate attempts to also apply MC at the neighbourhood scale. The basic quest in UA and SA, particularly global sensitivity analysis, is to balance quality and completeness of output information and computational force needed. Automating calculations, using lighter sampling methods and fast calculators should be further investigated. Finally, future studies could also focus on defining a minimum group of parameters to investigate and on which strategy to follow in specific data availability circumstances. Fuzzy sets seem better for environmental assessments with high degree of uncertainties and probabilistic distributions give results that are more precise. Dynamic models, future scenario uncertainty and spatial uncertainties propagation should also be further explored once the basic challenges for uncertainty assessment are overcome.