Enhancing relevance by regional life cycle impact assessment at global level

本研究计划旨在透过解决全球区域化问题，提高生命周期影响评估（LCIA）的环境关联性，并降低其不确定性。它解决了当今LCIA模型无法评估全球经济中产品生命周期的排放和资源消耗的问题。本研究计划侧重于两个影响类别：人类毒性和水的使用，将扩大和进一步发展当前的表征模型，以适当的分辨率水平覆盖全球范围。增强的建模能力将通过获得复杂的专家模型和具有全球覆盖的参数数据库来支持。此外，人类毒性表征模型将与室内模块相辅相成，以将区划缩小到工作场所和家庭水平。通过处理与（资源）水使用有关的重要和及时的问题，该提案还通过描述当前清单评估做法的区域差异，大大有助于扩大LCIA的范围。将通过概率（蒙特卡洛）或解析（泰勒展开级数）不确定性方法，广泛分析与地理分化、原型和一般表征因素相关的典型空间变异性范围和典型不确定性范围的影响参数。最后，这项工作将确保今后在加拿大和其他地方执行的lca将提高质量，并确保所获得的结果将导致更好和更有洞察力的决策。

This research proposal aims at enhancing the environmental relevance and reducing the uncertainty of Life Cycle Impact Assessment (LCIA) by addressing global regionalization. It tackles the inability of today's LCIA models to assess emission and resource consumption of product life cycles in a global economy. Focusing on two impact categories: human toxicity and water use, this research proposal will expand and further develop current characterization models to cover a global scale at an appropriate level of resolution. The enhanced modeling capability will be supported by the acquisition of sophisticated expert models and parameter databases with global coverage. Human toxicity characterization modeling will additionally be complemented with an indoor module to scale regionalization down to the working place and the household level. By addressing the important and timely issue related to the use of (resource) water, this proposal also significantly contributes to broaden the scope of LCIA by characterizing regional differences of current inventory assessment practices. Influential parameters in respect to their typical spatial variability range and typical uncertainty range linked to geographically- differentiated, archetypal and generic characterization factors will be extensively analyzed through probabilistic (Montecarlo) or analytical (Taylor expansion series) uncertainty approaches. Finally, this work will ensure that future LCAs performed in Canada and elsewhere will improve in quality and that the results obtained will lead to better and more insightful decision-making.