Enhancing relevance by regional life cycle impact assessment at global level

本研究提案旨在通过解决全球区域化问题，增强环境相关性，减少生命周期影响评估的不确定性。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。该研究提案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目前的表征模型，以适当的分辨率覆盖全球范围。将通过获得覆盖全球的先进专家模型和参数数据库来支持增强的建模能力。人体毒性特性建模还将补充一个室内模块，以将区域化范围缩小到工作场所和家庭一级。 通过处理与（资源）水的使用有关的重要和及时的问题，本提案还通过描述目前清单评估做法的区域差异，大大有助于扩大伦敦国际评估的范围。将通过概率（蒙特卡洛）或分析（泰勒展开级数）不确定性方法，广泛分析与地理差异、原型和通用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进行的生命周期评估的质量得到提高，所取得的结果将导致更好和更有见地的决策。

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.