Institute of Environmental Sciences (CML), Leiden University, P.O. Box 9518, 2300 RA Leiden, The Netherlands.
Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands.
Environ Sci Technol. 2023 Feb 28;57(8):3445-3454. doi: 10.1021/acs.est.2c05311. Epub 2023 Feb 13.
While wild pollinators play a key role in global food production, their assessment is currently missing from the most commonly used environmental impact assessment method, Life Cycle Assessment (LCA). This is mainly due to constraints in data availability and compatibility with LCA inventories. To target this gap, relative pollinator abundance estimates were obtained with the use of a Delphi assessment, during which 25 experts, covering 16 nationalities and 45 countries of expertise, provided scores for low, typical, and high expected abundance associated with 24 land use categories. Based on these estimates, this study presents a set of globally generic characterization factors (CFs) that allows translating land use into relative impacts to wild pollinator abundance. The associated uncertainty of the CFs is presented along with an illustrative case to demonstrate the applicability in LCA studies. The CFs based on estimates that reached consensus during the Delphi assessment are recommended as readily applicable and allow key differences among land use types to be distinguished. The resulting CFs are proposed as the first step for incorporating pollinator impacts in LCA studies, exemplifying the use of expert elicitation methods as a useful tool to fill data gaps that constrain the characterization of key environmental impacts.
虽然野生传粉媒介在全球粮食生产中发挥着关键作用,但它们的评估目前在最常用的环境影响评估方法(生命周期评估,LCA)中缺失。这主要是由于数据可用性和与 LCA 清单的兼容性方面的限制所致。为了弥补这一差距,本研究使用 Delphi 评估法获得了相对传粉媒介丰度的估计值,在该评估法中,25 名专家涵盖了 16 个国籍和 45 个专业领域,对与 24 个土地利用类别相关的低、典型和高预期丰度进行了评分。基于这些估计值,本研究提出了一套全球通用的特征化因子(CFs),这些因子可以将土地利用转化为对野生传粉媒介丰度的相对影响。还提出了 CFs 的相关不确定性,并通过一个实例说明了其在 LCA 研究中的适用性。建议将在 Delphi 评估中达成共识的估计值所得到的 CFs 作为可直接应用的方法,以区分不同土地利用类型之间的关键差异。提出的 CFs 旨在作为将传粉媒介影响纳入 LCA 研究的第一步,例证了使用专家启发法作为填补限制关键环境影响特征化的数据差距的有用工具。