文献类型：期刊文献

中文题名：前瞻性全生命周期评价在水处理领域的发展与应用

英文题名：Development and Application of Prospective Life Cycle Assessment in Water Treatment Field

作者：邹茂君[1,2,3];杨雪晶[2,3];张锦杨[2,4]

机构：[1]东方电气集团东方锅炉股份有限公司,四川自贡643000;[2]上海工业水系统精益运营工程技术研究中心,上海200237;[3]华东理工大学机械与动力工程学院,上海200237;[4]麦王环境技术有限公司,上海200082

年份：2026

卷号：45

期号：2

起止页码：22

中文期刊名：净水技术

外文期刊名：Water Purification Technology

基金：中节能集团重大科技创新项目(cecep-zdkj-2021-006)。

语种：中文

中文关键词：前瞻性;全生命周期评价(LCA);新兴技术;水处理;放大方法

外文关键词：prospectiveness;life cycle assessment(LCA);novel technology;water treatment;scale-up method

摘要：【目的】随着我国“30·60”“双碳”目标的推进,低碳、零碳及负碳技术不断涌现,准确评估这些新兴技术的发展前景并优化其发展路径则显得至关重要。前瞻性全生命周期评价(LCA)已被证实能有效评估新兴技术并指导其早期开发,可以被用于水处理领域新兴技术的评估与分析。【方法】本文对前瞻性LCA在水处理领域的现有案例进行选取梳理,主要分析了前瞻性LCA的建模过程,包括功能单位、系统边界、前景和背景数据的建立以及数据的不确定性与敏感性分析。【结果】水处理领域前瞻性LCA的功能单位和系统边界可与传统LCA保持一致。前景数据现有建立方法可以分为情景预测和放大,但尚无适合所有情景的方法,需根据研究需求进行选择。背景数据可通过对电力数据的预测,使评估结果更加准确。前瞻性LCA数据不确定性会显著加剧,必须进行严格的不确定性及敏感性分析,以保障评估结果的可靠性。【结论】前瞻性LCA通常聚焦于未来时间点的建模,功能单位定义、系统边界划定以及前景与背景数据的获取,均需依据技术在未来时间点进行预测。此外,现有前瞻性LCA研究通常沿用传统LCA的评估框架,缺乏可靠的评估系统,未来亟需更适合前瞻性LCA的框架和方法。
[Objective]With the advancement of China's"30·60""dual carbon"goals,low-carbon,zero-carbon,and negativecarbon technologies continue to emerge.Accurately assessing the development prospects of these emerging technologies and optimizing their development pathways is crucial.Prospective life cycle assessment(LCA)has been proven effective in evaluating emerging technologies and guiding early-stage development,making it applicable for assessing and analyzing emerging technologies in water treatment.[Methods]In this paper,the existing cases of prospective LCA in the water treatment field are selected and summarized,including functional unit definition,system boundary delimitation,foreground and background data establishment,and uncertainty and sensitivity analyses.[Results]In the water treatment field,functional units and system boundaries in prospective LCA can generally maintain consistency with conventional LCA.Foreground data establishment methods are categorized into scenario projection and scaling effect models.However,no universally applicable approach yet exists,so selection depends on the research objective.Background data can be enhanced through electricity mix projections to improve assessment accuracy.Prospective LCA exhibits significantly heightened data uncertainty,necessitating rigorous uncertainty and sensitivity analyzes to ensure result reliability.[Conclusion]Prospective LCA typically focuses on modeling a future time,requiring projections of functional units,system boundaries,foreground and background data based on anticipated technological states.Furthermore,existing prospective LCA studies predominantly adopt traditional LCA frameworks,lacking dedicated assessment systems.There is an urgent need to develop specialized prospective LCA frameworks and method ologies.