A new method of patent analysis to support prospective life cycle assessment of eco-design solutions

可比性 生命周期评估 质量(理念) 计算机科学 可靠性(半导体) 过程(计算) 产品(数学) 生产(经济) 一致性(知识库) 可靠性工程 持续性 数据提取 模块化设计 风险分析(工程) 工程类 数学 业务 经济 人工智能 哲学 梅德林 法学 宏观经济学 生态学 生物 操作系统 功率(物理) 几何学 认识论 量子力学 政治学 物理 组合数学
作者
Christian Spreafico,Daniele Landi,Davide Russo
出处
期刊:Sustainable Production and Consumption [Elsevier BV]
卷期号:38: 241-251 被引量:76
标识
DOI:10.1016/j.spc.2023.04.006
摘要

Prospective life cycle assessment (LCA) was introduced with the goal to evaluate the environmental sustainability of eco-design solutions (i.e., ideas, prototypes, immature products, emerging technologies) prospectively rather than existing products, at the present time, as in traditional LCA. The main difference lies in the inventory, which is foreground and is based solely on the extraction of data from prospective documents, including patents, although this task, is tricky and can make the final result uncertain. This study proposes a systematic method to collect all the flows about a specific function of the product lifecycle from patent literature for building the foreground inventory of prospective LCA, ensuring comparability, data quality and scale-up. This was done by studying the intersections between patent analysis techniques and LCA requirements for reducing the uncertainty, prescribed by ISO 14040, ISO 14044, Pedigree Matrix and Data Quality Indicators for Life Cycle Inventory Data. The application of the proposed method to a case study related to the production of titanium powders using an innovative process revealed its main advantages in collecting patents and extracting data. Patent search recall and precision are increased. Patents are filtered by seeking a trade-off to ensure time consistency and avoid anomalous fluctuations in the data resulting from predatory patenting strategies. Data reliability and significance are controlled. Results can be expressed without levelling them around the average value, but adding time evolution and forecasting considerations. For example, the global warming potential (GWP) of the innovative process is 1.5 % lower than the GWP of the current process, considering the average patent data of the last 10 years. In addition, this value showed a 1 % increase for each year.
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