The assessment of the potential environmental impacts of electronic products through Life Cycle Assessment method has been widely pro- posed in scientific literature. Despite this, issues related to data collection may occur when electronic components, constituting the final product, are included in the analysis. This study aims to compare two different scenarios to evaluate how the use of primary data and related assumptions (SC1) or secondary data (SC2) may influence the results related to the cradle-to-ga- te analysis of an electronic product, specifically focusing on raw materials and manufacturing processes of electronic components. The main findings allow highlighting that despite in SC1 the use of primary data and related assumptions results in lower impacts in terms of global warming, and has lower uncertainty, in SC2 the adoption of secondary sour es may be more representative for the components manufacturing process.

Primary vs secondary data in LCA: the case of an electronic product

Teresa Maria Gulotta;Giovanni Mondello;Roberta Salomone;Francesco Lanuzza;Giuseppe Saija;
2022-01-01

Abstract

The assessment of the potential environmental impacts of electronic products through Life Cycle Assessment method has been widely pro- posed in scientific literature. Despite this, issues related to data collection may occur when electronic components, constituting the final product, are included in the analysis. This study aims to compare two different scenarios to evaluate how the use of primary data and related assumptions (SC1) or secondary data (SC2) may influence the results related to the cradle-to-ga- te analysis of an electronic product, specifically focusing on raw materials and manufacturing processes of electronic components. The main findings allow highlighting that despite in SC1 the use of primary data and related assumptions results in lower impacts in terms of global warming, and has lower uncertainty, in SC2 the adoption of secondary sour es may be more representative for the components manufacturing process.
2022
9791221004564
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3231308
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