Uncertainty in the Carbon Footprint accounting and evaluation of textile and apparel products: A systematic review

Abstract

The textile and apparel industry is pivotal in the global economy but faces significant environmental challenges, particularly regarding Greenhouse Gas (GHG) emissions reduction. Carbon Footprint of a Product (CFP) serves as an effective measure of carbon emissions and has garnered widespread attention within the industry. However, there is a lack of comprehensive accounting and analysis of its uncertainties, especially in the textile and apparel sector. This study analyzes the main factors influencing uncertainties in CFP and categorizes them based on the accounting process and life cycle characteristics of textile and apparel products. It identifies the primary factors affecting uncertainty as the definition of system boundaries, the quality of activity data and emission factor data, the selection of allocation methods, and the setting of scenarios. Methodological uncertainty, in addition to parameter, scenario, and model uncertainties, should also be considered. A review of 1072 CFP related publications on textile and apparel products from 2000 to 2023 revealed that 38 mentioned uncertainties, with only 10 providing qualitative or quantitative assessments, primarily focusing on parameter uncertainty. The main techniques identified for summarizing uncertainty analysis methods are Monte Carlo simulation (MCS), pedigree matrix and Data Quality Indicator (DQI) methods, or a combination of both (DQI + MCS), and sensitivity analysis (SA). Future work should focus on enhancing attention to model, scenario, and methodological uncertainty to establish a more systematic and comprehensive evaluation framework. Digital acquisition technology, blockchain technology, modular theory and fuzzy methods should be used to improve data collection methods and optimize the selection and verification of data sources, thereby reducing uncertainties at the source.