Data and code for "Methodological completeness of LCA studies on waste printed circuit board recycling: a systematic review and a proposed six-layer framework"

This repository contains the data, methodological documentation and Python code supporting the study “Methodological completeness of LCA studies on waste printed circuit board recycling: a systematic review and a proposed six-layer framework”. The review includes 38 studies identified through searches in Scopus and Web of Science conducted on 17 March 2026. Methodological reporting completeness was assessed using six criteria covering the functional unit, system boundary, life cycle impact assessment method, treatment of multifunctionality, modelling platform and inventory data sources, and uncertainty, sensitivity and scenario characterization. Each criterion was scored from 0 to 2, yielding a total score of 0–12. The repository includes the coding manual and framework checklist; search strategies and database exports; eligibility decisions and full-text exclusions; study-level data extraction and supporting evidence; independent and consensus scores and the reconciliation log; the expert-appraisal questionnaire and aggregated results; and Python scripts, a notebook, input data, statistical outputs and figures. The analyses cover inter-rater agreement, descriptive statistics, exploratory associations, leave-one-criterion-out comparisons and sensitivity to weighting schemes, classification thresholds, scoring rules and the inclusion of borderline studies. A traceability table links reported results to the analysis code. Individual expert questionnaire responses are not publicly shared, in accordance with the consent conditions. Aggregated, anonymized results are provided. The code automatically skips analyses requiring these individual responses when the corresponding file is unavailable. Please consult README.md for the repository structure, software requirements, reproduction instructions and licensing information. The assessment instrument and six-layer framework are preliminary and have not been externally validated.