Digital technologies in the circular economy: a bibliometric analysis
Bibliographic record
Abstract
Purpose This study maps the scholarly landscape of digital technologies (DT) in the circular economy (CE) using bibliometric methods to identify key trends, contributors and thematic developments. Design/methodology/approach A bibliometric analysis of 722 Scopus-indexed publications (2016–early 2025) was conducted, using VOSviewer and Bibliometrix for citation, co-authorship, keyword co-occurrence and thematic evolution analyses. Findings The analyzed literature shows rapid growth post-2020, with key contributions from Europe. Influential journals include Sustainability, Journal of Cleaner Production, Procedia CIRP and Resources, Conservation and Recycling. Core themes evolved from foundational concepts toward Industry 4.0 technologies (IoT, AI and Blockchain) and policy-related topics like digital product passports (DPPs), suggesting growing interest in data-driven circularity. Research limitations/implications The dataset is limited to Scopus and English-language sources, which may affect comprehensiveness. Future research should explore empirical applications, integration strategies and sector-specific digital solutions. Practical implications The findings inform industry and policy actors on emerging digital enablers that support resource efficiency, transparency and sustainable innovation in circular transitions. Originality/value This paper offers an up-to-date, data-driven overview of the DT–CE research domain, extending prior qualitative reviews by mapping its structure and evolution over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.134 | 0.316 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".