Leveraging industry 4.0 technologies and industrial symbiosis: Advancing circular economy practices in BRICS economies
Bibliographic record
Abstract
In addressing the dynamics of a circular economy (CE), Industry 4.0 technologies (IN4.0T) and Industrial Symbiosis (IS) necessitate meticulous management strategies to optimize their advantageous impacts on circular practices. The present study investigates the influence of IN4.0T, such as Artificial Intelligence (AI), the Internet of Things (IoT), and IS on advancing CE principles in BRICS economies during 2011-2021. To estimate these nexuses, Panel Cross Sectionally Augmented Autoregressive Distributed Lag econometric approach is employed. The results reveal that IS, AI, and IoT significantly enhance CE efficiency in BRICS nations. The study's findings contribute to current literature in three discrete ways: first, it stipulates empirical evidence of how AI and IoT facilitate CE practices; second, it demonstrates the facilitating role of IS in strengthening the restorative-circularity nexus; and third, it offers insights specific to BRICS nations, where rapid economic growth intersects with environmental challenges. The results align with and extend theoretical frameworks, including the Natural-Resources-Based and the Business-Technology-Adoptions models. The findings suggest that policymakers should invest in Industrial Symbiosis and digital technologies to lessen waste, improve resource efficiency, nurture collaborations, and boost CE transitions in BRICS economies.
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
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".