The Circular Economy (CE) Rebound as a Paradox of Knowledge: Forecasting the Future of the CE–IoT Nexus through the Global E-Waste Crisis
Bibliographic record
Abstract
There are widespread assumptions to the effect that the real-time data generated through the 5G-enabled Internet of Things (IoT) will improve material traceability and accelerate the global transition to a circular economy (CE), thereby helping to achieve the UN Sustainable Development Goals and carbon neutrality. Many industries, governments, and NGOs are supporting this vision by investing in related digital infrastructure (5G networks, servers, computer hardware, etc.). Conversely, recent literature has highlighted a paradoxical phenomenon known as the CE rebound, whereby sound CE activities end up offsetting environmental gain(s). This challenges the assumption that the new 5G-enabled IoT will be conducive to greater circularity while carrying its own environmental weight. Resorting to applied epistemology—a perspective seldom used in sustainability research—and the global e-waste crisis as an intense case in point, we question the confidence with which actors predict positive outcomes from the CE–IoT nexus. We argue that avoiding circularity rebounds cannot be construed as a matter of methodological development or, by extension, modeling sophistication through real-time data exploitation. Instead, circularity rebounds need to be recognized and theorized as a paradox of knowledge that also narrows sustainability research’s horizons, despite AND because of the 5G-enabled IoT. As per this paradox, advanced digital technologies may well be compounding environmental issues at the same time as they illuminate them.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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, 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".