A Study on Assessing a Business Viability for Transition to a Circular Economy
Bibliographic record
Abstract
The shift from the existing linear model of the economy to a circular model is gaining traction across business entities, nationally and globally. Minimal studies have been done that would support the circular transition for a business from an existing linear model. There is a significant gap between the formulation and implementation of circular strategies in business. This literature review explores the preexisting concepts of the business model canvas (BMC) and Value Hill tool for the implementation of circular strategies in a business by determining a Good Point for Transition (GPT). The favorable condition, where a business can transition from a linear economy (LE) to a circular economy (CE) is defined as a GPT. This study suggests a three-step generic process that would provide a company with clarity on how to incorporate circular strategies into their structure. Firstly, this review paper defines and elaborates upon the business model canvas (BMC) based on the prior work of Osterwalder and Pigneur (2010) and Lewandowski (2016). Secondly, it analyzes the Value Hill diagram, a strategic tool for circular activities, that a business can use upon implementation of a circular model (Achterberg et al., 2016). Finally, this work will indicate how a circular strategy can be selected on the basis of assessment of the BMC and Value Hill diagram of a business. For a better understanding of the process, IKEA's initiatives for circular strategies are used in the study. The paper concludes with a three-step generic model for determining GPT and emphasizes that the adoption of circular strategies for companies depends upon the circular expertise and resources they and their value chain partners have across the Value Hill diagram. Keywords: circular economy, circular strategies, circular business model canvas, value hill diagram, circular transitions
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".