Assessing circular economy in Brazilian industries through the analytical hierarchy process
Bibliographic record
Abstract
Sustainable development has been pursued by organizations around the world ever since environmental and social issues were introduced into institutional agendas. In the various sectors of the economy, the factors that influence sustainable decisions are multidisciplinary and systemic, and address the concept of Circular Economy (CE). This study aimed to develop a method to measure the level of commitment of companies and sectors to CE. The method allows investigating institutional factors associated with sustainable development and assessing the depth of CE practices. A circularity index is originated that can assist decision makers in the development of specific strategies, investment plans, and policies to guide organizations towards the achievement of a CE. The proposed method was then applied to 75 Brazilian companies recognized for their sustainability initiatives, analyzing practices associated with CE actions, as well as their depth. The results, using the Analytic Hierarchical Process (AHP), indicate that the sectors analyzed do not have a significant difference among them and that the majority of the companies analyzed (80%) do not practice any circular action despite claiming the opposite. Therefore, CE is still incipient in Brazil. The application of the proposed method to a large sample showed its potential for global use, and that it can also be employed to guide actions of single companies or entire sectors towards sustainable development using a CE path.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".