Barriers, Challenges, and Opportunities in the Adoption of the Circular Economy in Mexico: An Analysis through Social Perception
Bibliographic record
Abstract
This study explores the transition toward sustainable economic models through the circular economy (CE) in Mexico. Utilizing a mixed-methods approach, this research incorporates a comprehensive literature review and analyzes responses from 42 stakeholders, gathered through surveys and focus groups. These stakeholders comprise a diverse group including PhD students, professors, researchers, industry professionals in sustainability and the environment, and government advisors and coordinators from the Mexican Secretary of Environment. This representative sample provides a broad perspective on the barriers, opportunities, and societal perceptions regarding CE. The findings reveal significant challenges such as economic barriers, regulatory inadequacies, and a lack of awareness and education, all of which hinder the adoption of CE practices. Despite these challenges, there is a generally optimistic view among stakeholders about CE’s potential to positively impact societal needs, suggesting robust opportunities for innovation and policy enhancement to foster sustainable development. Key recommendations include intensifying educational programs to elevate public understanding and engagement, formulating supportive policies that facilitate CE adoption, and promoting intersectoral collaboration to leverage collective expertise and resources. Additionally, the research underscores the necessity of integrating CE principles into urban planning and policy frameworks to effectively address specific local challenges such as waste management, pollution, and urban sprawl. By providing a detailed analysis of the current state and potential of CE in Mexico, this paper contributes valuable insights to the global discourse on sustainability. It proposes strategic actions to overcome existing hurdles and capitalize on opportunities within the CE framework, charting a path forward for Mexico and serving as a model for other regions facing similar sustainability challenges
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".