Estimación y análisis de la relacion entre desarrollo económico y emisiones de CO2
Bibliographic record
Abstract
The environmental situation is an issue that concerns the world and that must be addressed from different angles for this reason this article aims to determine the relationship between economic development and CO2 emissions per capita, according to the premise of Kuznets. For this, an econometric model is applied with data from the year 2003 - 2016, for eleven (11) countries: Germany, Argentina, Australia, Brazil, Canada, Chile, China, Colombia, United States, United Arab Emirates and Russia. The results obtained from the econometric model used partially confirm the hypothetical relationship posed by the CAK environmental curve. A fuzzy logic model complements this finding where the variability of the behavior of future CO2 emissions is appreciated according to the input variable GDP per capita and CO2 per capita. Both models lead to a holistic, inclusive and creative understanding of the environmental situation. It is concluded from the data used for the two models of increase or decrease of CO2 emissions, which is conditioned to multiple variables, so the fuzzy logic opens a new field of exploration in this topic.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".