Analysis of the Profile of the Greenhouse Gas Emissions in Brazil
Bibliographic record
Abstract
The number of debates on sustainability and the emission of gases (GHG), that potentiate the greenhouse effect, have grown in recent years, mainly regarding structural changes in the organizational dynamics of society. Particularly in Brazil, which has an extensive territorial area and native vegetation, emission of greenhouse gases is responsible for harmful socio-environmental effects both externally and internally to the country. Thereby, this study aimed to characterize the GHG in Brazil and identify which sustainable factors influence GHG emissions and how these factors correlate with the variations in temperature between 1990 and 2022. For that, multiple linear regression models were used to develop the regression model and canonical correlation analysis to verify how the adjusted factors behaved with the temperature in this period. The results of this study show that the four adjusted indicators are of economic origin and closely related to the type of economic production in Brazil. Each indicator has a similar correlation with temperature variations. This GHG profile helps public decision makers gain an overview, particularly in relation to the drastic temperature changes and current weather conditions. It is expected that this work will have theoretical implications for a new line of research to be deepened, which can develop practices that allow public managers to plan how to reduce the greenhouse gases indicators analyzed in this research. Therefore this study can contribute as a research tool in the control of greenhouse gas emissions in Brazil.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".