A Study into the Demographics Having the Greatest Carbon Footprint
Bibliographic record
Abstract
Our aim was to determine factors predicting greenhouse gas emissions per capita and to identify the demographics having the greatest carbon footprint. The relationship between socioeconomic trends and greenhouse gas emissions is controversial, given that many past studies evaluated only a single factor. We analyzed the relationship between global greenhouse gas emissions per capita and literacy rate, GDP per capita, urban population percentage, adolescent fertility rate, unemployment percentage, percent of agricultural land, research and development expenditure, renewable energy consumption, food production, population growth, mobile cellular subscriptions, air transport freight, and forest area. We gathered data from 217 countries spanning a period of 20 years; 1993 to 2012. We analyzed the data using multiple regression models. We concluded food production, renewable energy consumption, air transport, mobile cellular subscriptions, literacy rate, and population growth have the greatest impact on predicting greenhouse gas (GHG) emissions in our model, suggesting the demographic with the greatest carbon footprint are wealthy, educated people living in urban centers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".