An Empirical Analysis Based on Per Capita GDP and Global Temperature Changes
Bibliographic record
Abstract
This study examines the statistical relationship between global economic development measured by global GDP per capita, and global temperature change. The main goal for this paper is to determine whether the increasing global economic development is associated to annually global temperature increase. Three regression models are employed in this study: linear regression, multiple linear regression, and polynomial regression. The linear regression model is considered as the base model for whole analysis. The numerical and figure outcomes provide the direct linear relationship between GDP and temperature change. The multiple regression model extends the analysis by using three control variables, which are urbanization rate, CO2emissions from land use, and CO2emissions from industry. The polynomial regression model is applied to test for potential nonlinear dynamic relationship. The results show that the linear models suggest a positive association between economic growth and global temperature rise. In contrast, the polynomial models reveal nonlinear patterns resembling the environmental Kuznets curve. Overall, the findings highlight the importance of including demographic and emissions-related variables in climate–economy research and provide new evidence for ongoing debates on the complex interaction between economic development and climate change.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".