Environmental Sustainability and Economic Growth: A Panel Data Analysis in East Africa
Bibliographic record
Abstract
This study investigated the environmental sustainability of economic growth: a Panel data analysis in East Africa for the period 1990-2020 And we use of OLS estimation, Fully Modified Least Squares (FMOLS), and the outcome of Descriptive statistics reveal the typical magnitudes, standard deviations, and ranges of variables such as ecological footprint (EF), carbon dioxide (CO2), biocapacity (BC), foreign direct investment (FDI), gross domestic product (GDP), and population. EF has an average of 17.15 with a low standard deviation, suggesting proximity to the mean. CO2 exhibits higher variability, with a range from -3.330 to 11.691. Similar patterns are observed for BC, FDI, GDP, and population. Correlation analysis reveals relationships, with positive correlations between EF and CO2, EF and population, CO2 and population, and BC and EF. Negative correlations exist between EF and GDP and CO2 and GDP, suggesting potential trends in the associations between ecological impact, economic development, and population dynamics. The Kao Residual Cointegration Test indicates that the variables in the analysis may be stationary after differencing, a crucial condition for cointegration. The p-values associated with residual V and HAC V are both less than 0.05, providing statistical significance and evidence against the null hypothesis of no cointegration. The overall low p-value (0.0004) for the Kao test further supports the rejection of the null hypothesis, suggesting the presence of cointegration among the variables. For biocapacity (BC) and foreign direct investment (FDI), individual intercept and trend co-integration tests show significant evidence.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".