Greening the Growth: A Comprehensive Analysis of Globalization, Economic Performance, and Environmental Degradation in Tanzania
Why this work is in the frame
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Bibliographic record
Abstract
The pursuit of economic growth in developing countries like Tanzania often intensifies environmental degradation, posing significant sustainability challenges. This study examined the interrelationships between globalization, economic growth, and environmental degradation in Tanzania from 1970 to 2022, using World Bank data and the autoregressive distributed lag (ARDL) model. The findings reveal a strong long-run positive relationship between GDP per capita and CO2 emissions, partially supporting the environmental Kuznets curve (EKC) hypothesis. Specifically, the analysis identifies an EKC threshold where emissions peak at 3 metric tons per capita and GDP per capita reaches approximately USD 1200 (TSH 3,120,000), after which further increases in emissions are associated with a decline in GDP per capita. In the short run, GDP per capita shows a weak negative association with CO2 emissions, indicating temporary environmental benefits during growth phases. Foreign direct investment (FDI) exhibits no significant short-term impact on emissions, mostly due to delays in the implementation of mega development projects and changes in the country’s economic policies as the result of change in the political regime. Additionally, trade openness is a significant driver of long-run emissions, emphasizing the environmental costs of globalization. To address these challenges, this study recommends that Tanzania attract sustainable FDI for integrating eco-friendly technologies, promote green trade practices by embedding environmental safeguards into trade agreements, and invest in renewable energy infrastructure to decouple growth from emissions. Strengthening environmental regulations, enhancing institutional capacity, and fostering international collaboration are crucial to achieving long-term sustainability. These measures can help Tanzania balance economic development and environmental preservation, aligning with the goals of Tanzania Development Vision 2025 (TDV 2025) and paving the way for a sustainable growth trajectory.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it