Analysis of the Impact of Climate Change on Economic Growth, Financial Development, and Investment — Evidence from the ASEAN Region
Bibliographic record
Abstract
By using the ASEAN region as a case study, the research attempts to examine how climate change has an impact on financial development, foreign direct investment, and economic growth. ASEAN region is considered among the highest growing region across the world hence this study assesses the climate change consequences by taking into consideration the growth of region. This study conducted an empirical test using regression and focuses on the five nations with the greatest economies in the ASEAN region. These countries are Singapore, Malaysia, Thailand, the Philippines, and Indonesia, and they cover the years 2000–2022. The study collects data from the World Bank. This study used five independent variables while taking one climate change variable (CO2 emission) as independent variable. This study used the data in the panel format and after reviewing multiple literature the hypothesis could be tested. The result states that climate change has had a mixed impact on economic growth, performance, and investment.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".