Macroeconomic Variables and Its Impact on CO2 Emissions: An Empirical Study on Selected ASEAN Economic Community (AEC) Countries
Bibliographic record
Abstract
Environmental issues are a crucial aspect in promoting sustainable economic growth.Specifically, CO2 gas emission is an environmental-economic phenomenon that needs to be concerned for all parties to maintain a balance between economic growth and environmental sustainability to realize sustainable economic development.This study aims to analyze the social and economic factors that affect CO2 gas emissions in selected ASEAN economic community (AEC) member countries.The research used panel data analysis from 2010 to 2019 on selected ASEAN economic community (AEC) member countries: Indonesia, Malaysia, Singapore, Thailand, Laos, Vietnam, Cambodia, Brunei Darussalam, and the Philippines.The research variables consisted of CO2 gas emissions as the dependent variable and economic growth (GDP), population (POP), energy consumption (EC), external debt (ED), foreign direct investment (FDI), inflation (INF), and exports (X) as independent variables.The results showed that the variables of economic growth (GDP), population (POP), energy consumption, and exports had a positive and significant effect on CO2 gas emissions.Meanwhile, the variables of foreign debt, foreign direct investment (FDI), and inflation did not affect CO2 gas emissions in ASEAN economic community (AEC) member countries.This research concludes that it is necessary to carry out an integrated policy to reduce CO2 gas emissions by implementing sustainable development strategies involving related parties, providing incentives to reduce fossil energy consumption, and replacing it with environmentally friendly new, renewable energy.The novelty of this research is to analyze the social and economic factors affecting CO2 emissions in ASEAN economic community (AEC).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".