CO2 Emissions, Energy Consumption, and Economic Growth: New Evidence in the ASEAN Countries
Bibliographic record
Abstract
The members of the Association of Southeast Asian Nations (ASEAN) have made several attempts to adopt renewable energy targets given the economic, energy-related, environmental challenges faced by the governments, policy makers, and stakeholders. However, previous studies have focused limited attention on the role of renewable energy when testing the dynamic link between CO2 emissions, energy consumption and renewable energy consumption. As such, this study is conducted to test a common hypothesis regarding a long-run environmental Kuznets curve (EKC). The paper also investigates the causal link between carbon dioxide (CO2) emissions, energy consumption, renewable energy, population growth, and economic growth for countries in the region. Using various time-series econometrics approaches, our analysis covers five ASEAN members (including Indonesia, Myanmar, Malaysia, the Philippines, and Thailand) for the 1971–2014 period where required data are available. Our results reveal no long-run relationship among the variables of interest in the Philippines and Thailand, but a relationship does exist in Indonesia, Myanmar, and Malaysia. The EKC hypothesis is observed in Myanmar but not in Indonesia and Malaysia. Also, Granger causality among these important variables varies considerably across the selected countries. No Granger causality among carbon emissions, energy consumption, and renewable energy consumption is reported in Malaysia, the Philippines, and Thailand. Indonesia experiences a unidirectional causal effect from economic growth to renewable energy consumption in both short and long run and from economic growth to CO2 emissions and energy consumption. Interestingly, only Myanmar has a unidirectional effect from GDP growth, energy consumption, and population to the adoption of renewable energy. Policy implications have emerged based on the findings achieved from this study for each country in the ASEAN region.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".