Sectoral Growth and Energy Consumption in South and Southeast Asian Countries: Evidence from a Panel Data Approach
Bibliographic record
Abstract
This study examines the dynamic relationship between energy consumption and the three major sectoral outputs (agricultural, manufacturing and service) in thirteen South and Southeast Asian countries using a panel data framework for the period 1971¨C2012. It undertakes panel cointegration analysis to investigate the long-run relationship between the variables. Also, the panel vector error correction model (PVECM) and impulse response functions (IRFs) are employed to examine the short- and long-run direction of causality and the effect of responses between energy consumption and the three sectoral outputs. The empirical results reveal that the long-run equilibrium relationship between energy consumption and the three sectoral outputs is positive and statistically significant, indicating the existence of long-run co-movement among the variables. The short- and long-run causality results support the existence of bidirectional causality between energy consumption and the three sectoral outputs except for the short-run causality between energy consumption and service sector output, which is unidirectional, running from service sector output to energy consumption. The IRFs show that all variables reach the equilibrium level within three to seven years from the initial shock.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".