Dynamics between Power Consumption and Economic Growth at Aggregated and Disaggregated (Sectoral) Level Using the Frequency Domain Causality
Bibliographic record
Abstract
We investigated the Granger causal relationship between the consumption of power both at the aggregate and sectoral level and economic growth in India using the frequency domain approach, which would help policy makers seek the efficient allocation of electricity via proper policy initiatives at different frequencies. We find that at the aggregate level, unidirectional causality runs from the total power consumption to economic growth, starting from the second up to the seventh quarter. In the sectoral context, the results are different. Since there is no causality between industrial power consumption and economic growth; therefore, an energy conservation policy can thus be implemented for the industrial sector. Moreover, since a bidirectional causality exists after 15 quarters for the commercial sector, a short-term policy but not an energy conservation policy could also be initiated for this sector. In the industrial and agricultural sectors, a promotional policy should be initiated because a unidirectional causality exists from sectoral power consumption to economic growth. Therefore, different and sector-specific policies would be more appropriate than a single policy for all power sectors in India in order to orient the efficient utilisation of power towards better economic development.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".