Hydroelectricity Consumption and Economic Growth Nexus: Evidence from a Panel of Ten Largest Hydroelectricity Consumers
Bibliographic record
Abstract
This paper explores the long-run and causal relationships between hydroelectricity consumption and economic growth for a panel of the 10 largest hydroelectricity consuming countries over the period 1965–2012. The countries include Brazil, Canada, China, France, India, Japan, Norway, Sweden, Turkey and the U.S.A. Using the Bai and Perron (2003) [9] tests for cointegration, the results indicate that real GDP per capita and hydroelectricity consumption per capita appear to be cointegrated around a broken intercept. Granger causality results from a nonlinear panel smooth transition vector error correction model suggest different results depending on the regimes, which we identified based on structural break tests. The test identified three breaks at 1988, 2000 and 2009. For the pre-1988 period, there is evidence of unidirectional causality running from real GDP per capita to hydroelectricity per capita in both the short- and long-run. Over the post-1988 period, there exists evidence of bidirectional causality between hydroelectricity energy consumption per capita and real GDP per capita in both the short- and the long-run. The results imply the existence of a feedback hypothesis with both hydroelectricity consumption and growth promoting each other in more recent periods, as the importance of hydroelectricity as a renewable energy, has become more prominent.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".