Economic Growth and a Qualitative Shift in the Composition of Energy Use: Which of the Energy Ladder or Energy Stacking Models is the Most Suitable in the Long Run?
Bibliographic record
Abstract
At the household level, numerous studies in developing countries have demonstrated that the energy ladder hypothesis does not hold, with energy stacking remaining the prevailing pattern of fuel choice. This paper extends the debate to the macroeconomic level, examining whether national energy transitions in developed and developing economies align more closely with the energy ladder or energy stacking framework. Using data from the World Development Indicators (2022) and the World Energy and Climate Statistics Yearbook (2022), and applying an ARDL model, we assess the dynamics of income growth and energy use. Our results indicate that, with the exception of Canada, the energy ladder hypothesis is supported in developed economies, where rising incomes are associated with a shift toward cleaner energy sources. By contrast, in developing countries, energy stacking persists as the dominant pattern. These findings suggest that, apart from Canada, developed economies can achieve environmental sustainability alongside economic growth. However, in African economies and in Canada, growth alone does not guarantee sustainable energy use. For these cases, targeted environmental regulations and policy interventions are essential to advance sustainable 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".