Do Structural Transformations in the Energy Sector Help to Achieve Decarbonization? Evidence from the World’s Top Five Green Leaders
Bibliographic record
Abstract
The purpose of this study is to examine the role of structural transformation in the energy sector to accelerate the decarbonization process in the world’s top five green leaders, Germany, Canada, Sweden, Denmark, and Poland. To test this empirically, we collected annual data from a panel of the top five green leaders from 2000–2023. A key contribution of our study lies in assessing multiple critical metrics, including CO2 emissions, carbon intensity, carbon intensity of electricity, production-based carbon emissions, and consumption-based carbon emissions, to capture holistic progress towards carbon neutrality. We applied the augmented mean group (AMG) model to estimate the long-term results. The Dumitrescu–Hurlin test is used to test the causal relationship among the modeled variables. The findings of the AMG model reveal that renewable energy production and consumption significantly reduce CO2 emissions, production-based CO2 emissions, consumption-based CO2 emissions, carbon intensity, and the carbon intensity of electricity. Conversely, fossil-fuel-derived energy exacerbates these metrics. However, the impact of these energy sources varies by country in terms of their magnitude. The outcomes of the Dumitrescu–Hurlin test indicate that a bidirectional causality exists between renewable energy production and CO2 emissions and between renewable energy consumption and carbon intensity. However, a unidirectional causality exists between fossil fuel consumption and CO2 emissions and between renewable energy consumption and the carbon intensity of electricity. Our results indicate the detrimental impacts of continued fossil fuel use and conclude that a structural transformation in the energy sector is critical to decarbonization. Based on our results, we suggest that policy efforts should prioritize structural reforms in the energy sector by emphasizing a shift towards renewable energy sources. Such reforms are essential for achieving net-zero carbon emissions and mitigating broader environmental degradation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".