Sowing the seeds of sustainability: Digitalization, renewable energy, and carbon emissions in emerging economies' global value chains
Bibliographic record
Abstract
This study explores how digitalization moderates the link between renewable energy consumption and carbon emissions within global value chains across 31 emerging economies from 2001 to 2022. Employing a robust panel data approach, the research utilizes advanced econometric techniques such as fixed and random effects models with Driscoll and Kraay standard errors, and feasible generalized least squares (FGLS) estimators to account for cross-sectional dependence, heteroscedasticity, and autocorrelation. Our findings reveal that renewable energy consumption significantly lowers carbon emissions in global value chains, emphasizing its environmental benefits. However, digitalization shows a dual role, as it improves operational efficiency, its dependence on energy-intensive digital infrastructures contributes to higher carbon emissions. The interaction between digitalization and renewable energy consumption suggests that high levels of digitalization weaken the positive environmental impact of renewable energy. This moderating effect underscores the importance of aligning digital strategies with sustainable energy policies, particularly in emerging economies where the energy mix often relies heavily on traditional sources. This study sheds light on the pollution haven hypothesis by emphasizing the risks associated with technology transfers that lack sufficient backing in renewable energy. The insights offer important policy implications, urging governments to adopt integrated low-carbon strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".