Digitalization's contribution towards sustainable development and climate change mitigation: An empirical evidence from EU economies
Bibliographic record
Abstract
), a hydrocarbon component of natural gas, as a proxy of carbon emission, GDP as economic growth, financial development (FIN), and medium and high technologies as a proxy of information technology (ICT) and human development (HDI). This study observes two extended moderating effect models of human development index and financial development via medium and high technologies on carbon emissions over the 15-year periods from 2007 to 2021 for the 27 EU economies. Results indicate that when considered solely, ICT, economic growth, and HDI improve environmental quality and contribute to climate change mitigation, reducing methane emissions, whereas financial development seems to damage environmental quality. However, the crossed effects of ICT with HDI, and that of ICT with FIN, were considered in estimations, with results pointing out that those favorably affect climate change mitigation. Jointly considering ICT, HDI, and financial development proves to have a synergistic effect in promoting environmental health than each element on its own. Green and yellow countries were also identified revealing the countries for which a reduction and increase, respectively, in the value of methane emissions is predicted after three years. In the case of the entire panel, the STR (linear regression tree) algorithm predicts an average growth in methane emissions of around 3.64 %. Important policy directions are drawn considering the results obtained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".