An Ideology of Sustainability under Technological Revolution: Striving towards Sustainable Development
Bibliographic record
Abstract
The recent decades have witnessed an unprecedented surge in global warming occasioned by human anthropogenic activities. The ensuing effects have brought devastating threats to human existence and the ecosystem, with the sustainability of the future generations highly uncertain. Resolving this pervasive issue requires evidence-based policy implications. To this end, this study contributes to the ongoing sustainable development advocacy by investigating the impacts of renewable energy and transport services on economic growth in Germany. The additional roles of digital technology, FDI, and carbon emissions are equally evaluated using data periods covering 1990 to 2020 within the autoregressive distributed lag (ARDL) framework. The results show the existence of cointegration among the variables. Additionally, renewable energy and transport services positively drive economic growth. Furthermore, economic growth is equally stimulated by other explanatory variables, such as digital technology and carbon emissions. These outcomes are robust for both the long-run and short-run periods. More so, departures in the long run are noted to heed to corrections at an average of 60% speed of adjustment. The estimated models are confirmed to be valid based on the outcomes of the postestimation tests. Policy implications that support the path to sustainability are highlighted based on the findings.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".