Digital and sustainable synergies: Insights into green investment, technological innovation, and low-carbon economies
Bibliographic record
Abstract
Climate change and rapid depletion of the environmental resources pose critical threat to world economies, particularly to those who are heavily dependent on fossil fuels. The United States (US), as one of the leading carbon emitters, requires innovative strategies that integrate technology, policy, and investment to transition toward the sustainable low-carbon economy. Against this backdrop, this study examines how artificial intelligence (AI), carbon pricing mechanisms, and the green investment collectively influence energy transition and long-term emission reduction pathways. The study examines US time-series data from 1990 to 2022 using a combination of econometric modeling, such as the Autoregressive Distributed Lag technique and the Augmented Dickey–Fuller test, and Bayesian neural network forecasting. According to the findings, a 1% increase in the use of renewable energy lowers carbon emissions by roughly 0.033% in the short term. Long-term estimates, assuming continued investment in carbon pricing and technological advancement, imply a 15% reduction in emissions by 2040. Furthermore, it is anticipated that over the course of two decades, AI-driven research and development integration will increase renewable energy efficiency by 18%. In addition to offering evidence-based insights for policymakers looking to align economic and environmental goals through digital innovation and sustainability policy frameworks, our findings highlight the revolutionary potential of AI in strengthening climate mitigation initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".