Integrating Economic Development, Innovation, and Environmental Performance for Sustainable Futures: Insights from a Global Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to investigate the complex interplay between GDP per capita, innovation index, and environmental performance across a comprehensive dataset comprising 102 countries spanning from 2008 to 2022. Utilizing descriptive statistics, cointegration tests, and Granger causality analysis, the research delves into the intricate relationships among these pivotal variables. The findings unveil significant long-term equilibrium relationships among environmental performance, innovation, and economic development. Furthermore, bidirectional causal links are discerned, indicating that enhancements in environmental performance stimulate innovation, and conversely, innovation influences environmental performance. Likewise, economic development exerts influence on both innovation and environmental performance. These insightful revelations underscore the multifaceted dynamics among economic growth, innovation, and sustainability, thereby underscoring the imperative for integrated approaches to foster sustainable development. The comprehension and harnessing of these interrelationships are paramount for crafting efficacious policies and strategic business initiatives geared towards steering humanity toward a more sustainable and prosperous future.
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.
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 | 0.001 |
| 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 it