Balancing growth and sustainability: The role of women's empowerment, innovation, and green transitions
Bibliographic record
Abstract
Climate change and environmental degradation remain urgent global challenges driven by rapid economic growth, fossil fuel dependence, and unsustainable consumption. While prior studies have explored the roles of technology, energy, and growth, limited research examines how women's empowerment shapes the economy–energy–technology–environment nexus. This study investigates the joint impact of economic development, technological innovation, renewable energy, and women's empowerment on environmental sustainability using data from 189 countries spanning 1990–2022. The Autoregressive Distributed Lag (ARDL) model is employed to assess both short- and long-run dynamics, with robustness verified through Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR). Results show that economic growth intensifies greenhouse gas (GHG) emissions, while technological innovation, renewable energy use, and women's empowerment significantly reduce emissions across time horizons. These findings underscore the need to integrate gender equity with technological and energy transitions to decouple economic progress from ecological degradation. Policy recommendations include expanding renewable energy, incentivizing green technologies, and strengthening women's participation in economic and political decision-making. By positioning gender empowerment as a structural driver of sustainability, this study advances an inclusive framework for achieving a resilient, low-carbon global future.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".