From fossil fuels to green growth: rethinking new models through digital innovation, labor force and sustainable investment
Bibliographic record
Abstract
Over the past three decades, accelerating environmental degradation driven largely by rising carbon emissions has posed serious challenges to global ecological stability. In response, this study investigates the asymmetric and nonlinear effects of key macroeconomic and structural factors on environmentally sustainable growth within the G-7 economies (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) from 1990 to 2023. Specifically, it examines the differentiated impacts of fossil fuel consumption, digital economy expansion, labor force participation, gross fixed capital formation, trade openness, and natural resource utilization on green growth. To address slope heterogeneity and cross-sectional dependence across countries, the analysis employs the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators. Results reveal clear evidence of asymmetric dynamics: positive shocks in digital development, trade openness, capital investment, and labor force participation significantly enhance green growth, whereas increases in fossil fuel consumption and unregulated resource extraction hinder environmental performance. Negative shocks in digital and trade activity, by contrast, exhibit muted or statistically insignificant effects highlighting path dependency and structural inertia in green development processes. To reinforce the reliability of the results, robustness checks were conducted using Fully Modified Ordinary Least Squares (FMOLS) and Dynamic OLS (DOLS) estimators. These alternative approaches confirmed the direction, magnitude, and statistical significance of key relationships, underscoring the validity of the asymmetric modeling approach. The findings carry substantial policy implications: G-7 economies must reduce fossil fuel dependency, foster inclusive digital infrastructure, and align capital and trade policies with long-term environmental goals. The study contributes novel insights into the shock-sensitive nature of green growth transitions, offering a methodological and policy framework relevant to both advanced and emerging economies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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".