Financial, markets, impact of environmental stability on economic development and sustainable development goals, evidence from developed and emerging countries
Bibliographic record
Abstract
The study seeks to investigate the developed and emerging countries forecasting the financial business market, environmental strategy and impact on sustainable development Goals and the country's economic development from 1991 to 2021. Annually date of Stock Market Index, exchange rate index, Sustainable development index, eco-efficiency and Countries GDP of developed countries, i.e. UK, USA, Canada, Australia, Japan, Germany, France and emerging countries, i.e. Brazil, Malaysia, Thailand, Philippines, China, Indonesia, India, and Pakistan has been considered as sample data for this study. Unit root test (ADF) for stationary test, Johansen’s Co-integration test, Granger Causality, GMM (panel data) applied to test the short run/long-run impact, association, and behavior of variables. Models 1& 2 result of finance sustainability with Stock Market Index, exchange rate index, SDGs ~ GDP, and eco-efficiency indicates highly significant and asymmetrical relationships exist with countries growth as similar with Models 3 & 4 SDGs. UK, France in developed markets and India, Thailand and Malaysia in emerging countries markets high influencing potential. Meanwhile, Australia and USA in developed and China, Indonesia, and Pakistan Markets in emerging have more space for investor. The Robustness test validates the finding of study. Financial sustainability implication and recommendation are cleared for investors; forecast market behavior, financing efficiency, investment diversification, multi corporations and exchange management have make significant investment decision. Further, these finding helps policy makers and regulatory authorities to design appropriate finance strategies for market-economic sustainability.
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 | 0.002 | 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 | 0.001 | 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) | 0.001 | 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".