Green Growth Strategies for Sustainable Economic Development
Bibliographic record
Abstract
The study aimed to address the impact of different green policy concepts on economic growth and environmental sustainability.For this purpose, existing green concepts and statistical indicators were analysed, and current green growth strategies in different countries and regions of the world were compared.The study employed conceptual and theoretical analyses to identify the complex interrelationships between economic growth, environmental sustainability, and social wellbeing that need to be addressed to develop effective green growth and sustainable development strategies.Statistical indicators have led to the conclusion that the world has made progress in social well-being thanks to the Sustainable Development Goals: child mortality, extreme poverty, and the income gap between the rich and poor have been reduced, but environmental problems remain unresolved.It has been established that green growth significantly affects gross domestic product (GDP) and economic growth, opening up new potential for the clean technology, energy efficiency, and renewable energy industries.The examination of Azerbaijan's green strategy revealed the country's efforts to create sustainable resource management practices and renewable energy sources.The policies and initiatives of the United States of America, the European Union, Germany, China, India, Sweden, Norway, Canada, and Australia were also taken into consideration.Through a comparative analysis of the evaluated techniques, suggestions were developed to enhance existing strategies and policies.The findings indicated that in order to achieve sustainable economic growth and environmental improvement, more funding is required as well as the creation of practical measures.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".