Bibliographic record
Abstract
Green Economy pertains to a quite complex approach to economic development. This concept is present in many segments of life, so the Green Economy can be said to be an economic, but at the same time multidisciplinary development phenomenon. The goal of Green Economy is to achieve economic growth and development while reducing the risk of environmental destruction. Thus, it is closely related to environmental economy, but with a higher degree of participation of public policies in its implementation. Given the limited natural resources, continuous and progressive population growth in the world, the Green Economy is emerging as an economic development necessity. Hence, there is a growing interest of public policy makers around the world in this approach to economic development. In accordance with world tendencies, there is a need to analyse the situation and prospects of the Green Economy. By comparing relevant elements of the green economy at the international level (i.e. in the European Union), the region, but also in Bosnia and Herzegovina, this paper seeks to identify advantages and disadvantages as well as key potentials for future economic development in this context. Analyses have shown that European countries have an advantage in terms of Green Economy. It can be concluded that these countries have specific policies for the development of Green Economy, which is reflected in their overall development. Also, economies with a significant share of world GDP (Canada, USA, Japan and China) are far from the 90th percentile in the Green Economy Index, which implies that creating their production is more at the expense of limited resources and less in the direction of economic and environmental sustainability. When it comes to countries from our immediate surroundings, most countries are in the 3rd and 4th quarters on the ranking list of the Global Green Economy Index. The lag of the neighbouring countries behind the developed Europe in the field of green economy is visible in almost all indicators incorporated in GGEI. A better perspective in this area for the surrounding countries is possible with continuous raising of awareness of all actors about the importance of ecology and the Green Economy. Recommendations to economic policy makers are aimed at all areas of the Global Green Economy Index, including the following improvements: decarbonisation, energy efficiency, investment in renewable energy sources, raising public awareness of the importance of economic and environmental sustainability, etc. As a precondition for the implementation of the recommendations, it is necessary to harmonize with the standards of the European Union in all areas of Green Economy and Environmental Protection.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".