Analysis of current investment projects and their economic justification
Bibliographic record
Abstract
The purpose of this study was to analyse existing investment projects in Albania and assess their economic feasibility to determine their impact on the country’s economic growth and development. The study examined 30 major investment projects in key sectors such as energy, infrastructure, and tourism. The study showed that the main investors in Albania are the Netherlands, Switzerland, Canada, Italy, Turkey and Austria, which are actively investing in the development of renewable energy sources, transport infrastructure and light industry. The analysis confirmed that the volume of investment, macroeconomic stability and government support are key factors in the successful implementation of projects. The SWOT analysis showed that Albania has significant potential to attract foreign investment due to its favourable geographical location, political stability and natural resources, but faces challenges such as underdeveloped infrastructure and high levels of bureaucracy. Investment projects have had a positive economic and social impact, including reducing energy dependence, developing tourism infrastructure, and improving logistics capabilities. Recommendations were made to optimise state support for investors, invest in infrastructure, improve the skills of the workforce, and support small and medium-sized businesses. The proposed recommendations will help improve Albania’s investment climate and ensure further growth in foreign investment, which will have a positive impact on the country’s economic development. At the same time, these recommendations may also be useful for other countries with similar economic levels of development seeking to attract foreign investment and stimulate their economic growth
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".