Data–driven analysis of Romania's renewable energy landscape and investment uncertainties
Bibliographic record
Abstract
In recent years, there has been a significant transformation in the energy sector in the European Union as a whole. The shift towards producing renewable energy has had a significant impact on the economic development of most countries, requiring substantial investments through public procurement. This study aims to analyse the evolution of the energy sector in Romania from an economic perspective by using a data-driven approach. The data used in this research is collected from publicly available sources and pertains to energy production and public acquisitions in Romania, carried out through the Electronic Public Procurement System. By using a mixed approach, combining documentary analysis, literature review, and predictive modelling, the study reveals a shift towards more sustainable energy options. There is a significant decrease in the production of thermal power and an increase in solar and wind power. The findings provide an overview and potential scenario of Romania's electricity production levels in 2023, shedding light on the relative uncertainties associated with such a transition. The findings also suggest a clear and growing commitment in Romania towards the adoption of alternative energy sources, as reflected in the trends of public procurement. These procurement trends offer a valuable perspective on policy-making, investment planning, and progress monitoring in Romania's energy transition. Despite the inherent uncertainties in such a transition, the study demonstrates Romania's potential in terms of diverse sources for electricity production as well as the role of public procurement in achieving energy transformation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".