Modelling of the Effects of Renewable Energy Establishments towardsthe Economic Growth of a Nation
Bibliographic record
Abstract
Renewable energy is one of the current hot topics in the global energy forum and many of the conventional fossil fuelsbased establishments have been replaced with renewable sources over the last few decades. Countries such as China, USA and India have already made huge investments on installing renewable energy infrastructure. Hence, many of these countries are in need of investigating the effects of their investments on the countries' economic growth, carbon footprint and the well-being of their environment. This study provides a comprehensive discussion on how renewable energy usage can contribute towards the economic enhancements mainly to the Gross Domestic Production (DGP). A conceptual model were established to understand the effects of the development of renewable energy establishments on some key economic performance indicative parameters such as the household consumption, government consumption, capital formation, trade balance and energy import and then eventually on the GDP formation. Then, the data collected from an emerging economy were analysed incorporating a path analysis by using SPSS Amos software. Chi square ( 2 ) test and maximum likelihood indices are used to assess the overall fit of the model. Overall, the findings of this study clearly show that the promotion of renewable energy establishments can cause a significant reduction in energy related imports while increasing the GDP of a nation. Accordingly, it is apparent that Sri Lanka has aligned their economic strategies in terms of becoming a 100% sustainable energy driven nation by 2050 as their major economic indicators are positively correlated with the promotion of renewable energy establishments.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".