Electricity Production and Sustainable Development: The Role of Renewable Energy Sources and Specific Socioeconomic Factors
Bibliographic record
Abstract
An eco-friendly and sustainable power production system constitutes the cornerstone of every country’s strategic plan to tackle climate change and enhance energy resource autonomy. Carbon dioxide abatement in electricity generation, in addition to being a necessary condition for t “green” energy transition, can contribute greatly to cleaner industrial production and sustainable development. Emphasizing this key role of the power sector, the present research focuses on shedding light on the impact of renewable energy resources (RES), per capita gross domestic product (GDP), electricity gross fixed capital formation (GFCF) and urbanization in the CO2 intensity, and the sustainability level of electricity production. The analysis is based on a comprehensive dataset of 31 countries including 26 European countries, U.S.A., Japan, Australia, Canada, and New Zealand from 1995 until 2018. The econometric outcomes revealed the strong statistical significance of all variables and a plethora of causality relationships, upon which several policy suggestions are made. Interestingly, GDP per capita beyond a certain level can gradually become an aggravating factor for the electricity carbon footprint. Similarly, the vital role of RES in clean electricity production was confirmed as expected, yet surprisingly, this effect also appears to reverse after a certain percentage of total RES reliance. In contrast to urbanization, the electricity GFCF parameter is estimated to have an adverse effect on electricity CO2 intensity, indicating that the vast amount of new investments in the power sector concerns carbon-intensive technologies. Finally, a dynamic analysis is carried out, revealing to policy makers the necessary time frame after which the implementation of new energy policies can have the full impact on the carbon emissions of electricity generation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".