Assessing Hybrid Solar-Wind Potential for Industrial Decarbonization Strategies: Global Shift to Green Development
Bibliographic record
Abstract
The global energy mix is shifting from fossil fuels to combinations of multiple energy storage and generation types. Hybrid energy system advancements provide opportunities for developing and deploying innovative green technology solutions that can further reduce emissions and achieve net-zero emissions by 2050. This study examined the impact of an increasing share of wind and solar electricity production on reducing carbon intensity by controlling coal and lignite domestic consumption and the production of refined oil products in a world aggregated data panel. Data covering the last three decades were used for the analysis by the ARDL bounds testing approach. The results showed that an increasing share of wind and solar electricity production would be helpful to decrease carbon intensity in the short and long term. On the other hand, a 1% increase in coal and domestic lignite consumption increased carbon intensity by 0.343% in the short run and 0.174% in the long run. The production of refined oil products decreases carbon intensity by 0.510% in the short run and 0.700% in the long run. However, refining oil products is associated with positive and negative environmental externalities. The positive aspect depends upon the removal of harmful pollutants and the production of cleaner-burning fuels, while the negative part is related to the operational side of refineries and processing plants that may release contaminants into the atmosphere, affecting global air and water quality. Hence, it is crucial to improve processing and refining capacity to produce better-refined oil products by using renewable fuels in energy production. It is proposed that these are the most cost-effective pathways to achieve industrial decarbonization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".