Natural Resources, Renewable Energy Sources, GHG-emission and Demographic Profiles in United States: A Broad Analysis for Developing Sustainable Low-Carbon Energy Sector
Bibliographic record
Abstract
Renewable energy production is a priority policy agenda in US. The natural and renewable energy resource availability, energy use trends and demographic profiles are all critical components for correctly gearing the proper and sustainable development of this sector. Co-assessment of the natural and renewable energy resources in US is a must for renewable energy industry growth without dramatic environmental detrimental effects. For analyzing the natural and renewable energy resources and its developmental potential, this concept paper divides US into seven different regions (R-1: Northeast; R-2: Southeast; R-3: Midwest; R-4: Southcentral; R-5: Northwest; R-6: Southwest; R-7: Alaska & Hawaii). Based on parameters such as land availability, water resource availability, demographic patterns, and renewable energy sources, natural resource index (NRI), renewable energy index (REI) and development potential index (DPI) were defined and calculated for these various regions. Our analysis showed that R-6 had high NRI (6) and REI (14). Therefore it had the highest DPI (20). There were also marked differences in various regions with respect to energy use and GHG-emissions. The R-3, R-4 and R-5 regions had high-energy use and GHG-emissions. In light of these broader trends, the implications and the need for regional prioritization, resource coupling, investment allocation, and future policy directions for optimal and sustainable renewable energy production were discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".