Methodology for Quantitative Assessment of Greenhouse Gas Emissions Reduction of Solar-Powered Wi-Fi
Bibliographic record
Abstract
In this paper, a modeling framework to quantify the reduction of potential greenhouse gas (GHG) emissions when using solar-powered Wi-Fi as an internet access point is developed and tested. This framework includes newly developed MATLAB code and the use of the ATOLL tool for energy consumption and network optimization, respectively. A practical case study is described with scenarios enabling different signal coverage on a university campus. These scenarios are based on technical requirements, including number of access points, budget link, and access duration. Four hundred tons of GHG can be reduced each year if solar Wi-Fi is deployed in solar campuses, which represents 5.5 × 10−5 percent of the total GHG produced by the telecommunications sector. A direct relationship between the number of access points and coverage signal quality on the one hand and energy consumption on the other hand is established. We use this case study to forecast the potential GHG mitigation if a wider deployment of the community Wi-Fi is achieved. This methodology could also be used to estimate GHG reductions when other wireless technologies are deployed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".