The Impact of Built-in Ad-Blockers in Web Browsers on Computer Power Consumption
Bibliographic record
Abstract
This study investigates the power consumption of various web browsers, specifically focusing on those with built-in ad-blockers compared to standard browsing without ad-blocking features. Using detailed measurements of CPU and GPU power consumption across multiple browsers i.e. Chrome without Ad blocker, Brave, Opera, Firefox, Vivaldi, Librewolf, and Tor—this research highlights the significant impact of ad-blocking on power consumption during web browsing. Experiments were conducted on different types of websites, including video-heavy, news, and entertainment sites, to evaluate how browser optimizations affect overall power usage. Results indicate that browsers with integrated ad-blockers, such as Brave and Librewolf, use significantly reduce power consumption up to 44% compared to traditional browsing setups. The findings also reveal that video content significantly increases CPU and GPU load, with ad-blocking browsers demonstrating superior performance in minimizing energy use. This study emphasizes the importance of browser selection in reducing power consumption, particularly for mobile and battery-dependent devices, and suggests that adopting ad-blocking technologies can lead to substantial energy savings.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".