Impact of Conversion to Compact Fluorescent Lighting, and other Energy Efficient Devices, on Greenhouse Gas Emissions
Bibliographic record
Abstract
Selecting appropriate boundaries for energy systems can be as challenging as it is important. In the case of household lighting systems, where does one draw these boundaries? Spatial boundaries for lighting should not be limited to the system that consumes the energy, but also consider the environment into which the energy flows and is used. Temporal boundaries must assess the energy system throughout its life cycle. These boundary choices can dramatically influence the analysis upon which energy strategies and policies are founded. This study applies these considerations to the "hot" topic of whether to ban incandescent light bulbs. Unlike existing light bulb studies, the system boundaries are expanded to include the effects incandescent light bulbs have on supplementing household space heating. Moreover, a life cycle energy analysis is performed to compare impacts of energy consumption and greenhouse gas emissions for both incandescent light bulbs and compact fluorescent light bulbs. This study focuses on Canada, which not only has large seasonal variations in temperature but which has announced a ban on incandescent light bulbs. After presenting a short history and description of incandescent light bulbs (ILBs) and compact fluorescent light bulbs (CFLBs), the notion that a ban on ILBs could alter (or even increase) greenhouse gas (GHG) emissions in certain regions of Canada are introduced. The study then applies a life cycle framework to the comparison of GHG emissions for the ILB and CFLB alternatives. Total GHG emissions for both alternatives are calculated and compared for the provinces of Canada and again a physical rebound effect sometimes occurs. Finally, the policy and decision making implications of the results are considered for each of these locations. 2 www.intechopen.com
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".