An Integrated Analysis of the Use of Woodstoves to Supplement Fossil Fuel–Fired Domestic Heating
Bibliographic record
Abstract
Consumers are constantly being presented with choices that have economic, environmental, and lifestyle/social dimensions. For example, is an energy‐efficient hybrid car (with regenerative braking) a better choice than a regular petroleum‐only vehicle when considered from all three dimensions? Surprisingly, although each and all of these dimensions is clearly important to optimal long‐term choices, integrated analyses are rare, and there is a great need for better education on how best to approach such consumer decisions. Here, we present a case study by a small class of primarily final‐year undergraduate students on renewable vs. fossil–fuel based options for domestic heating to explore the actual economic and environmental advantages and disadvantages of each option. We analyzed 4 years of fuel consumption data for a household in Kingston, ON, Canada that installed a wood stove to supplement (i.e., reduce their reliance on) natural gas for domestic heating. Furthermore, we conducted a survey of local householders to identify those factors that are most important to consumers in deciding on future heating options. Supplemental use of the woodstove for home heating reduced natural gas consumption by 60%. Total annual operating costs before and after installation were similar because woodfuel costs matched the savings from lowered natural gas consumption. Consideration of projected fuel price rises and ongoing maintenance and replacement costs, however, strongly suggests that substantial overall cost savings would accrue, especially after the first decade of woodstove installation. Since wood can be a renewable resource, annual net CO2 emissions associated with domestic heating were also reduced by 60%. Survey respondents consistently ranked heating effectiveness, operating costs, and environmental issues among the most important factors in choosing a replacement heating system, but those who do not currently have a woodstove ranked safety as the primary concern. Together, these results suggest that promotion of eco‐friendly options for consumers could be greatly enhanced by supplying clearly focused information on the critical economic, environmental, and lifestyle/social dimensions of that choice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".