Innovations to reduce residential energy use and carbon emissions: an integrated approach
Bibliographic record
Abstract
Research in energy sustainability is gaining renewed priority because of the growing importance of climate‐change issues and the ratification of the Kyoto Protocol by many countries. Increased energy efficiency and substitution of less carbon‐intensive fuels are proposed as the principal means to reduce greenhouse‐gas (GHG) emissions and associated climate change. The residential sector is an important area for improvement, as it accounts for 22 percent of global energy consumption. This paper illustrates the integration of four dimensions of energy issues within a single community study in Waterloo Region, Canada. First, it overcomes the limitations of single‐discipline approaches to energy studies by recognizing the importance of social context in measuring the potential to reduce energy consumption. The ‘sociotechnical’ potential to reduce residential consumption by 25 percent is lower in our analysis than traditional measures of the technical potential, but is considered more achievable. Second, the paper examines how community‐based implementation can enhance the effectiveness of a national energy efficiency program (EnerGuide for Houses, or EGH). Controlled marketing experiments demonstrated higher response rates for materials highlighting local partners. Third, the paper outlines how the local capacity developed by diverse stakeholders (city councils, regional government, federal government agencies, local utilities, local businesses, environmental nongovernmental organizations and the local university) was an important means of overcoming many of the barriers to taking action. Fourth, the paper details the examination of issues of energy efficiency and fuel substitution through a survey of residents’ attitudes and comparison to behaviour. For example, stated ‘willingness to pay’ was compared to the actual sign‐up rate for the first introduction of ‘green’ electricity in the Ontario residential market. The integration of these four dimensions in a single study offers a framework that can be reviewed and adapted to meet the needs of other projects.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".