Emergy-based sustainability rating system for buildings : case study of Canada
Bibliographic record
Abstract
The building and construction industry significantly contributes to the global environmental problems as it accounts for 30-40% of energy and material consumption of the society and around 30% of the global greenhouse gas emissions. Considering growing population, resource scarcity and environmental effects of the building industry on Earth, there is an urgent need for paradigm shift toward sustainability and green buildings. However, studies show that 28-35% of the current LEED-certified green buildings actually use more energy than conventional buildings. This thesis addresses weaknesses in current green building rating systems in North America, by implementing the “emergy” methodology. Emergy measure provides a holistic method to estimate the true value of environmental resources and services that was previously used to make a product/service. In this thesis, emergy methodology is used to assess the environmental and associated socioeconomic impacts of construction projects over lifecycle of buildings, including: resource extraction, manufacturing, transportation, construction, operation and maintenance, demolition and end of life scenarios (recycle, reuse and landfill). The main objective of this research is to develop an emergy-based sustainability rating system for buildings in Canada, named the “Em-Green sustainability rating system”. This sustainability evaluation system is a user-friendly framework for building and construction industry in Canada that covers the Triple Bottom Line (TBL) of sustainability (i.e.: environmental, social, and economical). The Em-Green sustainability fills the gap of a comprehensive building rating system that covers complete life-cycle of buildings (Cradle-to-Cradle/Grave approach) based on local practices in Canada. The framework developed for Em-green sustainability rating system can be adopted for other nations and can be expanded to develop a global sustainability measure for the built environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".