The Transition of Manchester, Calgary into a Sustainable, Low-Carbon, 100% Clean Energy District
Bibliographic record
Abstract
The transition into a post‐carbon world will require collaboration across all sectors and system‐wide changes. This design exploration models the transformation of an entire Canadian city district into a low‐carbon development using urban planning, 100% clean energy, and sustainable development. Sustainable buildings and transportation systems are designed first. The community’s heat, electricity, and transport energy demands are then modelled. Clean energy sources and technologies are analyzed to model their potential energy generation. After evaluating the space requirements and the economics between different energy system choices, an ideal energy system for the community was proposed and the potential greenhouse gas emissions reductions were calculated. Solar, wind, hydro, geothermal, nuclear, and wastewater heat recovery energy are combined with electrifying sectors, reducing energy demands, green buildings and infrastructure, mixed‐use developments, reducing automobile dependence, electric vehicles, and sustainable public transport to transition the Manchester district in Calgary, Canada into a post‐carbon city district. This study found that it is possible to meet Manchester’s energy needs using local clean energy sources and presents a ceiling for the number of energy generation technologies, the capital costs, and the space required for the energy mix. The proposed energy mix includes rooftop solar panels, a wind farm, geothermal heat pumps within the district, a geothermal power plant, a small modular reactor nuclear power plant, and a wastewater heat recovery plant. Sustainable infrastructure and energy will significantly reduce the district’s GHG emissions compared to implementing Alberta’s current unsustainable buildings, ICE vehicles, and fossil fuel-based energy mix. The final product of this research is a method to analyze the transformation of entire large‐scale communities into low‐carbon developments, which can be individualized and applied to communities to estimate their unique energy demands and create a customized clean energy mix to meet them.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".