Local Material and Energy Flow Analysis for the Region of Waterloo, Ontario, Canada
Bibliographic record
Abstract
To address global environmental challenges, resource use patterns at local and sub-national scales can provide relevant insights into drivers and how these link to local policy and decision-making. The Region of Waterloo is often referred to as “Canada’s Silicon Valley” consists of the townships of Wellesley, Woolwich, Wilmot, and North Dumfries and the tri-cities of Kitchener, Cambridge, and Waterloo. Close to Toronto, and reputation of being the tech hub, Waterloo’s population (mainly from immigration) is on the rise and region is under rapid transition from a rural to an increasingly urban system being connected by rapid transit systems. To better understand the region from a systems perspective and to provide input into sustainability policies of the region, the concept of social metabolism is applied. \nSociety’s metabolism measures pressures on the environment where increasing throughput can have negative impacts on ecosystem and human health in the short and long term. Material and Energy Flow Analysis (MEFA) is conducted to calculate derived indicators of biomass and energy use for the Region of Waterloo in accordance with established conventions. Using a number of MEFA indicators, the paper will outline some of the major sustainability challenges in resource use patterns in the region since 2006. This study is the first to conduct a material and energy flow analysis for the Region of Waterloo in three points in time. Data on extraction, consumption, imports, and exports of various biomass and energy flow indicators will be presented. \nResults from MFA show that the Region of Waterloo has a high import dependency for food crops and high domestic extraction of feed crops mainly fodder corn And results observed from Energy Flow Analysis indicates that the Region of Waterloo imports 100% of its technical energy and is highly dependent on outside markets. This study will encourage discussions on food and energy security in the region and help the policymakers in the region to make informed decisions. \nThis research adds to the growing data points of research on Material and Energy flow analysis and social metabolism and serves as a starting point for more related research in sub-national socio-metabolic studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".