ENERGY EFFICIENCY AND ENVIRONMENTAL IMPACT ANALYSIS IN GROCERY STORE MARKET IN CANADA
Bibliographic record
Abstract
There is growing pressure to limit Global Warming Potential (GWP) as it is the most significant way to reduce serious threats to people. The increase of average temperature in the atmosphere causes adverse effects on the environment, and this is changing the way business operates. Together with undergoing changes to be more environmentally friendly, the majority of business sectors has been launching many projects to reduce environmental impacts. Similar to grocery sector in Canada, many companies have been reported to investigate the environmental performance. Loblaw Companies Limited, the largest food distributors in Canada, has put more concerns on environmental impacts and worked diligently to reduce greenhouse gas (GHG) emissions through actions such as improving energy efficiency reducing refrigerant leaks, and incorporating renewable energy sources. This study analyzed Loblaw’s energy efficiency and environmental performance and provided suggestions in the applications of skylight improvement, geothermal, refrigeration systems, kinetic energy to electricity, and e-grocery shopping. The results showed that the environmental concerns were substantially decreased. However, types of lights and refrigerators, distance driven, vehicle types, fuel used in e-grocery shopping results in the different amount of emissions; consequently, the GHG emissions varied depending on these factors. By integrating more environmentally technology, Loblaw could further reduce their emissions and waste and become a more sustainable company.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".