Bibliographic record
Abstract
This paper explores the extent to which the logistics sector contributes to global greenhouse gas emissions and energy consumption and the barriers to greening operations within this industry. Four case studies examined national postal operators around the world to assess their progress; Canada Post, United States Postal Service, Australia Post, and Royal Mail (U.K). Using a common template, these case studies looked specifically at greenhouse gas emissions and energy consumption in both transportation and building operations. Evaluation was based on industry standards and expectations as set out by the 2016 International Post Corporation Sustainability Report and Environmental Measurement and Monitoring System (EMMS) protocol. Conclusions were drawn based on the information provided in annual and sustainability reports and climate change policy and mitigation protocols in place for each postal operator. Although postal operators are making strides in improving transport and building operations, their reliance on fossil fuels for energy and the subsequent lock-in to internal combustion engines and the network externalities they have cultivated remain key barriers to greening this industry. Despite this, the postal sector appears to be engaged with the issues and is making improvements in greening its operations. However this engagement varies greatly and there seems to be more concern with financial viability, profit margins and reliability of service. In addition, the absence of an enforcing governing body within the logistics sector may be a why reason postal operators and private logistics firms have yet to make progress at a faster rate. Ultimately, more effort and growth is needed to transition to low-carbon operations in the global logistics industry.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".