State-of-the-Art in E-Commerce Carbon Footprinting
Bibliographic record
Abstract
This paper provides a survey of the state-of-the-art in E-Commerce Carbon Footprinting. This comprehensive literature survey informs the research community on past and recent objective and subjective efforts towards measuring Eco-Efficiency and Eco-Efficacy of E-Commerce. Moreover, this paper provides a framework for categorizing research in this critical area. We also provide a summary of some very promising future research directions in Carbon Footprinting of E-Commerce. Our survey corroborates that Carbon Footprinting is now considered a widely recognized broad framework of gauging Eco-Efficiency and Eco-Efficacy of E-Commerce. Furthermore, it informs us that the research in this discipline is fast expanding and evolving. Such a survey in this critical research field is significant for government and corporate policy-makers in formulating informed decisions regarding Sustainability. In addition, the research in this domain may be useful to environmentally conscious consumers who want to make informed choices on their consumption habits for reducing their personal Carbon Footprints.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".