Bibliographic record
Abstract
Purpose As an enabler of environmental sustainability, Green information technology (IT) has become an emerging topic of interest in both academic and business communities. Despite its importance, confusions exist in the content and scope of Green IT practice. The purpose of this paper is to provide an overview of the current state of Green IT practice. Design/methodology/approach First 14 widely accepted Green IT practice topics were identified from prior research and a taxonomy was developed to categorize them. Using the content analysis method, these topics were examined in the sustainability reports of 30 IT companies in 2014 Fortune 500 . A quantity–quality portfolio framework was developed and applied to measure and assess the Green IT practices of the selected samples. Findings Currently, the Green IT practice is still in its infancy. Both research and practice attention are now focusing on IT’s direct impacts and enabling impacts, while overlooking the systemic impacts, on natural environment. The possible reasons for the current state and the recommendations for future research and practice are provided. Originality/value Theoretically, this paper identified 14 widely accepted Green IT practice topics and developed a taxonomy for categorizing them. The taxonomy and topics provide a theoretical basis for future examination on Green IT practice-related issues. Practically, the findings of this paper provide guidelines for Green IT practice and directions for both Green IT developers and adopters in their decision-making.
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 | 0.034 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.052 | 0.017 |
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".