Advancing Sustainability Through Digital Maturity: An Open Approach for Evaluating Quebec Organizations’ Environmental Responsibility
Bibliographic record
Abstract
Abstract The exponential growth of digital technologies has brought about substantial benefits in operational efficiency and communication. However, it has also led to increased energy consumption and greenhouse gas emissions (GHG), highlighting a growing concern: the environmental impact of information systems (IS). This chapter introduces an innovative approach designed to assess the environmental impact of digital technologies within organizations, tailored specifically to the North American context with a focus on Quebec. Key considerations include the local energy mix, regulations, and cultural factors. It addresses the challenges, opportunities, and prerequisites for successful adaptation, ensuring relevance to regional specificities. The proposed platform aims to facilitate collective efforts toward environmental preservation by promoting responsible and sustainable digital practices aligned with the United Nations Sustainable Development Goals. Adapted from the WeNR platform developed by the Institute of Responsible Digital Technology at the University of La Rochelle in France, this chapter discusses the requirement, description, and adaptation of the platform in Quebec. It addresses the pressing challenges of socio-ecological transition in the digital age and fulfills the pressing need for organizations to measure and mitigate the digital carbon footprint of their IS. Utilizing a life cycle assessment (LCA) methodology, the platform considers all phases of electronic equipment and data lifecycle. It emphasizes accessibility with an intuitive user interface and an open database featuring regularly updated impact factors. Users can complete a concise questionnaire to receive a comprehensive report on their digital carbon footprint, maturity level in responsible digital practices, and recommendations for reducing their carbon footprint. In conclusion, this chapter not only raises awareness but also actively promotes the implementation of responsible digital usage. By offering an innovative and adaptable platform, it addresses the environmental impact of digital technology within organizations, supporting their transition toward a more sustainable and environmentally respectful digital future.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".