Method and tool to assess the environmental impacts of cyber-physical systems with a life-cycle approach
Bibliographic record
Abstract
The increasing environmental footprint of the information and communication technology sector calls for innovative strategies for assessing and improving its sustainability. Life Cycle Assessment (LCA) methods are suitable for estimating the negative environmental impacts of products or services. While some LCA methods start to be applied during the engineering of Cyber-Physical Systems (CPS), some challenges remain unresolved. For instance, the energy consumption, and therefore the greenhouse gas emissions, of CPS is influenced by the specific configuration of components in their architecture, as well as by the geographical location where the components are deployed. Also, the skills and efforts required by LCA projects, remain prohibitive to many CPS engineering projects. This paper presents an LCA-based method tailored for CPS that facilitates the analysis of the environmental impact and the comparison of architectural and location variants. Moreover, we have developed a supporting tool that guides the process and automates part of the data collection activity. Through an illustrative case and expert assessment, we have been able to assess the benefits and drawbacks of our proposal. With these contributions, we hope to lower the barrier to adopting LCA practices in CPS engineering projects, both in industry and academia.
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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 | 0.001 | 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".