Assessing the Environmental Sustainability of Cloud Computing: A Life Cycle Assessment Approach
Bibliographic record
Abstract
Cloud computing has emerged as a popular technology platform that allows businesses to store, access, and process data and applications over the internet. This technology has the potential to reduce costs, increase scalability, and improve efficiency, making it an attractive option for businesses of all sizes. However, as the adoption of cloud computing continues to grow, concerns have been raised about its environmental impact. This has led to the need for an assessment of the environmental sustainability of cloud computing. In this research, we take a life cycle assessment approach to assess the environmental sustainability of cloud computing. The life cycle assessment approach involves the evaluation of the environmental impact of a product or service throughout its entire life cycle, from the extraction of raw materials to the disposal of waste. We apply this approach to cloud computing by analyzing the environmental impact of the hardware, software, and data center infrastructure required to support cloud computing. Our research shows that cloud computing can have a significant environmental impact, particularly in terms of energy consumption and carbon emissions. The hardware required to support cloud computing, such as servers and data storage devices, consumes a significant amount of energy and contributes to greenhouse gas emissions. Additionally, the cooling systems required to maintain optimal temperatures in data centers also consume a significant amount of energy. We further identify several strategies that can be implemented to improve the environmental sustainability of cloud computing. These strategies include the use of renewable energy sources to power data centers, the optimization of data center cooling systems, and the use of energy-efficient hardware and software. Our findings highlight the need for businesses and policymakers to consider the environmental impact of cloud computing when making decisions about technology adoption. By implementing strategies to improve the environmental sustainability of cloud computing, businesses can reduce their carbon footprint and contribute to a more sustainable future.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".