Energy Efficiency in the Cloud: An Empirical Analysis of Information Technology Outsourcing, Cloud Computing, and Energy Efficiency
Bibliographic record
Abstract
Improving energy efficiency is one of the most cost-effective ways to address the challenges of energy security and climate change. Using U.S. economy-wide data from 57 private industries during 1998-2014, this study examines the impacts on client industry’s energy efficiency of in-house information technology (IT) capital and data processing and hosting services (DP&HS) outsourcing which is closely related to cloud computing. Based on a two-stage stochastic frontier approach, we find that IT capital and DP&HS outsourcing play a complementary role in reducing energy consumption. Specifically, IT capital contributes to technical progress toward less energy-consuming production, whereas DP&HS outsourcing improves energy efficiency. Notably, DP&HS outsourcing substantially improves the client industry’s energy efficiency after 2007, when cloud computing services began to rapidly grow. Furthermore, the contribution of DP&HS outsourcing to energy efficiency appears to be amplified with more intensive investments in internal IT capital. Relevant implications for research and practice are discussed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".