Measuring IT Carbon Footprint: What is the Current Status Actually?
June 12, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tom Kennes
arXiv ID
2306.10049
Category
cs.SE: Software Engineering
Cross-listed
cs.CY
Citations
4
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Despite the new Corporate Sustainability Reporting Directive from the European Union, which presses large enterprises to be more transparent about their GHG emissions, and though large technology- or advisory firms might peddle otherwise, there are plenty of challenges ahead when it comes to measuring GHG emissions from IT activities in the first place. This paper categories those challenges into 4 categories, and explains the current status, shortcomings and potential future research directions. These categories are: measuring software energy consumption, server overhead energy consumption, Energy Mix and emissions from embodied carbon. Next to that, various non-profit and open-source initiatives are introduced as well as a mathematical framework, based on CPU consumption, that can act as a rule-of-thumb for quick and effortless assessments.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted