Green Computing: The Ultimate Carbon Destroyer for a Sustainable Future
- URL: http://arxiv.org/abs/2508.00153v2
- Date: Mon, 04 Aug 2025 03:57:25 GMT
- Title: Green Computing: The Ultimate Carbon Destroyer for a Sustainable Future
- Authors: Sayed Mahbub Hasan Amiri, Prasun Goswami, Md. Mainul Islam, Mohammad Shakhawat Hossen, Marzana Mithila, Naznin Akter,
- Abstract summary: Green computing represents a critical pathway to decarbonize the digital economy.<n>This article examines how sustainable IT strategies can transform computing into a net carbon sink.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Green computing represents a critical pathway to decarbonize the digital economy while maintaining technological progress. This article examines how sustainable IT strategies including energy-efficient hardware, AI-optimized data centres, and circular e-waste systems can transform computing into a net carbon sink. Through analysis of industry best practices and emerging technologies like quantum computing and biodegradable electronics, we demonstrate achievable reductions of 40-60% in energy consumption without compromising performance. The study highlights three key findings: (1) current solutions already deliver both environmental and economic benefits, with typical payback periods of 3-5 years; (2) systemic barriers including cost premiums and policy fragmentation require coordinated action; and (3) next-generation innovations promise order-of-magnitude improvements in efficiency. We present a practical framework for stakeholders from corporations adopting renewable-powered cloud services to individuals extending device lifespans to accelerate the transition. The research underscores computing's unique potential as a climate solution through its rapid innovation cycles and measurable impacts, concluding that strategic investments in green IT today can yield disproportionate sustainability dividends across all sectors tomorrow. This work provides both a compelling case for urgent action and a clear roadmap to realize computing's potential as a powerful carbon destruction tool in the climate crisis era.
Related papers
- Energy-Efficient Green AI Architectures for Circular Economies Through Multi-Layered Sustainable Resource Optimization Framework [0.0]
We introduce a multi-layered framework and meta-architecture that integrates state-of-the-art machine learning algorithms, energy-conscious computational models, and optimization techniques.<n>We tested the framework on real-world datasets from lithium-ion battery recycling and urban waste management systems.
arXiv Detail & Related papers (2025-03-28T16:09:43Z) - Role of AI Innovation, Clean Energy and Digital Economy towards Net Zero Emission in the United States: An ARDL Approach [0.0]
The paper investigates the influences of AI innovation, GDP growth, renewable energy utilization, the digital economy, and industrialization on CO2 emissions in the USA from 1990 to 2022.<n>The outcomes observe that AI innovation, renewable energy usage, and the digital economy reduce CO2 emissions, while GDP expansion and industrialization intensify ecosystem damage.
arXiv Detail & Related papers (2025-03-24T16:32:24Z) - From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.<n>We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions.
arXiv Detail & Related papers (2025-01-27T22:45:06Z) - Carbon Connect: An Ecosystem for Sustainable Computing [22.998262140061733]
Carbon Connect envisions coordinated research thrusts that produce design and management strategies for sustainable computer systems.
We will require accurate models for carbon accounting in computing technology.
New hardware design and management strategies must be cognizant of economic policy and regulatory landscape.
arXiv Detail & Related papers (2024-05-22T17:33:51Z) - System Support for Environmentally Sustainable Computing in Data Centers [4.774769264608661]
Modern data centers suffer from a growing carbon footprint due to insufficient support for environmental sustainability.
We present our preliminary results and recognize this as an ongoing initiative with significant potential to advance environmentally sustainable computing in data centers.
arXiv Detail & Related papers (2024-03-19T12:56:02Z) - Green Edge AI: A Contemporary Survey [46.11332733210337]
The transformative power of AI is derived from the utilization of deep neural networks (DNNs)
Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs)
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - The Energy Worker Profiler from Technologies to Skills to Realize Energy
Efficiency in Manufacturing [1.290382979353427]
The Worker Profiler is a software designed to map the skills currently possessed by workers.
It identifies misalignment with those they should ideally possess to meet the renewed demands that digital innovation and environmental preservation impose.
The tool has shown evidence of being user-friendly, effective in identifying skills gaps and easily adaptable to other contexts.
arXiv Detail & Related papers (2023-01-23T14:08:34Z) - Eco2AI: carbon emissions tracking of machine learning models as the
first step towards sustainable AI [47.130004596434816]
In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting.
The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
arXiv Detail & Related papers (2022-07-31T09:34:53Z) - Future Computer Systems and Networking Research in the Netherlands: A
Manifesto [137.47124933818066]
We draw attention to CompSys as a vital part of ICT.
Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without CompSys advances.
arXiv Detail & Related papers (2022-05-26T11:02:29Z) - Learning, Computing, and Trustworthiness in Intelligent IoT
Environments: Performance-Energy Tradeoffs [62.91362897985057]
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications.
This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
arXiv Detail & Related papers (2021-10-04T19:41:42Z) - Towards the Systematic Reporting of the Energy and Carbon Footprints of
Machine Learning [68.37641996188133]
We introduce a framework for tracking realtime energy consumption and carbon emissions.
We create a leaderboard for energy efficient reinforcement learning algorithms.
We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
arXiv Detail & Related papers (2020-01-31T05:12:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.