Toward Federally Aligned Green AI: Reducing Data Processing Time and Carbon Emissions with Sustainable Vendor-Marketplace Architectures

Data Vendors and data marketplaces are seeing increasing adoption in research and marketing analytics. The carbon footprint of AI has increased rapidly as its adoption has increased, with data processing and data storage contributing to the majority of the impact of AI on the environment. In this paper, we can explore how data vendors and data marketplaces can play a key role in helping reduce the carbon footprint of AI by reducing the amount of data processing by using existing datasets or pre-processed data and can make the whole process not just leaner but also good for the environment. We also discuss how data vendors and data marketplaces can use sustainable practices to further help reduce the impact of AI on the environment.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-05-25
DOI
https://doi.org/10.52783/jisem.v10i49s.10053
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Toward Federally Aligned Green AI: Reducing Data Processing Time and Carbon Emissions with Sustainable Vendor-Marketplace Architectures

Siddig Omer
Journal of Information Systems Engineering & Management
Big Data and Business Intelligence
article

Toward Federally Aligned Green AI: Reducing Data Processing Time and Carbon Emissions with Sustainable Vendor-Marketplace Architectures

Siddig Omer
article en

Abstract

Data Vendors and data marketplaces are seeing increasing adoption in research and marketing analytics. The carbon footprint of AI has increased rapidly as its adoption has increased, with data processing and data storage contributing to the majority of the impact of AI on the environment. In this paper, we can explore how data vendors and data marketplaces can play a key role in helping reduce the carbon footprint of AI by reducing the amount of data processing by using existing datasets or pre-processed data and can make the whole process not just leaner but also good for the environment. We also discuss how data vendors and data marketplaces can use sustainable practices to further help reduce the impact of AI on the environment.

Journal of Information Systems Engineering & ManagementVol. 10(49s)
Openalex Percentile: Top 80%
Big Data and Business Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.