AI & Environment Resource HubGazi Mohammad Imdadul Alam; Sharia Arfin Tanim; Sumit Kanti Sarker; Yutaka Watanobe; Rashedul Islam; M. F. Mridha; Kamruddin Nur · 2025-01-29
A deep learning model with explainable AI predicts vehicle CO2 emissions, aiding strategies for reducing transportation-related environmental impacts.
Gazi Mohammad Imdadul Alam; Sharia Arfin Tanim; Sumit Kanti Sarker; Yutaka Watanobe; Rashedul Islam; M. F. Mridha; Kamruddin Nur. 2025-01-29. Deep Learning Model Based Prediction Of Vehicle CO2 Emissions With EXplainable AI Integration For Sustainable Environment. https://www.nature.com/articles/s41598-025-87233-y (AI & Environment Resource Hub; record paper-109; collection snapshot 2026-09-15).
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Source sheet: Scientific Paper · Row 113 · Original ID: paper-109.
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Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Transport and logistics
By the end of this course, you will be able to explain how data centers power AI‑driven decarbonization across the global economy and why sustainable digital infrastructure is foundational to achieving net‑zero goals. You will learn how AI, enabled by cloud and edge data centers, supports clean energy grids, optimizes buildings and manufacturing operations, and accelerates electrified, efficient transportation systems
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Transport and logistics
Join Adnan Kordab, Head of Business Technologies at Emircom, and Kunal Aman, Director of Marketing at SAS, as they explore the latest AI trends revolutionizing transportation by enhancing efficiency, sustainability, and climate impact through cutting-edge technology.
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Transport and logistics
A carbon tracking data tool for sustainable software engineering.
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Transport and logistics
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