The paper develops an ECW nexus framework using differentiable optimization layers and fixed-point coordination to internalize virtual water in data center power dispatch, showing 3-5% water withdrawal reductions on IEEE test systems.
Islam, and Shaolei Ren
9 Pith papers cite this work, alongside 158 external citations. Polarity classification is still indexing.
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158
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Full development of 7B and 32B Olmo 3 models used 12.3 GWh datacenter energy and emitted 4,251 tCO2eq, with development overheads accounting for 82% of compute and reasoning models costing 17x more to post-train than instruction-tuned ones.
Analysis of a compiled dataset shows steadily rising production-related environmental impacts for NVIDIA workstation graphics cards from 2013 to 2025.
GreenZ is a conceptual three-layer sustainable UX framework built on ten principles, five operational systems, and practical tools, centered on an eight-type Digital Waste Taxonomy and a model questioning AI necessity before implementation.
Constraint-aware greedy heuristics jointly allocate models, heterogeneous GPUs, TP/PP, and routing for SLO-constrained LLM inference in seconds, with better out-of-sample robustness than cost-minimal MILP plans.
MADP multi-agent pipeline with human-in-the-loop achieves 97% full automation on 955 real documents, 98.5% accuracy on ablation set, and 69-70% reductions in FTE, energy, and emissions versus manual processing.
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.
Surveys energy footprints of image ML and proposes modest technical solutions including tiny models, low-precision hardware, and true-cost accounting driven by critiques of shareholder efficiency metrics.
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.
citing papers explorer
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From Accounting to Coordination: A Virtual Water-Aware Electricity-Computation-Water Nexus Framework for Data Center Dispatch
The paper develops an ECW nexus framework using differentiable optimization layers and fixed-point coordination to internalize virtual water in data center power dispatch, showing 3-5% water withdrawal reductions on IEEE test systems.
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The Hidden Cost of Thinking: Energy Use and Environmental Impact of LMs Beyond Pretraining
Full development of 7B and 32B Olmo 3 models used 12.3 GWh datacenter energy and emitted 4,251 tCO2eq, with development overheads accounting for 82% of compute and reasoning models costing 17x more to post-train than instruction-tuned ones.
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The Rising Unsustainability of AI Graphics Cards Production
Analysis of a compiled dataset shows steadily rising production-related environmental impacts for NVIDIA workstation graphics cards from 2013 to 2025.
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GreenZ: A Sustainable UX Framework for Complex Digital Systems
GreenZ is a conceptual three-layer sustainable UX framework built on ten principles, five operational systems, and practical tools, centered on an eight-type Digital Waste Taxonomy and a model questioning AI necessity before implementation.
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Scalable Joint Resource Allocation for SLO-Constrained LLM Inference in Heterogeneous GPU Clouds
Constraint-aware greedy heuristics jointly allocate models, heterogeneous GPUs, TP/PP, and routing for SLO-constrained LLM inference in seconds, with better out-of-sample robustness than cost-minimal MILP plans.
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MADP: A Multi-Agent Pipeline for Sustainable Document Processing with Human-in-the-Loop
MADP multi-agent pipeline with human-in-the-loop achieves 97% full automation on 955 real documents, 98.5% accuracy on ablation set, and 69-70% reductions in FTE, energy, and emissions versus manual processing.
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From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.
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Modest, artistic, and radical solutions to the environmental impact of image-generating machine learning
Surveys energy footprints of image ML and proposes modest technical solutions including tiny models, low-precision hardware, and true-cost accounting driven by critiques of shareholder efficiency metrics.
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LLM Harms: A Taxonomy and Discussion
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.