Flash endurance is priced via shadow price η making placement cost-optimal for any sign of value-write correlation χ, with χ positive only in recurrent long-horizon manipulation and the budget binding only on low-endurance commodity hardware.
Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends
8 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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GreenFPGA modeling indicates FPGAs can achieve lower total carbon footprints than ASICs, GPUs, and CPUs for low-volume applications with frequently changing and diverse workloads under iso-performance assumptions.
AI data centers raise surrounding land surface temperatures by 2°C on average, potentially affecting over 340 million people via local climate changes.
ICT LCAs form error-prone model systems requiring explicit dependency graphs and open versioned repositories for credible assessments.
The paper calls for life cycle assessment to capture embodied hardware costs and full pipeline operational costs in AI development and deployment.
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.
Taxation of AI activities can correct externalities, redistribute costs and gains, and support regulation, though instruments like corporate taxes, consumption taxes, and excises vary in feasibility, measurement challenges, and effects on innovation.
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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Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So
Flash endurance is priced via shadow price η making placement cost-optimal for any sign of value-write correlation χ, with χ positive only in recurrent long-horizon manipulation and the budget binding only on low-endurance commodity hardware.
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Evaluating Computing Platforms for Sustainability: A Comparative Analysis of FPGAs against ASICs, GPUs, and CPUs
GreenFPGA modeling indicates FPGAs can achieve lower total carbon footprints than ASICs, GPUs, and CPUs for low-volume applications with frequently changing and diverse workloads under iso-performance assumptions.
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The data heat island effect: quantifying the impact of AI data centers in a warming world
AI data centers raise surrounding land surface temperatures by 2°C on average, potentially affecting over 340 million people via local climate changes.
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All LCA models are wrong. Are some of them useful? Towards open computational LCA in ICT
ICT LCAs form error-prone model systems requiring explicit dependency graphs and open versioned repositories for credible assessments.
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Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
The paper calls for life cycle assessment to capture embodied hardware costs and full pipeline operational costs in AI development and deployment.
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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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Taxing Artificial Intelligence
Taxation of AI activities can correct externalities, redistribute costs and gains, and support regulation, though instruments like corporate taxes, consumption taxes, and excises vary in feasibility, measurement challenges, and effects on innovation.
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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.