Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers

Paper 2 has higher likely scientific impact due to strong real-world relevance and timeliness (AI-driven data-center growth), clear policy and industry applications (energy planning, emissions accounting, regulation), and broad cross-field reach (energy systems, climate science, CS/AI infrastructure, economics). Its facility-level dataset and transparent attributional methodology using current EPA eGRID data support rigor and reuse. Paper 1 is novel within LLM-agent memory evaluation and may influence agent design, but its impact is narrower and more contingent on rapid shifts in LLM tooling and benchmarks.

Paper 2 addresses a critically timely and broadly relevant topic—the environmental footprint of hyperscale data centers driven by AI growth—with novel facility-level empirical data (403 HDCs) that fills a major knowledge gap. Its findings (HDC carbon intensity 48% above grid average) have immediate policy implications and broad interdisciplinary impact across energy, environmental science, computer science, and policy. Paper 1, while technically sound, addresses a narrower ML/epidemiology niche with incremental improvements to LLM-based forecasting. Paper 2's empirical contribution and societal relevance give it substantially broader citation potential and impact.

Paper 2 has higher potential impact: it provides a timely, facility-level, policy-relevant quantification of U.S. hyperscale data center electricity use and CO2 emissions using recent eGRID data, enabling benchmarking and informing regulation, grid planning, and corporate decarbonization across energy, climate, and computing. Its dataset and attributional tool can be widely reused and updated. Paper 1 is technically innovative for real-time trajectory prediction and valuable for autonomy, but its impact is more domain-specific and depends on reproducibility and adoption in deployed stacks.

Paper 1 addresses a critically timely and high-visibility topic—the environmental footprint of AI-driven hyperscale data centers—with novel facility-level empirical data covering 403 US data centers. Its finding that HDC carbon intensity is 48% above the national grid average has immediate policy relevance and broad societal impact across energy, environmental, and technology domains. Paper 2, while technically sound, presents an incremental improvement (~1.95% MASE reduction) to a specific time series forecasting framework, limiting its breadth of impact to a narrower ML audience.

While Paper 1 provides a highly relevant empirical analysis of AI's environmental impact, Paper 2 offers a concrete, novel methodological solution to this challenge by drastically reducing the computational and memory requirements of LLM deployment. The proposed quantization framework achieves remarkable performance gains at ultra-low bitrates, which will directly drive widespread adoption in AI research and industry, leading to higher technological impact and citations.

Paper 2 has higher likely scientific impact due to its timely, quantitative, facility-level assessment of AI-driven hyperscale data center electricity use and CO2 emissions, producing actionable national-scale estimates and a replicable attributional methodology using current eGRID data. Its results directly inform energy policy, grid planning, corporate reporting, and climate research, with broad cross-field relevance (computing, power systems, environmental science, economics). Paper 1 is integrative and relevant but relies largely on secondary datasets and yields more qualitative, less methodologically novel conclusions, limiting incremental impact.

Paper 2 addresses a highly timely and socially significant topic—the environmental impact of hyperscale data centers driven by AI growth. It provides the first comprehensive facility-level assessment of 403 US hyperscale data centers, offering concrete empirical data (68-99 TWh consumption, carbon intensity 48% above grid average) that will be widely cited in policy, sustainability, and computing research. Its broad interdisciplinary relevance spanning energy policy, environmental science, and computer science, combined with immediate real-world policy implications, gives it higher potential impact than Paper 1's more incremental technical contribution to multimodal time series modeling.

Paper 2 addresses a highly urgent and globally relevant issue: the environmental impact and energy consumption of hyperscale data centers driven by the AI boom. Its findings have broad implications for climate policy, energy grid planning, and the tech industry. While Paper 1 presents an interesting AI/robotics framework for spatial reasoning, Paper 2's potential to influence real-world sustainability efforts, policy decisions, and cross-disciplinary research gives it a higher scientific and societal impact.

Paper 2 addresses a critical, globally relevant issue—the environmental footprint of AI infrastructure. Its empirical assessment of hyperscale data centers has broad implications across environmental science, energy policy, and the tech industry, offering foundational data for future sustainability efforts. Paper 1 presents a practical but relatively incremental solution for LLM security, which, while useful, is narrower in scope and cross-disciplinary impact compared to the macro-level climate and energy insights provided by Paper 2.

Paper 2 addresses a timely, high-stakes environmental question with rigorous empirical methodology—compiling facility-level data on 403 US hyperscale data centers and quantifying their carbon footprint. Its finding that HDC carbon intensity is 48% above the national grid average is a striking, policy-relevant result with broad implications for AI sustainability, energy policy, and climate discussions. Paper 1 proposes a conceptual framework for entropy-based agent evaluation that, while interesting, lacks empirical validation and offers incremental contributions to an already crowded evaluation-metrics space. Paper 2's cross-disciplinary relevance (CS, environmental science, policy) gives it substantially broader impact potential.

Paper 1 likely has higher scientific impact due to its large-scale, facility-level empirical dataset (403 hyperscale data centers) and direct policy/industry relevance to energy, climate, and infrastructure planning. Its attributional emissions accounting using recent EPA eGRID data is methodologically grounded and broadly useful across environmental science, power systems, and tech policy, with immediate real-world applicability. Paper 2 is timely for AI reliability, but CHARM appears as an engineering framework evaluated on standard QA benchmarks; its novelty and generalizability beyond specific agentic RAG setups may be narrower and faster-moving/shorter-lived.

Paper 2 has higher likely scientific impact due to its broad, timely relevance to AI-driven infrastructure growth, energy policy, and climate mitigation, with immediate real-world applicability (facility-level accounting across 403 data centers) and cross-field utility (energy systems, environmental science, policy, computing). Its quantified national-scale estimates and reusable attributional methodology can inform regulation, siting, procurement, and lifecycle assessments. Paper 1 is novel for LLM-agent reliability and efficiency, but its impact is narrower to tool-using agents and depends on adoption of its contract-based framework; methodological rigor seems solid but less societally expansive.

While Paper 1 offers a valuable technical optimization for LLM serving, Paper 2 addresses a highly urgent, cross-disciplinary global issue: the environmental footprint of AI. Its findings on energy consumption and carbon emissions of hyperscale data centers have broad implications for policy, sustainability, and the tech industry, giving it a much wider potential impact across multiple fields compared to the narrowly focused systems optimization in Paper 1.

Paper 2 has higher potential scientific impact due to its extreme timeliness and broad interdisciplinary relevance. The environmental footprint of AI and hyperscale data centers is a critical global issue affecting tech policy, energy grid planning, and climate science. While Paper 1 offers a rigorous methodological advancement in pharmaceutical supply chain optimization, Paper 2 provides crucial empirical data on a rapidly scaling industry with massive societal implications. Paper 2's findings are highly likely to inform policy regulations, guide sustainable AI development, and spark widespread research across multiple disciplines.

Paper 1 addresses a fundamental challenge in AI safety—hallucination detection in LLMs—by establishing a novel theoretical connection between OOD detection and hallucination detection. This geometric reframing offers a principled, training-free methodology with broad applicability across reasoning tasks and potential to spawn new research directions. Paper 2 provides valuable empirical data on data center emissions but is more descriptive and geographically/temporally bounded. Paper 1's methodological innovation and relevance to the rapidly growing LLM safety field give it higher potential for lasting scientific impact and citations.

Paper 1 provides novel, granular facility-level data on a critically important and timely topic—the environmental footprint of US hyperscale data centers driven by AI growth. Its methodology linking 403 specific facilities to EPA eGRID data yields a surprising finding (48% higher carbon intensity than grid average) with immediate policy relevance for energy planning, climate policy, and corporate sustainability. Paper 2 introduces a useful AI benchmark but enters a crowded benchmark landscape, and its impact depends on adoption. Paper 1 addresses a concrete, urgent societal concern with quantitative evidence that will be widely cited across energy, environmental, and policy fields.

Paper 2 addresses a critically timely and societally important question—the environmental footprint of AI-driven hyperscale data centers—with novel facility-level empirical data (403 US HDCs). Its finding that HDC carbon intensity is 48% above the national grid average is striking and policy-relevant. The methodology provides a reusable attributional framework using public EPA data, enabling broad adoption. Its interdisciplinary impact spans energy policy, environmental science, and computer science. Paper 1, while technically solid, is more incremental—another LLM-agent framework for time series—in an increasingly crowded space with less distinctive empirical contribution.

While Paper 1 provides highly timely empirical data on a critical environmental issue, Paper 2 introduces a foundational benchmark for continual learning in AI. In the rapidly advancing AI field, comprehensive, expert-validated benchmarks typically drive extensive future research, establishing the standard for evaluating new models. Consequently, Paper 2 is likely to generate a massive citation volume and broadly shape future AI development methodologies.

Paper 2 is more novel methodologically, proposing a new training-free, token-selective decoding approach (FIDES) using internal model signals to resolve retrieval–memory conflict in RAG, with broad applicability to many LLM systems and tasks. It reports extensive benchmarking across multiple datasets and model scales up to 70B, suggesting strong rigor and immediate relevance to a fast-moving area with wide cross-field impact (NLP, IR, trustworthy AI). Paper 1 is timely and societally important but is primarily an attributional measurement study with narrower methodological innovation and more limited transferability.

Paper 2 likely has higher scientific impact due to greater novelty (a general framework for distilling transferable agent skills from trajectories), broad applicability across many LLM-agent domains, and strong evidence of cross-model and out-of-distribution transfer—suggesting a reusable method that can influence multiple subfields (agentic AI, tool use, instruction/skill learning). Paper 1 is timely and societally relevant with clear real-world applications, but is primarily an attributional assessment using existing data sources, with narrower methodological innovation and field breadth.