Toward a Time-Aware Assessment Framework for the Carbon Cost of AI-Enabled Decarbonization

arXiv:2609.18029v1 Announce Type: new Abstract: AI is increasingly used to support decarbonization decisions across the built environment, yet the development, training, and use of AI consume energy and induce CO2e emissions. However, existing assessments often report physical-system savings while omitting AI-side emissions. Moreover, they rarely account for the mismatch between when AI costs occur and when decarbonization benefits materialize, which may be substantial for infrastructure-scale projects. To address these issues, we present a time-aware assessment framework that models avoided emissions and AI-induced emissions as discrete-time streams over a finite time horizon. In demonstrating this process, we seek to show that time-aware assessment can support temporal decision-making, identify cases in which accounting for time value of carbon can change preferred rankings relative to time-invariant totals, and explore how decisions may vary with slightly different governance priorities. Using four representative interventions with intentionally different temporal profiles (multi-project low-carbon concrete design support, AI-assisted construction logistics, agentic HVAC control, and predictive maintenance), we demonstrate how discounting can change preferred rankings relative to time-invariant totals and supports ranking sensitivity analysis, discounted payback screening, and break-even discount-rate analysis. We also provide decision guidelines that support go/no-go screening, timing decisions, and minimum "bang-for-your-buck" thresholds. Ultimately, this work contribu

AI helps reduce carbon emissions.

The paper presents a nuanced framework for evaluating AI's carbon impact over time, showing both emissions costs and decarbonization benefits. This balanced approach informs better decision-making but does not claim AI solves decarbonization nor causes significant harm alone. The framework helps optimize AI use for environmental benefits with some caution about emissions.

This is a methodological framework paper, not an empirical finding about AI's societal impact. It offers tools for better decision-making around AI carbon costs but makes no claims about actual outcomes or adoption, so implications for human flourishing are indirect and uncertain.

This paper proposes a framework to better assess the carbon costs and benefits of AI in decarbonization efforts. It highlights a current oversight in assessments, which could lead to more informed and effective deployment of AI for climate goals. The implications are largely neutral, addressing a methodological gap rather than claiming a specific societal impact.

The paper presents a framework to better assess the carbon cost of AI-enabled decarbonization. It highlights the potential for AI to support significant environmental benefits. The framework aims to improve decision-making by accounting for temporal mismatches in emissions and benefits.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   f2887e87-5015-4cc2-bb50-015a87b96892
           https://arxiv.org/abs/2609.18029
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -2  (Mixed or uncertain)
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CONFIDENCE 78%