面向AI赋能脱碳碳成本的时间感知评估框架
Toward a Time-Aware Assessment Framework for the Carbon Cost of AI-Enabled Decarbonization
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一个时间感知评估框架，将AI排放与脱碳效益建模为离散时间流，通过折现分析支持何时部署AI脱碳干预的决策，并用四个案例验证其能改变偏好排序。
AI中文摘要：
AI正越来越多地被用于支持建筑环境中的脱碳决策，然而，AI的开发、训练和使用会消耗能源并产生CO2e排放。然而，现有评估往往报告物理系统的节省，却忽略了AI侧的排放。此外，它们很少考虑AI成本发生时间与脱碳效益实现时间之间的不匹配，而这种不匹配对于基础设施规模的项目可能相当显著。为解决这些问题，我们提出了一个时间感知评估框架，将避免的排放和AI引起的排放建模为有限时间范围内的离散时间流。在演示这一过程时，我们力求表明时间感知评估能够支持时间性决策，识别考虑碳时间价值会改变相对于时间不变总量的偏好排序的情况，并探讨决策如何随略有不同的治理优先级而变化。通过使用四个具有刻意不同时间特征的代表性干预措施（多项目低碳混凝土设计支持、AI辅助施工物流、智能体HVAC控制和预测性维护），我们演示了折现如何相对于时间不变总量改变偏好排序，并支持排序敏感性分析、折现回收期筛选和盈亏平衡折现率分析。我们还提供了支持进行/不进行筛选、时机决策和最低“性价比”阈值的决策指南。最终，这项工作贡献了一个轻量级框架，用于在明确时间偏好下决定是否以及何时部署AI赋能的脱碳干预措施。
英文摘要：
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 contributes a lightweight framework for deciding whether and when to deploy AI-enabled interventions for decarbonization under explicit time preference.
