迈向可持续AI部署：面向企业供应链系统的碳感知决策框架

中文摘要 AI 辅助

企业在为供应链决策部署AI时，通常默认使用最大的可用语言模型，这种采购启发式方法既忽视了经验性能，也忽视了环境成本。我们对六个大型语言模型在520项供应链任务上进行了基准测试，同时测量决策质量和估算的与生成相关的运营碳排放。借鉴技术-组织-环境（TOE）框架，我们开发了碳感知AI采购框架（CAAPF），这是一种绿色信息系统设计工件，将可持续AI治理落实到企业采购中。在此有限样本内，质量得分范围为0.497至0.723，且公开参数总量最大的模型并未取得最高得分。该设计未隔离规模、提供商、架构或基准构建的影响。一个按类别分层校准的GreenRoute概念验证在估算的0.402克二氧化碳/任务下达到了0.733的平均样本外质量。静态Haiku在0.022克二氧化碳/任务下达到0.699，而Sonnet在0.401克二氧化碳/任务下达到0.723，这表明首选策略取决于组织的质量要求。我们的“先基准测试，再选择绿色”原则表明，环境责任和决策质量可以相互促进，为符合可持续发展目标12和13的可持续数字基础设施治理做出贡献。

英文摘要

Enterprises deploying AI for supply chain decisions commonly default to the largest available language model, a procurement heuristic that neglects both empirical performance and environmental cost. We benchmark six large language models across 520 supply chain tasks, simultaneously measuring decision quality and estimated generation-related operational carbon. Drawing on the Technology-Organization-Environment (TOE) framework, we develop a Carbon-Aware AI Procurement Framework (CAAPF), a Green IS design artifact that operationalizes sustainable AI governance for enterprise procurement. Within this bounded sample, quality spans 0.497-0.723, and the models with the largest disclosed parameter totals do not achieve the highest scores. The design does not isolate size, provider, architecture, or benchmark-construction effects. A category-by-tier calibrated GreenRoute proof of concept reaches 0.733 mean out-of-sample quality at an estimated 0.402 gCO2/task. Static Haiku reaches 0.699 at 0.022 gCO2/task, while Sonnet reaches 0.723 at 0.401 gCO2/task, demonstrating that the preferred strategy depends on the organization's quality requirement. Our "benchmark first, select green" principle suggests that environmental responsibility and decision quality can be mutually reinforcing, contributing to sustainable digital infrastructure governance aligned with SDG 12 and SDG 13.

发表机构

• University of Florida（佛罗里达大学）
• Wichita State University（威奇托州立大学）
• Nanchang Hangkong University（南昌航空大学）

机构由 AI 辅助整理，请以论文原文为准。