Enterprise supply chains balance ai quality and carbon footprint
Toward Sustainable AI Deployment: A Carbon-Aware Decision Framework for Enterprise Supply Chain Systems
Software EngineeringComputers and Society
Summary
Enterprises often choose the biggest AI language models for supply chain decisions without considering how well they actually perform or their environmental impact. The authors tested six language models on many supply chain tasks, measuring both decision quality and the carbon emissions generated per task. They created a framework to help businesses pick AI models that are good enough while producing less carbon, supporting greener technology use. These findings show that bigger AI models are not always better and that companies can be smart about choosing AI to reduce environmental harm.
What this means in practice
- •For enterprise technology teams: Introduce a decision framework to choose AI models that balance supply chain decision quality with carbon emission reductions.
- •For enterprise sustainability officers: Use benchmark data and carbon estimates to guide procurement policies that meet environmental goals without sacrificing AI performance.
- •For cloud service providers: Develop offerings that highlight AI model choices optimized for both performance and reduced carbon output to attract eco-conscious enterprise customers.$Commercial implications: Enables selling carbon-aware AI selection tools to enterprises aiming to reduce emissions while maintaining performance.
Authors
Haoran Yu, Lifei Liu, Danping Zhang
Abstract
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.