On the Carbon Footprint of Economic Research in the Age of Generative AI
Generative artificial intelligence (AI) is increasingly used to write and refactor research code, expanding computational workflows. At the same time, Green AI research has largely measured the footprint of models rather than the downstream workflows in which GenAI is a tool. We shift the unit of analysis from models to workflows and treat prompts as decision policies that allocate discretion between researcher and system, governing what is executed and when iteration stops. We contribute in two ways. First, we map the recent Green AI literature into seven themes: training footprint is the largest cluster, while inference efficiency and system level optimisation are growing rapidly, along...
| Authors | Andres Alonso-Robisco, Carlos Esparcia, Francisco Jareño |
|---|---|
| Primary category | cs.SE |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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