[2603.26712] On the Carbon Footprint of Economic Research in the Age of Generative AI

Computer Science > Software Engineering arXiv:2603.26712 (cs) [Submitted on 17 Mar 2026] Title:On the Carbon Footprint of Economic Research in the Age of Generative AI Authors:Andres Alonso-Robisco, Carlos Esparcia, Francisco Jareño View a PDF of the paper titled On the Carbon Footprint of Economic Research in the Age of Generative AI, by Andres Alonso-Robisco and 2 other authors View PDF HTML (experimental) Abstract: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, alongside measurement protocols, green algorithms, governance, and security and efficiency trade-offs. Second, we benchmark a modern economic survey workflow, an LDA-based literature mapping implemented with GenAI assisted coding and executed in a fixed cloud notebook, measuring runtime and estimated CO2e with CodeCarbon. Injecting generic green language into prompts has no reliabl...