On the Carbon Footprint of Economic Research in the Age of Generative AI

TL;DR

This paper examines the environmental impact of generative AI in economic research workflows, highlighting how prompt engineering and human governance can reduce carbon footprints.

Contribution

It shifts analysis from AI models to research workflows, benchmarking a GenAI-assisted economic survey process and identifying effective green prompt strategies.

Findings

Operational constraints and decision rule prompts significantly reduce CO2 emissions.

Generic green language prompts have no reliable impact on footprint.

Human governance is a practical lever for environmental efficiency.

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 reliable effect, whereas operational constraints and decision rule prompts deliver large and stable footprint reductions while preserving decision equivalent topic outputs. The results identify human in the loop governance as a practical lever to align GenAI productivity with environmental efficiency.