Toward Sustainable Generative AI: A Scoping Review of Carbon Footprint and Environmental Impacts Across Training and Inference Stages

TL;DR

This paper reviews methodologies for assessing AI's carbon footprint, highlighting gaps in current practices, and proposes standardized, dynamic, and comprehensive frameworks to promote sustainable AI development across training and inference stages.

Contribution

It provides a comprehensive scoping review of AI carbon footprint assessment methods and proposes future directions for standardized, dynamic, and holistic sustainability evaluation frameworks.

Findings

Current practices have methodological inconsistencies.

Inference phase's environmental impact is underexplored.

Proposed frameworks aim to improve measurement and sustainability assessment.

Abstract

Generative AI is spreading rapidly, creating significant social and economic value while also raising concerns about its high energy use and environmental sustainability. While prior studies have predominantly focused on the energy-intensive nature of the training phase, the cumulative environmental footprint generated during large-scale service operations, particularly in the inference phase, has received comparatively less attention. To bridge this gap this study conducts a scoping review of methodologies and research trends in AI carbon footprint assessment. We analyze the classification and standardization status of existing AI carbon measurement tools and methodologies, and comparatively examine the environmental impacts arising from both training and inference stages. In addition, we identify how multidimensional factors such as model size, prompt complexity, serving environments, and system boundary definitions shape the resulting carbon footprint. Our review reveals critical limitations in current AI carbon accounting practices, including methodological inconsistencies, technology-specific biases, and insufficient attention to end-to-end system perspectives. Building on these insights, we propose future research and governance directions: (1) establishing standardized and transparent universal measurement protocols, (2) designing dynamic evaluation frameworks that incorporate user behavior, (3) developing life-cycle monitoring systems that encompass embodied emissions, and (4) advancing multidimensional sustainability assessment framework that balance model performance with environmental efficiency. This paper provides a foundation for interdisciplinary dialogue aimed at building a sustainable AI ecosystem and offers a baseline guideline for researchers seeking to understand the environmental implications of AI across technical, social, and operational dimensions.