🤖 AI Summary
Carbon emissions from generative AI inference have been systematically underestimated due to ambiguous system boundaries, lack of standardized metrics, and methodological biases in existing assessments. This study employs a scoping review to systematically analyze carbon footprint measurement tools, classification frameworks, and research trends across the full training–inference lifecycle. We propose (i) a standardized measurement protocol, (ii) a dynamic evaluation framework, and (iii) a multidimensional sustainability assessment system—novelly integrating user behavior and system boundary definitions into a unified analytical model. Our analysis uncovers nonlinear relationships between carbon emissions and model scale, prompt complexity, and deployment environment, identifying key leverage points for mitigation. The findings establish a reusable methodological benchmark and practical guidelines for AI environmental governance across technical, social, and operational dimensions.
📝 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.