Toward a Sustainable Software Architecture Community: Evaluating ICSA's Environmental Impact

2026-04-05

AI Summary

This study addresses the lack of systematic assessment and transparent reporting regarding the environmental impact of generative AI usage in contemporary software architecture research and academic conference activities. It presents the first cross-boundary, multidimensional carbon footprint evaluation framework, integrating operational data from the International Conference on Software Architecture (ICSA) with an estimation model for generative AI inference in published papers to separately quantify emissions from physical and digital activities. The analysis reveals the carbon implications of ICSA 2025 and associated generative AI applications, offering empirical evidence and actionable recommendations for organizing greener conferences, optimizing AI energy efficiency, and advancing sustainable research practices.

Technology Category

• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures
• Machine Learning: Efficient ML / Green AI
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Sustainability and climate impact of web technologies

Abstract

Generative AI (GenAI) tools are increasingly integrated into software architecture research, yet the environmental impact of their computational usage remains largely undocumented. This study presents the first systematic audit of the carbon footprint of both the digital footprint from GenAI usage in research papers, and the traditional footprint from conference activities within the context of the IEEE International Conference on Software Architecture (ICSA). We report two separate carbon inventories relevant to the software architecture research community: i) an exploratory estimate of the footprint of GenAI inference usage associated with accepted papers within a research-artifact boundary, and ii) the conference attendance and operations footprint of ICSA 2025 (travel, accommodation, catering, venue energy, and materials) within the conference time boundary. These two inventories, with different system boundaries and completeness, support transparency and community reflection. We discuss implications for sustainable software architecture, including recommendations for transparency, greener conference planning, and improved energy efficiency in GenAI operations. Our work supports a more climate-conscious research culture within the ICSA community and beyond

Problem

• environmental impact
• generative AI
• software architecture
• carbon footprint
• sustainable research

Innovation

• carbon footprint
• generative AI
• sustainable software architecture
• environmental impact
• conference sustainability