Sahil Garg
Decision Science Area, Indian Institute of Management Lucknow, Lucknow, Uttar Pradesh, India.

Garima Mittal
Decision Science Area, Indian Institute of Management Lucknow, Lucknow, Uttar Pradesh, India.

DOI https://doi.org/10.33889/IJMEMS.2026.11.5.083

  ;

Abstract

Artificial Intelligence (AI) in contemporary industry and society is omnipresent, with its applications permeating across enterprise functions such as manufacturing, supply chain and research & development, and enterprise goals such as efficiency, innovation, and sustainability. However, an important question that has been raised, quite often recently, is: Is AI environmentally sustainable? This study proposes an extension to the CRoss-Industry Standard Process for Machine Learning with Quality Assurance (CRISP-ML(Q)), to include ‘sustainability’ into Artificial Intelligence and Machine Learning (AI-ML) development lifecycle. This paper conducts a systematic literature review with 171 papers to understand the current state of sustainable AI research and synthesizes these studies onto the stages of the model development lifecycle – Business and Data Understanding, Data Preparation, Model Building, Evaluation, Deployment and Monitoring and Maintenance. The proposed framework, Cross-Industry Standard Process for Machine Learning with Quality and Sustainability assurance CRISP-ML(QS), highlights that design choices in various stages of the AI model development lifecycle can have a significant impact on the carbon footprint and energy consumption of the resulting AI system. The framework addresses the environmental dimension of sustainability and focuses on the direct environmental impacts of the AI development lifecycle. Energy-aware model development lifecycle, by utilizing smaller datasets, optimal number of features, algorithms with low energy profiles, efficient neural architecture search, pruning, compression and making efficient model design choices such as modelling frameworks, coding language, batch size, optimizers, training location, can lead to a significant reduction in energy consumption. Energy-aware hyperparameter tuning, deployment, and inference of models also contribute to reducing the environmental impact of AI.

Keywords- Green AI, Sustainability of AI, Energy consumption, Carbon footprint, AI-ML model development lifecycle.

Citation

Garg, S., & Mittal, G. (2026). CRISP-ML(QS): Embedding Sustainability in AI-ML Development Lifecycle. International Journal of Mathematical, Engineering and Management Sciences, 11(5), 2079-2104. https://doi.org/10.33889/IJMEMS.2026.11.5.083.