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Working paper

Considerations on the environmental impact of AI in science

This paper examines the environmental implications of applying artificial intelligence (AI) in scientific research. It serves as a primer for scientists, research institutions and science policy-makers who seek to understand various approaches to addressing the environmental impact of AI in science. In addition, it offers guidance on how reducing environmental costs can contribute to the broader goals of sustainability and ethical AI use in research environments.

The paper is part of a series of three primers that explore various technical dimensions of AI and its impact on science:

  1. Types of AI in science
  2. Considerations on the environmental impact of AI in science
  3. Data for AI in science

Although evidence on the specific environmental impacts of AI in scientific research is still emerging, the paper provides conceptual frameworks and practical tools to help assess the environmental implications of the full AI lifecycle within scientific projects.

The first section introduces key frameworks for understanding environmental impacts in a holistic way.

The second section outlines an approach for defining and measuring environmental costs across the AI lifecycle.

The third section presents concrete strategies for reducing the direct environmental footprint of scientific projects that use or depend on resource-intensive AI applications.

Key takeaways

  • There is limited awareness and evidence about the environmental costs of using artificial intelligence (AI) in scientific research. This article offers frameworks and tools scientists and research institutions can consider to assess the environmental impacts of their research as part of a more sustainable, ethical and responsible use of AI in science.
  • Addressing the environmental impact of AI requires a multi-dimensional approach. Scientists and researchers who are planning to incorporate AI into their workflows need to assess tools in light of their scientific value, social equity and environmental costs across the entire AI life cycle, with attention to rebound effects and long-term consequences.
  • Adopting more resource-efficient AI models has environmental and social benefits. Smaller, local and frugal approaches to AI can improve accessibility, affordability, transparency and social inclusion around the use of AI, especially in diverse and resource-constrained research contexts.

Considerations on the environmental impact of AI in science

September 2025

DOI: 10.24948/2025.10


This work was carried out with the aid of a grant from the International Development Research Centre (IDRC), Ottawa, Canada. The views expressed herein do not necessarily represent those of IDRC or its Board of Governors.