The dual footprint of artificial intelligence: environmental and social impacts across the globe

2025-12-01

AI Summary

This study addresses the dual environmental and social externalities—ecological degradation and socioeconomic inequality—exacerbated by the global expansion of artificial intelligence (AI). Employing a political economy lens and the global value chain (GVC) framework, it conducts cross-national case analyses of raw material extraction and data labor to trace how AI industries systematically offshore environmental burdens and social costs onto marginalized Global South nations. Its key contribution lies in integrating ecological and social costs into a unified analytical dimension, revealing their shared logic of capital accumulation and geographically uneven distribution. Findings indicate that AI-driven, resource-intensive growth entrenches North–South asymmetries, erodes developmental sovereignty in vulnerable countries, and promotes ecologically unsustainable pathways. The study thus provides a theoretical foundation and policy entry points for reimagining digital globalization as equitable and environmentally sustainable.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Other Foundations of Human Computation & AI
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Security and Privacy: Data transparency and provenance

Abstract

This article introduces the concept of the 'dual footprint' as a heuristic device to capture the commonalities and interdependencies between the different impacts of artificial intelligence (AI) on the natural and social surroundings that supply resources for its production and use. Two in-depth case studies, each illustrating international flows of raw materials and of data work services, portray the AI industry as a value chain that spans national boundaries and perpetuates inherited global inequalities. The countries that drive AI development generate a massive demand for inputs and trigger social costs that, through the value chain, largely fall on more peripheral actors. The arrangements in place distribute the costs and benefits of AI unequally, resulting in unsustainable practices and preventing the upward mobility of more disadvantaged countries. The dual footprint grasps how the environmental and social dimensions of the dual footprint emanate from similar underlying socioeconomic processes and geographical trajectories.

Problem

• Analyzes AI's environmental and social impacts globally
• Examines how AI value chains perpetuate global inequalities
• Studies unequal distribution of AI's costs and benefits

Innovation

• Dual footprint concept captures AI's environmental and social interdependencies
• Case studies show AI value chain perpetuates global inequalities
• Unequal cost-benefit distribution leads to unsustainable practices