Digitalization and Automation and AI: A Theoretical Framework of rethinking the Pollution Haven Hypothesis

Abstract

This theoretical paper investigates the impact of digitalization, automation, and artificial intelligence (AI) on environmental regulations, specifically through the lens of the Pollution Haven Hypothesis (PHH). It explores how these advancements influence pollution intensity and environmental compliance costs, challenging the traditional understanding of the PHH. Methodology. The study employs a Cobb-Douglas production function to model the relationship between technological innovations and environmental regulations. By integrating digitalization, automation, and AI into the model, the paper examines how these factors affect the economic incentives for firms to relocate to regions with lenient environmental standards. Findings. The analysis reveals that advancements in digitalization and automation reduce pollution intensity and lower the costs of complying with strict environmental standards. As a result, the economic incentive to relocate to pollution havens diminishes. In an open economy, the combination of stringent environmental policies and technological innovations leads to reduced pollution levels and a shift toward cleaner production processes. Practical Implication. The findings suggest that integrating technological innovations into environmental policy can make adherence to stricter regulations more economically viable, thereby weakening the appeal of pollution havens. This has significant implications for global sustainability efforts, as it highlights the potential for technology to support more effective and equitable environmental regulations. Originality. This study introduces a novel perspective by directly linking technological innovations to shifts in capital allocation and the efficacy of environmental policies. It offers a fresh understanding of the PHH in the context of modern advancements, providing new understanding into the relationship between innovation and environmental regulation.