ARTIFICIAL INTELLIGENCE ADOPTION, MANUFACTURING ENERGY INTENSITY, AND R&D EXPENDITURE ON GREEN MANUFACTURING PERFORMANCE: THE MODERATING ROLE OF ENVIRONMENTAL POLICY STRINGENCY

Abstract

Purpose: This research investigates the contingent impacts of artificial intelligence (AI) adoption, the energy intensity of manufacturing, and the national research and development (R&D) spending on green manufacturing performance in 30 countries in the period of 2015-24 with the theorized moderating variable being environmental policy stringency (EPS).

Design/methodology/approach: The study is based on the Resource-Based View (RBV) and the Technology-Organization-Environment (TOE) paradigm and involves compilation of a 30-country balanced panel based on the Oxford Insights Government AI Readiness Index, International Energy Agency (IEA) energy data, World Bank World Development Indicators (WDI), OECD Environmental Policy Stringency (EPS) database, and a home cooked Green Manufacturing Performance Index. The main estimator used is the fixed effects (FE) regression using Driscoll-Kraay standard errors; random effects and Hausman tests are used as robustness checks. Sub-sample tests examine the heterogeneity of income-groups of people, and moderation is tested using interaction terms.

Findings: EAI and R&D investment has a large positive impact on green manufacturing performance, which is significant whereas manufacturing energy intensity has a large negative impact. The AI adoption-green manufacturing nexus is positively mediated by environmental policy stringency, which suggests AI technologies are stimulated to open their latent environmental potential.

Research limitations/implications: The composite nature of the Green Manufacturing Performance Index and aggregate annual data on adoption the level of AI may pose imprecision in measurement. Digital industrial strategies must be optimally counter-balanced by environmental regulation to ensure that the green dividend of the AI-based transformation of manufacturing is optimized.