THE TWIN TRANSITION: SYNERGIZING AI AND GREEN INNOVATION-ORIENTED INVESTMENT FLOWS FOR AGGREGATE PRODUCTIVITY
TL;DR
To prevent AI expansion from impeding global sustainability goals, policymakers should consider financial screening mechanisms for state-directed innovation finance and require that datacentric expansions be paired with new renewable energy generation.
Abstract
This study examines the macroeconomic premise that the "Twin Transition"—the concurrent advancement of digitalization and decarbonization—inherently produces complementary productivity gains. Utilizing a three-way matched firm-level microdata infrastructure integrating FactSet RSCR, PATSTAT, and Orbis, this research evaluates whether data-intensive investments promote clean-tech efficiency or generate competing resource demands across the global supply chain. The empirical findings challenge the synergy hypothesis. At the firm level, scaling artificial intelligence infrastructure displaces the capital expenditure, Research and Development (R&D) resources, and organizational capacity required for concurrent green innovation. Furthermore, dynamic network modeling indicates that upstream digital modernization heavily utilizes regional energy grids and critical hardware outputs, creating bottlenecks that delay downstream sustainability targets. To prevent AI expansion from impeding global sustainability goals, policymakers should consider financial screening mechanisms for state-directed innovation finance and require that datacentric expansions be paired with new renewable energy generation.