Germany's sustainable future: How artificial intelligence and energy innovation shape the carbon neutrality roadmap?

In the era of artificial intelligence (AI), technological innovation and environmental sustainability are no longer separate agendas but intertwined forces shaping society's future. This study investigates the asymmetric effects of AI on environmental sustainability, offering additional insights into how energy innovation can influence the carbon neutrality agenda. Using a non-linear autoregressive distributed lag approach with Fourier terms, it explores the asymmetric effects of AI in Germany over the period 1985-2022. Grounded in the Load Capacity Curve (LCC) hypothesis, it offers both theoretical and empirical contributions by exploring how AI and energy innovation influence ecological outcomes beyond linear assumptions. Empirically, the findings reveal that positive AI shocks have no significant impact on ENS, while negative shocks improve it in the short run but worsen it in the long run. In contrast, energy innovation shows no short-term effects but contributes positively over time. The results reject the LCC hypothesis, suggesting that growth alone is insufficient for ecological sustainability. These outcomes reveal the nonlinear and asymmetric environmental effects of AI, highlighting the potential of AI and energy innovation to accelerate progress toward net-zero goals and the achievement of Sustainable Development Goals 7, 9, and 13. These insights suggest the importance of sustained, policy-driven technological change. Policymakers should align AI development and energy R&D with climate goals through consistent regulation and targeted investment to ensure AI contributes meaningfully to long-term sustainability.