Innovation Dynamics and Ethical Considerations of Agentic Artificial Intelligence in the Transition to a Net-Zero Carbon Economy

As climate action becomes increasingly urgent, nations and institutions worldwide seek advanced technologies for practical mitigation efforts. This study examines how agentic artificial intelligence systems capable of decision-making and learning from experience drive innovation dynamics in climate change mitigation, with a particular focus on ethical considerations during the net-zero transition. The current urgency of climate action demands advanced technologies, yet organisations struggle to effectively deploy agentic AI for climate mitigation due to unclear implementation pathways and ethical consideration. This study examines the relationships among agentic AI capabilities, innovation dynamics, and net-zero transition performance, using survey data from 340 organisations across the manufacturing, energy, and technology sectors, and analysed using structural equation modelling. Based on dynamic capabilities theory, this research proposes a novel theoretical model that examines how agentic AI drives innovation dynamics in climate change mitigation within governance frameworks that encompass transparency, accountability, and environmental justice. Results reveal significant mediation effects of innovation dynamics, dynamic capabilities, and ethical considerations, while environmental context negatively moderates innovation and ethical pathways. Findings suggest that overly restrictive ethical considerations can lead to implementation delays that undermine the urgency of climate action. This study proposes three solutions: (1) adaptive ethical protocols adjusting governance intensity based on climate risk severity, (2) pre-approved ethical templates reducing approval delays by 60%, and (3) stakeholder co-design processes building consensus during development. The research advances dynamic capabilities theory for AI contexts by demonstrating how AI-enabled sensing, seizing, and reconfiguring capabilities create differentiated pathways to climate performance. This study provides empirical validation of the responsible innovation framework, identifies asymmetric environmental contingencies, and offers evidence-based guidance for organisations implementing agentic AI for climate action.