Artificial Intelligence: facilitator or destroyer of carbon neutrality? Evidence from China

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:The rolling-window causality method offers a novel approach to study time-varying relationships between AI and carbon neutrality, applicable to other countries.

🏢実務担当者:Corporate sustainability teams should monitor AI's energy footprint and align AI deployment with carbon reduction targets.

🏛政策担当者:Governments need integrated policies that promote AI efficiency and leverage CN milestones to drive AI innovation, as shown by the bidirectional causality.

Against the escalating backdrop of climate change, investigating whether Artificial Intelligence (AI) can enable carbon neutrality (CN) attainment is critically imperative. Focusing on China, this research employs a methodological framework of full-sample and sub-sample rolling-window bootstrap causality testing to explore the dynamic linkages between AI and CN. The empirical results indicate the absence of a stable causal relationship in the full-sample scenario; however, the rolling-window approach reveals significant time-varying causality. As shown by the rolling window analysis, AI shows a negative association on CN (July 2020 to December 2020), suggesting that the high energy consumption attributable to AI may impede CN objectives. Conversely, CN exhibits a positive association on AI across several periods (December 2019 to March 2020, July 2020 to October 2020, and May 2022 to September 2022), indicating that CN policies may support AI advancement. According to this research, accelerating CN progress has proven to be an effective pathway for catalysing AI innovation and enhancing technological capabilities. This suggests a complex long-term relationship in which AI and CN may both complement and constrain each other over time. Governments may consider developing policies to improve the energy efficiency of AI systems and to mitigate their associated energy consumption, to better align AI development with CN objectives. Achieving CN milestones boosts AI development by creating application scenarios and commercial opportunities, suggesting that policies promoting CN can indirectly enhance AI capabilities. Governments should establish dynamic monitoring and adjustment frameworks that encourage technological innovation while strengthening carbon-efficiency constraints to align AI development with CN objectives. Policymakers should strengthen interdisciplinary collaboration and international cooperation to facilitate knowledge sharing and coordinated progress in AI-driven carbon neutrality efforts. Policymakers must foster collaboration between AI and CN sectors to leverage synergies, ensuring that AI advancements contribute effectively to CN efforts.

• openalex https://doi.org/10.6084/m9.figshare.32411575.v1first seen 2026-05-28 04:57:33 · last seen 2026-06-03 04:56:56