Quantifying responsible AI's role in global climate policy stabilization

Despite the growing intersection of environmental governance and digital transformation, empirical evidence on the dynamic interplay between climate policy uncertainty (CPU) and responsible artificial intelligence (RAI) remains sparse. This study addresses this research gap by investigating the contemporaneous and lagged relationships between CPU and RAI from November 2018 to June 2025 using the R ² decomposing linkage method. The results showed that the contemporaneous and lagged TCI had an opposite picture. Contemporaneous TCI exhibits notable temporal fluctuations, with peaks in early 2021, mid-2022, late 2024, and a trough around Q4 2021, Q3 2023. Responsible AI was a net shock transmitter in both linkages prior to 2021. Responsible AI acted as a net shock receiver through 2021-early 2022, mid-2023, and 2024 in lagged linkage. The role of responsible AI in contemporaneous linkage is opposite to its lagged result from 2021 onwards. Responsible AI generally dominated climate policy uncertainty, primarily through the lagged channel from 2020 to Q2 2021, in the second half of 2022, and in 2024. However, the relationship became more volatile, and responsible AI gradually shifted to being dominated by climate policy uncertainty in the lagged linkage from mid-2020 to early 2022 and in 2023. These results indicate that policymakers have to formalize binding governance standards for responsible AI, require transparency and accountability in AI systems that interact with climate and energy policy, and integrate AI oversight directly into climate-policy design and energy security planning. • Investigates the complicated dynamics between energy uncertainty and responsible AI. • Uses the R2 decomposing method to analyze contemporaneous/lagged relationships. • Contemporaneous and lagged Total Connectedness Index (TCI) exhibit opposite temporal patterns. • Responsible AI shifts its role, notably acting as a net shock receiver in lagged linkage post-2021. • Recommends formalizing binding governance standards for AI in climate policy.