The sustainability paradox: A systematic review of carbon footprints and decarbonization strategies for artificial intelligence in climate science

Artificial intelligence has become essential infrastructure for climate science yet operates within a sustainability paradox—deploying carbon-intensive technologies to address climate challenges. Despite 3,000% publication growth (2021–2025), methodological fragmentation prevents systematic understanding of AI's environmental costs and effective decarbonization strategies. This PRISMA 2020-compliant systematic review analyzed 59 peer-reviewed studies from Scopus (2021–2025) examining carbon footprints and mitigation strategies for AI in climate applications. Data synthesis employed narrative synthesis for carbon quantification (Theme 1, n = 31, 52.5%), random-effects meta-analysis for technical mitigation (Theme 2, n = 42, 71.2%), and content analysis for governance frameworks (Theme 3, n = 30, 50.8%). Bibliometric analysis using Bibliometrix identified temporal patterns and keyword co-occurrence networks. Carbon footprint quantification reveals systematic 300–500% underestimation through exclusion of inference phases (69% of studies), hardware embodied carbon (10–50% lifecycle impact), and geographic variance (5–15 × across regions). Meta-analysis demonstrates technical mitigation achieves pooled carbon reduction of −32.4% (95% CI: −27.1 to −37.8%, I² = 78%, p < 0.001), with algorithmic optimization superior (−38.2%) to hardware upgrades (−24.8%) and operational strategies (−29.7%). Critical governance gaps include inconsistent system boundaries (77% of studies, 200–400% measurement variance), incomplete disclosure (only 32%), and zero universal protocols. Network centrality analysis reveals carbon quantification terms occupy peripheral positions (betweenness = 0.4) despite 52.5% coverage, while policy vocabulary dominates centrality (betweenness = 91.5). We propose the Sustainable AI Carbon Accounting Protocol (SAICAP) addressing identified gaps through five mandatory disclosure categories, three-tier compliance structure, and multi-stakeholder coordination. Integrated technical mitigation can achieve 30–45% carbon reduction while maintaining model performance, contingent upon adopting standardized measurement protocols and governance mechanisms.