A systematic review comparing how AI is actually used in climate impact assessment across Africa and Europe, and where oversight gaps remain.
As climate finance grows, scrutiny of whether mitigation and adaptation investments deliver anticipated outcomes has intensified. Artificial intelligence is increasingly proposed to strengthen impact assessment across its lifecycle, from ex-ante prediction to monitoring, evaluation and reporting, yet evidence of actual practice remains fragmented. This paper presents a systematic review of AI applications in climate-related impact assessment, comparing practice in Africa and Europe using peer-reviewed literature, multilateral development bank project documents (World Bank, African Development Bank, European Investment Bank), and policy publications from 2015 to 2026. Using PRISMA-guided search and thematic synthesis of openly available sources, the review maps which AI methods are used at which lifecycle stage (machine learning for impact prediction, natural language processing for document and stakeholder-comment review, remote sensing analytics for outcome monitoring), and identifies a clear maturity gap: Europe concentrates AI use in monitoring and reporting under emerging regulatory obligations, while African applications cluster around ex-ante screening in donor-funded projects with limited monitoring follow-through. The review distils cross-cutting benefits, risks to transparency and professional judgement, and safeguard practices, offering practitioners and regulators an evidence base for where AI currently strengthens climate impact assessment, and where oversight gaps remain most acute.
A Lecturer and a consultant specializing in climate change adaptation and environmental impact assessment. He is currently a scientific researcher at RWTH Aachen University Germany.