With the establishment of the Sustainable Development Goals (SDG) framework, practitioners in environmental impact assessment have an increasing requirement to detect relevant information centered on this frame of reference. The task of automatically identifying evidence that supports the project actually addressing a particular SDG target becomes crucial for enabling assessment digitalization across long, heterogeneous documents. In this work, we tackle SDG evidence identification via the well-suited Retrieval-augmented Generation (RAG) approach powered by Large Language Models (LLM). The identified evidence may also support further related tasks in conceptual modeling where reports or parts of their content are to be assigned to entries in a structured resource such as a domain-specific ontology. Beyond the measurement of performance of a series of method configurations on this task, we also assess RAG abilities for making this kind of decisions when the LLM is requested to explain its own mechanisms alongside the answer it generates. Our evaluation resources are made publicly available.