SEEK-Policy: a data-driven cross-modal retrieval framework for climate policy recommendation

Information from the abstract

Timely and context-sensitive climate policy recommendation requires intelligent systems that can seam-lessly integrate evolving environmental trends with domain-specific knowledge. However, existing tools rely predominantly on static keyword-based searches, lacking the ability to combine structured time-series signals with unstructured policy texts or to perform semantic reasoning across modalities. This limitation hinders policymakers from formulating rapid, evidence-based responses to emerging climate challenges, especially in resource-limited and data-constrained contexts. In this work, we present SEEK-Policy (Semantic Embeddings for Environment and Knowledge-based Policy) an open and reproducible cross-modal retrieval framework that directly aligns multivariate time-series data with climate policy documents using semantic embeddings and retrieval-augmented generation (RAG). The framework introduces three key modules: TimeTranscriber, which transforms multivariate time-series signals into concise, policy-relevant summaries; ChunkAlign, which performs fine-grained retrieval by mapping these summaries to semantically aligned segments within large corpora of climate policy documents; and PolicySynthesizer, which composes these segments into coherent, interpretable recommendations. To support open science, we develop POLiMATCH, a dataset linking over 1,200 climate policy documents with 10 years of structured time-series data across multiple countries and sectors, enabling rigorous evaluation of cross-modal retrieval tasks. Empirical results demonstrate that SEEK-Policy consistently outperforms strong baselines, including a Siamese network with contrastive learning and a role-based multi-agent summarization approach, achieving improvements of approximately 22% in Hit@1 and 8% in MRR@5 over the best-performing baseline. Beyond retrieval performance, SEEK-Policy provides an interpretable pipeline that links quantitative environmental indicators to climate policy documents, aiming to reduce the technical burden on decision-makers in resource-constrained settings. The framework and POLiMATCH dataset are openly available to support further research in cross-modal climate policy retrieval.

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Related topics: Computational and Text Analysis Methods · Topic Modeling · Multimodal Machine Learning Applications