Using Natural Language Processing for Automating the Identification of Climate Action Interlinkages within the Sustainable Development Goals

Climate action, Goal 13 of the UN Sustainable Development Goals (SDG), cuts across almost all SDGs. Achieving climate goals can reinforce the achievements in many other goals, but at the same time climate mitigation and adaptation measures may generate trade-offs, such as levelling the cost of energy and transitioning away from fossil fuels. Leveraging the synergies and minimizing the trade-offs among the climate goals and other SDGs is an imperative task for ensuring policy coherence. Understanding the interlinkages between climate action and other SDGs can help inform about the synergies and trade-offs. This paper presents a novel methodology by using artificial intelligence-based natural language processing (NLP) to automate the process of systematically identifying the key interlinkages between climate action and SDGs from a large amount of climate literature. A qualitative SDG interlinkages model for climate action was automatically generated and visualized in a network graph. This work contributes to the understanding of the interlinkages between climate action and SDGs based on scientific evidence and to integrated policy making based on an interlinkage perspective.