Document deals with using artificial intelligence (AI) to explore the interconnections between the Sustainable Development Goals (SDGs) and nationally determined contributions (NDCs) to reduce greenhouse gas emissions. The goal is provide a basis for better policy coordination and maximising synergies between these two important international initiatives.

Main ideas and findings:

• SDGs and the Paris Agreement: The document emphasizes that the SDGs and the Paris Agreement share common goals and principles. Both initiatives strive for globally beneficial solutions, inclusive societies, multilateral partnerships, and a bottom-up approach.
• Different approaches: However, the implementation of SDGs and NDCs is often separate, with different ministries responsible for them, leading to a lack of coordination.
• AI as a tool: The paper suggests using AI to analyze large data sets and identify interrelationships between SDGs and NDCs. AI can uncover patterns that traditional methods overlook.
• Methods: The study uses machine learning and natural language processing (NLP) methods. Machine learning is used to select the most important SDG indicators that are related to the NDC targets. NLP is used to analyze the texts in the Voluntary National Review Reports (VNRs) and explore their relationship to the NDC targets.
• Key indicators:
  • Machine learning models: Different machine learning models (Logistic Regression (LR), Extra Trees (ET), and Random Forest (RF)) selected different top indicators.
    • LR chose the unemployment rate.
    • ET selected the export of plastic waste.
    • RF selected the Corruption Perceptions Index.
  • Common indicator: However, all three models identified government spending on health and education as one of the important indicators.
  • Biodiversity protection: Protected areas for biodiversity were also often selected.
  • Export of plastic waste: The export of plastic waste was important for the ET and RF models.
• NLP analysis of VNR: Analysis of VNR using NLP showed that high-income countries tend to cluster into one group, indicating similar perspectives in their VNR.
• Link to NDC: Countries with high emissions often have lower NDC targets. Countries with high plastic waste exports and low NDC targets tend to have different content in their VNR reports.
• Keywords: Keyword analysis revealed that Korea focuses on connecting the economy, environment and society. Italy and Canada have stronger connections within communities, with Italy focusing on quality education and Canada on indigenous peoples.
• Impact of the economy: The document emphasizes that the level of ambition of NDCs and SDGs targets is influenced by national economic systems, performance and political leadership.
• Decoupling growth from emissions: Green growth strategies that seek to decouple economic growth from emissions are considered important.
• National development plans: Integrating climate action into national development plans is important.
• Different approaches: Countries have different approaches to implementing climate change mitigation and adaptation strategies.
• Importance of data: Data collaboration and access to AI are key to addressing the SDGs and NDCs.
• AI limits: The document also highlights the limits of AI, such as a lack of interpretability, and emphasizes the need for transparency and regulation.

The study shows that AI has the potential to improve policymaking on climate change and sustainable development. Analyzing the interlinkages between SDGs and NDCs using AI can help prioritize and coordinate policy actionsIt is important focus on the connection between economy, environment and society and take into account national economic systems and capacitiesIt is also necessary international cooperation and transparent use of AI. Spring