Artificial Intelligence (AI)-driven approach to climate action and sustainable development

A new analysis uses ML classifiers (Extra Trees, Random Forest) and natural language processing (TF-IDF vectorization) to examine how 67 countries align climate commitments with sustainable development goals. This computational approach analyzes patterns in national policy documents and identifies key indicators that bridge climate and development policies.

The study uses feature importance analysis to reveal that middle and low-income countries with high emissions typically set lower climate targets and show strong textual similarities in their sustainable development reporting. High-income countries, particularly in the EU, demonstrate less alignment between their Nationally Determined Contributions and Voluntary National Reviews.

Through classification tree analysis, specific policy linkages emerge such as government spending on health and education, biodiversity protection areas, and waste management. They serve as the strongest predictive features. This quantitative analysis provides a methodological framework for evaluating policy coherence through automated text analysis.

Source:

https://www.linkedin.com/posts/charlescozette01_ai-driven-approach-approach-to-climate-action-ugcPost-7291907174635585536-PGCG/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAtGGkQBsxwMBmX3lEJO8btihnfBCaHqTz4