The urgency to mitigate humanity's impact on global biodiversity necessitates innovative strategies. Artificial Intelligence (AI) holds transformative potential for navigating the complex policy landscapes of biodiversity conservation and accelerating action. When applied through a human-centered approach that minimizes risk, AI can democratize access to cutting-edge analytics, empowering a broader range of stakeholders. UNDP has now supported more than 50 countries to use AI to uncover patterns in the alignment of national policies with global biodiversity aims. 

Developed on an on-demand basis through the Early Action Support (EAS) Project implemented by UNDP, NBSAP Target Similarity Assessments offer customized insights on synergies between global and national biodiversity targets and provide recommendations for enhanced alignment to bring about a transformation in our societies’ relationship with biodiversity by 2030. Developed with governments to address their unique needs, these assessments can foster dynamic, inclusive, and effective national stakeholder engagement to fill gaps, raise political will, and improve sectoral collaboration, resulting in accelerated progress towards global biodiversity commitments. 

This publication releases the methodology behind the National Biodiversity Strategies and Action Plans (NBSAP) Target Similarity Assessments and identifies key lessons learned and opportunities for future applications.