Fast-Tracking Biodiversity Action: How AI is Transforming Policy Alignment for Nature’s Future

UNDP and GEF are using AI-driven assessments to help countries rapidly align national biodiversity targets with the Kunming-Montreal Global Biodiversity Framework, enhancing policy coherence and stakeholder engagement. This innovative approach accelerates biodiversity action while ensuring responsible AI use and inclusive decision-making.

The United Nations Development Programme (UNDP), in collaboration with the Global Environment Facility (GEF), has pioneered a transformative approach to aligning national biodiversity strategies with global goals by applying artificial intelligence (AI) to accelerate policy alignment processes. This initiative, supported by UNDP’s Early Action Support (EAS) project, harnesses OpenAI’s Generative Pre-trained Transformer (GPT-3.5) to help countries meet the ambitious objectives of the Kunming-Montreal Global Biodiversity Framework (GBF), adopted at the 15th Conference of the Parties (COP15) under the Convention on Biological Diversity (CBD). Despite three decades of global efforts to mitigate biodiversity loss, the world has continued to witness a significant decline in biodiversity, often exacerbated by human activities. The GBF establishes a set of four goals and 23 targets designed to curb this loss and promote sustainability. However, many countries have struggled to align their national policies with the new framework, often due to limitations in technical expertise, time, and financial resources. Recognizing these challenges, UNDP and GEF introduced AI to create a rapid, structured, and standardized approach to policy evaluation. Through the NBSAP Target Similarity Assessments, powered by GPT-3.5, countries can quickly assess how closely their national biodiversity targets (NBTs) align with the GBF, reducing what was traditionally a months-long process to a matter of hours.

A Fast-Track Solution for Policy Assessment

This AI-based assessment process is designed to function as an initial baseline, with findings intended to guide further national expert review and validation rather than act as stand-alone recommendations. For instance, it generates insights into areas where a country’s targets may either align or diverge from the GBF, suggesting concrete actions for improvement. During these assessments, GPT-3.5 evaluates similarities between national and global targets, highlighting alignment gaps and recommending strategies to bolster policy coherence. In turn, this process empowers countries to bring in diverse stakeholders for targeted consultations, addressing various social, economic, and environmental factors that influence biodiversity. The model’s ability to swiftly generate analyses has not only enhanced countries' capacity to engage with complex biodiversity goals but has also led to time and resource savings, enabling countries to focus on in-depth stakeholder engagement and local consultation efforts. By offering this rapid assessment tool, UNDP and GEF hope to support over 50 countries in addressing the biodiversity crisis more efficiently, ultimately fostering a whole-of-society approach to sustainable development.

A Global Analysis of Biodiversity Alignment Trends

The broader scope of the AI application was demonstrated through a global proof-of-concept analysis in which GPT-3.5 assessed over 3,000 pre-COP15 NBTs from 129 countries, providing a comprehensive view of global biodiversity policy trends. This study revealed that some global targets, such as those related to species management and resource-sharing, were well-represented across national strategies. In contrast, goals addressing gender equality, green urban spaces, and biosafety were less frequently aligned with national policies. These insights illustrate not only where biodiversity policies are relatively strong but also where critical gaps remain. For instance, less than 10% of countries had NBTs that aligned with the GBF's gender-related goals, underscoring the need for greater integration of gender equality considerations within national biodiversity policies. Similarly, urban planning goals received little attention despite the increasing importance of sustainable city development in protecting ecosystems. This large-scale analysis validates AI's potential in identifying policy gaps across global biodiversity goals and serves as a valuable resource for countries as they update their biodiversity strategies under the GBF.

Mitigating AI’s Challenges and Biases

Despite its potential, using AI in biodiversity policy-making is not without challenges. AI models like GPT-3.5 can inherit biases from their training data, which may impact the reliability of policy recommendations. UNDP has addressed these concerns by using only non-personal, publicly available policy data and by ensuring that assessments are reviewed and validated by national experts. The NBSAP Target Similarity Assessments are designed to complement expert-led discussions rather than replace them, offering a standardized foundation upon which countries can build their policy analyses. Furthermore, UNDP has made the underlying methodology and code available to all stakeholders, enhancing transparency and adaptability. This open-access approach allows countries to further customize the model according to their specific needs, ensuring that AI remains a supportive tool rather than a prescriptive solution.

Environmental and Ethical Safeguards in AI Implementation

The UNDP-GEF initiative has also implemented additional safeguards to mitigate environmental and ethical risks associated with large-scale AI usage. For instance, the project team ensured data efficiency by focusing AI assessments specifically on NBTs rather than entire policy documents, reducing the computational burden. Countries were encouraged to validate AI-generated results through their national frameworks, and all assessments were conducted on secure servers. These measures help balance the benefits of rapid, scalable AI-based policy analysis with the need for responsible and transparent technology use. Moreover, the initiative emphasizes human-centered AI, involving stakeholders from government, academia, and civil society to ensure that the technology aligns with both global standards and local realities.

A Collaborative Pathway for Biodiversity Conservation

Overall, this pioneering use of AI in biodiversity policy aligns with UNDP's broader Nature Pledge, which supports over 140 countries in incorporating nature into national development strategies. By enhancing countries' capabilities to assess and align their biodiversity targets with the GBF, AI-driven assessments are helping to fast-track national efforts to reverse biodiversity loss and safeguard ecosystems. As countries update their biodiversity policies, this AI-supported methodology offers an efficient, scalable way to meet global biodiversity commitments while promoting inclusive, equitable, and sustainable solutions. The combination of AI’s analytical power and human oversight in biodiversity planning showcases a path forward where technological innovation and collaborative governance coalesce to address the urgent global biodiversity crisis.