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【UNDP-2024研报】利用人工智能加强昆明-蒙特利尔全球生物多样性框架的早期行动(英).pdf
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1
Leveraging Artificial Intelligence
to enhance early action towards
the Kunming-Montreal Global
Biodiversity Framework
2
Authors
Nicole DeSantis, Lea Phillips, Christina Supples, Julien Pigot, Jamison Ervin, Doley Tshering, Juan Calles
Lopez, Dharshani Seneviratne, Enrique Paniagua, and Monica Mora.
Acknowledgements
The authors would like to acknowledge UNDP colleagues Reina Otsuka, Midori Paxton, Vishal Patil, Gayan
Peris, Anneke Lincoln Schoeman, Goetz Schroth, and Diwen Xu for their expert review and contribution and
Georgina de Moya and Alexis Legigand for their role in facilitating review and application of the methodology
among countries. The authors would also like to acknowledge the support of the 54 countries that participated
in the development and refinement of the methodology. Notably, the team thanks the Costa Rican Ministry
of the Environment and Energy (MINAE), UNDP Costa Rica, and Roxana L. García Huezo, whose data
visualizations in “Similarity Analysis of Costa Rica’s National Biodiversity Strategy with the Kunming-Montreal
Global Biodiversity Framework” served as a model for those in the NBSAP Target Similarity Assessments.
Disclaimer
The views expressed in this publication are those of the author(s) and do not necessarily represent those of
the United Nations, including UNDP, or the UN Member States.
UNDP is the leading United Nations organization fighting to end the injustice of poverty, inequality, and climate
change. Working with our broad network of experts and partners in 170 countries, we help nations to build
integrated, lasting solutions for people and planet. Learn more at undp.org or follow @UNDP.
©UNDP (2024). Leveraging Artificial Intelligence to enhance early action towards the achievement of the
Kunming-Montreal Global Biodiversity Framework. United Nations Development Programme: New York.
3
Contents
Executive Summary 2
Introduction 5
Methodology 13
National NBSAP Target Similarity Assessments 17
NBSAP Target Similarity Assessment: Policy summary 17
NBSAP Target Similarity Assessment: In-depth analysis 20
Proof of concept for using AI to assess global trends
in NBTs 24
Limitations, risk mitigation, and lessons learned 28
Conclusions 30
1
2
3
4
5
6
4
Executive Summary
Nature is interconnected, intertwined, and indivisible with human life, our
societies, and economies. Yet we are polluting and destroying our land,
air, seas, and freshwater, and threatening current and future generations.
Incremental change is not enough.
— UNDP Nature Pledge
“
”
5
The urgency to mitigate humanity’s impact on global biodiversity necessitates innovative strategies
within the framework of international conservation eorts. Central to these endeavors is the
Convention on Biological Diversity (CBD), an international convention established in 1992 to guide
the conservation of biodiversity, the sustainable use of its components, and the equitable sharing of
benefits from genetic resources. Despite global commitments from 196 Parties across more than 30
years, biodiversity continues to decline rapidly due to human activities, with no global targets fully
achieved yet.
National Biodiversity Strategies and Action Plans, or NBSAPs, are the main policy instruments for
implementing the CBD goals at the national level, and critical for countries to establish and monitor
their contributions towards global commitments. They include National Biodiversity Targets (NBTs)
and national actions on nature and related environmental and sustainable development policies. At
the 15th CBD Conference of the Parties (COP15) in 2022, 196 CBD Parties adopted the Kunming-
Montreal Global Biodiversity Framework (GBF), which aims to put nature on a path to recovery by
2030 and achieve harmony with nature by 2050. Through updating and revising NBSAPs in line with
the GBF, countries can contribute to a more sustainable future that leaves no one behind.
Yet, countries have struggled to set NBTs that match the scope and ambition of global biodiversity
commitments. And, since the aims of the GBF exceed those of previous CBD frameworks, the gap
between national and global targets is widening further. Less than half of CBD Parties have submitted
NBTs aligned with the GBF by COP16, and even fewer have submitted updated NBSAPs. This raises
concerns that the GBF could fall short of galvanizing accelerated global action at a scale sucient to
halt and reverse biodiversity loss and its impacts on humankind.
These delays are reflective of the breadth of challenges and capacity gaps countries often face when
developing updated national policies toward the CBD. In many cases, Parties must first strengthen
the underlying conditions for national achievement of the GBF, such as building political will and
increasing capacities. Countries may also develop or fortify national coordination mechanisms with
gender and biodiversity focal points, data holders, Indigenous Peoples, local communities, non-
governmental organizations, and the business and finance community, among other key groups.
These important activities can leave minimal time or capacity remaining for conventional manual
review of NBTs. The occurrence of extreme weather events and associated economic losses,
especially in Small Island Developing States (SIDS), can further delay eorts, as well as challenges
accessing national technical experts due to the emigration of skilled professionals.
Novel approaches are needed to support governments in rapidly aligning biodiversity policies
with the GBF to realize global biodiversity ambitions. Artificial Intelligence (AI) holds transformative
potential for navigating the complex policy landscapes of biodiversity conservation. Advanced AI
models oer capabilities unheard of at the start of this decade, such as analyzing and synthesizing
large volumes of policy data and providing actionable written insights that facilitate quick and
eective engagement on target alignment and strategic planning. When applied through a human-
centered approach that minimizes risk, AI can also democratize access to cutting-edge analytics,
empowering a broader range of stakeholders. AI-informed assessments can oer a systematic and
standardized foundation for policy discussions and facilitate collaboration among diverse groups.
By providing a clear basis for evaluating harmony between national and global policies, AI can help
prioritize actions and enhance the overall eectiveness of biodiversity strategies.
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