关键词: 生物多样性公约, 获取与惠益分享, 人工智能, 生态数智化, 遗传资源数字序列信息

Abstract

Background & Aims: The Convention on Biological Diversity (CBD) and its Nagoya Protocol have established a core international legal framework for the access and benefit-sharing (ABS) of genetic resources and associated traditional knowledge, aiming to advance biodiversity conservation and sustainable use through fair and equitable benefit distribution. Global biodiversity governance is undergoing rapid digital and intelligent transformation. Artificial intelligence (AI) is deeply integrated into genetic resource monitoring, digital sequence information (DSI) collection, AI-assisted breeding, and digital conservation of traditional knowledge, bringing both opportunities and new challenges to the ABS system under the Convention on Biological Diversity (CBD) and its Nagoya Protocol. As a major biodiversity country and key contracting party, China has incorporated ABS into national strategies, promoted the Kunming-Montreal Global Biodiversity Framework, and established the Kunming Biodiversity Fund. However, China’s ABS system is not fully adapted to AI-driven digital scenarios, especially in the governance of DSI and digitalized traditional knowledge. This study aims to identify the core challenges in terminology adaptation, ownership allocation, operational procedures, and international coordination, and to propose digital-oriented and feasible pathways for improving China’s ABS implementation. The objectives are to enhance compliance with CBD obligations, safeguard national biodiversity sovereignty and the rights of indigenous and local communities, and achieve fair and equitable benefit-sharing and sustainable use of genetic resources and associated traditional knowledge in the AI era. 

Results: As an early contracting party, China has integrated the ABS system into its national strategy, fulfilled major-country responsibilities by issuing policy documents such as the Outline for Building a Powerful Country in Intellectual Property (2021–2035), contributed to international outcomes including the Kunming-Montreal Global Biodiversity Framework, and set up a biodiversity conservation fund for financial support. However, as AI reshapes ecological governance and global biodiversity conservation enters a new digital and intelligent era, China’s ABS system still faces challenges: insufficient adaptability of core term definitions, outdated ownership separation and operational procedures, and weak coordination with international conventions. Meanwhile, practical constraints such as enterprise resistance, high technical costs, international negotiation divergences, and emerging risks including algorithm opacity, data security, and cross-border abuse of digital sequence information on genetic resources (DSI) further impede implementation. A mature institutional system compatible with digital and intelligent development has not yet been established. 

Suggestions: To adapt to the AI era, China needs digital-oriented institutional innovation for upgrading its ABS system. We suggest establishing digital-adapted evaluation system to clarify the qualification of local communities and the ownership of digitalized traditional knowledge. An AI-empowered mechanism for ownership separation and whole-process tracing supported by blockchain and big data platforms should be constructed to regulate DSI collection, cross-border flow, and benefit distribution. Coordination with related international conventions should be strengthened through digital rule alignment and genetic resource source disclosure requirements. These pathways can improve the efficiency, fairness, and enforceability of the ABS system, protect national biodiversity security and community rights, and provide a Chinese solution for the global digital governance of genetic resources and traditional knowledge under the post-2020 global biodiversity framework.

Key words: Convention on Biological Diversity, access and benefit-sharing, artificial intelligence, ecological digitalization, digital sequence information on genetic resources