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Jiachen Gao

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Open access Jul 2026

From Ambition to Action: Barriers and Enablers for the Inclusion of Indigenous Peoples and Local Communities in the Kunming–Montreal Biodiversity Framework and the Role of Non‐State Bridge Organizations

The Kunming–Montreal Global Biodiversity Framework (KMGBF), adopted in 2022, set forth ambitious targets to halt and reverse biodiversity loss by 2030. However, translating these commitments into actionable strategies has proven challenging due to several factors. These include insufficient financing of biodiversity initiatives, lack of capacity and institutional coordination at the national and subnational level, fragmented implementation of the framework, limited integration of biodiversity into sectoral policies (agriculture and infrastructure, etc.), and gaps in monitoring, reporting, and accounting mechanisms. Moreover, geopolitical tension and uneven commitments among parties have further slowed collective progress. The 16th Conference of the Parties (COP16) to the Convention on Biological Diversity (CBD) was aimed at addressing these challenges. This paper analyzes the key outcomes of COP16, focusing on resource mobilization, monitoring frameworks, and the role of Indigenous Peoples and Local Communities (IPLCs). Building on institutional experience from the China Biodiversity Conservation and Green Development Foundation (CBCGDF), this Perspective highlights the important role that accompanying organizations and civil society can play in strengthening biodiversity governance. By synthesizing lessons from COP16 and identifying practical pathways for improving implementation, monitoring, financing, and inclusive participation, the paper offers recommendations that not only support delivery of the Kunming–Montreal Global Biodiversity Framework (KMGBF) but also help inform policy discussions and implementation priorities in the lead‐up to COP17.

S. Ogbe, Jinfeng Zhou, Linda Wong et al. · 0 citations
Conference Aug 2026

Evaluation of the BERT model for text semantic similarity

This study validates the effectiveness of the BERT model in semantic similarity calculation, providing more accurate technical support for related application scenarios, and laying the foundation for subsequent model optimization and lightweighting research.

Jiachen Gao · 0 citations