Conservation Science and Practice (Feb 2024)

A mixed black and whitelist approach for wildlife trade regulation in China: Biodiversity conservation is made of shades of gray

  • Lingyun Xiao,
  • Emilio Pagani‐Núñez,
  • Xuesong Han,
  • Peng Zhao,
  • Xueyang Li,
  • Yixuan Hong,
  • Ruocheng Hu,
  • Xiang Zhao,
  • Ge Sun,
  • Cynthia Wardhana,
  • Zhi Lu

DOI
https://doi.org/10.1111/csp2.13062
Journal volume & issue
Vol. 6, no. 2
pp. n/a – n/a

Abstract

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Abstract The Kunming‐Montreal Global Biodiversity Framework requires effective actions to bend the curve of biodiversity loss by 2030. Wildlife trade, a direct drive of biodiversity decline, calls for more effective regulations to both protect wildlife populations in the wild and facilitate sustainable use of wildlife resources to meet human needs. This call has become particularly urgent in light of the COVID‐19 pandemic. In 2021, China's List of State Key Protected Wild Animals, a list of fauna under the strictest protection by national legislation, has been updated in the year 2021, 32 years after its first release, increasing its coverage (from the original 13%) an 11% of species across taxa. Combined with the updated List of State Protected Terrestrial Wild Animals which covers species with lower protection priority, these two national lists already cover 77% terrestrial vertebrate species of China. Such a blacklist approach, placing threatened species under a list of legal protection, is a common practice globally in species conservation. We discussed pros and cons of this dominant strategy and further explored the potential integration with a whitelist approach, listing all wildlife and only permitting regulated uses of certain species. We propose a mixed approach combining black and whitelists at different administration levels which could perhaps be first adopted in China. This is mainly due to the fact that in addition to illegal harvesting from the wild, traded wildlife in China are mostly from captive breeding and related laundering of wild‐caught animals.

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