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Research Article

SDG- and GMAG-oriented analysis of multi scenarios spatiotemporal changes and evaluation of the effectiveness and potential of mangrove forests

, , , , , , , & show all
Article: 2346274 | Received 22 Sep 2023, Accepted 17 Apr 2024, Published online: 26 Apr 2024
 

ABSTRACT

It is crucial to mangrove management by understanding its conservation efforts. This study proposed a spatiotemporal analysis model to evaluate mangrove changes in the Guangxi Beibu Gulf (GBG) and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), focusing on past, present, and future dynamics. The analysis covered mangrove loss, stability, and gain from 1990 to 2020 and provided projections for 2020 to 2035 across multiple scenarios. Then further assessed the efficacy of past mangrove conservation with the Sustainable Development Goals (SDGs) and the prospective conservation potential with the Global Mangrove Alliance Goal (GMAG) indicators. Findings indicate that by 2020, the GBG and GBA had 81.65 km² and 40.92 km² of mangroves, respectively, with increases of 31.51% and 164.09% since 1990. Projected expansions are anticipated under all three scenarios during 2020- 2035, with the highest growth projected under the trend continuation scenario, followed by ecological restoration and protection, and economic development. While mangrove protection in these regions met SDG targets 6.6.1 and 14.5.1 from 2015 to 2020, the 80% conservation milestone set by GMAG for 2030 was not achieved, necessitating the establishment of nature reserves. The study offers novel insights for sustainable governance of mangrove forests at local, regional, and global levels.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors were unable to specify which data were used.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China: [Grant No. U21A2022, 42101369, U1901219, and 42071393], and Natural Science Foundation of Guangxi: [Grant No. 2023GXNSFBA026278].