Impact of socioeconomic factors on vegetation restoration in humid karst areas of China: Evidence from a survey of 45 villages DOI
Qiuwen Zhou,

Ershuang Yuan,

Song Feng

и другие.

Journal of Rural Studies, Год журнала: 2024, Номер 114, С. 103546 - 103546

Опубликована: Дек. 12, 2024

Язык: Английский

Does socioeconomic status of farmers determine the adoption of forest landscape restoration practices? Evidence from Central Togo DOI Creative Commons
Hamza Moluh Njoya, Kossi Hounkpati, Kossi Adjonou

и другие.

Sustainable Environment, Год журнала: 2025, Номер 11(1)

Опубликована: Апрель 3, 2025

Язык: Английский

Процитировано

0

Exploration of Ecosystem Asset-Economy Coupling Coordination and Its Endogenous and Exogenous Drivers in Mountainous Regions DOI
Yuan Huang, Shidong Zhang, Jian Zhang

и другие.

Journal of Cleaner Production, Год журнала: 2024, Номер 486, С. 144460 - 144460

Опубликована: Дек. 17, 2024

Язык: Английский

Процитировано

2

Natural Factors Rather Than Anthropogenic Factors Control the Greenness Pattern of the Stable Tropical Forests on Hainan Island during 2000–2019 DOI Open Access
Binbin Zheng, Rui Yu

Forests, Год журнала: 2024, Номер 15(8), С. 1334 - 1334

Опубликована: Авг. 1, 2024

Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. This study used the annual mean Normalized Difference Vegetation Index (NDVI) as vegetation greenness indicator. The variation in NDVI on Hainan Island was analyzed using Theil–Sen median trend analysis Mann–Kendall test during 2000–2019. influence factors driving mechanism spatial pattern explored Multiscale Weighted Regression (MGWR) model. Additionally, we employed Boosted Tree (BRT) model explore their contribution NDVI. Then, MGWR utilized predict future patterns based precipitation temperature data from different Shared Socioeconomic Pathway (SSP) scenarios for period 2021–2100. results showed that: (1) forests significantly increased 2000 2019, with an average increase rate 0.0026/year. (2) R2 0.93, which more effective than OLS (R2 = 0.42) explaining relationship. regression coefficients ranged −10.05 0.8 (p < 0.05). Similarly, Gross Domestic Product (GDP) varied between −5.98 3.28 0.05); (3) played most dominant role influencing activities result relative contributions 83.2% forest changes (16.8% contributed activities). (4) under SSP119, SSP245, SSP585 2021 2100, projected have overall decreasing all scenarios. reveals change relationship factors, can guide medium long-term dynamic monitoring evaluation tropical Island.

Язык: Английский

Процитировано

0

Impact of socioeconomic factors on vegetation restoration in humid karst areas of China: Evidence from a survey of 45 villages DOI
Qiuwen Zhou,

Ershuang Yuan,

Song Feng

и другие.

Journal of Rural Studies, Год журнала: 2024, Номер 114, С. 103546 - 103546

Опубликована: Дек. 12, 2024

Язык: Английский

Процитировано

0