The fragile ecosystem of desert and desertification-prone regions (D & DPRs) in China is highly sensitive to climate change, landuse intensification, and human interventions such as deforestation and overgrazing. In response, large-scale ecological restoration programs have been implemented over the past decades, yet their effect and cost-effectiveness remain insufficiently understood. Here, by applying multi-source remote sensing data, employing the Geodetector model, and developing a Return on Investment (RI) index, we established a framework to quantify the ecological restoration effect and assess the cost-effectiveness of the ecological restoration programs launched in China’s D & DPRs. The results indicated that a marked shift in ecosystem dynamics occurred in 1999. A comparison of the pre-restoration (1982–1998) and post-restoration (1999–2020) periods revealed that the restoration and degradation occur simultaneously, with the proportions increasing by 15.5% and 21%, respectively. Spatially, the identified ecological restoration effect was concentrated in the northern Loess Plateau, the northeastern Inner Mongolia Plateau, and the Hexi Corridor, which were strongly linked to population, land management strategies and infrastructural accessibility. However, the cost-effectiveness analysis revealed that higher levels of ecological investment did not necessarily lead to greater ecological restoration effect. Instead, restoration efficiency varied substantially across different ecological and socio-economic contexts. These findings suggest that ecological restoration in China’s D & DPRs is not a uniform process but is contingent on social-ecological characteristics and investment strategies. Our results emphasize the need for adaptive, region-specific approaches to optimize restoration efforts and ensure the sustainable management of China’s D & DPRs.
Assessing the Cost-Effectiveness of Ecological Restoration Programs Across China’s Desert and Desertification-Prone Regions by Integrating Vegetation Dynamics and Investment Data
Published 2025 in Land
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2025
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Land
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2025-11-10
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