Disagreements in Equivalent-Factor-Based Valuation of County-Level Ecosystem Services in China: Insights from Comparison Among Ten LULC Datasets
Valuation of ecosystem services (ESs) is crucial for understanding the benefits provided by ecosystems and informing sustainable management and policy decisions related to ecosystem protection. This study explores the disagreements in ecosystem service value (ESV) at the county level across China in...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2320 |
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| Summary: | Valuation of ecosystem services (ESs) is crucial for understanding the benefits provided by ecosystems and informing sustainable management and policy decisions related to ecosystem protection. This study explores the disagreements in ecosystem service value (ESV) at the county level across China in 2020 by comparing ten land cover datasets of varying resolutions from 500 to 10 m, using the equivalent factor method. Significant disagreements in ESV estimates are identified, revealing spatial heterogeneity and large inconsistencies among estimates from different datasets, even with high spatial resolution (10 m). Across all counties, the typical discrepancy in ESV estimates between any two datasets reaches 3503 CNY/ha, and the ESV estimates for each county show an average coefficient of variation (CV) of 0.186 across the ten datasets, indicating considerable inconsistency attributable to dataset selection. The results highlight that ESV evaluations based on the CLCD, Globeland30, and GLC-FCS30 datasets demonstrate higher consistency and reliability, making them suitable for regional ecosystem service valuation. Both the landscape configurations and the area disparities of different land types have significant impacts on ESV disagreement. This study provides valuable insights into the applicability of different datasets for ESV evaluation, thereby enhancing the reliability of ESV assessments and supporting informed decision making in ecosystem management. |
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| ISSN: | 2072-4292 |