Spatial assessment of forest soil carbon and climate-related value inside and outside protected areas in China

Protected areas are widely used as spatial instruments for climate mitigation, yet their contribution to below-ground carbon storage remains insufficiently quantified at national scales. This study assesses how forest soil organic carbon (SOC) and its climate-related value differ between protected and non-protected areas across China under historical and future climate conditions. Using field-based SOC observations and spatial machine-learning models, we estimated SOC density at two soil depths (0-20 cm and 0-100 cm) from 2000 to 2100 under multiple Shared Socioeconomic Pathway scenarios. Spatially explicit SOC projections were generated using Random Forest models driven by climatic, vegetative, soil, and topographic variables, and SOC differences associated with protected-area status were evaluated through comparative analysis of forested regions inside and outside established reserves. The results reveal strong spatial differentiation in SOC trajectories, with stability or accumulation under low-emission pathways and widespread losses under high-emission scenarios, particularly in deeper soil layers. Forests within protected areas consistently exhibit higher SOC densities than unprotected forests, with mean differences of 45.5% in topsoil and 33.4% across the full soil profile. Extrapolation to protected areas established after 2000 indicates potentially substantial additional SOC stocks, corresponding to climate-related values ranging from tens to over one hundred billion USD under alternative carbon price assumptions. Although spatially independent validation indicates reduced predictive performance at fine scales, the model robustly captures broad climatic and edaphic gradients. Overall, this study provides a spatially explicit assessment of forest SOC dynamics to support conservation planning and climate-mitigation strategies.

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Publication Details

Journal
Environmental Monitoring and Assessment
Published
2026-06-26
DOI
https://doi.org/10.1007/s10661-026-15630-5
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatial assessment of forest soil carbon and climate-related value inside and outside protected areas in China

Y Z Zhang, Pedro Cabral, Guojie Wang
Environmental Monitoring and Assessment
Remote Sensing in Agriculture
article

Spatial assessment of forest soil carbon and climate-related value inside and outside protected areas in China

Y Z Zhang, Pedro Cabral, Guojie Wang
article en

Abstract

Protected areas are widely used as spatial instruments for climate mitigation, yet their contribution to below-ground carbon storage remains insufficiently quantified at national scales. This study assesses how forest soil organic carbon (SOC) and its climate-related value differ between protected and non-protected areas across China under historical and future climate conditions. Using field-based SOC observations and spatial machine-learning models, we estimated SOC density at two soil depths (0-20 cm and 0-100 cm) from 2000 to 2100 under multiple Shared Socioeconomic Pathway scenarios. Spatially explicit SOC projections were generated using Random Forest models driven by climatic, vegetative, soil, and topographic variables, and SOC differences associated with protected-area status were evaluated through comparative analysis of forested regions inside and outside established reserves. The results reveal strong spatial differentiation in SOC trajectories, with stability or accumulation under low-emission pathways and widespread losses under high-emission scenarios, particularly in deeper soil layers. Forests within protected areas consistently exhibit higher SOC densities than unprotected forests, with mean differences of 45.5% in topsoil and 33.4% across the full soil profile. Extrapolation to protected areas established after 2000 indicates potentially substantial additional SOC stocks, corresponding to climate-related values ranging from tens to over one hundred billion USD under alternative carbon price assumptions. Although spatially independent validation indicates reduced predictive performance at fine scales, the model robustly captures broad climatic and edaphic gradients. Overall, this study provides a spatially explicit assessment of forest SOC dynamics to support conservation planning and climate-mitigation strategies.

Environmental Monitoring and AssessmentVol. 198(7)
Nanjing University of Information Science and Technology (CN), Universidade Nova de Lisboa (PT)
Universidade Nova de Lisboa
Climate action
Openalex Percentile: Top 8%
Remote Sensing in Agriculture
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