To address climate change through enhanced forest carbon sequestration and sustainable resource management, this study develops and integrates two complementary modeling approaches: an EPIC-based carbon sequestration model and a meta-analysis-driven decision model for optimal forest resource utilization. First, using the Win EPIC version 6.0 model, we simulate forest growth and carbon dynamics by incorporating daily meteorological, soil, and management data. The model accounts for key physiological processes such as photosynthesis, biomass accumulation, water and nutrient uptake, and environmental stress to predict carbon dioxide sequestration under various forest management scenarios. Second, through a comprehensive meta-analysis of forest ecosystem services in China, we construct a multivariate regression model to evaluate the full spectrum of forest values, including carbon fixation, water conservation, biodiversity protection, soil retention, recreation, and economic benefits. The meta-regression incorporates factors such as ecosystem service type, vegetation zone, forest area, surrounding forest abundance, infrastructure such as railway length, and socioeconomic indicators such as population and GDP per capita. By applying both models to representative forest regions over a 100-year horizon, we identify long-term optimal management strategies that balance carbon sequestration with multifunctional ecosystem services. Results indicate that strategic forest management, including controlled logging of up to 2.37 percent of the area for wood products, can significantly enhance carbon storage while maintaining other ecological and socioeconomic values. The integrated framework provides a robust tool for policymakers and forest managers to design strategies that maximize both climate mitigation and the sustainable provision of forest ecosystem services.
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Share and Cite
Sun, C., Wang, C. (2025) Forest Management Strategy Model Based on EPIC and Meta-analysis. Scientific Research Bulletin, 2(3), 59-68.