• Introduces a novel “Atmosphere-Biomass-Soil (ABS) model” integrating carbon stock and flow via System Dynamics. • ABS model enables comparative carbon offset analysis between monoculture and synecoculture. • Synecoculture achieves net-negative carbon budgets across temperate and tropical zones. • Proposes theoretical sampling optimization framework reducing bias in ecosystem function assessments, including carbon credit evaluation. • Provides a robust framework to evaluate carbon offsets from land-use conversion strategies. To improve the accuracy and integrity of carbon offset evaluations, this study proposes an integrated framework using System Dynamics modeling that accounts for both carbon stock and flow in natural and managed ecosystems. We introduce the Atmosphere–Biomass–Soil (ABS) model, which simulates carbon budgets by integrating fluxes among atmospheric, biomass, and soil pools, while considering growth, sequestration, inputs, and harvests. Three case studies are examined: (1) Japanese average monoculture vs. locally practiced polyculture (synecoculture), (2) global temperate monoculture vs. standardized synecoculture, and (3) tropical paddy fields, rubber plantations, and forest regeneration vs. synecoculture in Indonesia, with simulated beta diversity scenarios. Results show that conventional monoculture generally yields positive carbon budgets due to high input-related emissions and limited retention. In contrast, synecoculture consistently achieves net-negative budgets by reducing emissions and enhancing sequestration. In temperate zones, converting monoculture to synecoculture can offset carbon equivalent to natural forests within 6–18 years. In tropical contexts, synecoculture shows early-stage offset benefits over paddy and rubber systems; however, its long-term advantage narrows relative to high-productivity systems like rubber plantations, whose offset potential may be overstated due to short product lifespans and limited recyclability. Beyond simulation, we propose a theory-driven framework for optimizing ecosystem sampling, incorporating normalized proxies, offset capacity metrics, and a cost-aware strategy based on statistical confidence. This framework enables robust and comprehensive assessments of ecosystem multifunctionality while reducing the risk of overvaluation and greenwashing.
Sasaki et al. (Tue,) studied this question.