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Integrated stock and flow system dynamics and optimized sampling for comprehensive carbon offset evaluation of natural and farming ecosystems

自然生態系と農業生態系の包括的カーボンオフセット評価のためのストック・フロー統合システムダイナミクスと最適化サンプリング (AI 翻訳)

Takahiro Sasaki, Godai Suzuki, Masatoshi Funabashi

Ecological Complexity📚 査読済 / ジャーナル2026-05-19#炭素会計Origin: JP対象セクター: agriculture
DOI: 10.1016/j.ecocom.2026.101168
原典: https://doi.org/10.1016/j.ecocom.2026.101168

🤖 gxceed AI 要約

日本語

本研究は、システムダイナミクスを用いて炭素ストックとフローを統合した大気-バイオマス-土壌(ABS)モデルを提案する。単作とシンファーム(混作)の比較分析により、シンファームが温帯・熱帯で正味負の炭素収支を達成することを示した。また、炭素クレジット評価の偏りを低減するためのサンプリング最適化フレームワークを提供する。

English

This study proposes an Atmosphere-Biomass-Soil (ABS) model integrating carbon stock and flow via system dynamics. It compares monoculture and synecoculture, showing synecoculture achieves net-negative carbon budgets in temperate and tropical zones. A theoretical sampling optimization framework is introduced to reduce bias in carbon credit evaluations.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

本論文は、日本の農地におけるシンファーム(混作)のカーボンオフセット効果を定量的に示し、J-クレジット制度や土地利用戦略に示唆を与える。SSBJや有報でのカーボンアカウンティングにも応用可能なフレームワークを提供する。

In the global GX context

This paper provides a rigorous framework for evaluating carbon offsets from land-use changes, directly relevant to global carbon accounting standards (e.g., ISO 14064, GHG Protocol) and voluntary carbon markets. The synecoculture case offers a nature-based solution for net-negative emissions, informing ISSB and CSRD disclosures.

👥 読者別の含意

🔬研究者:The ABS model and sampling optimization framework provide a novel approach for carbon accounting in managed ecosystems, applicable to further research on land-use emissions.

🏢実務担当者:Synecoculture as a carbon-negative farming practice can be integrated into corporate sustainability strategies and carbon offset portfolios.

🏛政策担当者:The results support policies promoting polyculture systems for carbon sequestration, and the sampling framework can improve the integrity of carbon credit programs.

📄 Abstract(原文)

• 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.

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