数字农科院2.0

Farmers’ decisions on crop residues utilization, greenhouse gases reduction and subsidy of crop residue-based bioenergy: An agent-based life cycle model

文献类型： 外文期刊

作者： Jiaqi Zhang;Chengxiang Zhuge;Qitong Huang;Bin Wang;Yu'e Li;Peter Oosterveer

作者机构：

关键词： Agent-based model;Crop residue-based bioenergy;Environmental and economic impacts;Farmer decision making;Life cycle thinking

期刊名称： Sustainable Production and Consumption

ISSN： 2352-5509

年卷期： 2025 年 55 卷

页码：

收录情况： SCIE（2025版） ; ; SSCI（2025版） ; ; EI（2025版）

摘要： To further advance the crop residue-based bioenergy (CRB) industry for climate change mitigation, it is crucial to better understand the influence of stakeholders' behaviours on greenhouse gases (GHG) mitigation potentials. However, the heterogeneity and social dynamics of stakeholders, particularly farmers, have received less attention. This study develops an Agent-based Environmental and Economic assessment (AEE) model that integrates agent-based model and life cycle thinking methods to simulate the CRB system. The AEE model was applied in Heilongjiang Province of China, to investigate how stakeholder decisions affect CRB's GHG reduction potential and government subsidies. Scenario analyses explore the effects of grain markets, subsidies, and collection distance on environmental and economic outcomes. The findings indicate that more farmers are willing to adopt crop residues collection than those currently practicing it, primarily due to logistical constraints. Key factors influencing adoption include farming income, age, farm size and crop types. CRB contributed to 70.6 % of overall GHG reductions with only 41.6 % of the subsidy, demonstrating higher mitigation efficiency. In conclusion, the government must address the deficiency in crop residues logistics to promote CRB development. Additionally, agricultural policies play a crucial role in ensuring CRB feedstock availability by guiding crop types selection. The results suggest that AEE model is adequate in simulating both micro and macro dynamics in the context of CRB, highlighting the robustness of integrating agent-based model and life cycle thinking methods to study complex issues.

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