Econ Papers Daily / 期刊论文
从规则到学习：一种集成GIS的基于智能体的强化学习框架，用于模拟农户土地利用决策与可持续政策设计
From rules to learning: A GIS-integrated agent-based reinforcement learning framework for simulating farmers’ land-use decisions and sustainable policy design
摘要
Developing countries face critical tensions between farmland-use policies prioritizing food security and environmental sustainability, and farmers’ livelihood-centered land-use strategies. To address these challenges, this study develops a human-centered agent-based model that integrates dual-layer Q-learning with GIS-based spatial representation. This framework simulates farmers’ adaptive decision-making under dynamic policy and environmental conditions while formalizing behavioral heterogeneity through Theory of Planned Behavior (TPB)-informed typologies. Agents dynamically evolve land-transfer and cropping strategies via perception–action–feedback learning cycles. Applied to and validated against empirical observations from a representative Chinese case, the model reveals non-linear scale–efficiency relationships, with medium-scale farms exhibiting the comparatively balanced performance across yield and multiple resource-use efficiency dimensions. The analysis further identifies evolutionary pathways of adaptive behavior under policy incentives and quantifies trade-offs among area-weighted crop output, resource-use efficiency, and regional gross income. Scenario simulations show that rice-oriented subsidies increase rice output but reduce resource-use efficiency, rapeseed output, and gross income, whereas rotation-oriented incentives increase gross income while maintaining rice output near the baseline level. These findings support performance-linked subsidies based on multidimensional outcomes, tiered land-transfer regulation, and diversified cropping incentives. By linking adaptive behavioral dynamics with spatial and institutional constraints, this study provides a robust analytical framework for evaluating land-use policies and supports more effective governance strategies for balancing food security, environmental sustainability, and rural livelihoods.
中文摘要
发展中国家面临着优先保障粮食安全和环境可持续性的农田利用政策与以农民生计为中心的土地利用策略之间的关键矛盾。为应对这些挑战，本研究开发了一个以人为中心的基于主体的模型，将双层Q学习与基于GIS的空间表征相结合。该框架模拟了农民在动态政策和环境条件下的适应性决策，同时通过基于计划行为理论（TPB）的类型学将行为异质性形式化。主体通过感知—行动—反馈学习循环动态演化土地流转和种植策略。将该模型应用于中国一个代表性案例并与实证观测结果进行验证，结果揭示了非线性的规模—效率关系，其中中等规模农场在产量和多个资源利用效率维度上表现出相对均衡的绩效。分析进一步识别了政策激励下适应性行为的演化路径，并量化了面积加权作物产出、资源利用效率和区域总收入之间的权衡。情景模拟表明，以水稻为导向的补贴增加了水稻产出，但降低了资源利用效率、油菜籽产出和总收入，而以轮作为导向的激励措施在将水稻产出维持在基线水平附近的同时增加了总收入。这些发现支持基于多维结果的绩效挂钩补贴、分级土地流转监管和多元化种植激励。通过将适应性行为动态与空间和制度约束相联系，本研究为评估土地利用政策提供了一个稳健的分析框架，并支持更有效的治理策略，以平衡粮食安全、环境可持续性和农村生计。