用真实农场数据模拟15年施肥减排，看政策如何影响奶农环保行为。
Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data
- 基于295个爱尔兰农场的实证数据，构建社会网络驱动的智能体模型。
- 预测准确率达97.9%，91%农场最终采用低排放肥料，符合现实饱和趋势。
- 适合政策制定者评估碳税、补贴等措施对农业减排的长期影响。
为理解乳品农业中复杂的系统动态，需兼顾农场异质性、社会互动与累积环境影响。本研究提出一种基于智能体的建模（ABM）框架，模拟15年间295个爱尔兰乳品农场的氮管理及低排放肥料采纳情况。利用实证数据，模型复现了农场间通过社交网络的信息传播，采纳概率由社会传染、农场自身特征及政策干预（如补贴和碳税）共同驱动。该框架可计算行业温室气体排放、累计减排量及私人-社会成本，并通过蒙特卡洛与敏感性分析量化不确定性。模型预测精度高（R² = 0.979，RMSE = 0.0274），经科尔莫戈罗夫-斯米尔诺夫检验验证（D = 0.2407，p < 0.001）。采纳动态符合罗杰斯逻辑曲线，再现91%的现实饱和平台，同时体现结构性滞后效应。研究将脱碳视为社会技术演进过程，而非单纯经济算计，建立了一个用于评估气候策略扩散的前瞻性政策分析框架。
原文摘要 · Abstract (English)
To understand complex system dynamics in dairy farming requires tools that capture farm heterogeneity, social interactions, and cumulative environmental impacts. This study proposes an agent-based modelling(ABM) framework to simulate nitrogen management and low-emission fertiliser adoption across 295 Irish dairy farms over a 15-year period. Using empirical data, the model replicates farm communication through a social network, where adoption probabilities are driven by social contagion, farm-scale factors, and policy interventions such as subsidies and carbon taxes. The framework computes sectoral greenhouse gas emissions, cumulative abatement, and private-social costs, with Monte Carlo and sensitivity analyses quantifying uncertainty. The model achieved high predictive accuracy (R2 = 0.979, RMSE = 0.0274) and was validated against observed adoption data using a Kolmogorov-Smirnov test (D = 0.2407, p < 0.001). Adoption dynamics were fitted to Rogers logistic curves, reproducing a realistic saturation plateau (91%) while acknowledging structural laggard effects. By conceptualizing decarbonization as a socio-technical evolution rather than a purely monetary calculation, this study establishes an exploratory policy framework for evaluating the diffusion of climate strategies prior to implementation.
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