用蒙特卡洛模拟评估海南智能农业平台的减碳效益，量化了农药化肥等减少效果。
Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan
- 构建从生产到田间全链条碳核算模型，结合AI与物联网数据进行量化分析。
- 全范围推广下，农药、化肥、灌溉水和碳强度平均降低23.5%、21.0%、16.5%和21.5%。
- 发现土壤测试推荐与有机替代是碳减排主因，适合政策评估与试点设计参考。
智能农业平台被视为实现中国减药、减肥、节水和降碳目标的关键载体，但缺乏统一的绿色价值量化框架。本文以集成大语言模型问答、多模态病虫诊断、物联网传感、卫星遥感与闭环田间记录系统的热带农业AI决策平台为研究对象，构建覆盖农药化肥生产、田间N2O排放、灌溉用电及稻田CH4的从产地到田头的农业碳核算模型，将平台干预转化为可量化的传输参数，并通过蒙特卡洛模拟在海南三种场景（芒果、冬菜、水稻/南繁）下进行评估，面积加权比例为40%:30%:30%。在全面推广条件下，农药使用中位数减少23.5%（90%区间15.0%-33.2%），化肥减少21.0%（13.8%-28.9%），灌溉用水减少16.5%（10.9%-23.5%），碳强度下降21.5%（16.1%-27.2%）。肥料减量≥15%的概率达90.6%，碳排放明显下降概率为98.1%，但综合节水≥20%的概率仅约20%，建议分场景表述。Sobol一阶指数显示，土壤测试推荐与有机替代共同解释约83%的碳强度降幅方差。收敛性测试表明10,000次迭代已稳定所有统计量；报告保守/基准/乐观情景边界。该框架提供可复现、可校准的前向绿色价值评估方法，适用于试点观测设计。
原文摘要 · Abstract (English)
Smart agriculture platforms are widely regarded as key carriers for implementing China's pesticide and fertilizer reduction, water-saving and carbon-reduction agendas, yet a unified quantitative framework for assessing their green value is still lacking. Taking an AI-driven decision platform for tropical agriculture as the object (integrating large-language-model question answering, multimodal pest diagnosis, IoT sensing, satellite remote sensing, and a closed-loop field record system), this study builds a cradle-to-farm-gate agricultural carbon accounting model covering pesticide and fertilizer production, field N2O, irrigation electricity and paddy CH4, translates platform interventions into quantifiable transmission parameters, and propagates parameter uncertainty by Monte Carlo simulation over three Hainan scenarios (mango, winter vegetable, rice/nanfan, area-weighted 40%:30%:30%). Under full adoption, median reductions are 23.5% (90% interval 15.0%-33.2%) for pesticide use, 21.0% (13.8%-28.9%) for fertilizer, 16.5% (10.9%-23.5%) for irrigation water, and 21.5% (16.1%-27.2%) for carbon intensity. Attainment probabilities are high for fertilizer reduction >=15% (90.6%) and clear carbon decline (98.1%), but only about 20% for aggregate water saving >=20%, favoring scenario-specific statements. Sobol first-order indices show soil-test recommendation and organic substitution jointly explain about 83% of the variance of aggregate carbon-intensity reduction. Convergence tests show 10,000 iterations stabilize all statistics; conservative/baseline/optimistic scenario bounds are reported. The framework offers a reproducible, calibration-ready methodology for ex-ante green-value assessment and pilot observation design.
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