BACKGROUNDThe agricultural sector faces a challenge in balancing increasing food demand while minimizing environmental impacts. Crop protection products are crucial for achieving high crop yields and ensuring food security. However, life cycle assessment (LCA), the standard framework for evaluating environmental impact, is time‐consuming and costly, especially during early product development. To address this, a novel tool correlating process mass intensity (PMI) with greenhouse gas (GHG) emissions has been developed as a streamlined alternative.RESULTSA strong linear correlation (R2 = 0.95) was identified between PMI and product GHG emissions, enabling rapid carbon footprint estimation using simplified PMI data. The model was validated using 13 small molecule active ingredients (AIs), showing a mean absolute error (MAE) of 55 g CO₂/kg AI and a root mean square error (RMSE) of 64 kg CO₂/kg AI. Residual analysis demonstrated random distribution, suggesting reliable predictions.CONCLUSIONThe PMI‐based tool provides rapid, accurate estimates of product carbon footprint (PCF), supporting early‐stage decision‐making in research and development for agrochemical processes. Its simplicity makes it applicable across various chemical sectors and valuable for sustainability efforts. © 2024 Society of Chemical Industry.

中文翻译：

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农用化学品开发中碳足迹的快速估算：过程质量强度与 CO2 排放的相关性


背景农业部门在平衡不断增长的粮食需求和最大限度地减少环境影响方面面临着挑战。作物保护产品对于实现高作物产量和确保粮食安全至关重要。然而，生命周期评估 （LCA） 是评估环境影响的标准框架，既耗时又昂贵，尤其是在产品开发初期。为了解决这个问题，已经开发了一种将过程质量强度 （PMI） 与温室气体 （GHG） 排放相关联的新工具作为一种简化的替代方案。结果确定了 PMI 和产品温室气体排放之间的强线性相关性 （R2 = 0.95），从而能够使用简化的 PMI 数据快速估算碳足迹。该模型使用 13 种小分子活性成分 （AI） 进行了验证，显示平均绝对误差 （MAE） 为 55 g CO₂/kg AI，均方根误差 （RMSE） 为 64 kg CO₂/kg AI。残差分析显示随机分布，表明预测可靠。结论基于 PMI 的工具提供快速、准确的产品碳足迹 （PCF） 估计，支持农用化学品工艺研发的早期决策。它的简单性使其适用于各种化工行业，对可持续发展工作很有价值。© 2024 化工学会.