| 基于机器学习的中国省域农业碳达峰情景预测 |
| 摘要点击 926 全文点击 33 投稿时间：2025-06-17 修订日期：2025-09-28 |
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| 中文关键词 农业碳排放 机器学习 碳达峰 情景预测 支持向量机模型 |
| 英文关键词 agricultural carbon emissions machine learning carbon peak scenario prediction support vector machine model |
| DOI 10.13227/j.hjkx.202506197 |
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| 中文摘要 |
| 准确预测农业碳排放峰值及达峰时间对于加快推进农业强国建设、早日实现“双碳”目标具有重要意义. 基于2005~2023年中国省域农业碳排放数据，利用Lasso回归模型和主成分分析法筛选农业碳排放影响因素，构建多种机器学习预测模型，遴选出性能最优的预测模型展开农业碳达峰预测. 结果表明：①全国层面，观测期内农业碳排放呈现下降态势；省际层面，有16个省域的农业碳排放呈现下降态势，其中以北京降幅最大. ②比较5种机器学习算法发现，基于Lasso特征选择下的贝叶斯优化调参的支持向量机模型（LASSO-BO-SVR）表现最佳. ③在基准情景下，北京等23个省域已率先实现农业碳达峰，黑龙江和陕西这2个省域预计能在2030年前实现农业碳达峰，而广西、云南、青海、宁夏和新疆这5个省域则如期达峰困难；在政策情景与低碳情景下，5个未达峰型省域的峰值均有所下降，但峰年提前程度出现一定差异. 研究可为中国各省域未来农业碳减排路径提供决策参考. |
| 英文摘要 |
| Accurately predicting the peak of agricultural carbon emissions and its timing is crucial for accelerating the development of an agricultural powerhouse and achieving the dual carbon goals at an early date. Based on provincial-level agricultural carbon emission data from China covering 2005 to 2023， this study employed Lasso regression models and principal component analysis to identify key factors influencing agricultural carbon emissions. Multiple machine learning prediction models were constructed， and the optimal model was selected to forecast the peak of agricultural carbon emissions. The results indicate： ① Nationally， agricultural carbon emissions showed a declining trend during the observation period； provincially， 16 provinces exhibited decreasing emissions， with Beijing demonstrating the largest reduction. ② Among five machine learning algorithms， the support vector machine model with Bayesian optimization parameter tuning under Lasso feature selection （LASSO-BO-SVR） performed best. ③ Under the baseline scenario， 23 provinces including Beijing have already achieved agricultural carbon peak， while Heilongjiang and Shaanxi are projected to peak before 2030. Conversely， Guangxi， Yunnan， Qinghai， Ningxia， and Xinjiang face challenges in meeting their peak targets on schedule. Under the policy and low-carbon scenarios， the peak levels for these five provinces declined， though the timing of peak attainment varied. This research provides decision-making references for future agricultural carbon emission reduction pathways across China's provinces. |
