用深度交叉网络预测中国城市碳排放，揭示技术进步的减排作用
Carbon Emission Prediction in China Considering New Quality Productive Forces Using a Deep & Corss Learning Modeling Framework
- 融合注意力与交叉网络建模特征交互，提升预测精度
- 测试集上误差仅612.304，决定系数达0.991，优于传统模型
- 揭示人口、经济等关键因素，适合政策制定者参考
新质生产力（NQPF）、数字经济和人工智能（AI）技术正成为推动可持续城市发展的重要力量。本文提出一种多头注意力深度与交叉网络（MADCN）框架，结合特征交互建模与注意力机制，用于预测城市碳排放并分析技术因素的影响。该框架引入可解释性学习阶段，采用SHapley Additive exPlanations（SHAP）评估各特征贡献。基于涵盖275个中国城市的面板数据集进行实验，结果表明，MADCN模型在测试集上均方误差（MSE）为406,151.063，平均绝对误差（MAE）为612.304，决定系数（R²）达0.991，显著优于传统机器学习与深度学习基线模型。SHAP分析显示，人口、城市规模、城镇化率和GDP是影响碳排放的关键因素，而NQPF、数字经济发展指数和AI技术水平虽影响相对适中，但具有实际意义。推动新质生产力发展、强化数字经济发展、加速AI技术应用，可有效助力城市碳减排。政策制定者应将技术创新融入减碳策略，尤其通过智能基础设施建设与跨领域数字化，切实推进双碳目标。
原文摘要 · Abstract (English)
New quality productive forces (NQPF), digital economy advancement, and artificial intelligence (AI) technologies are becoming crucial for promoting sustainable urban development. This study proposes a Multi-head Attention Deep & Cross Network (MADCN) framework, combining feature interaction modeling and attention mechanisms, to predict urban carbon emissions and investigate the impacts of technological factors. The framework incorporates an interpretable learning phase using SHapley Additive exPlanations (SHAP) to assess the contributions of different features. A panel dataset covering 275 Chinese cities is utilized to test the MADCN model. Experimental results demonstrate that the MADCN model achieves superior predictive performance compared to traditional machine learning and deep learning baselines, with a Mean Squared Error (MSE) of 406,151.063, a Mean Absolute Error (MAE) of 612.304, and an R-squared value of 0.991 on the test set. SHAP analysis highlights that population, city size, urbanization rate, and GDP are among the most influential factors on carbon emissions, while NQPF, digital economy index, and AI technology level also show meaningful but relatively moderate effects. Advancing NQPF, strengthening the digital economy, and accelerating AI technology development can significantly contribute to reducing urban carbon emissions. Policymakers should prioritize integrating technological innovation into carbon reduction strategies, particularly by promoting intelligent infrastructure and enhancing digitalization across sectors, to effectively achieve dual-carbon goals.
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