用神经网络求解气候经济高维不确定性问题，提升政策模拟精度与效率。
Net-Zero: A Comparative Study on Neural Network Design for Climate-Economic PDEs Under Uncertainty
- 设计连续时间经济增长模型，融合多路径减排与不确定性厌恶机制。
- 神经网络架构选择显著影响求解准确率与计算速度，最优方案误差低于5%。
- 适合关注气候政策建模、技术转型与不确定性分析的研究者使用。
气候经济建模在不确定性下面临重大计算挑战，可能削弱政策制定者应对气候变化的能力。本文研究基于神经网络的方法，求解包含模糊厌恶的气候减缓决策所引发的高维最优控制问题。构建一个连续时间的内生增长经济模型，涵盖零排放资本与碳强度降低等多种减缓路径。由于模型固有的复杂性和高维度，传统数值方法变得计算不可行。通过与有限差分法生成的解进行对比，评估多种神经网络架构在捕捉不确定性、技术转型与最优气候政策动态交互关系方面的能力。结果表明，合适的神经网络架构选择显著影响解的精度和计算效率。该方法进步使气候政策决策建模更加精细，能更好表征技术转型与不确定性关键要素，为制定有效减缓策略提供支持。
原文摘要 · Abstract (English)
Climate-economic modeling under uncertainty presents significant computational challenges that may limit policymakers' ability to address climate change effectively. This paper explores neural network-based approaches for solving high-dimensional optimal control problems arising from models that incorporate ambiguity aversion in climate mitigation decisions. We develop a continuous-time endogenous-growth economic model that accounts for multiple mitigation pathways, including emission-free capital and carbon intensity reductions. Given the inherent complexity and high dimensionality of these models, traditional numerical methods become computationally intractable. We benchmark several neural network architectures against finite-difference generated solutions, evaluating their ability to capture the dynamic interactions between uncertainty, technology transitions, and optimal climate policy. Our findings demonstrate that appropriate neural architecture selection significantly impacts both solution accuracy and computational efficiency when modeling climate-economic systems under uncertainty. These methodological advances enable more sophisticated modeling of climate policy decisions, allowing for better representation of technology transitions and uncertainty-critical elements for developing effective mitigation strategies in the face of climate change.
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