Dynamical analysis and near-optimal control strategy of a stochastic carbon emissions model

报告主题：Dynamical analysis and near-optimal control strategy of a stochastic carbon emissions model

报告时间：2026-06-06 09:30

报告地点：1C207

专家姓名：原三领

专家简介：原三领，上海理工大学教授，博士生导师，中国数学会生物数学专业委员 会副主任。研究方向为:微分方程与动力系统、生物数学。曾先后主持多项国家自然科学基金面上项目和上海市项目的研究工作。研究内容涉及微分方程与动力系统、种群动力学、流行病动力学、海洋生态学以及生物化学工程等诸多领域，具有多学科交叉特点。曾多次受 邀 到 国 内和 国 际多所 高 校 进 行合 作 研究和 学 术 交 流， 在 SIAM Journal on Applied Mathematics、Journal of Mathematical-Biology、Journal of Differential-Equations等国内外重要学术刊物上发表SCI论文150余篇。

报告内容：To better understand the dynamic relationship between CO2 concentration, human population, and energy consumption in a stochastic environment, we propose and investigate a stochastic carbon emissions model, and further consider its near-optimal control (NOC) problem. We first focus on the natural evolution scenario without intervention measures to analyze the dynamic behavior of the carbon emissions system under environmental fluctuations. The results suggest that when environment noise is sufficiently large, it will lead the population to collapse, thereby reducing energy consumption to zero, and eventually returning CO2 concentration to pre-industrial level. This is an unsustainable scenario ecologically for the model. When environment noise is not too large, there exists a unique ergodic stationary distribution. To effectively reduce the CO2 concentration while ensuring a reasonable population size, we then develop a near-optimal control system that incorporates two intervention strategies. Using the Pontryagin stochastic maximum principle, we establish necessary and sufficient conditions for the existence of the near-optimality. Theoretical and numerical results demonstrate that effective CO2 mitigation strategies must consider both ecological sustainability and economic feasibility. From the perspective of policymakers, this study emphasizes the importance of dynamically adjusting emission reduction strategies across different development stages. Such adaptive decision-making can effectively alleviate atmospheric CO2 concentration while ensuring economic and ecological sustainability.