用集成模型量化碳库的时空空间动态和驱动因素,在中国的博海湾
Feng Yan1, Zimo Zhao1, Jing Shan2
1School of Land and Resources, Hebei Agricultural University, Baoding, 071001, China.
Carbon balance and management
|October 8, 2025
概括
沿海地区的快速城市化影响了碳储存. 生态恢复可以扭转下降,人口密度被确定为影响未来碳汇的关键驱动因素.
科学领域:
- 环境科学,生态学,城市规划,气候变化研究
背景情况:
- 城市化和生态保护存在冲突,导致沿海地区的碳汇功能变化.
- 沿海地区面临着平衡发展与维持碳储存能力的挑战.
研究的目的:
- 分析快速城市化的沿海地区碳储存的时空动态.
- 确定影响碳储存变化的主要驱动因素.
- 为开发沿海地区的低碳空间规划提供科学基础.
主要方法:
- 历史评估,未来预测和机制分析的综合框架.
- 使用了InVEST,PLUS,空间错误模型 (SEM) 和最佳主要组件一般差异 (OPGD) 模型.
- 在北大,曹,和黄华,博海边缘,中国研究碳储存动力学.
主要成果:
- 碳储存呈现周期性变化,由于低碳土地扩张 (2000-2010年) 而下降,随后通过生态恢复 (2010-2020年) 恢复.
- 所有未来的场景 (2030年) 预测碳储量增加,耕地保护 (CP) 场景显示增幅最大.
- 人口密度被确定为主要驱动因素 (q=0.2126-0.2129),与社会经济因素非线性相互作用,塑造空间模式.
结论:
- 人类活动在城市化和生态冲突中显著影响碳储存模式.
- 了解时空异质性和关键驱动因素对于有效的环境管理至关重要.
- 研究结果支持为快速发展的沿海地区制定差异化低碳空间规划策略.
相关概念视频
Growth Models with Integration: Problem Solving
9
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
9
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
285
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
285
Modeling with Differential Equations
5
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
5


