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相关概念视频

The Carbon Cycle01:14

The Carbon Cycle

Carbon is the basis of all organic matter on Earth, and is recycled through the ecosystem in two primary processes: one in which carbon is exchanged among living organisms, and one in which carbon is cycled over long periods of time through fossilized organic remains, weathering of rocks, and volcanic activity. Human activities, including increased agricultural practices and the burning of fossil fuels, has greatly affected the balance of the natural carbon cycle.
Net Change Theorem01:22

Net Change Theorem

The Net Change Theorem is a fundamental principle in calculus that establishes a direct relationship between a function’s rate of change and its accumulated change over an interval. Mathematically, it states that the definite integral of a function's derivative over a given interval [a,b] yields the net change in the original function:This theorem has significant applications in various real-world scenarios, including physics, economics, and engineering. A particularly useful application is in...

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Theoretical study of the intermolecular recognition mechanism between Survivin and substrate based on conserved binding mode analysis.

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The asymmetry of neural symptoms in Wilson's disease patients detecting by diffusion tensor imaging, resting-state functional MRI, and susceptibility-weighted imaging.

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Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
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深度循环Q网络算法用于碳排放许可证交易策略.

Chao Wu1, Wenjie Bi2, Haiying Liu3

  • 1School of Business Administration, South China University of Technology, Guangzhou, 510640, China.

Journal of environmental management
|November 16, 2024
PubMed
概括

一个新的深度循环Q网络 (DRQN) 战略提供了智能碳交易决策. 这种深度强化学习方法在碳市场上实现了显著的年化回报,优于其他方法.

关键词:
算法交易是一种算法交易.碳交易市场是指碳交易市场.深度循环的Q网络.深度强化学习的学习.全球变暖全球变暖.

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科学领域:

  • 环境经济学环境经济学
  • 人工智能的人工智能
  • 金融市场 金融市场

背景情况:

  • 全球变暖需要有效的减排战略.
  • 碳交易市场对环境和经济参与者来说越来越重要.
  • 智能决策工具对于应对碳交易的复杂性至关重要.

研究的目的:

  • 为碳市场开发一种新的深度强化学习 (DRL) 交易策略.
  • 为了实现碳交易投资机会的自动识别.
  • 为市场参与者实现智能碳交易决策.

主要方法:

  • 关于深度反复Q网络 (DRQN) 算法的建议,这是一个新的DRL策略.
  • 使用DRQN算法开发碳排放权交易模型.
  • 分析折扣因素和交易成本对模型性能的影响.

主要成果:

  • 基于DRQN的模型展示了最佳的交易策略和适应市场变化的能力.
  • 对DRQN战略的年化回报率在广东碳市场达到15.43%,在湖北碳市场达到34.75%.
  • 两个市场都确定了0.4的最佳折扣因子,偏差会对交易产生不利影响.

结论:

  • DRQN算法为智能碳交易提供了强大而有效的方法.
  • 了解折扣因素和交易成本的影响对于优化交易策略至关重要.
  • 政府对交易成本的监管可以促进市场公平性和限制投机.