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Related Concept Videos

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.
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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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Deep recurrent Q-network algorithm for carbon emission allowance trading strategy.

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
Summary

A new Deep Recurrent Q-Network (DRQN) strategy offers intelligent carbon trading decisions. This deep reinforcement learning approach achieves significant annualized returns in carbon markets, outperforming other methods.

Keywords:
Algorithm tradingCarbon trading marketsDeep recurrent Q-NetworkDeep reinforcement learningGlobal warming

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Area of Science:

  • Environmental Economics
  • Artificial Intelligence
  • Financial Markets

Background:

  • Global warming necessitates effective emission reduction strategies.
  • Carbon trading markets are increasingly important for environmental and economic participants.
  • Intelligent decision-making tools are crucial for navigating carbon trading complexities.

Purpose of the Study:

  • To develop a novel deep reinforcement learning (DRL) trading strategy for carbon markets.
  • To enable automatic identification of carbon trading investment opportunities.
  • To achieve intelligent carbon trading decisions for market participants.

Main Methods:

  • Proposal of a Deep Recurrent Q-Network (DRQN) algorithm, a novel DRL strategy.
  • Development of a carbon allowance trading model utilizing the DRQN algorithm.
  • Analysis of the impact of discount factors and trading costs on the model's performance.

Main Results:

  • The DRQN-based model demonstrated optimal trading strategies and adaptability to market changes.
  • Annualized returns for the DRQN strategy reached 15.43% in the Guangdong carbon market and 34.75% in the Hubei carbon market.
  • An optimal discount factor of 0.4 was identified for both markets, with deviations adversely affecting trading.

Conclusions:

  • The DRQN algorithm provides a robust and effective approach for intelligent carbon trading.
  • Understanding the impact of discount factors and trading costs is vital for optimizing trading strategies.
  • Government regulation of trading costs can promote market fairness and limit speculation.