JOURNAL ARTICLE

Dynamic Optimal Control Analysis of CCUS Green Technology Innovation in Coal-Fired Power Plants Under Dual Carbon Policy.

  • Published In: International Game Theory Review, 2025, v. 27, n. 4. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Pu, Han; Wang, Xinping; Su, Chang; Zhang, Cheng 3 of 3

Abstract

To enhance the environmental benefits of Carbon Capture, Utilization, and Storage (CCUS) in coal-fired power plants (CFPPs), the government has implemented carbon trading and tax policies. These policies aim to encourage green technological innovations in these plants. Consequently, CFPPs have increased their investments in emission reduction and carbon capture technologies, alongside accumulating knowledge from these investments. Understanding the relationship between these technology inputs is crucial to help plants balance their efforts and to guide government regulation to foster green innovation. This paper constructs a dynamic optimal control model that incorporates the dual carbon policy's impact on emission reduction and carbon capture technology inputs, factoring in knowledge accumulation. It examines changes in inputs and benefits under profit and social welfare optimization. A comparative analysis using numerical simulation reveals that the two inputs have a complementary substitution effect, knowledge accumulation enhances input stability, and social incentives are more effective than monopoly incentives. Additionally, the impact of carbon trading and tax policies on these inputs varies with policy intensity. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Game Theory Review. 2025/12, Vol. 27, Issue 4, p1
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2025
  • ISSN:0219-1989
  • DOI:10.1142/S0219198924400140
  • Accession Number:191260294
  • Copyright Statement:Copyright of International Game Theory Review is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.