Blockchain-enabled carbon emission trading data quality regulation: A tripartite evolutionary game analysis.

Area of Science:

• Environmental Economics
• Game Theory
• Blockchain Technology

Background:

• Data quality issues are a significant challenge to the stability and effectiveness of carbon trading markets.
• Existing models often overlook the psychological factors influencing participant decision-making in these markets.

Purpose of the Study:

• To propose an innovative tripartite evolutionary game model incorporating prospect theory to analyze the carbon trading market.
• To investigate the impact of blockchain technology and a blockchain-based penalty mechanism on participant strategies and market stability.
• To identify key factors influencing strategic choices for government, enterprises, and the public.

Main Methods:

• Development of a tripartite evolutionary game model integrating prospect theory.
• Simulation analysis to explore the influence of various parameters on participant strategies.
• Introduction of a blockchain-based penalty mechanism to address data quality issues.

Main Results:

• Carbon price, blockchain costs, penalties for enterprise dishonesty, integrity gains, and public rewards significantly affect strategy stability.
• Government's willingness to adopt blockchain, carbon pricing, penalty enforcement, and public engagement are crucial drivers of strategic decisions.
• Blockchain application, public participation, and robust penalties enhance market stability and emission reduction efforts.

Conclusions:

• Blockchain technology and a well-designed penalty system are vital for improving carbon trading market data quality and stability.
• Government policy, including carbon pricing and enforcement, alongside public engagement, is essential for successful carbon emission reduction.
• Integrating behavioral economics (prospect theory) into game models provides deeper insights into participant decision-making in environmental markets.