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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Carbon allowance decision optimization with multi-agent simulation: Incorporating behavioral drivers.

Lihui Zhang1, Jing Luo1, Jinrong Zhu1

  • 1School of Economics and Management, North China Electric Power University, Beijing, 102206, China.

Journal of Environmental Management
|July 25, 2025
PubMed
Summary
This summary is machine-generated.

This study optimizes carbon allowance auctions by modeling corporate bidding behavior and auction parameters. Findings show risk-seeking bidders and social networks impact efficiency, while reserve prices drive emission reductions.

Keywords:
Carbon allowance auction mechanismCarbon emission allowance allocationCarbon emission reductionInformation feedbackMulti-agent simulationRisk attitude

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Area of Science:

  • Environmental Economics
  • Climate Policy
  • Computational Economics

Background:

  • Carbon trading is crucial for emissions reduction, with allowance allocation a key challenge.
  • Emerging markets are transitioning from free allocation to auction mechanisms for carbon allowances.

Purpose of the Study:

  • To develop an optimization model for carbon allowance allocation auctions.
  • To analyze the impact of auction parameters and corporate behavior on auction efficiency and emission reduction.

Main Methods:

  • Multi-agent simulation and multi-objective particle swarm optimization (MOPSO).
  • TOPSIS method for selecting optimal solutions from the Pareto set.
  • Incorporation of corporate risk attitude and information feedback in bidding behavior.

Main Results:

  • Risk-seeking companies tend to win bids; social network density can improve auction efficiency but may cause convergence.
  • False information decreases auction efficiency and increases costs, especially in uniform-price auctions.
  • Higher reserve prices and secondary market prices encourage emission reductions, while increased allowance supply lowers costs but may reduce reduction incentives.

Conclusions:

  • Governments can design better carbon allowance auction mechanisms by considering efficiency, costs, and reduction outcomes.
  • Enterprises can optimize carbon compliance strategies based on insights into bidding behavior and market dynamics.