Paper: arXiv 2401.01427

Abstract

One approach to reducing greenhouse gas (GHG) emissions is to incentivize carbon capturing and carbon reducing projects while simultaneously penalising excess GHG output. In this work, we present a novel market framework and characterise the optimal behaviour of GHG offset credit (OC) market participants in both single-player and two-player settings. The single player setting is posed as an optimal stopping and control problem, while the two-player setting is posed as optimal stopping and mixed-Nash equilibria problem. We demonstrate the importance of acting optimally using numerical solutions and Monte Carlo simulations and explore the differences between the homogeneous and heterogeneous players. In both settings, we find that market participants benefit from optimal OC trading and OC generation.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced stochastic control, optimal stopping, and game theory (e.g., solving QVIs via finite-difference schemes) and is validated through Monte Carlo simulations and numerical solutions for market behavior.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Optimize GHG Offset Credit<br>Market Participation"] --> B["Key Methodology<br>Optimal Stopping & Control<br>Game Theory (Nash Equilibrium)"]
  B --> C["Data & Inputs<br>Simulation Parameters<br>Player Characteristics"]
  C --> D["Computational Process<br>Monte Carlo Simulations"]
  D --> E["Key Outcomes<br>Optimal OC Trading Strategies<br>Value of Acting Optimally<br>Homogeneous vs Heterogeneous Players"]

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0 of 59 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.