AI-Enhanced Carbon Management: A Strategic Leap Beyond Compliance

As international climate policy transitions from voluntary disclosure toward mandatory regulatory compliance—marked by the European Union’s Corporate Sustainability Reporting Directive (CSRD) and the Carbon Border Adjustment Mechanism (CBAM)—carbon emissions have shifted from a sustainability metric to a critical financial liability.

For modern organizations, these regulatory changes introduce operational requirements that carry significant compliance risks and economic costs. While a multitude of mitigation actions are available, corporations struggle to identify the optimal pathways that align with evolving regulations while fitting their unique operational workflows.

The Disconnect Between Data and Strategy

Current carbon management practices are hindered by a fundamental disconnect between data and strategy. Most existing tools function as retrospective ledgers, recording what has already happened, rather than prospective strategic aids.

Master's Thesis Spotlight: AI-Enhanced Carbon Management

To address these challenges, Zheng Dong, under my supervision, proposed an AI-Enhanced Carbon Management System in his master's thesis. This system leverages the power of Large Language Models (LLMs) integrated with robust stochastic optimization to transform carbon management into a dynamic decision-support process.

1. From Unstructured Data to Executable Logic

The system implements a Human-in-the-Loop (HITL) workflow where LLMs act as both parser and copilot.

• Abstract Syntax Tree (AST) Validation: Ensures that unstructured technical reports are translated into executable calculation logic accurately.
• Mandatory User Confirmation: Mitigates hallucination risks by keeping human experts in the driver's seat.

2. Overcoming Uncertainty with Robust Optimization

Deterministic planning often fails when faced with the volatility of energy markets and regulatory updates. To overcome this, the system integrates:

• Sample Average Approximation (SAA)
• Monte Carlo Simulation (MCS)

This approach identifies "robust" reduction strategies—plans that remain feasible and effective across a range of plausible future scenarios, not just the "likely" one. A linear approximation calculator ensures these iterative evaluations remain computationally efficient for real-world applications.

Key Findings & Impact

The system’s performance was validated using a complex synthetic dataset and a case study involving industrial data from a Taiwanese manufacturing company.

Research Insight: Results confirm that as carbon prices rise, early-stage decarbonization investments become increasingly profitable.

The system provides a framework for Return on Investment (ROI) projections and payback periods, bridging the critical gap between semantic documentation and mathematical rigor.

This work demonstrates a capable and reliable approach to corporate carbon management in an era of uncertainty, proving that with the right tools, sustainability can drive profitability.