Sustainable Operational Decision-Making for Thermal Power Enterprises’ Carbon Assets Oriented Toward Medium- and Long-Term Risk Exposure

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🔬研究者:Novel integration of deep learning and financial derivatives for carbon risk management, advancing operational research in carbon markets.

🏢実務担当者:Corporate sustainability teams can apply the LSTM-option hedging framework to proactively manage carbon cost exposure and enhance financial resilience.

🏛政策担当者:The framework demonstrates how carbon price risk can be mitigated via market instruments, informing the design of carbon market stability mechanisms.

Against the background of deepening “dual carbon” goals and the continuously tightening policies of the national carbon market, the carbon asset risks faced by thermal power enterprises have shifted from short-term compliance cost fluctuations to medium- and long-term systemic risks. Managing these risks effectively is essential for ensuring the financial viability of thermal power operations during the low-carbon transition, thereby supporting the long-term sustainability of the energy sector. This study constructs a risk management framework for carbon assets in thermal power enterprises based on the LSTM model and option portfolios. First, the multi-dimensional characteristics of medium- and long-term carbon asset risks are systematically identified at the policy, market, and enterprise levels. Second, a dual-layer LSTM model with Dropout regularization is employed to simulate medium- and long-term carbon prices. The prediction results indicate a moderate upward trend in future carbon prices, with the fluctuation range gradually narrowing. On this basis, a combined hedging strategy of “core call options + auxiliary put options” is designed, capping the maximum procurement cost at 72.63 CNY/ton and covering over 90% of the risk of carbon price increases. Monte Carlo simulations and rolling window backtesting, conducted using operational data from a thermal power enterprise to validate the framework, verify the effectiveness and robustness of the strategy. The study shows that, through the integration of accurate LSTM predictions and proactive option hedging, thermal power enterprises can transform their carbon asset management from passive compliance to active value creation, thereby enhancing their operational sustainability and resilience during the energy transition.

• openalex https://doi.org/10.3390/su18084094first seen 2026-05-05 08:00:56