LLM-enabled carbon emissions abatement in green production: Single or double hedging?

In green supply chains, as consumers become increasingly sensitive to environmental concerns, manufacturers striving for greater carbon reduction find it essential to capture more market share through green production. However, uncertainties in the production process often result in actual carbon emissions abatement (CEA) levels falling short of targets. With the rise of large language models (LLM), which is known for enhancing decision-making and process optimization, there is a growing tendency to use LLM for facilitating CEA. This study examines the effectiveness and impact of LLM-enabled carbon emissions hedging (L-CEH) on green production performance under uncertain production process within a supply chain involving an upstream manufacturer of semi-finished products and a downstream manufacturer of finished products. We formulate four downstream-led Stackelberg models, including upstream single hedging, downstream single hedging, double hedging, and a non-hedging benchmark, to delineate the applicability and performance of each mechanism. To the best of our knowledge, this paper is among the first to theoretically analyze LLM performance in CEA while considering uncertainty control. Our findings indicate that both manufacturers can maximize profits under double hedging. While in single hedging scenarios, L-CEH adoption of the manufacturer who is more vulnerable to production uncertainties allows both enterprises to achieve higher profits. Notably, a manufacturer with a higher failure rate, though required to hedge, can attain significant profits by setting a lower carbon reduction target, provided the other enterprise with a lower failure rate sets a higher target.