We study the strategic pricing decision models of manufacture-led dual-channel supply chains with the free-rider problem under the service level and cost. We use the Stackelberg model to study the impact of the degree with the free-rider problem of consumers on the optimal pricing strategy and the optimal service level of the dual-channel supply chain under various decision-making modes and carry out a numerical simulation. The main conclusions are as follows: In the retailer’s dual-channel supply chain, the deepening of consumer free-riding behavior will reduce the enthusiasm of retailers, but the weak position of the channel will lead to improved service levels and reduced prices, as well as to increase the wholesale price to cover costs. In the manufacturer’s dual-channel supply chain, the deepening of consumer free-riding behavior will lead to a decline in the retailers’ service level and enthusiasm, as well as to a decrease in the wholesale prices and retailers’ pricing. In the two types of dual-channel supply chains, the demand of manufacturers’ network channels increases, the price increases first and then decreases, and the profits of all supply chain members decrease with the increase in the free-rider coefficient of consumers. Finally, we use numerical simulation to verify the validity of the above conclusions, which provides a scientific basis to make optimal pricing decisions in the manufacturer-led dual-channel supply chain.

Against the combined backdrop of intensifying global climate governance, China’s carbon-peaking and carbon-neutrality goals, and the rapid deployment of artificial intelligence (AI) for low-carbon development, this study examines how AI adoption affects the carbon emission intensity of energy firms. Using panel data for listed Chinese energy firms from 2010 to 2023 and fixed-effect models, we find that AI adoption significantly reduces carbon emission intensity; this result remains robust to alternative variable measures, lagged specifications, and other sensitivity tests. Executive digital background strengthens this effect, while the low-carbon city pilot policy provides a complementary institutional incentive that generates additional emission reductions when combined with AI. Mechanism tests indicate that AI lowers carbon emission intensity by increasing capacity utilization, improving supply-chain efficiency, and raising production efficiency. The effect is stronger in eastern and central China, state-owned enterprises, conventional energy firms than in western China, non-state-owned enterprises, and new-energy firms. These findings provide firm-level evidence on how AI facilitates the low-carbon transition of the energy sector and offers actionable implications for policy design and corporate strategy.