Artificial Intelligence and Resilience Efficiency in China’s New Energy Industry: Evidence from Production, Storage, and Application Segments

Artificial intelligence (AI) is increasingly reshaping the new energy industry, yet its relationship with enterprise resilience remains insufficiently understood. Using 54 Chinese A-share listed new energy enterprises from 2018 to 2023, this study evaluates resilience efficiency across production, storage, and application segments. A parallel two-stage dynamic directional distance function data envelopment analysis (DDF-DEA) model is developed to assess operational, innovative, and sustainable capabilities, with AI incorporated as an exogenous environmental variable. Econometric models are further used to examine the association between AI and resilience efficiency. The results show that overall resilience efficiency remains relatively low, with the production segment performing best and the storage segment worst. Sustainable capability is close to the efficiency frontier, whereas innovative capability constitutes the main constraint. AI-conditioned efficiency scores are generally higher than unconditional scores, with the largest difference observed in the application segment and primarily in operational capability. Regression results provide modest statistical support for a positive association between AI and overall resilience efficiency. After controlling for firm characteristics and firm and year fixed effects, the association remains statistically significant at the 10% level. These findings highlight the importance of differentiated AI strategies for strengthening resilience across new energy industry segments.