The hydrogen fuel cell vehicle (HFCV) market is growing rapidly, but technological limitations, high costs, and market constraints are hindering enterprise performance. Existing studies often analyze isolated factors, overlooking their configurational interactions. This study applies the Technology–Organization–Environment (TOE) framework and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how R&D capability, human capital level, scale of enterprise, attention allocation, and government support shape high performance in 40 Chinese HFCV enterprises. The consistency of all antecedents does not exceed 0.9, indicating that high performance does not depend on any single factor. The sufficiency analysis identifies three effective configurations: technology-driven, internal–external synergy, and organization–policy-driven, with an overall solution consistency of 0.9206 and a coverage of 0.4167. Without adequate government support and human capital, achieving high performance in HFCV enterprises appears improbable. These findings reveal multiple pathways toward high performance and highlight the importance of condition combinations over isolated effects, offering theoretical and practical insights into sustainable development strategies for emerging green industries.

Achieving the low-carbon transformation of energy consumption structure is central to carbon-neutral development, yet how artificial intelligence (AI) contributes to sustained structural change remains insufficiently understood. Addressing this gap, this study examines not only whether AI facilitates the green and low-carbon transformation of energy consumption structure in China, but also through which mechanisms and under what conditions this effect is sustained. Using provincial panel data for 2013–2022, we estimate the direct effect of AI and test the mediating role of green innovation resilience and the moderating role of green finance. The results show that AI significantly promotes the low-carbon transformation of energy consumption structure. Green innovation resilience serves as an important transmission mechanism, while green finance strengthens this relationship. These findings deepen understanding of the AI-energy transition nexus and provide policy implications for effectively coordinating digital technology, green innovation, and green finance.

Agricultural carbon reduction (ACR) is important for the green transformation of agriculture and ecological protection in the Yellow River Basin. However, ACR is not driven by a single factor. How different conditions combine and change over time remains unclear. Based on the technology–organization–environment (TOE) framework, this study examines 77 cities in the Yellow River Basin from 2017 to 2022. Six antecedent conditions are considered: agricultural technological innovation (ATI), agricultural mechanization (AM), government environmental attention (GEA), government fiscal intervention (GFI), agricultural industrial structure (AIS), and urbanization (URB). Multi-period fuzzy-set qualitative comparative analysis (fsQCA) is used to identify the pathways to high ACR, their temporal evolution, and regional differences. The results show that (1) no single antecedent condition is necessary for high ACR in either period; (2) four high-ACR pathways are identified in the baseline period (2017–2019), comprising three types: agricultural structure–urbanization synergy, agricultural technological innovation-led, and technology–equipment–urbanization synergy; (3) four high-ACR pathways are also identified in the transition period (2020–2022). AIS is present in all four pathways, compared with only two pathways in the baseline period, and combines with AM or URB in different configurations; and (4) regional comparisons reveal alternative configurations of production and governance conditions. Upstream pathways include technology–equipment–government combinations, midstream pathways retain both technological and structural alternatives, and downstream pathways show a more pervasive role for AIS in the later period. These findings suggest that effective ACR policies should match local production and governance conditions rather than uniformly increase individual policy inputs.