Urban green development has become a crucial approach for balancing ecological conservation and socio-economic development. The digital economy (DE) and new-type urbanization (NTU), as technological and social systems, respectively, are both driving urban green development. In this context, furthering their synergistic effects could substantially improve urban sustainability outcomes. Grounded in sociotechnical systems theory, this study applied pooled and multi-period fuzzy-set qualitative comparative analysis (fsQCA) to analyze urban green development pathways in 79 Yellow River Basin cities (2020–2022). The pooled fsQCA indicates that urban green development is driven by synergistic interaction within the NTU-DE subsystem, especially industrial digitalization–spatial urbanization. The multi-period fsQCA further demonstrates that industrial digitization has always existed as a core condition, which means that it plays a more general role. In addition, the Yellow River Basin exhibits distinct regional variations in urban green development, where the downstream region is dominantly driven by DE and spatial urbanization, the upstream region by industrial digitization, and the midstream region demonstrates diversified pathways. This study enhances understanding of complex system interactions in urban green development and provides policy-relevant insights. For policy implementation, local governments should not only prioritize effective synergies between industrial digitization and spatial urbanization but also develop differentiated strategies for the DE and NTU subsystems based on local conditions.

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.