To comprehensively promote its rural revitalization, it is an important task for China to accelerate the formation of new quality productivity. Beginning with the three dimensions of elements, structure, and function, this study analyzes the logical relationship between new quality productivity and rural revitalization, constructs an analytical framework of “element–structure–function”, clarifies the mechanism of action of new quality productivity enabling rural revitalization, and categorizes four refined typical rural revitalization models. The study finds that: ① new quality productivity promotes rural revitalization by building a platform for factor flow to form rural value, guiding the optimal allocation of factors to reshape rural value, and driving the coupling of multiple functions to promote the co-creating and sharing of rural value; ② according to the degree of achievement of rural revitalization and the scope of empowerment of new quality productivity, the rural revitalization models are divided into four types, respectively, the rural project model, the new quality productivity exploration model, the traditional industry dominated model, and the rural system reconstruction model. Since rural development still faces uncertainty and complexity in the future, the new quality productivity is not only an important action which can be selected to drive the rural revitalization strategy but also a significant driving force for rural areas to maintain their normal operation and orderly development in uncertain situations.

As global climate change intensifies and economic transformation progresses, the agro-pastoral ecotone of northern China faces dual challenges of stopping ecological degradation and enhancing farmers’ livelihoods. Yunzhou District in Shanxi Province represents a typical ecologically fragile area, where the daylily industry contributes significantly to improving livelihood resilience. This study categorized farmers into three types based on their dependence on daylily income: major-job farmers (50–90% income from daylily), sole agriculture farmers (≥90%), and side-job farmers (<50%). Using questionnaire survey data and the optimal parameter-based geographical detector method, we evaluated and compared the livelihood resilience levels of these farmer types and identified their key explanatory factors. The results showed that (1) major-job farmers exhibited the highest livelihood resilience index (0.165), followed by sole agriculture farmers (0.152), whereas side-job farmers exhibited the lowest (0.138); (2) significant differences in livelihood resilience existed across farmer types (p < 0.05); and (3) health status was a common key factor across all types, while factors such as traffic accessibility, policy awareness, social security, and information acquisition capability exhibited differential effects among groups. These findings provide empirical evidence to guide targeted livelihood interventions and sustainable transitions in the agro-pastoral ecotone.