Investigating the coupling coordination between urban scale and vitality is critical for enhancing holistic urban development quality and advancing sustainability. Taking the Changsha-Zhuzhou-Xiangtan (ChangZhuTtan) metropolitan area as a case study, this research integrates multi-source raster and vector data to: (1) analyze spatial patterns of urban scale and virtual–substantive vitality; (2) delineate a “scale-vitality” hierarchical zonal structure; (3) quantify coupling relationships across subzones; and (4) propose synergistic spatial optimization strategies. Key findings reveal that, distinct core-periphery structure characterizes urban scale and vitality, with Changsha’s central districts dominating population, land use, and economic metrics, while Zhuzhou and Xiangtan exhibit moderate concentrations. Significant positive correlations exist between urban scale and dual vitality types, with scale-driven vitality enhancement being most pronounced in core agglomeration zones. Furthermore, in the metropolitan core, where both urban scale and vitality values are high, they exhibit a high-value coupling state. As they expanded outward, both metrics gradually decreased, resulting in a low-value coupling state. However, zonal comparisons (core agglomeration circle–peripheral expansion circle) reveal that the proportion of spatially coupled units progressively increases. By elucidating scale-vitality coupling in the ChangZhuTtan metropolitan area, this study provides actionable insights for spatial planning and sustainable urban transition. The methodology framework is replicable for similar metropolitan regions globally.

The United Nations 2030 Agenda for Sustainable Development (UN SDGs) frames sustainable agriculture as a core pathway to advance zero hunger (SDG 2) and protect life on land (SDG 15). As critical platforms for the green transition of agriculture, innovative perception among new-type agricultural business entities inside national modern agricultural industrial parks constitutes a prerequisite cognitive factor shaping sustainable farming practices. Taking Anhua National Modern Agricultural Industrial Park—China’s sole industrial park specialized in dark tea—as the research case, this study develops a four-dimensional measurement framework for innovative perception covering innovation cognition, motivation, demand, and intention, grounded in 363 field micro-survey questionnaires. Factor analysis is adopted to quantify township-level composite innovative perception scores, and the Geodetector model is integrated to detect driving factors as well as their interactive coupling effects behind spatial disparities. The results indicate the following: (1) Marked spatial heterogeneity characterizes innovative perception levels of new-type agricultural business entities across the park, which maintain an overall high standard. The core zone registers an average score of 4.209 versus 3.726 for the demonstration zone; innovative perception in the core zone is statistically higher, presenting a typical core–periphery gradient pattern across townships. (2) Individual endogenous attributes, including age, educational attainment, and farming experience, show strong explanatory capacity. The interaction between age and innovation applicability yields a q-statistic of 0.961, the strongest pairwise spatial association observed within the present sample; nevertheless, q-statistics from the Geographical Detector are sample-specific descriptive metrics for spatially stratified explained variance and do not represent causal effect sizes. This research uncovers the core–periphery polarization rule and multi-factor spatial coupling patterns underlying innovative perception of operators within specialized agricultural industrial zones. The empirical micro-evidence generated in this work informs the design of tiered, differentiated governance policies aimed at mitigating intra-park spatial disparities, delivering empirical insights into the cognitive foundations of the park’s green transition. This analysis targets innovative perception as the cognitive prerequisite for sustainable farming practices instead of direct sustainability outcomes; empirical tracing of the perception-to-behavior-to-outcome causal chain would require longitudinal outcome-focused datasets, which constitutes a key avenue for future inquiry.