关键词: 油茶产业, 空间引力模型, 跨区域协作, 两阶段数据包络分析

Abstract:

Objective: This study aims to quantitatively examine the spatial imbalance characteristics, network structure patterns, and driving factors of the two stages of production-processing in China’s Camellia oleifera industry, providing decision-making references for optimizing the division of labor and cooperation in major producing regions and improving the coordination efficiency between production and processing. Method: Based on panel data for 15 major C. oleifera producing provinces in China from 2014 to 2022, a two-stage additive network DEA model, a spatial gravity model, social network analysis (SNA), and QAP regression were comprehensively used to construct a bidirectional spatial association network of production?processing, systematically measure the efficiency of the two stages, characterize the network structure, and identify the key driving factors underlying spatial interactions. Result: 1) There was a significant efficiency mismatches exist between the production and processing stages in major C. oleifera production areas, forming two typical patterns:“strong upstream but weak downstream”and “weak upstream but strong downstream”. The average efficiency of raw material supply (0.63) was higher than that of processing (0.60), indicating pronounced mismatches in regional factor allocation and functional positioning. 2) The spatial network of the C. oleifera industry exhibits a radial diffusion pattern, characterized by a layout of“central production core–southwestern collaborative radiation–southeastern processing absorption”. From 2014 to 2022, the network density of the production→processing network increased from 0.338 to 0.386, and the number of ties rose from 71 to 81; the network density of the processing→production network increased from 0.333 to 0.390, and the number of ties rose from 70 to 82. However, the network efficiency of the production→processing network declined from 0.538 5 to 0.483 5, indicating a certain degree of path redundancy. 3) Geographic adjacency, mechanization level, and e-commerce development exerted stable positive effects on the formation of spatial associations, while the effects of planting area, urbanization rate, and economic development level varied across stages. In particular, the effect of planting area shifted from positive to negative as C. oleifera forests enter the full-fruiting stage. Conclusion: To alleviate the spatial structural imbalance between production and processing in the C. oleifera industry chain, efforts should focus on optimizing the regional division of labor and interprovincial cooperation mechanisms, improving post-harvest handling and logistics systems, and enhancing the coordination efficiency between the production and processing stages as well as the overall operational performance of the industry chain.

Key words: Camellia oleifera industry, spatial gravity model, interregional collaboration, two-stage data envelopment analysis