中国油茶生产–加工双向网络结构及驱动因素

doi:10.11707/j.1001-7488.LYKX20250767

Abstract

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

CLC Number:

S75
F326.2

Sulan Lu,Xinhui Hu,Yongwu Dai,Peifeng Huang,Yan Huang. Bidirectional Network Structure and Driving Factors of Camellia oleifera Production and Processing in China[J]. Scientia Silvae Sinicae, 2026, 62(10): 234-250.

Figures/Tables 16

Fig.1

Table 1

Meaning of relevant indicators of spatial association social network analysis (SNA) of camellia oleifera industry chain"

分析层面 Analysis level	社会网络分析指标Social network analysis indicators	指标定义 Indicator definition	内涵 Connotation
整体网络特征Overall network characteristics	网络密度 Network density	网络中实际存在的关系数量与所有可能关系数量的比值The ratio of the actual number of relationships in the network to the total possible relationships	衡量油茶主产省份之间空间关联的普遍性和紧密程度，值越高，省际协作更紧密Measures the universality and tightness of spatial connections among major C. oleifera producing provinces. A higher value indicates closer inter-provincial cooperation
	网络关系数Number of network	网络中实际存在的关联（边）的总数量 The total number of actual connections （edges） in the network	量化省际空间关联的总量规模，关系数增加表明跨区域协作联系在扩展Quantifies the total scale of inter-provincial spatial connections. An increase in relationships indicates the expansion of cross-regional cooperative links
	平均路径长度Average path length	网络中所有节点对之间最短路径长度的平均值The average of the shortest path lengths between all pairs of nodes in the network	资源/要素在省际网络中流动的平均“距离”环节数。值越低，空间流通效率越高The average "distance" （number of links） for the flow of resources/factors in the inter-provincial network. A lower value indicates higher spatial circulation efficiency
	网络效率 Network efficiency	衡量网络结构冗余度的指标（值越低，冗余连接越多）An indicator measuring network structure redundancy （lower value = more redundant connections）	评估空间关联网络结构的精炼性，效率下降提示存在重复或低效的流通路径，可能导致效率损失Evaluates the refinement of the spatial connection network structure. A decline in efficiency suggests redundant or inefficient circulation paths， which may lead to efficiency losses
个体节点特征Individual node characteristics	点出度 Out-degree	某节点指向其他节点的关联数量 The number of connections from a node to other nodes	反映该省份在网络中主动输出资源/要素或影响的强度Reflects the intensity of a province's proactive ability to export resources/factors or exert influence in the network
	点入度 In-degree	某节点被其他节点指向的关联数量 The number of connections pointing to a node from other nodes	反映该省份在网络中被动接收资源/要素或影响的强度Reflects the intensity of a province's passive reception of resources/factors or external influence in the network
	度中心性 Degree centrality	标准化后的点出度与点入度之和The standardized sum of out-degree and in-degree	综合衡量某省份在网络中的总体连接活跃度与影响力，值越高，其枢纽地位越核心。Comprehensively measures a province's overall connection activity and influence in the network. A higher value indicates a more core hub status
	接近中心性Closeness centrality	某节点到网络中所有其他节点平均最短路径的倒数 The reciprocal of the average shortest path from a node to all other nodes in the network	反映某省份在双向网络中获取资源（原料/反馈）或传播影响（输出原料/反馈）的便捷性，值越高，其信息通达与协作效率越高Reflects the convenience of a province in acquiring resources （raw materials/feedback） or disseminating influence （exporting raw materials/feedback） in the bidirectional network. A higher value indicates higher information accessibility and cooperation efficiency
	中介中心性Betweenness centrality	某节点位于其他节点对之间最短路径上的频率 The frequency with which a node lies on the shortest paths between other pairs of nodes	识别在连接不同区域、促进跨省资源流动中起关键“桥梁”用的省份，值越高，控制力越强Identifies provinces that play a key bridging role in connecting different regions and promoting cross-provincial resource flows. A higher value indicates stronger control power
块模型分析Block Model Analysis	净溢出板块 Net spillover block	板块内部连接较弱，向板块外输出资源远多于接收 A block with weak internal connections， exporting far more resources to outside the block than it receives	主要承担资源/要素净输出职能，是空间关联网络中的主要供给源Mainly undertakes the function of net resource/factor export and serves as the primary supply source in the spatial connection network
	净受益板块 Net benefit block	板块内部连接较强，接收板块外资源远多于输出A block with strong internal connections， receiving far more resources from outside the block than it exports	主要承担资源/要素吸纳与转化职能，是空间关联网络中的主要集聚中心Mainly undertakes the function of resource/factor absorption and transformation， acting as the primary agglomeration center in the spatial connection network
	双向溢出板块Bidirectional spillover block	板块内部连接较强，与板块外联系活跃且输入输出相对均衡A block with strong internal connections， active external links， and relatively balanced input and output	在省际协作中既输出也输入资源/要素，扮演内部协同与外部衔接的平衡角色Both exports and imports resources/factors in inter-provincial cooperation， playing a balanced role in internal coordination and external connection
	经纪人板块Broker block	板块内部连接较强，在板块间主要承担资源流动的中介传导作用A block with strong internal connections， mainly undertaking the intermediary role in resource flow between blocks	在不同功能区域之间发挥关键的连接与协调作用，是网络连通性的重要支撑Exercises key connecting and coordinating functions between different functional regions， serving as an important support for network connectivity

Table 1

Table 1

Variable description for the two-stage additive network DEA model"

阶段 Stage	变量符号 Variable symbol	变量 Variable	含义说明 Meaning explanation	均值 Average	标准差 Standard deviation	最大值 Maximum value	最小值 Minimum value
第一阶段（生产端）Phase Ⅰ （production side）	$ x_{1j}^{I} $	油茶种植面积 C. oleifera planting area/hm2	油茶生产环节土地要素投入规模 Scale of land input in the C. oleifera production process	265.54	368.55	1518.33	0.30
	$ x_{2j}^{I} $	劳动人数投入量Labor input measured in headcount	测算的油茶产业从业人员数量，用于衡量人力资源配置强度The estimated number of employees in the C. oleifera industry serves as a measure of the intensity of human resource allocation	53722.27	67255.13	352859.79	144.22
	$ x_{3j}^{I} $	资金投入量 Amount of capital investment/10⁴yuan	来源于《中国林业和草原统计年鉴》中国家油茶基地投资基金，表征资本要素投入水平 Sourced from China Forestry and Grassland Statistical Yearbook， the national C. oleifera base investment fund characterizes the capital?factor input level	29262.61	57105.53	282097.85	23.00
	$ {z}_{1j} $	油茶籽产量 C. oleifera seed yield/t	油茶生产环节的中间产出 Intermediate outputs in the C. oleifera production process	178668.12	282410.19	1716414.00	124.00
第二阶段（加工端）Phase Ⅱ （manufacturing stage）	$ x_{1j}^{II} $	中小加工企业数量Number of small and medium-sized processing enterprises	油茶加工环节的产业组织规模 Industrial organization scale in the C. oleifera processing sector	45.58	52.94	263.00	1.00
第二阶段（加工端）Phase Ⅱ （manufacturing stage）	$ x_{2j}^{II} $	机械总动力 Total mechanical power/ 10 MW	油茶加工环节的技术装备水平 Technical equipment level in the C. oleifera processing stage	271.19	375.42	1635.40	0.26
最终产出Final output	$ \mathrm{\mathit{y}}_{_{1\mathrm{j}}} $	油茶产业总产值Total output value of C. oleifera industry/ 100 million yuan	油茶产业链整体价值创造能力 The overall value creation capability of C. oleifera industry chain	77.37	134.62	688.63	0.04

Table 1

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Table 9

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变量类型Variable type	变量Variable	含义说明Explanation of meaning	均值Average	标准差 Standard deviation	最大值Maximum value	最小值Minimum value
因变量Dependent variable	油茶生产→加工/加工→生产空间关联网络C. oleifera production→processing/manufacturing→spatial production network	油茶产业空间关联联系强度矩阵Spatial correlation matrix for the C. oleifera industry	—	—	—	—
自变量Independent variable	城镇化水平Urbanization level(%)	各省城镇人口占总人口比重 Percentage of urban population in total population by province	55	10	75	34
	人均GDP GDP per capita/10⁴yuan	各省经济发展水平Level of economic development in each province	5.16	2.26	12.36	1.29
	交通通达性 Transportation accessibility/(km·km^–2)	公路里程与省域面积之比Ratio of highway mileage to provincial area	1.15	0.39	2.30	0.50
	油茶从业人员C. oleifera industry personnel	测算的油茶产业从业人员数量，用于衡量人力资源配置强度The estimated number of employees in C. oleifera industry serves as a measure of the intensity of human resource allocation	53722.27	67255.13	352859.79	144.22
	机械总动力Total mechanical power/10 MW	油茶加工环节的技术装备水平Technical equipment level in the C.oleifera processing stage	271.19	375.42	1635.40	0.26
	油茶种植面积C. oleifera planting area/hm2	油茶生产环节土地要素投入规模 Scale of land input in the C. oleifera production process	265.54	368.55	1518.33	0.30
	电商发展水平E-commerce sales/100 million yuan	各省电子商务业务销售总额Provincial e-commerce transaction value	4295.26	5669.53	37886.05	177.25
	地理邻接关系Geographical proximity	省域之间是否存在地理邻接Geographical contiguity exists between provinces	—	—	—	—

省（自治区、直辖市）Province (autonomous regions, and municipalities)	点出度 Out-degree		点入度 In-degree		度中心性 Degree centrality		接近中心性 Closeness centrality		中介中心性 Betweenness centrality
	生产→ 加工 Production→ processing	加工→ 生产 Processing→ production	生产→ 加工 Production→ processing	加工→ 生产 Processing→ production	生产→ 加工 Production→ processing	加工→ 生产 Processing→ production	生产→ 加工 Production→ processing	加工→ 生产 Processing→ production	生产→ 加工 Production→ processing	加工→ 生产 Processing→ production
浙江 Zhejiang	5	4	5	6	0.67	0.67	0.59	0.56	11.52	7.05
安徽 Anhui	4	4	4	5	0.53	0.60	0.52	0.52	2.46	6.15
福建 Fujian	3	3	8	5	0.73	0.53	0.72	0.56	10.14	2.90
江西 Jiangxi	8	5	3	5	0.73	0.67	0.46	0.56	5.33	4.17
河南 Henan	2	4	7	7	0.60	0.73	0.68	0.64	4.24	14.06
湖北 Hubei	10	12	8	6	1.20	1.20	0.68	0.58	48.28	22.5
湖南 Hunan	10	12	3	7	0.87	1.27	0.52	0.61	4.16	30.19
广东 Guangdong	2	4	6	4	0.53	0.53	0.65	0.54	4.57	7.69
广西 Guangxi	6	6	5	5	0.73	0.73	0.54	0.52	15.54	17.28
海南 Hainan	0	1	5	6	0.33	0.47	0.58	0.58	0	0.50
重庆 Chongqing	8	7	5	7	0.87	0.93	0.57	0.67	9.95	24.61
四川 Sichuan	6	4	6	7	0.8	0.73	0.62	0.67	5.47	6.44
贵州 Guizhou	8	7	5	4	0.87	0.73	0.59	0.54	6.93	7.11
云南 Yunnan	6	6	5	5	0.73	0.73	0.57	0.54	5.04	8.70
陕西 Shaanxi	3	3	6	3	0.60	0.40	0.62	0.50	4.38	0.64

板块 Block	接收关系 Number of received relationships				成员数目 Number of members	接受板块外关系数 Number of relationships received from outside the block	溢出板块外关系数 Number of relationships spilled over to outside the block	期望内部比例 Expected internal ratio (%)	实际内部关系比例 Actual internal relationship ratio (%)	板块特征 Block characteristics
板块 Block	Ⅰ	Ⅱ	Ⅲ	Ⅳ	成员数目 Number of members			期望内部比例 Expected internal ratio (%)	实际内部关系比例 Actual internal relationship ratio (%)	板块特征 Block characteristics
Ⅰ	0	3	5	0	3	16	8	0.14	0.27	浄收益 Net benefit
Ⅱ	12	10	0	10	4	8	22	0.21	0.15	净溢出 Net spillover
Ⅲ	3	1	5	4	4	17	8	0.21	0.33	净收益Net benefit
Ⅳ	1	4	12	7	4	14	17	0.21	0.19	双向溢出Bidirectional spillover

板块 Block	密度矩阵 Density matrix				像矩阵 Image matrix
板块 Block	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅰ	Ⅱ	Ⅲ	Ⅳ
Ⅰ	0	0.25	0.417	0	0	0	1	0
Ⅱ	1	0.833	0	0.625	1	1	0	1
Ⅲ	0.25	0.063	0.417	0.25	0	0	1	0
Ⅳ	0.083	0.25	1	0.583	0	0	1	1

板块 Block	接收关系数 Number of received relationships				成员数目 Number of members	接受板块外关系数 Number of relationships received from outside the block	溢出板块外关系数 Number of relationships spilled over to outside the block	期望内部比例 Expected internal ratio (%)	实际内部关系比例 Actual internal relationship ratio (%)	板块特征 Block characteristics
板块 Block	Ⅰ	Ⅱ	Ⅲ	Ⅳ	成员数目 Number of members			期望内部比例 Expected internal ratio (%)	实际内部关系比例 Actual internal relationship ratio (%)	板块特征 Block characteristics
Ⅰ	17	8	5	7	5	2	20	0.286	0.200	净溢出Net spillover
Ⅱ	0	0	1	0	2	10	1	0.071	0.667	净收益Net benefit
Ⅲ	0	1	5	2	4	8	3	0.214	0.571	经纪人 Broker
Ⅳ	2	1	2	8	4	9	5	0.214	0.444	双向溢出 Bidirectional spillover

Bidirectional Network Structure and Driving Factors of Camellia oleifera Production and Processing in China

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板块 Block	密度矩阵 Density matrix				像矩阵 Image matrix
板块 Block	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅰ	Ⅱ	Ⅲ	Ⅳ
Ⅰ	0.177	0.386	0.105	0	0	1	0	0
Ⅱ	0.023	0.208	2.349	0.41	0	0	1	1
Ⅲ	0.489	0.543	1.666	0.325	1	1	1	0
Ⅳ	0.018	0.204	0.338	1.147	0	0	0	1

变量 Variable	2014	2017	2022
城镇化 Urbanization	0.32^**	?0.26^*	?0.048
从业人员 Employment personnel	?0.24^**	0.219^*	?0.677^*
地理邻接关系 Geographical adjacency	0.451^***	0.259^***	0.237^***
机械化水平 Mechanical power	?0.101	1.129^*	1.10^**
人均GDP Per capita GDP	?0.28^*	0.151	0.189^*
种植面积Planting area	0.284^**	?1.133^*	?0.231^*
电子商务水平E-commerce sales	0.236^*	0.186^**	0.136^**
R²	0.341	0.215	0.208
调整后 R² Adjusted R²	0.319	0.192	0.181
样本数 Sample size	210	210	210

变量 Variables	2014	2017	2022
城镇化Urbanization	0.213^*	?0.288^*	?0.263^*
地理邻接关系 Geographical adjacency	0.463^***	0.365^***	0.126^*
机械化水平 Mechanical power	0.144^*	0.165^*	0.204^**
交通通达性 Transportation accessibility	?0.038	?0.126^*	0.263^*
人均GDP Per capita GDP	?0.156	0.208^*	0.239^*
电子商务水平E-commerce sales	0.04	0.17^**	0.15^**
R²	0.258	0.227	0.136
调整后 R² Adjusted R²	0.23	0.208	0.115
样本数Sample size	210	210	210