中国渔业碳排放效率空间关联网络及影响因素

SPATIAL CORRELATION NETWORK AND INFLUENCING FACTORS OF FISHERY CARBON EMISSION EFFICIENCY IN CHINA

关键词:  渔业  碳排放效率  超效率SBM模型  空间关联网络  低碳渔业

Key words:  fishery  carbon emission efficiency  super efficiency SBM model  spatial correlation network  low-carbon fishery

摘要:

目的 探究中国渔业碳排放效率的空间关联网络与影响因素，为区域渔业协调发展与低碳渔业发展提供科学支撑。方法 文章基于关系数据和网络视角，以考虑非期望产出的随机块模型（Stochastic Block Model，SBM）测度2011—2020年中国27个省（市、自治区）的渔业碳排放效率，构建渔业碳排放效率空间关联网络引力矩阵，并通过社会网络分析法（Social Network Analysis，SNA）分析空间关联网络的结构与属性特征，最后通过二次指派程序方法（Quadratic Assignment Procedure，QAP）探究空间关联网络的影响因素与作用。结果 （1）渔业碳排放效率在全国范围内存在显著的空间关联现象与溢出路径，形成了复杂的网络状空间关联关系，协同发展现象明显；（2）渔业碳排放效率空间关联网络结构相对松散，且存在一定的等级梯度特征，渔业碳排放效率的区域间合作有待进一步增强与优化；（3）上海、天津、北京、四川等发达省市区在空间关联网络中处于中心地位，对低碳渔业发展所需资源要素的掌控与支配作用较强，位于西部地区与东北地区的省市区位于关联网络的边缘位置，获取低碳渔业发展资源的能力偏弱；（4）渔业发展水平差异、社会消费水平差异、能源强度差异与空间邻接关系能够显著推动渔业碳排放效率空间关联网络的形成，税负水平差异、对外开放水平差异、交通运输水平差异越小，越有利于形成空间关联网络。结论 应探索渔业发展的区域联动机制，建立跨区域合作平台，强化生产要素的空间流动；在制定和实施政策和措施时需要因地制宜，充分发挥各地区的优势和潜力，提高整体效率水平；控碳治碳需要地区间相互协调、共同治理，以实现全国范围内的碳减排目标。

Abstract:

This research aims to investigate the spatial correlation network characteristics and influencing factors of China's fishery carbon emission efficiency, with a view to providing scientific support for the coordinated development of regional fishery and the low-carbon fishery development. Based on relational data and network perspectives, a Stochastic Block Model(SBM) taking into account undesirable output was used to measure fishery carbon emission efficiency in 27 provinces (city, autonomous region) across China from 2011 to 2020, and a modified gravity model was used to construct a gravity matrix of the spatial correlation network of fishery carbon emission efficiency, then the Social Network Analysis(SNA) was conducted on the correlation structure and attributes of spatial correlation network, and finally, the influencing factors of spatial correlation network and their roles were investigated by Quadratic Assignment Procedure(QAP). The results were found as follows. (1) There were significant spatial correlation and spillover paths of fishery carbon emission efficiency in China, forming a complex network-like spatial correlation and demonstrating obvious coordinated development; (2) The spatial correlation network of fishery carbon emission efficiency featured a loose structure and hierarchical gradient, and the spatial cooperation and interaction of fishery carbon emission efficiency still needed to be enhanced and optimized. (3) In the spatial correlation network, developed provinces, cities and districts, such as Shanghai, Tianjin, Beijing, and Sichuan were at the center and had a strong control and dominance over resource elements required for low-carbon fishery development, while those in the western and northeastern regions were at the periphery and were week in acquiring the resources. (4) Additionally, the formation of a spatial correlation network of fishery carbon emission efficiency was promoted by differences in fishery development, social consumption and energy intensity, and spatial proximity, and was facilitated by a small difference in tax burden, openness and transportation. In response, it is required to explore the regional linkage mechanisms of fishery development, establish a cross-regional cooperation platform, and strengthen the spatial mobility of factors of production; take into account local conditions while making and implementing policies and measures and giving full play to regional strengths and potentials and improving overall efficiency, in addition, carbon control and treatment needs mutual coordination and joint governance in all regions to achieve carbon emission reduction targets on a national scale.