中国渔业碳排放效率的空间关联网络结构及其影响因素

SPATIAL CORRELATION NETWORK STRUCTURE OF FISHERY CARBON EMISSION EFFCIENCY IN CHINA AND ITS INFLUENCING FACTORS

关键词:  渔业碳排放效率  空间关联网络  影响因素  社会网络分析  低碳转型

Key words:  carbon emission efficiency in fisheries  spatial correlation networks  influencing factors  social network analysis  low-carbon transition

摘要:

目的 推进绿色生态养殖，科学把握渔业碳排放效率的空间关联网络结构，不仅是水产行业可持续发展客观要求，更为“双碳”目标下农业低碳转型发展奠定基础。方法 文章创新性地从空间关联视角关注中国渔业碳排放效率，以2007—2021年中国30个省（市、区，不含港澳台和西藏）为研究对象，运用Super-SBM模型测度渔业碳排放效率，在此基础上利用修正引力模型和社会网络分析法分析其空间关联网络结构特征，QAP模型实证分析其影响因素。结果 （1）“双碳”目标驱动下，渔业碳排放效率在时间上呈波动提升态势；但各省市碳排放效率空间差异显著，沿海及沿河地区渔业碳排放效率普遍较高。（2）中国各省市渔业碳排放效率空间联系逐渐增强，网络密度、关联性与稳定性不断提升，渔业碳排放效率网络更加均衡稳定。（3）各省市在渔业碳排放效率空间关联网络中地位及作用存在显著差异，东部沿海渔业发达省市在网络中始终处于主导地位，西北、西南和东北省市则由于渔业经济基础差及养殖技术落后等原因在网络中处于被支配地位，而中部省市借助其地理位置发挥了“桥梁”与“中介”作用。（4）养殖品种结构、渔业技术、碳汇量、基础设施、渔业经济发展水平及养殖密度的地区差异显著地影响了渔业碳排放效率空间关联网络构建。结论 目前，中国渔业碳减排已取得一定成效，但仍有较大潜力；应重视中国渔业碳排放效率的区域联动效应，通过养殖结构优化、养殖技术提升、养殖密度调整及设施建设等途径推动中国渔业低碳转型发展。

Abstract:

Promoting green ecological aquaculture and scientifically comprehending the spatial correlation network structure of fishery carbon emission efficiency is not only an objective requirement for sustainable development of the aquaculture industry but also provides a foundation for the development of a low-carbon transformation of agriculture under the dual-carbon goal. Taking 30 provinces (cities and autonomous regions, excluding Hong Kong, Macao, Taiwan and Xizang) in China from 2007 to 2021 as research objects, this study innovatively focused on the carbon emission efficiency of the Chinese fishery industry from the perspective of spatial correlation, measured the carbon emission efficiency of the fishery industry by Super-SBM model, and analyzed the characteristics of the spatial correlation network structure on the basis of the modified gravitational model and social network analysis, then also analyzed its influencing factors by using the QAP model. The results were showed as follows. (1) Driven by the carbon peak and carbon neutrality goals target, the carbon emission efficiency of the fishery industry fluctuated but increased with time. However, there were significant spatial differences of carbon emission efficiency among provinces and cities, with the fishery carbon emission efficiency generally being higher in coastal and river-side areas. (2) The spatial correlation of fishery carbon emission efficiency among provinces and cities in China was gradually enhanced, the network density, correlation, and stability were continuously improved, and the fishery carbon emission efficiency network became more balanced and stable. (3) There were significant differences among provinces and cities in the spatial correlation network of fisheries' carbon emission efficiency. The eastern coastal provinces and cities with developed fisheries always held a dominant position in the network, while the northwest, southwest, and northeast provinces and cities were inferior in the network because of the poor economic foundation of the fisheries and their outdated aquaculture technology. Some provinces and cities in central China seemed to play the role of "bridge" and "intermediary" by virtue of their geographical location. (4) The regional differences in aquaculture variety structure, fishery technology, carbon sink, infrastructure, fishery economic development level, and aquaculture density significantly affected the construction of the spatial correlation network of fishery carbon emission efficiency. Therefore, at present, China has achieved some fisheries carbon reduction, but there is still great potential. Attention should be paid to the regional linkage effect of China's fisheries carbon emission efficiency, and low-carbon transformation of China's fisheries should be promoted through optimization of aquaculture structure, improvements of aquaculture technology, adjustments of aquaculture density, and the construction of facilities.