摘要
天然气价格市场空间关联是反映天然气市场运行效率的关键指标,探究天然气价格的空间传导机制,对于建设和完善天然气统一大市场具有重要的现实意义。基于中国2012年1月—2023年2月各省会城市的工业天然气面板数据,验证工业天然气价格市场空间关联情况,并运用社会网络分析法描绘工业天然气价格市场空间关联网络。结果表明:工业天然气价格市场空间网络整体上结构稳定,并呈现复杂性和多线程性,省份之间存在空间溢出。具体而言,山东、广西、河北等省份中心度较高;中国工业天然气价格网络分为经纪人、净受益、主受益和净溢出四个板块,板块之间存在“溢出效应”和“梯度效应”。加强天然气市场内部的协作机制,特别是以政商合作促进信息交流,对推动天然气行业向市场化转型至关重要。
The spatial correlation of natural gas price market is a key indicator reflecting the operating efficiency of the natural gas market.It is of great practical significance to construct and improve the unified natural gas market and explore the spatial transmission mechanism of natural gas price.Based on the inter-provincial industrial natural gas panel data of China from January 2012 to February 2023,this paper verifies the spatial correlation of industrial natural gas price market,and uses social network analysis to describe the spatial correlation network of industrial natural gas price market.The results show that the overall structure of the industrial natural gas price space market network is stable and presents complexity and multi threading,with spatial spillovers among provinces.Specifically,provinces such as Shandong,Guangxi,and Hebei have higher degree of centrality;the price network of China’s industrial natural gas is divided into four sectors:broker,net benefit,main benefit and net spillover,among which there are“spillover effects”and“gradient effects”between these sectors.The next step is to strengthen the collaboration mechanism within the natural gas market,especially to promote information exchange through cooperation between government and business,which is crucial to promote the transformation of the natural gas industry to market.
作者
马远
张同蕾
MA Yuan;ZHANG Tong-lei(Xinjiang University of Finance and Economics,Urumqi,Xinjiang 830012)
出处
《价格月刊》
北大核心
2024年第5期68-76,共9页
基金
国家社科基金一般项目“‘一带一路’建设对中国天然气贸易格局及国内价格形成机制影响研究”(编号:20BGL289)
新疆财经大学校级科研创新项目“‘双碳’目标下碳排放权交易政策对能源结构调整的影响研究”(编号:XJUFE2023B004)。
关键词
统一大市场
工业天然气价格
市场空间关联
社会网络分析
unified national market
industrial natural gas price
market spatial correlation
social network analysis