| 全生命周期视角下中国建筑碳排放空间关联网络演化及影响因素分析 |
| 摘要点击 950 全文点击 171 投稿时间：2023-03-06 修订日期：2023-06-06 |
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| 中文关键词 建筑碳排放 引力模型 空间关联 社会网络分析 二次指派程序 |
| 英文关键词 construction industry carbon emissions the gravity model spatial correlation social network analysis quadratic assignment program |
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| 中文摘要 |
| 基于全生命周期视角核算2011~2019年中国省际建筑碳排放量，采用社会网络分析方法，探究碳排放空间关联网络演化及其影响因素.结果表明：①中国建筑碳排放空间关联网络形态明显存在，网络密度和网络关联数逐渐上升，网络紧密性和稳定性逐渐提高.②上海、浙江、天津、北京和江苏处于碳排放空间关联网络的核心和支配地位.③北京、天津、江苏、内蒙古、上海和山东属于“净受益”板块，接收其他地区的建筑碳排放；广东、重庆、福建和浙江属于“经纪人”板块，实现了建筑碳排放生产端和消费端的动态平衡；其余省份均扮演“净溢出”角色，主动向外省发出建筑碳排放量.板块间的关联关系远大于板块内部的关联关系.④经济发展、空间邻接关系、城镇化、建筑业过程结构和产业结构差异对建筑碳排放空间关联产生显著影响.研究结果可为建筑业区域协同减排提供参考. |
| 英文摘要 |
| Based on the whole life cycle perspective, the carbon emissions of the provincial construction industry in China from 2011 to 2019 were calculated from the production, construction, operation, and demolition stages of building materials. A spatial correlation network matrix of the carbon emissions in the construction industry was constructed by using the modified gravity model, and the structural characteristics of the correlation network were described by introducing social network analysis. Through the quadratic assignment program, the spatial correlation matrix of carbon emissions in the construction industry and its influencing factors were regressed and analyzed. The conclusions were as follows:① the spatial correlation network of carbon emissions in China's construction industry clearly existed. The network density and network correlation numbers were gradually rising, and the network tightness and stability were gradually improving. ② Shanghai, Tianjin, Beijing, and Jiangsu had a higher degree centrality and closeness centrality, which are the core and dominant positions of the spatial correlation network of carbon emissions in the construction industry. Zhejiang replaced Shanghai in the top four from 2013 to 2018, and the betweenness centrality of each province had unbalanced characteristics. ③ Beijing, Tianjin, Jiangsu, Inner Mongolia, Shanghai, and Shandong were "net beneficiaries" blocks, receiving the carbon emissions from other regions. Four provinces, Guangdong, Chongqing, Fujian, and Shandong, belonged to the "broker" sector, achieving a dynamic balance between the production and consumption sides of building carbon emissions. The remaining 20 provinces played a "net spillovers" role, actively sending carbon emissions from the construction industry to other provinces. The correlation between blocks was much greater than the correlation relationship within the blocks. ④ Industrial structure, urban population, spatial adjacency, consumption level, and construction industry process structure had a significant influence on the spatial correlation of carbon emissions in the construction industry. The greater the inter-provincial differences in industrial structure, urban population, spatial adjacency, and consumption level, the greater the similarity of inter-provincial construction industry process structure, and the stronger the spatial correlation and spatial spillover of the construction industry carbon emissions. Finally, according to the evolution characteristics and influencing factors of the spatial correlation network of building carbon emissions, relevant countermeasures and suggestions were provided for the collaborative carbon reduction development of the construction industry region. |
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