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中国工业能-水消耗系数的空间差异及脱钩分析 被引量:15

Spatial difference and decoupling analysis of industrial energy-water consumption coefficients in China
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摘要 厘清工业领域中能源、水资源等生产要素的配置关系,以及它们与经济增长的曲线关系,对于集约高效的工业发展模式和生态文明建设具有重要的意义。根据2005—2016年的数据,通过构建能源耗水系数,运用空间自相关方法分析中国工业领域水资源-能源之间联系,并采用Tapio脱钩模型分析水资源-能源-经济三者的两两关系,以寻求工业领域能-水消耗的时空演变规律。结果表明:①中国工业用水强度、用能强度和能源耗水系数在省级范围内均有显著的空间正相关关系。②局部莫兰指数的结果表明工业用水强度H-H集聚区主要在南部省份,L-L集聚区主要在北方省份;工业用能强度H-H集聚区主要在西部省份,L-L集聚区主要在东部省份;工业能源耗水系数H-H集聚区主要在华东和华南省份,L-L集聚区主要在北方省份,其中上海、福建和广东省份的能源耗水系数状态较为理想。③研究期内经济与资源之间的脱钩更为明显,其发展更为协调;而水资源与能源之间的脱钩关系与此相比有一定差距,但是从时间趋势来看,其协调关系正在进一步加强。据此,提出全国层面应该协同推进工业领域中能源、水资源等生产要素的配置关系,强化高质量发展。结合能源和水资源的集聚性做好区域层面的产业规划,大力发展绿色低碳节约型产业,推进资源全面可持续和循环利用。省级层面在国家产业规划指导下根据产业实际发展加快产业升级和供给侧改革,对工业企业的管理严格执行相关政策,同时引进高新设备,改进生产工艺,提高资源利用效率和回收循环利用效率。 Clarifying the allocation relationship between energy, water resources and other production factors in the industrial field, and their relationship with economic growth, is important to intensive and efficient industrial development model and ecological civilization construction. According to the data from 2005 to 2016, through the construction of energy consumption coefficient, we used the spatial autocorrelation method to analyze the water-energy relationship in China’s industrial fields and used the Tapio decoupling model to analyze the relationship between water resources, energy and economy, so as to seek the laws of space-time evolution of energy-water consumption in the industrial sector. The results showed that:①There was notable spatial positive correlation between China’s industrial water intensity, energy intensity and energy consumption coefficient at the provincial level.②The results of the local Moran’s I index indicated that the concentrated areas of the H-H industrial water intensity were mainly located in southern provinces, the L-L concentrated areas were mainly in the northern provinces;the industrial energy intensity H-H concentrated areas were mainly in the western provinces, while the L-L concentrated areas were mainly in the eastern provinces;the concentrated areas of industrial energy-water consumption coefficient H-H were mainly in the eastern and southern China provinces, and the L-L concentrated areas were mainly in the northern provinces;the energy-water consumption coefficient status in Shanghai, Fujian and Guangdong was ideal.③During the research period, the decoupling between economy and resources was more obvious and its development was more coordinated. There was a gap between the decoupling of water and energy, but in terms of trends among time, the coordination was strengthened further. Based on this, it is proposed that the national level should promote the allocation of production factors such as energy and water resources in the industrial sector, and strengthen high-quality development. We should combine the agglomeration of energy and water resources to make the industrial planning at the regional level, and develop green and low carbon economy industries, to promote comprehensive, sustainable and circular use of resources. At the provincial level, under the guidance of the national industrial planning, industrial upgrading and supply-side reform should be accelerated in accordance with the actual industrial development. We should strengthen the management of industrial enterprises and strictly implement relevant policies. Meanwhile, high-tech equipment should be introduced to improve the production process, so as to improve the efficiency of resource utilization and recycling.
作者 马海良 李倩 庞庆华 MA Hai-liang;LI Qian;PANG Qing-hua(Institute of Low Carbon Economy, Hohai University, Changzhou Jiangsu 213022, China;Business School, Hohai University, Changzhou Jiangsu 213022, China)
出处 《中国人口·资源与环境》 CSSCI CSCD 北大核心 2019年第3期62-70,共9页 China Population,Resources and Environment
基金 国家自然科学基金项目"绿色水资源利用效率的空间异质性及其与新型城镇化的耦合"(批准号:41301620) 江苏省研究生科研与实践创新计划项目"江苏省工业耗水的空间差异及协调性研究"(批准号:KYCX18_0496) 中央高校业务基金项目"基于碳水耦合系统的区域碳排放测量与调控"(批准号:20191322814)
关键词 工业用水强度 工业用能强度 能源耗水系数 空间自相关 脱钩分析 industrial water intensity industrial energy intensity energy-water consumption coefficient spatial autocorrelation decoupling analysis
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