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机构地区:[1]华南理工大学工商管理学院,广东广州510641 [2]广东省电力设计研究院,广东广州510663
出 处:《管理工程学报》2016年第3期99-105,共7页Journal of Industrial Engineering and Engineering Management
基 金:国家社会科学基金重大资助项目(11&ZD154)
摘 要:本文从要素、空间和时间三个维度构建了智慧城市视角下电力消费的理论分析框架,从协整分析的角度引入了新的空间权重度量方法,考察电力消费的空间相关性与动态增长路径。研究发现:电力消费具有显著的空间溢出效应,生产要素的价值实现过程不仅显著促进本地区电力消费增长,还将通过要素融合与产业链延伸带动周边地区的电力消费增长。构建时间变化模型发现电力消费的增长路径具有动态演变特征,即空间溢出效应逐年增强,各要素对电力消费的长期贡献此消彼长。最后为构建动态调整、供需均衡的智慧型电力发展模式提供了相关政策建议。Energy source is essential to sustaining economic growth in China. Understanding energy sources' development pattern is critical to shitting the growing path of China's economy. Therefore, an increasing number of researchers have started paying attention to the regulation of electricity consumption. Currently, the existing literature has discussed the features of electricity consumption mainly ~om either the aspect of temporal evolution or spatial distn"oution. However, there is a lack of comprehensive framework combining these two dimensions to explain the features of electricity consumption. Through literature review, this paper proposes the concept of intelligent power to construct a new framework integrating the time, space and elements. This framework can be applied in electricity consumption's spatial correlation and its dynamic growth path in China. This paper can provide theoretical support for constructing the patterns of intelligent power's development. The research uses the dynamic spatial panel econometric model based on energy economics panel data from 16 cities in Yangtze River Delta Area from year 1995 to 2010. This study assumes that spatial correlation and its dynamic growth path of electricity consumption occur linearly through GDP, electricity price, population growth, industry structure and others. Spatial correlation mainly discusses the correlation between electricity consumption with its location and the contribution of related elements. This study further discusses temporal evolution in size and the significance of electricity consumption's spatial correlation. The research compares the estimated results of static spatial panel model with that of dynamic spatial panel model. Moreover, this study separates the elements which is difficult to quantify, such as power-using efficiency and policy regulation, GDP, population growth and industry structure upgrade. As a result, this study can make more significant positive contribution to electricity consumption, and electricity pric
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