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作 者:刘菁[1] 杨天娇 凡培红 Liu Jing;Yang Tian-jiao;FAN Pei-hong(School of Economics and Management,Beijing Jiaotong University,Beijing 100044;Science and Technology and Industrialization Development Center of Ministry of Housing and Urban-Rural Development,Beijing 100835)
机构地区:[1]北京交通大学经济管理学院,北京100044 [2]住房和城乡建设部科技与产业化发展中心,北京100835
出 处:《建筑科学》2020年第S02期312-318,共7页Building Science
基 金:国家重点研发计划项目“民用建筑‘四节一环保’大数据及数据获取机制构建”(2018YFC0704300)
摘 要:选取国内省域电力消耗量数据,运用最小二乘回归模型(OLS)、空间滞后模型(SLM)、空间误差模型(SEM)模型探讨中国省域间城镇住宅电力消耗增长的空间相关性并进行模型回归估计,发现空间误差模型(SEM)能够相对更好的反映出中国省域间城镇住宅居民电力消耗的空间效应。基于此识别出空间效应下影响中国城镇住宅电力消耗的关键因素,并发现城镇居民人口数量及居民消费水平对电力消耗具有正向推动作用。This paper selects domestic provincial power consumption data,uses the least squares regression model(OLS),spatial lag model(SLM),and spatial error model(SEM)models to explore the spatial correlation of urban residential power consumption growth between provinces in China After performing model regression estimation,it was found that the spatial error model(SEM)can better reflect the spatial effect of the electricity consumption of urban residential residents in China’s provinces.Based on this,the key factors influencing China’s urban residential power consumption under spatial effects were identified,and it was found that the number of urban residents and the level of residents’consumption have a positive driving effect on power consumption.
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