| 摘要: |
| 城镇居民生活用电问题是中国城市群城镇化进程中的重要问题,比较与分析经济社会发展水平相近的城市群城镇居民生活电力消费规律,对保障城镇电力供应安全、推动节能减排、完善电网建设有着十分重要的意义。运用STIRPAT模型分析中国京津冀、长三角和珠三角城市群城镇居民生活电力消费的影响因素,结果表明,三大城市群城镇居民用电模式各有特色:京津冀城市群城镇居民用电呈现“产业结构主导型”模式,长三角城市群城镇居民用电呈现“人口主导型”模式,珠三角城市群城镇居民用电呈现“收入主导型”模式。中国应依据不同的城镇居民生活用电模式,有针对性地采取城镇居民生活能源消费结构优化和节能减排措施。 |
| 关键词: 居民生活用电 城市群 STIRPAT模型 岭回归 |
| DOI：10.13216/j.cnki.upcjess.2018.06.0005 |
| 分类号:F062.1 |
| 基金项目:国家自然科学基金资助项目(7110364);国家留学基金委项目(201706410059) |
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| On Urban Household Electricity Consumption of Typical City-Agglomerations in China:Based on STIRPAT Model |
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LI Lin1,2, CHENG Jinhua1,2, SUN Han1,2
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(1.School of Economics and Management, China University of Geosciences (Wuhan), Wuhan,Hubei 430074,China;2.Research Centre of Resource and Environmental Economics, China University of Geosciences(Wuhan), Wuhan,Hubei 430074, China)
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| Abstract: |
| Urban household electricity consumption is an important issue in the process of urbanization of agglomerations in China. The comparison and analysis of urban household electricity consumption of typical city-agglomerations with similar levels of economic and social development is very significant for guaranteeing electricity supply security, promoting energy saving and emission reduction and improving power grid construction. This paper analyzes the factors that influence the urban household electricity consumption of Beijing-Tianjin-Hebei City-agglomeration, Yangtze-River-Delta City-agglomeration and Pearl-River-Delta City-agglomeration by using STIRPAT model. The result shows that the pattern of urban household electricity consumption is "industrial-structure-oriented" in Beijing-Tianjin-Hebei City-agglomeration,"population-oriented" in Yangtze-River-Delta City-agglomeration, and "income-oriented" in Pearl-River-Delta City-agglomeration. According to the results, targeted measures about optimizing the structure of urban household energy consumption, energy-saving and emission reduction are proposed. |
| Key words: household electricity consumption city agglomeration STIRPAT model ridge regression |
