The impacts of population structure and household size on residential energy consumption: empirical research based on provincial panel data

SHEN Ke1 SHI Qian2

(1.School of Social Development and Public Policy, Fudan University)
(2.Education Bureau of Pudong New Area in Shanghai)
【Knowledge Link】Qinling Mountains

【Abstract】Drawing upon provincial data between 1995 and 2015 in China, this study presents the regional distributions and dynamic changes of residential energy consumption per capita and also investigates the impacts of population structure and household size on residential energy consumption. Residential energy consumption per capita is on a rising trend across China, and the annual growth rates in Heilongjiang, Hainan and Chongqing between 2005 and 2015 rank the top list. Empirical analyses reveal that there is a U-shaped relationship between urbanization and residential energy consumption. That is to say, in the early stages of urbanization, domestic energy consumption can be suppressed, but with the advancement of urbanization, per capita living energy consumption would gradually increase. Population aging and the shrinkage of household size lead to the increased energy consumption. In order to achieve the dual targets of combating climate change and meeting the rising demand for prosperous life, China should further promote energy reservation and more importantly, improve the energy consumption structure and expand the utilization of clean energy.

【Keywords】 residential energy consumption; urbanization; population aging; household Size;

【DOI】

【Funds】 The National Social Science Fund of China (17CRK023) National Natural Science Foundation of China (71490734)

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    Footnote

    [1]. (1) IEA official website database. https://www.iea.org/energyaccess/database/ [^Back]

    [2]. (2) The energy consumption data of National Bureau of Statistics of China are calculated in kgce and the total amount of statistics is total energy consumption, which includes terminal energy consumption, energy processing and conversion losses and losses. The IEA data are based on kilotonne of oil equivalent (ktoe), and the total amount of statistics is the terminal energy consumption. Therefore, the two estimates of the proportion of residential energy consumption are slightly different. [^Back]

    [3]. (3) China Population Statistics Yearbook and China Population & Employment Statistics Yearbook over the years. [^Back]

    [4]. (4) Among them, the data of Guangxi from 1995 to 2011 and the data of Hainan and Fujian come directly from the per capita residential energy consumption in the statistical yearbooks. The data of Zhejiang Province are derived from the white book on Energy and Utilization in Zhejiang Province in various years. Energy consumption does not include the use of low calorific value energy, biomass energy and solar energy or only includes the use of the energy as commercial energy. [^Back]

    [5]. (5) Considering that the proportion of the elderly population and the proportion of the young population have strong multicollinearity, this paper only includes the variable of the proportion of the elderly population. [^Back]

    [6]. (6) According to the economic zones in the east, central and west regions, this paper classifies 11 provincial regions such as Beijing and Tianjin as the eastern region, 8 provincial regions such as Heilongjiang and Jilin as the central region and 10 provincial regions such as inner Mongolia and Guangxi as the western region. China’s Qinling Mountains and Huaihe River define the north as the central heating area (Zheng et al., 2015). Some provinces and municipalities straddle the Qinling-Huaihe line. According to the geographical location and actual heating situation, this paper classifies 14 provincial regions including Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shandong, Henan, Shaanxi, Gansu, Qinghai and Xinjiang into the northern region and 15 provincial regions including Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou and Yunnan into the southern region. [^Back]

    [7]. (7) http://shupeidian.bjx.com.cn/news/20171113/861210.shtml [^Back]

    [8]. (8) The abscissa of the vertex of the quadratic function is −(−8.94) / (2 * 0.10) = 44.7%. [^Back]

    [9]. (9) Statistics Bureau of Japan, http://www.stat.go.jp/data/nihon/02.html; Korean Statistical Information Service, http: //kosis.kr/eng/. [^Back]

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This Article

ISSN:1000-6087

CN: 11-1489/C

Vol 42, No. 06, Pages 100-110

November 2018

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Article Outline

Knowledge

Abstract

  • 1 Introduction
  • 2 Literature review
  • 3 Research methods and data sources
  • 4 Empirical analysis results
  • 5 Conclusion and enlightenment
  • Footnote

    References