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干旱区地理 ›› 2023, Vol. 46 ›› Issue (2): 294-304.doi: 10.12118/j.issn.1000-6060.2022.172

• 区域发展 • 上一篇    下一篇

基于物质流的青海高原城镇社区家庭能源消费研究——以西宁市为例

姜璐1,2(),刘艳娟1,史晓楠3,丁博文鹏4,陈虹宇1   

  1. 1.青海省人民政府-北京师范大学高原科学与可持续发展研究院,青海 西宁 810016
    2.北京师范大学地理科学学部,北京 100875
    3.中国科学院东北地理与农业生态研究所,吉林 长春 130102
    4.兰州大学资源环境学院,甘肃 兰州 730000
  • 收稿日期:2022-04-29 修回日期:2022-06-22 出版日期:2023-02-25 发布日期:2023-03-14
  • 作者简介:姜璐(1989-),女,博士,教授,博士生导师,主要从事能源地理与区域可持续发展等方面的研究. E-mail: jianglu@gdas.ac.cn
  • 基金资助:
    国家自然科学基金项目(42001130)

Household energy consumption in urban communities in Qinghai Plateau based on material flow: A case of Xining City

JIANG Lu1,2(),LIU Yanjuan1,SHI Xiaonan3,DING Bowenpeng4,CHEN Hongyu1   

  1. 1. Academy of Plateau Science and Sustainability of People’s Government of Qinghai Province & Beijing Normal University, Xining 810016, Qinghai, China
    2. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    3. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, Jilin, China
    4. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, Gansu, China
  • Received:2022-04-29 Revised:2022-06-22 Online:2023-02-25 Published:2023-03-14

摘要:

在后工业化时代和后疫情时代的叠加背景下,随着城镇家庭能源消费量持续增加,家庭已成为能源消费碳排放的主要贡献者。高原城市西宁市是生态安全的重要守卫地,分析其家庭能源消费的研究对我国生态文明建设起着至关重要的作用。基于2021年实地调研数据结合普通最小二乘法(OLS)回归模型,通过构建家庭能源流探索了其家庭能源消费特征、影响因素并对不同收入情况下家庭能源消费的全过程进行可视化。结果表明:(1) 西宁市城镇家庭年人均能源消费量为461.57 kgce·a-1。(2) 在能源用途方面,西宁市城镇家庭人均取暖能源消费量为307.52 kgce·a-1,是家庭能源消费的主要来源,而大型家用电器中的洗衣机耗能量最低。(3) 家庭年末总收入是影响西宁市家庭人均年能源消费的核心因素。(4) 高收入家庭人均能源消费量大,但随着家庭收入水平增加,取暖能源消费量随之减少。基于此,研究建议增加居民的清洁能源供给,增强所有居民对可再生能源的认识;增加对中高收入人群的节能宣传和自主节能意识。通过因地制宜制定能源政策,推动我国可持续能源发展进程。

关键词: 家庭能源消费, 高原城镇, 影响因素, OLS模型, 西宁市

Abstract:

Under the background of the postindustrial and post-epidemic eras, the amount of energy consumed by households in urban areas continuously increases. Thus, households have become the main contributor to carbon emissions and large amounts of energy consumed. Accordingly, a study on household energy consumption plays a vital role in the construction of ecological civilization in China. Plateau cities are important custodians of ecological security, and Xining City is a representative of plateau cities. Therefore, we selected it as the research object of this study. We aimed to explore the characteristics and influencing factors of household energy consumption and to visualize the process of household energy consumption under different income conditions. We utilized 2021 survey data, mainly using household energy estimates and ordinary least squares regression models and constructing household energy flows. We found that: (1) The annual per capita energy consumption of urban households in Xining City is 461.57 kgce·a-1. (2) Regarding energy use, the per capita heating energy consumption of urban households in Xining City is 307.52 kgce·a-1, which is the main cause of the high energy consumption (66.62%), followed by the per capita energy consumption of kitchen equipment (74.56 kgce·a-1). Conversely, large household appliances are relatively few, and washing machines especially consume the lowest energy. (3) The total year-end household income is the core factor that affects the annual per capita energy consumption of households in Xining City, China. (4) The annual per capita energy consumption of high-income households is large, and the type of energy consumed varies. Additionally, as household income levels increase, the amount of energy consumed in heating households decreases. Finally, to effectively promote energy transition, we suggest the residents should enhance their awareness of renewable energy, and governments should increase the supply of clean energy for residents by constructing rooftop photovoltaics and laying natural gas pipelines. What is more, governments should also increase the energy-saving publicity for middle- and high-income groups to enhance residents’ awareness of independent energy conservation and must customize energy policies for local conditions.

Key words: household energy consumption, plateau towns, influencing factors, OLS model, Xining City