IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v45y2023i2d10.1007_s10878-023-01004-x.html
   My bibliography  Save this article

Analyzing the spatial association of household consumption carbon emission structure based on social network

Author

Listed:
  • Jia-Bao Liu

    (Anhui Jianzhu University)

  • Xin-Bei Peng

    (Anhui Jianzhu University)

  • Jing Zhao

    (Hubei University)

Abstract

In recent years, the energy consumption and associated carbon emissions from household consumption are increasing rapidly. It is an essential indicator to evaluate the extent of building a low-carbon society in China under the background of carbon peaking and carbon neutrality. Thus, we firstly calculate the information entropy of direct household consumption-induced carbon emission structure (IDHCES) in China during 2005–2019. Secondly, the spatial association network of the IDHCES is constructed by using the modified gravity model. Finally, we apply the social network analysis (SNA) to investigate spatial association characteristics of the spatial association network and explore influential factors by constructing the quadratic assignment procedure (QAP) model. There are four primary discoveries: (1) The balance of inter-provincial direct carbon emission structure from residential consumption is quite different. And the spatial linkage of the IDHCES is not just geographical proximity, but shows the complex network pattern. The extent of this network linkage is getting higher over time. (2) The spatial association network of the IDHCES presents an evident core-edge distribution. Most of the eastern provinces situated at the core of this network, such as Shanghai, Beijing and Tianjin, play essential roles, while most of the central and western provinces such as Qinghai, Guizhou, Xiangjiang and Ningxia are on the edge and have slight influence to this network. (3) The spatial association network for the IDHCES can be divided into four blocks, which are strongly related to each other and have obvious stepwise spillover effects. (4) The expansion of differences in per capita GDP, energy consumption per unit of GDP, family size and government investment in science and technology promotes the formation of the spatial association network of the IDHCES. While, the expansion of differences in geographical distance, population density and engel coefficient acts as a barrier. Based on the above analysis, we put forward some related suggestions for optimizing the information entropy of the direct carbon emission structure from Chinese residents’ consumption.

Suggested Citation

  • Jia-Bao Liu & Xin-Bei Peng & Jing Zhao, 2023. "Analyzing the spatial association of household consumption carbon emission structure based on social network," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-34, March.
    • Handle: RePEc:spr:jcomop:v:45:y:2023:i:2:d:10.1007_s10878-023-01004-x
    DOI: 10.1007/s10878-023-01004-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-023-01004-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-023-01004-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jia-Bao Liu & Yan Bao & Wu-Ting Zheng, 2022. "Analyses Of Some Structural Properties On A Class Of Hierarchical Scale-Free Networks," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(07), pages 1-11, November.
    2. Streimikiene, Dalia, 2015. "Assessment of reasonably achievable GHG emission reduction target in Lithuanian households," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 460-467.
    3. Song, Tao & Zheng, Tingguo & Tong, Lianjun, 2008. "An empirical test of the environmental Kuznets curve in China: A panel cointegration approach," China Economic Review, Elsevier, vol. 19(3), pages 381-392, September.
    4. Lu, Heli & Liu, Guifang, 2014. "Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting," Applied Energy, Elsevier, vol. 131(C), pages 297-306.
    5. José Miguel Barrios & Willem W. Verstraeten & Piet Maes & Jean-Marie Aerts & Jamshid Farifteh & Pol Coppin, 2012. "Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases," IJERPH, MDPI, vol. 9(12), pages 1-19, November.
    6. Feng, Zhen-Hua & Zou, Le-Le & Wei, Yi-Ming, 2011. "The impact of household consumption on energy use and CO2 emissions in China," Energy, Elsevier, vol. 36(1), pages 656-670.
    7. Zheng, Heran & Shan, Yuli & Mi, Zhifu & Meng, Jing & Ou, Jiamin & Schroeder, Heike & Guan, Dabo, 2018. "How modifications of China's energy data affect carbon mitigation targets," Energy Policy, Elsevier, vol. 116(C), pages 337-343.
    8. Reinders, A. H. M. E. & Vringer, K. & Blok, K., 2003. "The direct and indirect energy requirement of households in the European Union," Energy Policy, Elsevier, vol. 31(2), pages 139-153, January.
    9. Jia-Bao Liu & Yan Bao & Wu-Ting Zheng & Sakander Hayat, 2021. "NETWORK COHERENCE ANALYSIS ON A FAMILY OF NESTED WEIGHTED n-POLYGON NETWORKS," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 29(08), pages 1-15, December.
    10. Liao, Chun-Hsiung & Lu, Chin-Shan & Tseng, Po-Hsing, 2011. "Carbon dioxide emissions and inland container transport in Taiwan," Journal of Transport Geography, Elsevier, vol. 19(4), pages 722-728.
    11. Fei Ma & Yixuan Wang & Kum Fai Yuen & Wenlin Wang & Xiaodan Li & Yuan Liang, 2019. "The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective," IJERPH, MDPI, vol. 16(12), pages 1-23, June.
    12. Qu, Jiansheng & Zeng, Jingjing & Li, Yan & Wang, Qin & Maraseni, Tek & Zhang, Lihua & Zhang, Zhiqiang & Clarke-Sather, Abigail, 2013. "Household carbon dioxide emissions from peasants and herdsmen in northwestern arid-alpine regions, China," Energy Policy, Elsevier, vol. 57(C), pages 133-140.
    13. Kadian, Rashmi & Dahiya, R.P. & Garg, H.P., 2007. "Energy-related emissions and mitigation opportunities from the household sector in Delhi," Energy Policy, Elsevier, vol. 35(12), pages 6195-6211, December.
    14. Liu, S. & Xiao, Q., 2021. "An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model," Energy, Elsevier, vol. 224(C).
    15. Wang, Yuan & Wang, Yichen & Zhou, Jing & Zhu, Xiaodong & Lu, Genfa, 2011. "Energy consumption and economic growth in China: A multivariate causality test," Energy Policy, Elsevier, vol. 39(7), pages 4399-4406, July.
    16. Tian, Xu & Geng, Yong & Dai, Hancheng & Fujita, Tsuyoshi & Wu, Rui & Liu, Zhe & Masui, Toshihiko & Yang, Xie, 2016. "The effects of household consumption pattern on regional development: A case study of Shanghai," Energy, Elsevier, vol. 103(C), pages 49-60.
    17. Druckman, Angela & Buck, Ian & Hayward, Bronwyn & Jackson, Tim, 2012. "Time, gender and carbon: A study of the carbon implications of British adults' use of time," Ecological Economics, Elsevier, vol. 84(C), pages 153-163.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qingsheng Zhu & Kai Gao & Jia-Bao Liu, 2023. "Cloud model for new energy vehicle supply chain management based on growth expectation," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-23, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Zhenshuang & Xie, Wanchen & Zhang, Chengyi, 2023. "Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on U.S. carbon emission," Resources Policy, Elsevier, vol. 81(C).
    2. Pedro J. Zarco-Periñán & Fco Javier Zarco-Soto & Irene M. Zarco-Soto & José L. Martínez-Ramos & Rafael Sánchez-Durán, 2022. "CO 2 Emissions in Buildings: A Synopsis of Current Studies," Energies, MDPI, vol. 15(18), pages 1-10, September.
    3. Zhang, Weishi & Xu, Ying & Wang, Can & Streets, David G., 2022. "Assessment of the driving factors of CO2 mitigation costs of household biogas systems in China: A LMDI decomposition with cost analysis model," Renewable Energy, Elsevier, vol. 181(C), pages 978-989.
    4. Rui Huang & Shaohui Zhang & Changxin Liu, 2018. "Comparing Urban and Rural Household CO 2 Emissions—Case from China’s Four Megacities: Beijing, Tianjin, Shanghai, and Chongqing," Energies, MDPI, vol. 11(5), pages 1-17, May.
    5. Lina Liu & Jiansheng Qu & Afton Clarke-Sather & Tek Narayan Maraseni & Jiaxing Pang, 2017. "Spatial Variations and Determinants of Per Capita Household CO 2 Emissions (PHCEs) in China," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    6. Zhang, Hongwu & Shi, Xunpeng & Wang, Keying & Xue, Jinjun & Song, Ligang & Sun, Yongping, 2020. "Intertemporal lifestyle changes and carbon emissions: Evidence from a China household survey," Energy Economics, Elsevier, vol. 86(C).
    7. Xinkuo Xu & Liyan Han, 2017. "Diverse Effects of Consumer Credit on Household Carbon Emissions at Quantiles: Evidence from Urban China," Sustainability, MDPI, vol. 9(9), pages 1-25, September.
    8. Yuan, Baolong & Ren, Shenggang & Chen, Xiaohong, 2015. "The effects of urbanization, consumption ratio and consumption structure on residential indirect CO2 emissions in China: A regional comparative analysis," Applied Energy, Elsevier, vol. 140(C), pages 94-106.
    9. Fei Ma & Yixuan Wang & Kum Fai Yuen & Wenlin Wang & Xiaodan Li & Yuan Liang, 2019. "The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective," IJERPH, MDPI, vol. 16(12), pages 1-23, June.
    10. Yongxia Ding & Wei Qu & Shuwen Niu & Man Liang & Wenli Qiang & Zhenguo Hong, 2016. "Factors Influencing the Spatial Difference in Household Energy Consumption in China," Sustainability, MDPI, vol. 8(12), pages 1-20, December.
    11. Zhihui Lv & Amanda M. Y. Chu & Michael McAleer & Wing-Keung Wong, 2019. "Modelling Economic Growth, Carbon Emissions, and Fossil Fuel Consumption in China: Cointegration and Multivariate Causality," IJERPH, MDPI, vol. 16(21), pages 1-35, October.
    12. Lan-Cui Liu & Gang Wu & Jin-Nan Wang & Yi-Ming Wei, 2010. "China's carbon emissions from urban and rural households during 1992-2007," CEEP-BIT Working Papers 12, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    13. Lina Liu & Jiansheng Qu & Tek Narayan Maraseni & Yibo Niu & Jingjing Zeng & Lihua Zhang & Li Xu, 2020. "Household CO 2 Emissions: Current Status and Future Perspectives," IJERPH, MDPI, vol. 17(19), pages 1-19, September.
    14. Wenwen Wang & Ming Zhang, 2015. "Direct and indirect energy consumption of rural households in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1693-1705, December.
    15. Zhang, Yimeng & Wang, Feng & Zhang, Bing, 2023. "The impacts of household structure transitions on household carbon emissions in China," Ecological Economics, Elsevier, vol. 206(C).
    16. Lan-Cui Liu & Gang Wu & Yue-Jun Zhang, 2015. "Investigating the residential energy consumption behaviors in Beijing: a survey study," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 243-263, January.
    17. Wei Shi & Zhiquan Sha & Fuwei Qiao & Wenwen Tang & Chuyu Luo & Yali Zheng & Chunli Wang & Jun Ge, 2023. "Study on the Temporal and Spatial Evolution of China’s Carbon Dioxide Emissions and Its Emission Reduction Path," Energies, MDPI, vol. 16(2), pages 1-16, January.
    18. Shu Yang & Dingtao Zhao & Yanrui Wu & Jin Fan, 2013. "Regional Variation in Carbon Emissions and its Driving Forces in China: An Index Decomposition Analysis," Energy & Environment, , vol. 24(7-8), pages 1249-1270, December.
    19. Hamamoto, Mitsutsugu, 2013. "Energy-saving behavior and marginal abatement cost for household CO2 emissions," Energy Policy, Elsevier, vol. 63(C), pages 809-813.
    20. Wei, Rui & Zhang, Wencheng & Peng, Shuijun, 2022. "Energy and greenhouse gas footprints of China households during 1995–2019: A global perspective," Energy Policy, Elsevier, vol. 164(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:45:y:2023:i:2:d:10.1007_s10878-023-01004-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.