IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v119y2020ics1364032119307506.html
   My bibliography  Save this article

A decision tree method for explaining household gas consumption: The role of building characteristics, socio-demographic variables, psychological factors and household behaviour

Author

Listed:
  • Namazkhan, Maliheh
  • Albers, Casper
  • Steg, Linda

Abstract

This research aims to develop a decision tree model for understanding actual gas consumption in residential buildings. Extending previous studies, this study examined to what extent four different type of factors, building characteristics, socio-demographics, psychological factors and household behaviour can explain actual gas consumption of Dutch households in 2017 and 2018. Data were collected from 601 households. A novel approach, a decision tree method, revealed that household gas consumption was related to building characteristics, socio-demographics, and psychological factors, while energy-related behaviour in households was not uniquely related to gas consumption. Specifically, house size, building age and residence type (building characteristics), household income and employment status (socio-demographics), and most notably egoistic values, hedonic values, environmental self-identity, perceived corporate environmental responsibility of the energy provider, and social norm (psychological factors) predicted total actual household gas consumption. These results illustrate that the novel integrated framework introduced in the paper yields a better understanding of actual household gas consumption. The results have important practical implications and suggest that it would be important to target these three type of factors in policy aimed to reduce household gas consumption.

Suggested Citation

  • Namazkhan, Maliheh & Albers, Casper & Steg, Linda, 2020. "A decision tree method for explaining household gas consumption: The role of building characteristics, socio-demographic variables, psychological factors and household behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
  • Handle: RePEc:eee:rensus:v:119:y:2020:i:c:s1364032119307506
    DOI: 10.1016/j.rser.2019.109542
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032119307506
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2019.109542?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. Azam, Muhammad & Khan, Abdul Qayyum & Zaman, Khalid & Ahmad, Mehboob, 2015. "Factors determining energy consumption: Evidence from Indonesia, Malaysia and Thailand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1123-1131.
    2. Hara, Keishiro & Uwasu, Michinori & Kishita, Yusuke & Takeda, Hiroyuki, 2015. "Determinant factors of residential consumption and perception of energy conservation: Time-series analysis by large-scale questionnaire in Suita, Japan," Energy Policy, Elsevier, vol. 87(C), pages 240-249.
    3. Namazkhan, Maliheh & Albers, Casper & Steg, Linda, 2019. "The role of environmental values, socio-demographics and building characteristics in setting room temperatures in winter," Energy, Elsevier, vol. 171(C), pages 1183-1192.
    4. Jeong, Jaehoon & Seob Kim, Chang & Lee, Jongsu, 2011. "Household electricity and gas consumption for heating homes," Energy Policy, Elsevier, vol. 39(5), pages 2679-2687, May.
    5. Baker, Keith J. & Rylatt, R. Mark, 2008. "Improving the prediction of UK domestic energy-demand using annual consumption-data," Applied Energy, Elsevier, vol. 85(6), pages 475-482, June.
    6. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    7. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
    8. Henri C. Moll & Klaas Jan Noorman & Rixt Kok & Rebecka Engström & Harald Throne‐Holst & Charlotte Clark, 2005. "Pursuing More Sustainable Consumption by Analyzing Household Metabolism in European Countries and Cities," Journal of Industrial Ecology, Yale University, vol. 9(1‐2), pages 259-275, January.
    9. Harold, Jason & Lyons, Seán & Cullinan, John, 2015. "The determinants of residential gas demand in Ireland," Energy Economics, Elsevier, vol. 51(C), pages 475-483.
    10. Brounen, Dirk & Kok, Nils & Quigley, John M., 2012. "Residential energy use and conservation: Economics and demographics," European Economic Review, Elsevier, vol. 56(5), pages 931-945.
    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. Jinhua Xie & Gangqiao Yang & Ge Wang & Yaying Zhu & Zhaoxia Guo, 2023. "Substitutes or complements? Exploring the impact of environmental regulations and informal institutions on the clean energy utilization behaviors of farmers," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 3893-3922, May.
    2. Yuan, Yue & Chen, Zhihua & Wang, Zhe & Sun, Yifu & Chen, Yixing, 2023. "Attention mechanism-based transfer learning model for day-ahead energy demand forecasting of shopping mall buildings," Energy, Elsevier, vol. 270(C).
    3. Justyna Patalas-Maliszewska & Hanna Łosyk, 2020. "An Approach to Assessing Sustainability in the Development of a Manufacturing Company," Sustainability, MDPI, vol. 12(21), pages 1-18, October.
    4. Carlos E. Galván-Tejada & Laura A. Zanella-Calzada & Karen E. Villagrana-Bañuelos & Arturo Moreno-Báez & Huizilopoztli Luna-García & Jose María Celaya-Padilla & Jorge Issac Galván-Tejada & Hamurabi Ga, 2020. "Demographic and Comorbidities Data Description of Population in Mexico with SARS-CoV-2 Infected Patients(COVID19): An Online Tool Analysis," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    5. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    6. Mehmet Efe Biresselioglu & Muhittin Hakan Demir, 2022. "Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data," Energies, MDPI, vol. 15(7), pages 1-15, March.
    7. Apostolos Arsenopoulos & Vangelis Marinakis & Konstantinos Koasidis & Andriana Stavrakaki & John Psarras, 2020. "Assessing Resilience to Energy Poverty in Europe through a Multi-Criteria Analysis Framework," Sustainability, MDPI, vol. 12(12), pages 1-22, June.
    8. Dalla Longa, Francesco & Sweerts, Bart & van der Zwaan, Bob, 2021. "Exploring the complex origins of energy poverty in The Netherlands with machine learning," Energy Policy, Elsevier, vol. 156(C).

    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. Lee, Soo-Jin & Song, Seung-Yeong, 2022. "Time-series analysis of the effects of building and household features on residential end-use energy," Applied Energy, Elsevier, vol. 312(C).
    2. Michael Chesser & Jim Hanly & Damien Cassells & Nikolaos Apergis, 2019. "Household Energy Consumption: A Study of Micro Renewable Energy Systems in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 50(2), pages 265-280.
    3. Boukarta Soufiane & Berezowska-Azzag Ewa, 2018. "Assessing Households’ Gas and Electricity Consumption: A Case Study of Djelfa, Algeria," Quaestiones Geographicae, Sciendo, vol. 37(4), pages 111-129, December.
    4. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    5. Coyne, Bryan & Lyons, Sean & McCoy, Daire, 2016. "The Effects of Home Energy Efficiency Upgrades on Social Housing Tenants: Evidence from Ireland," Papers WP544, Economic and Social Research Institute (ESRI).
    6. Chen, Guangwu & Zhu, Yuhan & Wiedmann, Thomas & Yao, Lina & Xu, Lixiao & Wang, Yafei, 2019. "Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models," Applied Energy, Elsevier, vol. 250(C), pages 1321-1335.
    7. Soltanisarvestani, A. & Safavi, A.A., 2021. "Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map," Utilities Policy, Elsevier, vol. 72(C).
    8. Wang, Jianming & Li, Yongqiang & He, Zhengxia & Gao, Jian & Wang, Jianguo, 2022. "Scale framing, benefit framing and their interaction effects on energy-saving behaviors: Evidence from urban residents of China," Energy Policy, Elsevier, vol. 166(C).
    9. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    10. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    11. Yu, Miao & Zhao, Xintong & Gao, Yuning, 2019. "Factor decomposition of China’s industrial electricity consumption using structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 67-76.
    12. Halim Tatli, 2018. "Multiple Determinants of Household Natural Gas Demand: A Panel Data Analysis in OECD Countries," Asian Development Policy Review, Asian Economic and Social Society, vol. 6(4), pages 243-253, December.
    13. Lawal, Abiola S. & Servadio, Joseph L. & Davis, Tate & Ramaswami, Anu & Botchwey, Nisha & Russell, Armistead G., 2021. "Orthogonalization and machine learning methods for residential energy estimation with social and economic indicators," Applied Energy, Elsevier, vol. 283(C).
    14. Tamaryn Menneer & Zening Qi & Timothy Taylor & Cheryl Paterson & Gengyang Tu & Lewis R. Elliott & Karyn Morrissey & Markus Mueller, 2021. "Changes in Domestic Energy and Water Usage during the UK COVID-19 Lockdown Using High-Resolution Temporal Data," IJERPH, MDPI, vol. 18(13), pages 1-21, June.
    15. Prami Sengupta & Randall Cantrell, 2021. "Context Matters: The effects of budgetary and knowledge constraints on residential energy conservation," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 11(4), pages 561-573, December.
    16. Wang, Yuanping & Hou, Lingchun & Cai, Weiguang & Zhou, Zhaoyin & Bian, Jing, 2023. "Exploring the drivers and influencing mechanisms of urban household electricity consumption in China - Based on longitudinal data at the provincial level," Energy, Elsevier, vol. 273(C).
    17. Claudy, Marius & Michelsen, Claus, 2016. "Housing Market Fundamentals, Housing Quality and Energy Consumption: Evidence from Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 37(4), pages 25-43.
    18. Copiello, Sergio & Grillenzoni, Carlo, 2017. "Is the cold the only reason why we heat our homes? Empirical evidence from spatial series data," Applied Energy, Elsevier, vol. 193(C), pages 491-506.
    19. Ali Movahedi & Sybil Derrible, 2021. "Interrelationships between electricity, gas, and water consumption in large‐scale buildings," Journal of Industrial Ecology, Yale University, vol. 25(4), pages 932-947, August.
    20. Walter, Travis & Sohn, Michael D., 2016. "A regression-based approach to estimating retrofit savings using the Building Performance Database," Applied Energy, Elsevier, vol. 179(C), pages 996-1005.

    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:eee:rensus:v:119:y:2020:i:c:s1364032119307506. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.