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How does slum rehabilitation influence appliance ownership? A structural model of non-income drivers

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  • Debnath, Ramit
  • Bardhan, Ronita
  • Sunikka-Blank, Minna

Abstract

This study explores the effect of slum rehabilitation on appliance ownership and its implications on residential electricity demand. The low-income scenario makes it unique because the entire proposition is based on the importance of non-income drivers of appliance ownership that includes effects of changing the built environment (BE), household practices (HP) and appliances characteristics (AC). This study demonstrates quantitatively that non-income factors around energy practices influence appliance ownership, and therefore electricity consumption. The methodology consists of questionnaire design across the dimension of BE, HP and AC based on social practice theory, surveying of 1224 households and empirical analysis using covariance-based structural equation modelling. Results show that higher appliance ownership in the slum rehabilitation housing is due to change in household practice, built environment and affordability criteria of the appliances. Change in HP shifts necessary activities like cooking, washing and cleaning from outdoor to indoor spaces that positively and significantly influences higher appliance ownership. Poor BE conditions about indoor air quality, thermal comfort and hygiene; and product cost, discounts and ease of use of the appliances also triggers higher appliance ownership. The findings of this study can aid in designing better regulatory and energy efficiency policies for low-income settlements.

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  • Debnath, Ramit & Bardhan, Ronita & Sunikka-Blank, Minna, 2019. "How does slum rehabilitation influence appliance ownership? A structural model of non-income drivers," Energy Policy, Elsevier, vol. 132(C), pages 418-428.
    • Handle: RePEc:eee:enepol:v:132:y:2019:i:c:p:418-428
    DOI: 10.1016/j.enpol.2019.06.005
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    3. Debnath, R. & Darby, S. & Bardhan, R. & Mohaddes, K. & Sunikka-Blank, M., 2020. "Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research," Cambridge Working Papers in Economics 2062, Faculty of Economics, University of Cambridge.
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    5. Debnath, Ramit & Bardhan, Ronita & Misra, Ashwin & Hong, Tianzhen & Rozite, Vida & Ramage, Michael H., 2022. "Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models," Energy Policy, Elsevier, vol. 164(C).
    6. Bangkim Kshetrimayum & Ronita Bardhan & Tetsu Kubota, 2020. "Factors Affecting Residential Satisfaction in Slum Rehabilitation Housing in Mumbai," Sustainability, MDPI, vol. 12(6), pages 1-22, March.
    7. Guo, Ji & Xu, Yuanjing & Qu, Yao & Wang, Yiting & Wu, Xianhua, 2023. "Exploring factors affecting household energy consumption in the internet era: Empirical evidence from Chinese households," Energy Policy, Elsevier, vol. 183(C).
    8. Neto-Bradley, André Paul & Choudhary, Ruchi & Bazaz, Amir, 2020. "Slipping through the net: Can data science approaches help target clean cooking policy interventions?," Energy Policy, Elsevier, vol. 144(C).
    9. Ramit Debnath & Gianna Monteiro Farias Simoes & Ronita Bardhan & Solange Maria Leder & Roberto Lamberts & Minna Sunikka-Blank, 2020. "Energy Justice in Slum Rehabilitation Housing: An Empirical Exploration of Built Environment Effects on Socio-Cultural Energy Demand," Sustainability, MDPI, vol. 12(7), pages 1-27, April.

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