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Time preferences and energy consumption of rural household in China

Author

Listed:
  • Mao, Hui
  • Shi, Chaoqian
  • Tang, Heyan
  • Lu, Yufeng

Abstract

Recent empirical evidence suggests that time preferences have significant effects on intertemporal investment, saving behavior and asset pricing. However, limited research has been conducted on the impact of time preferences on energy consumption. This study aims to investigate the relationship between time preferences and energy consumption among rural households in China, using survey data collected on energy consumption patterns. The results reveal that as time preferences increase, there is a reduction in the diversity of energy sources used by households, and this finding remains robust after rigorous testing. Moreover, our findings support the applicability of the energy ladder theory in rural China, indicating that higher household income can mitigate the inhibiting effect of time preferences on the transition and improvement of energy sources. Additionally, policies promoting energy accessibility, raising awareness about high-quality energy sources, and facilitating credit access significantly promote rural households' advancement on the energy ladder toward superior-quality sources. These policy measures, along with heightened awareness and credit access, can alleviate time preference constraints on the energy transition of rural households. Consequently, we recommend enhancing future policies focusing on efficiency and providing customized financial support for energy initiatives.

Suggested Citation

  • Mao, Hui & Shi, Chaoqian & Tang, Heyan & Lu, Yufeng, 2024. "Time preferences and energy consumption of rural household in China," Energy Economics, Elsevier, vol. 132(C).
    • Handle: RePEc:eee:eneeco:v:132:y:2024:i:c:s0140988324001865
    DOI: 10.1016/j.eneco.2024.107478
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    as
    1. Michielsen, T.O., 2011. "The Distribution of Energy-Intensive Sectors in the US," Other publications TiSEM 2f7d55fe-e610-4ec4-8559-0, Tilburg University, School of Economics and Management.
    2. Liu, Haiying & Liu, Zexiao & Zhang, Chunhong & Li, Tianyu, 2023. "Transformational insurance and green credit incentive policies as financial mechanisms for green energy transitions and low-carbon economic development," Energy Economics, Elsevier, vol. 126(C).
    3. Richard G. Newell & Juha Siikamäki, 2015. "Individual Time Preferences and Energy Efficiency," American Economic Review, American Economic Association, vol. 105(5), pages 196-200, May.
    4. Rong, Rong & Gnagey, Matthew & Grijalva, Therese, 2018. "“The less you Discount, the more it shows you really care”: Interpersonal discounting in households," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 1-23.
    5. Doremus, Jacqueline M. & Jacqz, Irene & Johnston, Sarah, 2022. "Sweating the energy bill: Extreme weather, poor households, and the energy spending gap," Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
    6. Ekholm, Tommi & Krey, Volker & Pachauri, Shonali & Riahi, Keywan, 2010. "Determinants of household energy consumption in India," Energy Policy, Elsevier, vol. 38(10), pages 5696-5707, October.
    7. Pang, Qinghua & Dong, Xianwei & Zhang, Lina & Chiu, Yung-ho, 2023. "Drivers and key pathways of the household energy consumption in the Yangtze river economic belt," Energy, Elsevier, vol. 262(PA).
    8. Damette, Olivier & Delacote, Philippe & Lo, Gaye Del, 2018. "Households energy consumption and transition toward cleaner energy sources," Energy Policy, Elsevier, vol. 113(C), pages 751-764.
    9. Ubfal, Diego, 2016. "How general are time preferences? Eliciting good-specific discount rates," Journal of Development Economics, Elsevier, vol. 118(C), pages 150-170.
    10. Michielsen, T.O., 2011. "The Distribution of Energy-Intensive Sectors in the US," Discussion Paper 2011-075, Tilburg University, Center for Economic Research.
    11. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2019. "Modelling dynamic impacts of urbanization on disaggregated energy consumption in China: A spatial Durbin modelling and decomposition approach," Energy Policy, Elsevier, vol. 133(C).
    12. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    13. Saidur, R., 2009. "Energy consumption, energy savings, and emission analysis in Malaysian office buildings," Energy Policy, Elsevier, vol. 37(10), pages 4104-4113, October.
    14. Pothitou, Mary & Hanna, Richard F. & Chalvatzis, Konstantinos J., 2016. "Environmental knowledge, pro-environmental behaviour and energy savings in households: An empirical study," Applied Energy, Elsevier, vol. 184(C), pages 1217-1229.
    15. Hurwitz, Abigail & Sade, Orly, 2020. "An investigation of time preferences, life expectancy, and annuity versus lump sum choices: Can smoking harm long-term saving decisions?," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 812-825.
    16. Schleich, Joachim & Gassmann, Xavier & Meissner, Thomas & Faure, Corinne, 2019. "A large-scale test of the effects of time discounting, risk aversion, loss aversion, and present bias on household adoption of energy-efficient technologies," Energy Economics, Elsevier, vol. 80(C), pages 377-393.
    17. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    18. Esther Duflo & Michael Kremer & Jonathan Robinson, 2011. "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 101(6), pages 2350-2390, October.
    19. Susanna B. Berkouwer & Joshua T. Dean, 2022. "Credit, Attention, and Externalities in the Adoption of Energy Efficient Technologies by Low-Income Households," American Economic Review, American Economic Association, vol. 112(10), pages 3291-3330, October.
    20. Cayla, Jean-Michel & Maizi, Nadia & Marchand, Christophe, 2011. "The role of income in energy consumption behaviour: Evidence from French households data," Energy Policy, Elsevier, vol. 39(12), pages 7874-7883.
    21. Stephan Meier & Charles D. Sprenger, 2015. "Temporal Stability of Time Preferences," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 273-286, May.
    22. Chen, Qiu & Huang, Jikun & Mirzabaev, Alisher, 2022. "Does fuel price subsidy work? Household energy transition under imperfect labor market in rural China," Energy Economics, Elsevier, vol. 107(C).
    23. Maximilian Auffhammer & Catherine D. Wolfram, 2014. "Powering Up China: Income Distributions and Residential Electricity Consumption," American Economic Review, American Economic Association, vol. 104(5), pages 575-580, May.
    24. S. Tao & M. Y. Ru & W. Du & X. Zhu & Q. R. Zhong & B. G. Li & G. F. Shen & X. L. Pan & W. J. Meng & Y. L. Chen & H. Z. Shen & N. Lin & S. Su & S. J. Zhuo & T. B. Huang & Y. Xu & X. Yun & J. F. Liu & X, 2018. "Quantifying the rural residential energy transition in China from 1992 to 2012 through a representative national survey," Nature Energy, Nature, vol. 3(7), pages 567-573, July.
    25. Niu, Shuwen & Zhang, Xin & Zhao, Chunsheng & Niu, Yunzhu, 2012. "Variations in energy consumption and survival status between rural and urban households: A case study of the Western Loess Plateau, China," Energy Policy, Elsevier, vol. 49(C), pages 515-527.
    26. Grossman, Michael, 1972. "On the Concept of Health Capital and the Demand for Health," Journal of Political Economy, University of Chicago Press, vol. 80(2), pages 223-255, March-Apr.
    27. Finke, Michael S. & Huston, Sandra J., 2013. "Time preference and the importance of saving for retirement," Journal of Economic Behavior & Organization, Elsevier, vol. 89(C), pages 23-34.
    28. Aldossary, Naief A. & Rezgui, Yacine & Kwan, Alan, 2014. "Domestic energy consumption patterns in a hot and humid climate: A multiple-case study analysis," Applied Energy, Elsevier, vol. 114(C), pages 353-365.
    29. Tomomi Tanaka & Colin F. Camerer & Quang Nguyen, 2010. "Risk and Time Preferences: Linking Experimental and Household Survey Data from Vietnam," American Economic Review, American Economic Association, vol. 100(1), pages 557-571, March.
    30. Donglan, Zha & Dequn, Zhou & Peng, Zhou, 2010. "Driving forces of residential CO2 emissions in urban and rural China: An index decomposition analysis," Energy Policy, Elsevier, vol. 38(7), pages 3377-3383, July.
    31. He, Shutong & Blasch, Julia & van Beukering, Pieter & Wang, Junfeng, 2022. "Energy labels and heuristic decision-making: The role of cognition and energy literacy," Energy Economics, Elsevier, vol. 114(C).
    32. Zhu, Lin & Liao, Hua & Burke, Paul J., 2023. "Household fuel transitions have substantially contributed to child mortality reductions in China," World Development, Elsevier, vol. 164(C).
    33. Drescher, Katharina & Janzen, Benedikt, 2021. "Determinants, persistence, and dynamics of energy poverty: An empirical assessment using German household survey data," Energy Economics, Elsevier, vol. 102(C).
    34. James Andreoni & Charles Sprenger, 2015. "Risk Preferences Are Not Time Preferences: Reply," American Economic Review, American Economic Association, vol. 105(7), pages 2287-2293, July.
    35. Wu, Shu & Han, Hongyun, 2022. "Energy transition, intensity growth, and policy evolution: Evidence from rural China," Energy Economics, Elsevier, vol. 105(C).
    36. Werthschulte, Madeline & Löschel, Andreas, 2021. "On the role of present bias and biased price beliefs in household energy consumption," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    37. Richard H. Thaler, 2016. "Behavioral Economics: Past, Present, and Future," American Economic Review, American Economic Association, vol. 106(7), pages 1577-1600, July.
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    More about this item

    Keywords

    Energy consumption; Time preferences; Policy intervention; Energy perception; Credit;
    All these keywords.

    JEL classification:

    • R22 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other Demand
    • R28 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Government Policy
    • D15 - Microeconomics - - Household Behavior - - - Intertemporal Household Choice; Life Cycle Models and Saving
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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