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The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods

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  • Wang, Xiqian
  • Bian, Yong
  • Zhang, Qin

Abstract

We examine the relationship between the usage of household clean cooking fuels in rural areas and elderly’s overall well-being using micro survey data from the China Health and Retirement Longitudinal Study (CHARLS). We make two key innovations to the literature. First, we use Double Machine Learning, a newly proposed non-parametric method as a consistent estimation of causal inference, to capture non-linear effects of clean energy usage on the elderly’s well-being using a large number of confounders. Second, we take multiple views to assess elderly’s overall well-being, including physical health, psychological health and life satisfaction. We find usage of clean cooking fuel in rural areas significantly enhances middle-aged and senior people’s physical and mental health status and improves their overall subjective life satisfaction. Overall, our results support the energy transition to the use of clean fuels for cooking in rural areas of China, particularly for the elderly population.

Suggested Citation

  • Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    • Handle: RePEc:eee:eneeco:v:125:y:2023:i:c:s0140988323003249
    DOI: 10.1016/j.eneco.2023.106826
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    as
    1. Tian, Zhihua & Tian, Yanfang & Shen, Liangping & Shao, Shuai, 2021. "The health effect of household cooking fuel choice in China: An urban-rural gap perspective," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Burlinson, Andrew & Giulietti, Monica & Battisti, Giuliana, 2018. "The elephant in the energy room: Establishing the nexus between housing poverty and fuel poverty," Energy Economics, Elsevier, vol. 72(C), pages 135-144.
    4. Lin, Youhong & Liu, Feng, 2020. "Indoor air quality and health: Empirical evidence from fluoride pollution in China," China Economic Review, Elsevier, vol. 63(C).
    5. Hosier, Richard H. & Dowd, Jeffrey, 1987. "Household fuel choice in Zimbabwe : An empirical test of the energy ladder hypothesis," Resources and Energy, Elsevier, vol. 9(4), pages 347-361, December.
    6. Behera, Bhagirath & Rahut, Dil Bahadur & Jeetendra, Aryal & Ali, Akhter, 2015. "Household collection and use of biomass energy sources in South Asia," Energy, Elsevier, vol. 85(C), pages 468-480.
    7. Alem, Yonas & Beyene, Abebe D. & Köhlin, Gunnar & Mekonnen, Alemu, 2016. "Modeling household cooking fuel choice: A panel multinomial logit approach," Energy Economics, Elsevier, vol. 59(C), pages 129-137.
    8. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    9. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    10. Aziz, Shakila & Barua, Suborna & Chowdhury, Shahriar Ahmed, 2022. "Cooking energy use in Bangladesh: Evidence from technology and fuel choice," Energy, Elsevier, vol. 250(C).
    11. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    12. Liu, Ziming & Li, Jia & Rommel, Jens & Feng, Shuyi, 2020. "Health impacts of cooking fuel choice in rural China," Energy Economics, Elsevier, vol. 89(C).
    13. Elena Druică & Zizi Goschin & Rodica Ianole-Călin, 2019. "Energy Poverty and Life Satisfaction: Structural Mechanisms and Their Implications," Energies, MDPI, vol. 12(20), pages 1-20, October.
    14. Xiao Ma & Feiran Wang & Jiandong Chen & Yang Zhang, 2018. "The Income Gap Between Urban and Rural Residents in China: Since 1978," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1153-1174, December.
    15. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    16. Jin Liu & Bingdong Hou & Xiao-Wei Ma & Hua Liao, 2018. "Solid fuel use for cooking and its health effects on the elderly in rural China," CEEP-BIT Working Papers 111, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    17. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.
    18. Wang, Yangjie & Chen, Xiaohong & Ren, Shenggang, 2019. "Clean energy adoption and maternal health: Evidence from China," Energy Economics, Elsevier, vol. 84(C).
    19. Pachauri, Shonali & Jiang, Leiwen, 2008. "The household energy transition in India and China," Energy Policy, Elsevier, vol. 36(11), pages 4022-4035, November.
    20. Liu, Pihui & Han, Chuanfeng & Teng, Minmin, 2022. "Does clean cooking energy improve mental health? Evidence from China," Energy Policy, Elsevier, vol. 166(C).
    21. Dmitry Arkhangelsky & Guido W Imbens, 2022. "Doubly robust identification for causal panel data models [Sufficient statistics for unobserved heterogeneity in structural dynamic logit models]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 649-674.
    22. Ma, Wanglin & Zheng, Hongyun & Gong, Binlei, 2022. "Rural income growth, ethnic differences, and household cooking fuel choice: Evidence from China," Energy Economics, Elsevier, vol. 107(C).
    23. Wang, Qiang & Kwan, Mei-Po & Zhou, Kan & Fan, Jie & Wang, Yafei & Zhan, Dongsheng, 2019. "Impacts of residential energy consumption on the health burden of household air pollution: Evidence from 135 countries," Energy Policy, Elsevier, vol. 128(C), pages 284-295.
    24. Kees, Marlis & Feldmann, Lisa, 2011. "The role of donor organisations in promoting energy efficient cook stoves," Energy Policy, Elsevier, vol. 39(12), pages 7595-7599.
    25. Dmitry Arkhangelsky & Guido W. Imbens, 2019. "Doubly Robust Identification for Causal Panel Data Models," Papers 1909.09412, arXiv.org, revised Feb 2022.
    26. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
    27. Paudel, Uttam & Khatri, Umesh & Pant, Krishna Prasad, 2018. "Understanding the determinants of household cooking fuel choice in Afghanistan: A multinomial logit estimation," Energy, Elsevier, vol. 156(C), pages 55-62.
    28. Fu, Hongqiao & Ge, Run & Huang, Jialin & Shi, Xinzheng, 2022. "The effect of education on health and health behaviors: Evidence from the college enrollment expansion in China," China Economic Review, Elsevier, vol. 72(C).
    29. Jeuland, M.A. & Bhojvaid, V. & Kar, A. & Lewis, J.J. & Patange, O. & Pattanayak, S.K. & Ramanathan, N. & Rehman, I.H. & Tan Soo, J.S. & Ramanathan, V., 2015. "Preferences for improved cook stoves: Evidence from rural villages in north India," Energy Economics, Elsevier, vol. 52(PB), pages 287-298.
    30. Matinga, Margaret Njirambo & Annegarn, Harold J. & Clancy, Joy S., 2013. "Healthcare provider views on the health effects of biomass fuel collection and use in rural Eastern Cape, South Africa: An ethnographic study," Social Science & Medicine, Elsevier, vol. 97(C), pages 192-200.
    31. Yang, Jui-Chung & Chuang, Hui-Ching & Kuan, Chung-Ming, 2020. "Double machine learning with gradient boosting and its application to the Big N audit quality effect," Journal of Econometrics, Elsevier, vol. 216(1), pages 268-283.
    32. Foell, Wesley & Pachauri, Shonali & Spreng, Daniel & Zerriffi, Hisham, 2011. "Household cooking fuels and technologies in developing economies," Energy Policy, Elsevier, vol. 39(12), pages 7487-7496.
    33. Jagger, Pamela & Shively, Gerald, 2014. "Land use change, fuel use and respiratory health in Uganda," Energy Policy, Elsevier, vol. 67(C), pages 713-726.
    34. Zhang, Lingyue & Li, Hui & Chen, Tianqi & Liao, Hua, 2022. "Health effects of cooking fuel transition: A dynamic perspective," Energy, Elsevier, vol. 251(C).
    35. Shu Wu, 2021. "The Health Impact of Household Cooking Fuel Choice on Women: Evidence from China," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    36. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
    37. Imelda,, 2020. "Cooking that kills: Cleaner energy access, indoor air pollution, and health," Journal of Development Economics, Elsevier, vol. 147(C).
    38. Kelly C. Bishop & Jonathan D. Ketcham & Nicolai V. Kuminoff, 2018. "Hazed and Confused: The Effect of Air Pollution on Dementia," NBER Working Papers 24970, National Bureau of Economic Research, Inc.
    39. Ma, Wanglin & Vatsa, Puneet & Zheng, Hongyun, 2022. "Cooking fuel choices and subjective well-being in rural China: Implications for a complete energy transition," Energy Policy, Elsevier, vol. 165(C).
    40. Kristoffersen, Ingebjørg, 2018. "Great expectations: Education and subjective wellbeing," Journal of Economic Psychology, Elsevier, vol. 66(C), pages 64-78.
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    More about this item

    Keywords

    Cooking fuel choice; Elderly’s Well-being; Double Machine Learning; Propensity Score Matching;
    All these keywords.

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development

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