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Determinants of Cookstoves and Fuel Choice Among Rural Households in India

EcoHealth volume 16, pages 21–60 (2019)Cite this article

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Abstract

Roughly 2.8 billion people depend on solid fuels for cooking needs, resulting in a tremendous burden of disease from exposure to household air pollution. Despite decades of effort to promote cleaner cooking technologies, displacement of polluting technologies has progressed slowly. This paper describes results of a randomized controlled trial in which eight communities in two regions of rural India were presented with a range of cooking choices including improved solid fuel stoves and clean cooking options like liquefied petroleum gas (LPG) and induction stoves. Using survey data and logistic and multinomial regression, we identify factors associated with two outcomes: (1) pre-intervention ownership of non-solid fuel technologies and (2) household preferences for clean fuels from the range of cooking options offered. The analysis allows us to examine the influence of education, wealth, gender empowerment, stove pricing, and stove exchanges, among other variables. The majority of participants across all communities selected the cleanest options, LPG and induction, irrespective of price, but there is some variation in preferences. Wealth and higher caste stand out as significant predictors of pre-intervention ownership and non-solid fuel cooking options as well as preference for cleaner technologies offered through the intervention. The experimental treatments also influence preferences in some communities. When given the opportunity to exchange, communities in one region are more likely to choose solid fuel stoves (P < 0.05). Giving free stoves had mixed results; households in one region are more likely to select clean options (P < 0.05), but households in the other region prefer solid fuels (P < 0.10).

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Notes

  1. The study imposed one constraint on stove choice: Households that already had a subsidized LPG connection could not select LPG through our intervention because the government program only allows one connection per household.

  2. Models were tested for multicollinearity using variance inflation factor (VIF). Details are shown in “Appendix 2.” Generalized VIF remains well below 2 for all combinations of variables, which indicates a low degree of collinearity among variables.

  3. These distributions are shown in “Appendix 3.”

  4. The informal connections were not included, because the objective of this analysis was to assess households’ choices from among all non-LPG options.

  5. Except for one model (out of total 8) in the logistic regression for Kullu full sample (Table 11).

  6. This section reports odds ratios (OR) with 95% confidence intervals in brackets.

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Acknowledgements

This article was developed under Assistance Agreement No. 83542102 awarded by the US Environmental Protection Agency (EPA) to Dr. Rob Bailis (with sub-award to Dr. Hisham Zerriffi) and received supplemental funding from the Global Alliance for Clean Cookstoves (award no. UNF-160798). It has not been formally reviewed by the EPA or GACC. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency or GACC. Neither EPA nor GACC endorses any products or commercial services mentioned in this publication.

Author information

Affiliations

  1. IRES, University of British Columbia, Vancouver, Canada

    Vikas Menghwani

  2. Faculty of Forestry, University of British Columbia, Vancouver, Canada

    Hisham Zerriffi

  3. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, USA

    Puneet Dwivedi

  4. Civil and Environmental Engineering, University of Washington, Seattle, USA

    Julian D. Marshall

  5. Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, USA

    Andrew Grieshop

  6. Stockholm Environment Institute – US Center, 11 Curtis Ave, Somerville, MA, 02144, USA

    Rob Bailis

Authors
  1. Vikas Menghwani
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  2. Hisham Zerriffi
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  3. Puneet Dwivedi
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  4. Julian D. Marshall
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  5. Andrew Grieshop
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  6. Rob Bailis
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Corresponding author

Correspondence to Rob Bailis.

Appendices

Appendix 1

See Tables 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, and 19.

Table 10 Logistic Regression Results for Koppal Baseline Stove Ownership.
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Table 11 Logistic Regression Results for Kullu Stove Choice (Full Sample).
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Table 12 Logistic Regression Results for Kullu Stove Choice (Only for HHs with an LPG Connection).
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Table 13 Logistic Regression Results for Kullu Stove Choice (Excluding HHs with LPG Connections).
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Table 14 Logistic Regression Results for Koppal Stove Choice (Full Sample).
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Table 15 Multinomial Regression Results for Kullu Stove Choice (Full Sample).
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Table 16 Multinomial Regression Results for Kullu Stove Choice (Only for HHs with an LPG Connection).
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Table 17 Multinomial Regression Results for Kullu Stove Choice (Excluding HHs with LPG Connections).
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Table 18 Multinomial Regression Results for Koppal Stove Choice (Full Sample).
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Table 19 Means Test Comparing Treatments and Controls in Each Set of Study Communities.
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Appendix 2

Model Diagnostics

The most important issue logistic regression models ought to be tested for is the issue of multicollinearity among the independent variables. The method used here to test for this is the variance inflation factor (VIF). The VIFs for different unique sets of independent variables considered across regression models are shown in Tables 20 and 21.

Table 20 Variance Inflation Factors (VIFs) for the Independent Variables in Kullu Communities.
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Table 21 Variance Inflation Factors (VIFs) for the Independent Variables in Koppal Communities.
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Appendix 3

See Figure 5.

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Cite this article

Menghwani, V., Zerriffi, H., Dwivedi, P. et al. Determinants of Cookstoves and Fuel Choice Among Rural Households in India. EcoHealth 16, 21–60 (2019). https://doi.org/10.1007/s10393-018-1389-3

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  • DOI: https://doi.org/10.1007/s10393-018-1389-3

Keywords

  • Improved cookstoves (ICS)
  • Household energy transition
  • Biomass
  • LPG
  • Rural India
  • Multinomial regression
  • Logistic regression