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Assessment of the potential for electricity savings in households in Cameroon: A stochastic frontier approach

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  • Calvin Nsangou, Jean
  • Kenfack, Joseph
  • Nzotcha, Urbain
  • Tamo, Thomas Tatietse

Abstract

Promoting cost-effective energy efficiency actions is an important objective for many countries, as this appears to be one of the ultimate and effective strategies to ensure reliable access to energy while reducing greenhouse gas emissions. As this is a key challenge facing developing countries, assessing the potential for energy savings in households appears as a crucial action to be considered by local policymakers, given the growing importance of residential electricity demand and the difficulties they face in managing the supply/demand balance of electricity. The literature review reveals a shortage of knowledge on residential electricity consumption behaviours in sub-African developing countries. Noting this gap, an energy demand frontier function is used to assess the potential for electricity savings in the residential sector in Cameroon, taking as a case study. The result showed that: households have an average efficiency of 62.4% with a range of 71%, which implies a significant potential for reducing electricity consumption. Rural households emerged more efficient than urban households. The study also clarify the potential for demand management at the household level which confirms that the efficiency level varies according to technical and social standards. This assessment is the very first of its kind in Cameroon.

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  • Calvin Nsangou, Jean & Kenfack, Joseph & Nzotcha, Urbain & Tamo, Thomas Tatietse, 2020. "Assessment of the potential for electricity savings in households in Cameroon: A stochastic frontier approach," Energy, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s0360544220316844
    DOI: 10.1016/j.energy.2020.118576
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    as
    1. Filippini, Massimo & Hunt, Lester C., 2015. "Measurement of energy efficiency based on economic foundations," Energy Economics, Elsevier, vol. 52(S1), pages 5-16.
    2. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    3. Gale A. Boyd, 2008. "Estimating Plant Level Energy Efficiency with a Stochastic Frontier," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 23-44.
    4. Wiesmann, Daniel & Lima Azevedo, Inês & Ferrão, Paulo & Fernández, John E., 2011. "Residential electricity consumption in Portugal: Findings from top-down and bottom-up models," Energy Policy, Elsevier, vol. 39(5), pages 2772-2779, May.
    5. Huebner, Gesche & Shipworth, David & Hamilton, Ian & Chalabi, Zaid & Oreszczyn, Tadj, 2016. "Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes," Applied Energy, Elsevier, vol. 177(C), pages 692-702.
    6. Zhou, P. & Ang, B.W. & Zhou, D.Q., 2012. "Measuring economy-wide energy efficiency performance: A parametric frontier approach," Applied Energy, Elsevier, vol. 90(1), pages 196-200.
    7. Hori, Shiro & Kondo, Kayoko & Nogata, Daisuke & Ben, Han, 2013. "The determinants of household energy-saving behavior: Survey and comparison in five major Asian cities," Energy Policy, Elsevier, vol. 52(C), pages 354-362.
    8. Enongene, K.E. & Murray, P. & Holland, J. & Abanda, F.H., 2017. "Energy savings and economic benefits of transition towards efficient lighting in residential buildings in Cameroon," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 731-742.
    9. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    10. Akihiro Otsuka, 2018. "Regional Determinants of Energy Efficiency: Residential Energy Demand in Japan," Energies, MDPI, vol. 11(6), pages 1-14, June.
    11. 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.
    12. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    13. Broadstock, David C. & Li, Jiajia & Zhang, Dayong, 2016. "Efficiency snakes and energy ladders: A (meta-)frontier demand analysis of electricity consumption efficiency in Chinese households," Energy Policy, Elsevier, vol. 91(C), pages 383-396.
    14. Zhou, Shaojie & Teng, Fei, 2013. "Estimation of urban residential electricity demand in China using household survey data," Energy Policy, Elsevier, vol. 61(C), pages 394-402.
    15. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
    16. 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.
    17. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    18. Boogen, Nina, 2017. "Estimating the potential for electricity savings in households," Energy Economics, Elsevier, vol. 63(C), pages 288-300.
    19. Khayyat, Nabaz T. & Heshmati, Almas, 2014. "Production Risk, Energy Use Efficiency and Productivity of Korean Industries," IZA Discussion Papers 8081, Institute of Labor Economics (IZA).
    20. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    21. Yu, Yihua & Guo, Jin, 2016. "Identifying electricity-saving potential in rural China: Empirical evidence from a household survey," Energy Policy, Elsevier, vol. 94(C), pages 1-9.
    22. Orea, Luis & Llorca, Manuel & Filippini, Massimo, 2015. "A new approach to measuring the rebound effect associated to energy efficiency improvements: An application to the US residential energy demand," Energy Economics, Elsevier, vol. 49(C), pages 599-609.
    23. Filippini, Massimo & Hunt, Lester C. & Zorić, Jelena, 2014. "Impact of energy policy instruments on the estimated level of underlying energy efficiency in the EU residential sector," Energy Policy, Elsevier, vol. 69(C), pages 73-81.
    24. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Yonghan Jeon & Jongoh Nam, 2023. "Estimating Energy Efficiency and Energy Saving Potential in the Republic of Korea’s Offshore Fisheries," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
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    3. 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).
    4. Wang, Yuanping & Hou, Lingchun & Hu, Lang & Cai, Weiguang & Wang, Lin & Dai, Cuilian & Chen, Juntao, 2023. "How family structure type affects household energy consumption: A heterogeneous study based on Chinese household evidence," Energy, Elsevier, vol. 284(C).
    5. Wang, Xia & Ding, Chao & Cai, Weiguang & Luo, Lizi & Chen, Mingman, 2021. "Identifying household cooling savings potential in the hot summer and cold winter climate zone in China: A stochastic demand frontier approach," Energy, Elsevier, vol. 237(C).
    6. Wang, Xia & Ding, Chao & Zhou, Mao & Cai, Weiguang & Ma, Xianrui & Yuan, Jiachen, 2023. "Assessment of space heating consumption efficiency based on a household survey in the hot summer and cold winter climate zone in China," Energy, Elsevier, vol. 274(C).

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