Skip to main content

Advertisement

SpringerLink
Log in

Multidimensional Energy Poverty in Pakistan: Empirical Evidence from Household Level Micro Data

  • Original Research
  • Published:
Social Indicators Research Aims and scope Submit manuscript

Abstract

This paper estimates household level incidence and intensity of multidimensional energy poverty in Pakistan using a multidimensional energy poverty index with seven dimensions weighted based on their relative importance. Although being widely discussed in the literature as a basic human right for addressing energy access, reliable background estimates, and official statistics of national-level energy poverty are not available for Pakistan. This study thus provides necessary support in understanding energy poverty severity and the incidence with multiple dimensions. Some of the existing measurement approaches analyse multiple dimensions like lack of electricity access, access to clean cooking and heating fuels and inability to obtain sufficient and reliable amount of different energy services but are deficient in other vital dimensions. Hence, the study carried out a more comprehensive measurement with additional dimensions and indicators. Results analysing the Pakistan Social and Living Standards Measurement (PSLM) survey data for 2014–15 suggest that 55 percent of the households are multi-dimensionally energy-deprived in 30 percent of the selected dimensions in Pakistan. Robustness analysis depicts the change in multidimensional energy poverty estimates as a result of changes in energy poverty cut-off scores and weights. Results also provide insights into the underlying factors affecting multidimensional energy poverty in Pakistan.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: Author’s own work,

Fig. 2

Source: Author’s own work, Day et al. (2016)

Fig. 3

Source: Author’s own work. Alkire and Foster (2007, 2011), Foster et al. (1984), Martins (2005), Ouedraogo (2006), Ogwumike and Ozughalu (2012), Barnes et al. (2011), Nussbaumer et al. (2012), Sanusi and Owoyele (2016), Garcia and Ed. (2016)

Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Availability of Data and Material

Will be available.

Code Availability

Will be available.

Notes

  1. 51 million people were without electricity and 27 percent of the population was without electricity access in Pakistan (IEA 2016). More than 144 million people across Pakistan did not have reliable access to electricity (National Electric and Power Regulatory Authority [NEPRA] 2017). Access to clean cooking fuel in rural and urban areas of Pakistan was 14 percent and 88 percent, respectively (World Energy Council [WEC] 2017).

  2. The domestic/consumer prices of natural gas increased from Rs.310.92 per million Btu in January, 2008 to Rs. 529.50/MBtu in June 2008. Then, the prices rose to Rs. 730.17/MBTU in 2009; Rs. 860.15/MBtu in January, 2010 and Rs.1006.40/MBtu in July, 2010. The consumer prices further increased to Rs.1142.75/MBtu in year 2011 and Rs. 1302.46/MBtu in 2012 ((Pakistan Energy Yearbook, Ministry of Petroleum and Natural Resources, Government of Pakistan 2014).

  3. Household spending on biomass fuels increased from 17.84 percent to 20.70 percent (Table 13 and Table 14 in Appendix 1).

  4. Pakistan’s per capita energy use/consumption is 484.44 kg of oil equivalent whereas per capita electricity consumption stands at 471.04 kWh with 97.5 percent of population with access to electricity (World bank 2014).

  5. Proportion of people without energy access in Kenya, Ethiopia, Pakistan, and Bangladesh is 84 percent, 83 percent, 38 percent and 59 percent, respectively (IEA 2012). The number of people using solid fuels for cooking in Kenya, Ethiopia, Pakistan, and Bangladesh are 33 million, 77 million, 122 million, and 143 million, respectively (IEA 2011).

  6. Existing evidence of cut-offs used to measure energy poverty include k = 0.2 (Sher et al. 2014); k = 0.3 (Mahmood and Shah 2017; Mbewe 2018; Nussbaumer et al. 2012; Nussbaumer et al. 2013); k = 0.33 (Bersisa 2016; Sadath and Acharya 2017); k = 0.5 (Edoumikumo, Tombofa, and Karimo 2013), k = 0.6 (Ogwumike and Ozughalu 2016).

  7. Energy poverty threshold proposed as 1200 kWh per person per year (UN 2010); Minimum need in ‘the poorest countries’ as 50 kgoe per person per year for just cooking and lighting equals to only 58.15 kWh (Modi, McDade, Lallement and Saghir 2006).

  8. The study initially intended to use latest data for the analysis and made efforts to obtain the datasets but data collected under PSLM 2018–19 is still not completely available for public use and is still in data cleaning process by the data collecting organization. Therefore, we employed 2014–15 survey data for the analysis. In addition, district wise data will not available in 2018–19 dataset as observed from the questionnaire (which was obtained through personal efforts from the relevant office).

References

  • Ahn, B. S. (2011). Compatible weighting method with rank order centroid: Maximum entropy ordered weighted averaging approach. European Journal of Operational Research, 212, 552–559.

    Google Scholar 

  • Alkire, S. (2002). Valuing freedoms: Sen’s capability approach and poverty reduction. New York: Oxford University Press.

    Google Scholar 

  • Alkire, S., & Fang, Y. (2018). Dynamics of multidimensional poverty and uni-dimensional income poverty: An evidence of stability analysis from China. Social Indicators Research. https://doi.org/10.1007/s11205-018-1895-2.

    Article  Google Scholar 

  • Alkire, S. & Foster, J. (2007). Counting and multidimensional poverty measurement (OPHI Working Paper No. 7). Oxford Poverty and Human Development Initiative. University of Oxford.

  • Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487.

    Google Scholar 

  • Alkire, S., Roche, J. M., Ballon, P., Foster, J., Santos, M. E., & Seth, S. (2015). Multidimensional poverty measurement and analysis. USA: Oxford University Press.

    Google Scholar 

  • Alkire, S. & Santos, M.E. (2010). Acute multidimensional poverty: A new index for developing countries (OPHI Working Paper No. 38). Oxford Poverty and Human Development Initiative. University of Oxford.

  • Aristondo, O., & Onaindia, E. (2018). Inequality of energy poverty between groups in Spain. Energy, 153, 431–442.

    Google Scholar 

  • Asian Development Bank (2013). Asia’s energy challenge. Asian Development Outlook.

  • Asian Development Bank (2014). Energy Access Assessment (Punjab) Pakistan. ADB Energy for All Program. https://www.energynet.co.uk/webfm_send/1183

  • Awan, R. U., Sher, F., & Abbas, A. (2013). An investigation of multidimensional energy poverty in Pakistan. The Pakistan Development Review, 52(4), 405–419.

    Google Scholar 

  • Barron, F. H. (1992). Selecting a best multiattribute alternative with partial information about attribute weights. Acta Psychologica., 80, 91–103.

    Google Scholar 

  • Barnes, D. F., Khandker, S. R., & Samad, H. A. (2011). Energy poverty in rural Bangladesh. Energy Policy, 39, 894–904.

    Google Scholar 

  • Bekele, G., Nagatu, W., & Eshete, G. (2015). Energy poverty in Addis Ababa City, Ethiopia. Journal of Economics and Sustainable Development. https://doi.org/10.11114/aef.v3i1.1193.

    Article  Google Scholar 

  • Bersisa, M. (2016). Multidimensional measure of household energy poverty and its determinants in Ethiopia. East Africa Research Papers in Economics and Finance.

  • Bhatia, M. & Angelou, N. (2015). Beyond Connections: Energy Access Redefined. ESMAP Technical Report;008/15. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/24368 License: CC BY 3.0 IGO.

  • Bouzarovski, S., & Petrova, S. (2015). A global perspective on domestic energy deprivation: Overcoming the energy poverty-fuel poverty binary. Energy Research and Social Science, 10, 31–40.

    Google Scholar 

  • Clancy, J.S, Skutsch, M. & Batchelor, S. (2003). The gender-energy-poverty nexus. DFID Project.

  • Day, R., Walker, G., & Simcock, N. (2016). Conceptualising energy use and energy poverty using a capabilities framework. Energy Policy, 93, 255–264.

    Google Scholar 

  • Edoumikumo, S. G., Tombofa, S. S., & Karimo, T. M. (2013). Multidimensional energy poverty in the South-South geopolitical zone of Nigeria. Journal of Economics and Sustainable Development, 4(20), 96–103.

    Google Scholar 

  • ESMAP. (2018). Annual Report 2018, Energy Sector Management Assistance Programme. World Bank Group.

  • ESMAP. (2019). Annual Report 2019, Energy Sector Management Assistance Programme. World Bank Group.

  • Fatmi, Z., Rahman, A., Kazi, A., Kadir, M. M., & Sathiakumar, N. (2010). Situational analysis of household energy and biomass use and associated health burden of indoor air pollution and mitigation efforts in Pakistan. International Journal of Environmental Research and Public Health, 7(7), 2940–2952. https://doi.org/10.3390/ijerph7072940.

    Article  Google Scholar 

  • Garcia Ochoa, R., & Ed Graizbord, B. (2016). Privation of energy services in Mexican households: An alternative measure of energy poverty. Energy Research and Social Sciences. https://doi.org/10.1016/j.erss.2016.04.014.

    Article  Google Scholar 

  • Goldemberg, J. (1990). One kilowatt per capita. Bulletin of the Atomic Scientists, 46(1), 13–14.

    Google Scholar 

  • González-Eguino, M. (2015). Energy poverty: An overview. Renewable and Sustainable Energy Reviews, 47, 377–385.

    Google Scholar 

  • Haughton, J., & Khandker, S. R. (2009). Handbook on poverty and inequality. Washington D.C USA: The World Bank.

    Google Scholar 

  • Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision-making: Methods and applications. Berlin: Springer.

    Google Scholar 

  • International Energy Agency. (2010). World energy outlook: Energy poverty: how to make modern energy access universal? Paris: OECD Paris: OECD. United Nations Industrial Development Organization: United Nations Development Programme.

    Google Scholar 

  • International Energy Agency. (2011). Special early excerpt of the world energy outlook 2011- energy for all- financing access for the poor. Technical Report. Paris: France.

  • International Energy Agency. (2012). World Energy Outlook 2012-Measuring energy and development. Technical Report. Paris: France.

  • International Energy Agency. (2016). World Energy Outlook-Energy Access Database. International Energy Agency. http://www.worldenergyoutlook.org/resources/energydevelopment/energyaccessdatabase/.

  • Israel-Akinbo, S., Snowball, J. & Fraser, G. (2016). An investigation of multidimensional energy poverty amongst South African low-income households (Working Paper 3281). Retrieved from https://2017.essa.org.za/fullpaper/essa_3281.pdf

  • Kaygusuz, K. (2011). Energy services and energy poverty for sustainable rural development. Renewable and Sustainable Energy Reviews, 15(2), 936–947.

    Google Scholar 

  • Khandker, S. R., Barnes, D. F., & Samad, H. A. (2012). Are the energy poor also income poor? Evidence from India. Energy Policy, 47, 1–12.

    Google Scholar 

  • Kitchen, T., & O’Reilly, P. (2016). Energy poverty amidst abundance in Malaysia: Placing energy in multidimensional poverty. Development in Practice, 26(2), 203–213. https://doi.org/10.1080/09614524.2016.1133562.

    Article  Google Scholar 

  • Lipu, M. S. H., Shrestha, P., & Kumar, S. (2016). Energy poverty and access to modern energy of urban poor: A case of Dhaka, Bangladesh. International Journal of Energy Technology and Policy, 12(3), 270–294.

    Google Scholar 

  • Mahmood, R., & Shah, A. (2017). Deprivation counts: An assessment of energy poverty in Pakistan. The Lahore Journal of Economics, 22(1), 109–132.

    Google Scholar 

  • Martins, J. (2005). The impact of the use of energy sources on the quality of life of poor communities. Social Indicators Research, 72(3), 373–402.

    Google Scholar 

  • Mbewe, S. (2018). Investigating household energy poverty in South Africa by using uni-dimensional and multidimensional measures. Energy Research Center. South Africa: University of Cape Town. Retrieved from https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=CSAE2018&paper_id=206.

  • Mirza, B. & A. Szirmai (2010). Towards a new measurement of energy poverty: A cross-community analysis of rural Pakistan.1–41. Technical report. United Nations University.

  • Modi, V., McDade, S., Lallement, D. & Saghir, J. (2006). Energy and the millennium development goals. 1–116. New York: Energy Sector Management Assistance Program, United Nations Development Programme, UN Millennium Project, and World Bank.

  • Nathan, H.S.K. & Reddy, B.S. (2010). Selection Criteria for Sustainable Development Indicators. Working Paper, Indira Gandhi Institute of Development Research, India: Mumbai. Retrieved from http://igidr.ac.in/pdf/publication/WP-2010-013.pdf.

  • National Electric and Power Regulatory Authority. (2017). State of industry report. Government of Pakistan: Islamabad.

    Google Scholar 

  • National Electric and Power Regulatory Authority. (2005). Performance standards (distribution) rules, 2005, Statutory notification. Islamabad: Government of Pakistan.

    Google Scholar 

  • Nussbaumer, P., Bazilian, M., & Modi, V. (2012). Measuring energy poverty: Focusing on what matters. Renewable and Sustainable Energy Reviews, 16(1), 231–243.

    Google Scholar 

  • Nussbaumer, P., Nerini, F. F., Onyeji, I., & Howells, M. (2013). Global insights based on the multidimensional energy poverty index (MEPI). Sustainability, 5, 2060–2076. https://doi.org/10.3390/su5052060.

    Article  Google Scholar 

  • Ogwumike, F. O., & Ozughalu, U. M. (2016). Analysis of energy poverty and its implications for sustainable development in Nigeria. Environment and Development Economics, 21, 273–290.

    Google Scholar 

  • Ouedraogo, B. (2006). Household energy preferences for cooking in urban Ouagadougou, Burkina Faso. Energy Policy, 34(18), 3787–3795.

    Google Scholar 

  • Pachauri, S., & Spreng, D. (2004). Energy use and energy access in relation to poverty. Economic and Political Weekly, 39(3), 271–278.

    Google Scholar 

  • Pachauri, S., Mueller, A., Kemmler, A., & Spreng, D. (2004). On measuring energy poverty in Indian households. World Development, 32(12), 2083–2104.

    Google Scholar 

  • Pachauri, S., & Spreng, D. (2011). Measuring and monitoring energy poverty. Energy Policy, 39(12), 7497–7504.

    Google Scholar 

  • Padam, G., Rysankova, D., Portale, E., Koo, B. B., Keller, S., Fleurantin, G. (2018). Ethiopia – Beyond Connections: Energy Access Diagnostic Report Based on the Multi-Tier Framework. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/30102 License: CC BY 3.0 IGO.

  • Pakistan Bureau of Statistics. (2013–14) Household income and expenditure survey (HIES). Retrieved from http://www.pbs.gov.pk/content/household-integrated-economic-survey-hies-2013-14.

  • Pakistan Bureau of Statistics. (2014–15). Pakistan Social and Living Standards Measurement Survey, Government of Pakistan.

  • Pakistan Bureau of Statistics. (2015–16). Household income and expenditure survey (HIES). Retrieved from http://www.pbs.gov.pk/content/household-integrated-economic-survey-hies-2015-16.

  • Pakistan Energy Yearbook Data. (2008–2018). Hydrocarbon Institute of Pakistan. Islamabad, Pakistan.

  • Ravallion, M. (1998). Poverty lines in theory and practice. living standards measurement study series living standards measurement study series. Washington, DC: World Bank.

    Google Scholar 

  • Roszkowska, E. (2013). Rank ordering criteria weighting method -a comparative overview. Optimum Studia Ekonomiczne NR, 5, 65.

    Google Scholar 

  • Sadath, A. C., & Acharya, R. H. (2017). Assessing the extent and intensity of energy poverty using multidimensional energy poverty index: Empirical evidence from households in India. Energy Policy, 102, 540–548.

    Google Scholar 

  • Sen, A. K. (1993). Capability and well-being. In M. Nussbaum & A. Sen (Eds.), The quality of life. Oxford: Oxford University Press.

    Google Scholar 

  • Sen, A. K. (1985). Commodities and capabilities. Amsterdam: North Holland.

    Google Scholar 

  • Sen, A. K. (1992). Inequality reexamined. Oxford: Oxford University Press.

    Google Scholar 

  • Sher, F., Abbas, A., & Awan, R. (2014). An investigation of multidimensional energy poverty in Pakistan: A province level analysis. International Journal of Energy Economics and Policy, 4(1), 65–75.

    Google Scholar 

  • Solymosi, T., & Dombi, J. (1985). Method for determining the weights of the criteria: The centralized weights. European Journal of Operational Research, 26, 35–41.

    Google Scholar 

  • Sovacool, B., Cooper, C., Bazilian, M., Johnson, K., Zoppo, D., Clarke, S., & Raza, H. (2012). What moves and works: Broadening the consideration of energy poverty. Energy Policy, 42, 715–719.

    Google Scholar 

  • Sovacool, B. K. (2014). Defining, measuring and tackling energy poverty. Book chapter In: Energy poverty: Global challenges & local Solutions (Ed.), 2, (pp. 21–53). Oxford University Press.

  • Stillwell, W. G., Seaver, D. A., & Edwards, W. (1981). A comparison of weight approximation techniques in multiattribute utility decision making. Organizational Behavior and Human Decision Processes, 28, 62–77.

    Google Scholar 

  • Tchereni, B. H. M., Grobler, W., & Dunga, S. H. (2013). Economic analysis of energy poverty in South Lunzu, Malawi. Journal of Economics and Sustainable Development, 4(4).

  • Tse, Y. K., & Zhang, X. (2003). A Monte Carlo Investigation of Some Tests for Stochastic Dominance. Department of Econometrics and Business Statistics: Monash University, Clayton, Victoria, Australia.

    Google Scholar 

  • United Nations. (2010). Secretary general advisory group on energy climate change (AGECC). Energy for a sustainable future: Summary report and recommendations. New York: United Nations Industrial Development Organization. http://www.un.org/wcm/webdav/site/climatechange/shared/Documents/AGECCsummaryreport.pdf.

  • WHO. (2007). Indoor air pollution: National burden of disease estimates. World Health Organization. Geneva, Switzerland: WHO Press.

    Google Scholar 

  • World Bank (2006). Pakistan strategic country environment assessment. 2 (36946-PK). Washington, DC: USA.

  • World Bank (2014). World Development Indicators Data. Retrieved online from

  • World Energy Council. (2017). World energy issues monitor 2017. London: World Energy Council. https://www.worldenergy.org/wp-content/uploads/2017/04/1.-World-Energy-Issues-Monitor-2017-Full-Report.pdf

  • World Health Organization. (2018). World health statistics 2018: Monitoring health for the SDGs. Geneva: World Health Organization. Retrieved from http://www.who.int/gho/publications/world_health_statistics/2018/en.

Download references

Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Department of Economics, University of Gujrat, Hafiz Hayat Campus, Jalalpur Jattan road, Gujrat, Pakistan

    Abre-Rehmat Qurat-ul-Ann & Faisal Mehmood Mirza

Authors
  1. Abre-Rehmat Qurat-ul-Ann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Faisal Mehmood Mirza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abre-Rehmat Qurat-ul-Ann.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 13 , 14 and 15.

Table 13 Household expenditure on fuel and lighting (Percentage).
Full size table
Table 14 Percentages of household expenditure on fuel & lighting by quintiles.
Full size table
Table 15 Ranking of districts based on mean MEPI scores
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qurat-ul-Ann, AR., Mirza, F.M. Multidimensional Energy Poverty in Pakistan: Empirical Evidence from Household Level Micro Data. Soc Indic Res 155, 211–258 (2021). https://doi.org/10.1007/s11205-020-02601-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11205-020-02601-7

Keywords

Search

Navigation