Access to financial services and lighting energy consumption: Empirical evidence from rural Ghana

Bismark Addai, Wenjin Tang, Martinson Ankrah Twumasi, Dennis Asante, Annette Serwaa Agyeman

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In view of the UN Sustainable Energy for all (SE4All) policy initiative, the Center for Renewable Energy and Energy Efficiency (ECREEE) and the Africa-EU Energy Partnership (AEEP) have been championing sustainable energy (batteries, electricity, solar bulbs) adoption for lighting and other daily activities. Extant research has demonstrated the nexus between financial services access and household expenditure; however, there is no single study on how access to financial services affects lighting energy use in any developing country. Therefore, this study examines the lighting energy consumption impact of access to financial services (AFS) and the heterogeneous impacts of gender and income. Cross-sectional data from rural Ghana was analyzed using Poisson regression and the IV-probit model. The results of the study depict that access to financial services significantly improves the consumption of lighting energy such as electricity, batteries, and lanterns. The heterogeneous analysis shows that AFS improves the lighting energy consumption of households irrespective of the household income level and the householder's gender. The findings also show that demographic and socio-economic characteristics significantly influence rural households' access to financial services. The results of the study imply that policymakers should prioritize expanding access to finance in enhancing clean lighting energy consumption to salvage the rural folks from adverse health and environmental conditions, facilitate energy transition, and achieve sustainable rural and national development.

Original languageEnglish
Article number124109
Number of pages11
Publication statusPublished - 15 Aug 2022


  • Energy consumption
  • Financial services
  • IV-Probit model
  • Lighting energy
  • Poisson regression


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