Journal Article2026

Capturing the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime lights

Amin Masud Ali, Muntasir Wahed, Amin Ahsan Ali, Moinul Zaber

Journal of the Asia Pacific Economy

Taylor & Francis, Vol. 31, Issue 2, pp. 397-426, ISBN: 1354-7860

  • Affordable and Clean Energy — Relevance: 87%
  • Reduced Inequalities — Relevance: 89%
DOI: 10.1080/13547860.2024.2426872URL

Abstract

This paper examines the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime light (NTL) data. The NTL data, sourced from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) for the period from 2013 to 2020, reveals persistent variability in electricity consumption among the districts. Notably, the gap between urban and non-urban areas has widened. While within district inequality (measured by NTL Gini) has declined over time, it remains high in several districts. Convergence analysis confirms that while lagging districts are showing a catching up effect, the sub-districts are diverging among themselves (in terms of mean NTL per capita). Interestingly, the rural sub-districts are converging among themselves despite urban sub-district divergence. The study also identifies regions with significant imbalance between NTL, population, and built-up area density values. These findings have implications for policymakers aiming to ensure electricity for all and reduce inequality.

Keywords

  • VIIRS Day/Night Band
  • electricity consumption
  • built-up area density
  • nighttime lights
  • rural electrification
  • urban-rural divide
  • subnational inequality
  • Bangladesh

CCDS Authors

Amin Masud Ali, PhD
Amin Masud Ali, PhD

Amin Masud Ali, PhD

  • Supervisor, DS Wing

Professor

Department of Economics

Jahangirnagar University

Research Interests

Development Economics, Public Sector Economics

Muntasir Wahed
https://mmiakashs.github.io/

Muntasir Wahed

  • Collaborator, AI/ML Wing

Ph.D Candidate

University of Illinois Urbana-Champaign

Research Interests

Machine Learning

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

Moinul I Zaber, PhD
Moinul I Zaber, PhD

Moinul I Zaber, PhD

  • Director, HCI Wing
  • Supervisor, DS Wing

Professor

+2 more affiliations

Department of Computer Science & Engineering

Dhaka University

Published at

ICPR, HCII, JMIR

Research Interests

Computational Social Science, Human-Computer Interaction, Telecommunication Policy

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