As the world’s fourth most populous country, the population growth rate in Indonesia is expected to stay high. Owing to a combination of high-speed urbanization and increasing population density, economic growth is predicted to increase the demand for energy consumption. Thus, it is crucial to understand the relationship between population density and energy consumption in any country. This study evaluates the impact of population density on total energy consumption and the disaggregated electricity and fuel consumption at the provincial level in Indonesia. It uses the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. It also employed a balanced panel data of 33 provinces from 2010 to 2018. The results indicated that population density positively impacts energy consumption for total, electricity, and fuel consumption. This study suggests that incorporating population growth into national energy plans is crucial. Additionally, reducing energy inequality and uneven spatial distribution of the population is also needed.

This study estimated Indonesian households' Multidimensional Energy Poverty Index (MEPI) using Alkire-Foster's multidimensional poverty concept to capture the incidence, intensity, decomposition, and changes over time. The study used monetary and non-monetary variables to identify the availability, accessibility, affordability, consumption, and deprivation of modern energy services; and compared existing affordability indicators. Redundancy, robustness, and sensitivity tests were conducted with three weighting schemes and deprivation cut-offs. The study decomposed and determined household-head (HH) socio-economic, demographic, and geographic factors for MEP using the Logit, Probit, Tobit, and Heckman Selection models. The results show that the low-income and high cost (LIHC) was the most robust affordability indicator, followed by the ten percent rule (TPR). The complement-frequency weighting scheme gave the smallest and most robust MEPI compared to equal and principal component analysis (PCA) weighting. Three alternative deprivation cut-offs can show households as "vulnerable," "moderately," or "severely" energy poor. The MEP incidence decreased, but its intensity remained high and increased. Energy-poor households were averagely deprived of 55-60 % of all weighted indicators. The lack of modern cooking services was the primary cause. MEPI differed by geographical location and HH gender, education, business field, and employment status. Policies that boost education levels, raise household income, and increase the availability, accessibility, and affordability of modern cooking technology in the rural, hinterland, or non-coastal forested locations in the eastern islands of Indonesia may minimize the number of households experiencing MEP.