Recent energy price spikes have led to increased energy poverty among low-income households living in inefficient homes. Accurate statistics on energy poverty help inform resource allocation and better target relief schemes and retrofit funds. Existing indicators are predominantly defined in terms of a headcount ratio – the share of population living below a certain threshold or poverty line. In this paper we draw from the literature on income poverty evaluation to argue that the use of more elaborate energy poverty gap indices can substantiate the design and monitoring of energy poverty policies, by not only considering incidence but also intensity and inequality of energy poverty across households. We demonstrate that the choice for a particular energy poverty (gap) indicator makes the implicit welfare choices of energy poverty policies explicit. We illustrate our arguments for the case of the Netherlands, using recently developed microdata statistics on energy poverty, and an imposed energy price shock. We show that spatial targeting of relief funds based on incidence would neglect the full depth of energy poverty deprivation. Finally, we argue that visualisation techniques from the income poverty literature help to comprehend different poverty orderings and draw comparisons between time periods, regions, and subgroups.@en

We test an adjusted version of the classic monocentric-city model to explain the spatial sorting of rich versus poor people in Jakarta. We find that in Jakarta (1) the urban rich tend to live in the city centre; (2) because of extreme congestion levels, the elasticity between income and the opportunity cost of time spent commuting is higher than the elasticity between income and demand for larger plots of residential land; and (3) the motorbike is the most important and fastest mode of transport for the urban poor. These findings contrast with existing evidence from the United States. Both the logic of the monocentric-city model and empirical evidence suggest that the urban rich in Jakarta tend to cluster in the city centre. However, empirical evidence also suggests that the sorting of the rich and poor in Jakarta—as indicated by spatial variation in income, expenditure and land prices—depends not only on distance from the city centre but also on other neighbourhood characteristics, especially flood risk, crime rates and the proximity of a commercial area.@en