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| Srl | Item |
| 1 |
ID:
136239
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| Summary/Abstract |
In this paper, we discuss a number of ways to define and measure the affordability of energy consumption, and we examine the emergence of energy poverty in Italy in the period from 1998 to 2011. The paper examines the eligibility criteria for claiming the benefits available to support energy consumption for vulnerable families and it identifies the potential beneficiaries. The study assesses the appropriateness of the eligibility criteria by comparing the population targeted by the policy with the population actually facing affordability problems. A simulation exercise, using the hypothetical scenario most likely to result in energy benefits being made available, shows that, regardless of the affordability index adopted, the provision of state energy benefits has little impact on fuel poverty.
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| 2 |
ID:
179692
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| Summary/Abstract |
Energy poverty is receiving increased attention in developed countries like the Netherlands. Although it only affects a relatively small share of the population, it constitutes a stern challenge that is hard to quantify and monitor, hence difficult to effectively tackle through adequate policy measures. In this paper we introduce a framework to categorize energy poverty risk based on income and energy expenditure. We propose the use of a machine learning classifier to predict energy poverty risk from a broad set of socio-economic parameters: house value, ownership and age, household size, and average population density. While income remains the single most important predictor, we find that the inclusion of these additional socio-economic features is indispensable in order to achieve high prediction reliability. This result forms an indication of the complex nature of the mechanisms underlying energy poverty. Our findings are valid at different geographical scales, i.e. both for single households and for entire neighborhoods. Extensive sensitivity analysis shows that our results are independent of the precise position of risk category boundaries. The outcomes of our study indicate that machine learning could be used as an effective means to monitor energy poverty, and assist the design and implementation of appropriate policy measures.
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| 3 |
ID:
150818
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| Summary/Abstract |
Energy, whether from electricity, natural gas, heating oil, propane, kerosene, or wood, is essential for the well-being of many Americans, yet those who spend more than 10 percent of their income of energy services can be considered “fuel poor.” This study assesses the extent and severity of fuel poverty in Vermont. It analyzes energy burdens in Vermont by household income deciles, using data from the Census Bureau's American Community Survey. Approximately 71,000 people suffered from fuel poverty in Vermont in 2000, and in 2012 the number rose to 125,000, or one in five Vermonters. Startlingly, fuel poverty grew 76 percent during this period. Excess winter deaths, caused potentially by fuel poverty, kill more Vermonters each year than car crashes. The article then provides 12 policy recommendations based on a small sample of elite semi-structured research interviews. These include suggestions that the Vermont legislature better fund investments in weatherization among low-income households; that community groups and social service agencies scale up the training of energy efficiency coaches; that state agencies endorse improvements in housing efficiency and appropriate fuel switching; and that utilities and fuel providers offer extra assistance for disconnected households and allow for on-bill financing of efficiency improvements.
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