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Using a fixed effects model to isolate the effects of dynamic societal conditions, this research examines the drivers of utility burdens in three U.S. cities at a neighborhood scale. Energy burden reduction opportunities are forecasted through an evaluation of potential energy efficiency technologies using several utility cost tests. A discussion around leveraging current and future energy policies to redress systemic inequities in the residential energy ecosystem concludes.

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In this study, novel datasets and machine learning
modeling techniques are applied to evaluate the relationship between urban tree canopy, energy burden, redlining, energy use intensities, population migration, socio-economic indicators, and displacement, among others, at the neighborhood level in three U.S. cities.

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This study used advanced data analytics to develop three energy-use forecasts for the City of Tampa – Business-as-Usual (BAU), Moderate (MOD),
and Ambitious (AMB) Electricity Scenarios.

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