Abstract
The focus of this paper is to bring to light the vital issue of energy poverty alleviation and how big data could improve the data collection quality and mechanism. It also explains the vicious circle of low productivity, health risk, environmental pollution and energy poverty and presents currently used energy poverty measures and alleviation policies and stresses the associated problems in application due to the underlying dynamics.
Highlights
Energy poverty is a term widely used to define living conditions under unaffordable and inaccessible energy resources
Zhao et al [98] used a random forest regression (RFR) model by combining features extracted from multiple data sources, including National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) nighttime light (NTL) data, Google satellite imagery and land cover map, road map and division headquarter location data to estimate poverty based on household wealth index (WI) at a 10-km spatial resolution
This article deals with the problem of energy poverty on a global scale and what necessary measures must be taken in order to provide energy access and affordability
Summary
Energy poverty is a term widely used to define living conditions under unaffordable and inaccessible energy resources. We summarize the current discussion on energy poverty, the definitions and measures, as well as the resulting problems in policy implementation and their implications up to now. We highlight the issue that these indicators most often just capture the space dimension and neglect the time dimension This issue is problematic since policy measures usually do not take into account the dynamics of changing factors that lead to energy poverty and risk maintaining policies that are not efficient and effective over the course of time. We contribute to the current discussion by considering the potential of big data and usage of AI for the enhancement of socioeconomic data through environmental, political and climatic data for policy decision making. We show how big data could improve energy poverty alleviation policies and discuss the challenges that the data collection process would bear
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