Analyzing energy poverty with Fuzzy Cognitive Maps: A step-forward towards a more holistic approach

ABSTRACT

Energy poverty is a widespread problem across Europe with serious socioeconomic, environmental, political and health implications. The primary contributing factors are related to low-income levels, high energy prices, and energy inefficient housing. However, household characteristics, political, and social circumstances, and other drivers such as severe weather conditions also play a vital role. Due to the complex nature of the problem, previous research efforts have usually focused on a limited number of factors. This paper comprises the first attempt to give a holistic picture of the problem of energy poverty, using Greece as a case study, by means of Fuzzy Cognitive Mapping (FCM). The FCM model provides an insight into the energy poverty system’s structure and function. Thus, it may prove useful to policy makers interested in developing and testing alternative measures for tackling energy poverty. There are, however, certain shortcomings and challenges, which should be taken into account for future work.

KEYWORDS:

• Energy poverty
• fuzzy cognitive mapping
• nonlinear Hebbian learning

Acknowledgments

This work was supported by the STEP-IN Project (Using Living Labs to roll out Sustainable Strategies for Energy Poor Individuals) funded under the HORIZON Framework Programme of the European Commission (Contract No. 785125).

Additional information

Funding

This work was supported by the STEP-IN project [Grant agreement ID: 785125], which is funded under H2020-EU.3.3.7 and H2020-EU.3.3.1.