Energy poverty is a serious problem affecting many people in the world. To address it and alleviate it, the first action is to identify and measure the intensity of the population living in this condition. This paper seeks to generate information regarding the actual state of energy poverty by answering the research question: is it possible to measure the intensity of energy poverty between different Latin American countries with sufficient and equivalent data? To achieve this, the Multidimensional Energy Poverty Index (MEPI), proposed by Nussbaumer et al., was used. The results present two levels of lack of access to energy services: Energy Poverty (EP) and Extreme Energy Poverty (EEP). The last one, is a concept introduced by the authors to evaluate energy poverty using MEPI. Results of people living on EP (EEP within parentheses) are as follow: Colombia 29% (18%), Dominican Republic 32% (14%), Guatemala 76% (61%), Haiti 98% (91%), Honduras 72% (59%), Mexico 30% (17%) and Peru 65% (42%). A clear correlation between the Human Development Index (HDI) and MEPI is displayed, however some countries have relatively high values for the HDI, but do not perform so well in the MEPI and vice versa. Further investigation is needed.

Nowadays the challenge for humanity is to find pathways towards sustainable development. Decision makers require a set of sustainability indicators to know if the sustainability strategies are following those pathways. There are more than one hundred sustainability indicators but they differ on their relative importance according to the size of the locality and change on time. The resources needed to follow these sustainability indicators are scarce and in some instances finite, especially in smaller regions. Therefore strategies to select set of these indicators are useful for decision makers responsible for monitoring sustainability. In this paper we propose a model for the identification and selection of a set of sustainability indicators that adequately represents human systems. In developing this model, we applied evolutionary dynamics in a space where sustainability indicators are fundamental entities interconnected by an interaction matrix. we used a fixed interaction that simulates the current context for the city of Cuernavaca, México as an example. We were able to identify and define relevant sets indicators for the system by using the Pareto principle. In this case we identified a set of sixteen sustainability indicators with more than 80% of the total strength. This set presents resilience to perturbations. For the Tangled Nature framework we provided a manner of treating different contexts (i.e., cities, counties, states, regions, countries, continents or the whole planet), dealing with small dimensions. This model provides decision makers with a valuable tool to select sustainability indicators set for towns, cities, regions, countries, continents or the entire planet according to a coevolutionary framework. The social legitimacy can arise from the fact that each individual indicator must be selected from those that are most important for the subject community.

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Research on energy poverty (EP) started in the United Kingdom and other Western European countries in response to the Oil Crisis in 1973. In the last few years, the European community has made important breakthroughs on the topic, by establishing clear terminology as well as funding different multidisciplinary and intersectoral task groups that have EP understanding and alleviation as their goal. Several different methodologies have been developed to measure EP. For instance, the multidimensional energy poverty index (MEPI) by Nussbaumer et al. (2012) has been successfully used in Africa and in seven Latin American countries. Mexico does not have an official measure, indicator, or index on EP. However, a very important energy service has been overlooked: thermal comfort. In the present work, MEPI was understood as an energy services deprivation calculation, and thermal comfort was included. Understanding the regional nature of thermal comfort, we searched for weather-based regionalizations that could address a whole country diversity. We applied two regionalizations, one strongly related to political divisions (called climatic), and a another used for household design and construction standards (bioclimatic). The bioclimatic regionalization had a better fit when assessing energy services deprivation, since it addresses exclusively geographical and weather conditions, instead of the artificial political divisions. Having better ways to assess the level of EP in the local context is a key factor to develop effective public policies that might alleviate EP in a sustainable way.

This paper describes an innovative method to evaluate energy access in any of size population by applying fuzzy logic. The obtained results allow ranking regions of Mexico according to their overall energy access. The regions were determined by the country’s political division (32 states). The results presented herein are in close correspondence with other studies undertaken. This method is recommended because it is possible to use as an assessment tool due to its representativeness—that is, it poses a heuristic alternative to quantify the level of Energy Access in a particular region through qualitative data. It is also efficient and cost-effective in terms of computer resources. This is extremely important to public policy makers that require more accurate, faster and cheaper methodologies to assess energy access as an indicator of well-being.