**5. Conclusions**

We can reach the conclusion that the method used is pertinent in most of the federate entities when evaluating the EA in each state, especially those with similar bio-climate and/or socioeconomic regions. This method shows that fuzzy logic might be used to measure energy access and to highlight where it is low and deserves special attention.

Nevertheless, to obtain more accurate calculus, we should undertake several possible actions: increase the number of linguistic variables; adjust the values of those linguistic variables; use another survey or even organize our own survey.

We have concluded that the analysis of results by states might be an alternative to the geographic region analysis where the exactitude will depend on the number of variables taken into consideration. Especially when the aim is to implement energy access recovering measures, it is important to precisely define where they will be implemented. It is not the same to define an energy access recovering program for a small city, a municipality or a town as it is for a geographic area in general. That is why it is necessary to include more variables that give a better characterization to the reality of the entities in all-important energy dimensions regarding access.

If we take into consideration all the processing and calculus advantages that fuzzy logic offers us, and combine this with the analysis made, we find out that this approach for evaluating energy access should be taken into consideration by researchers in the field and public policy makers.

The most important result of this paper is that it provides researchers with another tool that has been shown to be useful in the assessment of energy access—fuzzy logic. This technique entails neither high mathematical complexity nor an excessive use of computer time and intensity, while providing a useful model to evaluate and monitor energy access through time.

**Author Contributions:** Conceptualization, D.S.-J. and K.G.C.; methodology, D.S.-J.; software, T.R.-B.; validation, T.R.-B.and K.G.C.; formal analysis, D.S.-J.; investigation, D.S.-J., T.R.-B. and K.G.C.; data curation, T.R.-B.; writing—original draft preparation, D.S.-J.; writing—review and editing, K.G.C.; visualization, T.R.-B.; supervision, K.G.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** I want to thank the National Science and Technology Council (CONACYT, its Spanish acronym), for the scholarship awarded.

**Conflicts of Interest:** The authors declare no conflict of interest.
