Greenhouse gases emissions and electric power generation in Latin American countries in the period 2006–2013

Leadina Sánchez, Carmen Vásquez, Amelec Viloria, Lizeth Rodríguez Potes

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

9 Citas (Scopus)

Resumen

The objective of this study is to classify the group of countries that contributes to the emissions of GHG gases in relation to their electric power generation. The data used for this classification are those reported by OLADE during the period from 2006 to 2013. The results show that they were organized in four (4) clusters, dividing them into efficient and inefficient levels. In the highest efficient level is Brazil, while in the lowest efficient level are Colombia, Costa Rica, El Salvador, Panama, Paraguay, Peru, Uruguay, and Venezuela. Argentina and Mexico are in the highest inefficient level, while the rest of the Latin American countries are in the lowest inefficient level. The countries located at the efficient and inefficient levels were labeled as most emitting countries and least emitting countries.

Idioma originalInglés
Título de la publicación alojadaData Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings
EditoresYuhui Shi, Qirong Tang, Ying Tan
EditorialSpringer Verlag
Páginas777-785
Número de páginas9
ISBN (versión impresa)9783319938028
DOI
EstadoPublicada - 2018
Evento3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China
Duración: 17 jun. 201822 jun. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10943 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018
País/TerritorioChina
CiudadShanghai
Período17/06/1822/06/18

Nota bibliográfica

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

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