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On a previous study, the carbon footprint (CF) of all production and consumption activities of Galicia, an Autonomous Community located in the north-west of Spain, was determined and the results were used to devise strategies aimed at the reduction and mitigation of the greenhouse gas (GHG) emissions. The territorial LCA methodology was used there to perform the calculations. However, that methodology was initially designed to compute the emissions of all types of polluting substances to the environment (several thousands of substances considered in the life cycle inventories), aimed at performing complete LCA studies. This requirement implies the use of specific modelling approaches and databases that in turn raised some difficulties, i.e., need of large amounts of data (which increased gathering times), low temporal, geographical and technological representativeness of the study, lack of data, and presence of double counting issues when trying to combine the sectorial CF results into those of the total economy. In view of these of difficulties, and considering the need to focus only on GHG emissions, it seems important to improve the robustness of the CF computation while proposing a simplified methodology. This study is the result of those efforts to improve the aforementioned methodology. In addition to the territorial LCA approach, several Input-Output (IO) based alternatives have been used here to compute direct and indirect GHG emissions of all Galician production and consumption activities. The results of the different alternatives were compared and evaluated under a multi-criteria approach considering reliability, completeness, temporal and geographical correlation, applicability and consistency. Based on that, an improved and simplified methodology was proposed to determine the CF of the Galician consumption and production activities from a total responsibility perspective. This methodology adequately reflects the current characteristics of the Galician economy, thus increasing the representativeness of the results, and can be applied to any region in which IO tables and environmental vectors are available. This methodology could thus provide useful information in decision making processes to reduce and prevent GHG emissions.