Publications

@article{reference_tag,
  author = "Arto, Iñaki and Rueda-Cantuche, José M. and Peters, Glen P.",
  title = "Comparing the GTAP-MRIO and WIOD Databases for Carbon Footprint Analysis",
  journal = "Economic Systems Research",
  year = 2014,
  abstract = "We explore two different worldwide multi-regional input-output (MRIO) databases (Global Trade Analysis Project-MRIO and World Input-Output Database) for the calculation of the global carbon footprint (CF) of nations. We start our analysis with a description of the main characteristics of the databases and then make a comparison between their main components. Then, we calculate the CF with both databases and identify (from a global perspective) the most relevant factors underlying their differences using structural decomposition analysis. On average, certain parts of both databases (e.g. intermediate uses and final demand) can be said to be similar for around 75% to 80%, with only a few elements in each part mainly driving the major differences. The divergences in the datasets of four countries explain almost 50% of the differences in the CF (the USA, China, Russia and India). Industry-wise, 50% of the differences can be explained by the divergences in electricity, refining and inland transport industries.",
  doi = "10.1080/09535314.2014.939949",
}

TY  - JOUR
T1 - Comparing the GTAP-MRIO and WIOD Databases for Carbon Footprint Analysis
AU - Arto, Iñaki and Rueda-Cantuche, José M. and Peters, Glen P.
Y1 - 2014
DO - 10.1080/09535314.2014.939949
N2 - We explore two different worldwide multi-regional input-output (MRIO) databases (Global Trade Analysis Project-MRIO and World Input-Output Database) for the calculation of the global carbon footprint (CF) of nations. We start our analysis with a description of the main characteristics of the databases and then make a comparison between their main components. Then, we calculate the CF with both databases and identify (from a global perspective) the most relevant factors underlying their differences using structural decomposition analysis. On average, certain parts of both databases (e.g. intermediate uses and final demand) can be said to be similar for around 75% to 80%, with only a few elements in each part mainly driving the major differences. The divergences in the datasets of four countries explain almost 50% of the differences in the CF (the USA, China, Russia and India). Industry-wise, 50% of the differences can be explained by the divergences in electricity, refining and inland transport industries.
ER -