Spain | Households’ CO2 Footprint: Enhancing Measurement, Economic Analysis and Big Data

Published on Tuesday, May 24, 2022 | Updated on Thursday, May 26, 2022

Spain | Households’ CO2 Footprint: Enhancing Measurement, Economic Analysis and Big Data

This note proposes a path to shore up the measurement of the carbon footprint of the representative household of an economy, enhancing traditional macroeconomic analysis and with the use of Big Data in order to obtain real time outcomes and insights into the impact of the lifestyle of different households.

Key points

  • Key points:
  • The measurement of the carbon footprint of individual companies and households will be key to allow them to make informed decisions to reduce it. While it is still under development, calculating individual CO2 emissions requires a holistic approach that combines data and analysis from different sources and venues.
  • Spanish households emitted 66 million tonnes of CO2 directly into the atmosphere in 2016, mainly due to the use of private vehicles, heating and cooking at home, 24% of the country's emissions. However, their total carbon footprint should also include embodied emissions from all goods and services they consume (e.g. the food that they eat or the clothes they wear), information that is not provided by official statistics.
  • BBVA Research estimates that these indirect emissions stood at 144 million tons in 2016 so all households’ (direct and indirect) emissions summed up 210 million tons, 52% of all Spanish CO2 emissions produced and imported. Among households’ indirect emissions, those generated by food, shelter, mobility and manufactured goods were 88%. Services and clothing made the rest.
  • The granular information included in Big Data allows us to go beyond the “representative” household of macroeconomic estimations and go deeper in the particular characteristics of the households in real time. This approach allows monitoring the decarbonization efforts of different households estimating the individual carbon footprint according to their different characteristics and lifestyles. Exploitation of BBVA’s with Big Data techniques can help in this endeavor enhancing both update and granularity.

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