Quantifying households’ carbon footprint in cities using socioeconomic attributes: A case study for The Hague (Netherlands)

R.G. Patel, Antonino Marvuglia, Paul Baustert, Yilin Huang, Abhishek Shivakumar, I. Nikolic, T. Verma

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Cities consume almost 80 percent of world’s energy and account for 60 percent of all the emissions of carbon dioxide and significant amounts of other greenhouse gases (GHG). The ongoing rapid urbanization will further increase GHG emissions of cities. The quantification of the environmental impact generated in cities is an important step to curb the impact. In fact, quantifying the consumption activities taking place inside a city, if differentiated by socioeconomic and demographic groups, can provide important insights for sustainable-consumption policies. However, the lack of high-resolution data related to these activities makes it difficult to quantify urban GHG emissions (as well as other impacts). This paper presents a methodology that can quantify the carbon footprint of households in cities using consumption data from a national or European level, where the resource consumption is linked to socioeconomic attributes of a population. The methodology is applied to analyzing the environmental impact by household resource consumption in the city of The Hague in the Netherlands. The key insights reveal potential intervention areas regarding resource consumption categories and demographic groups that can be targeted to reduce GHG emissions due to consumption-driven activities in the city.
Original languageEnglish
Article number104087
JournalSustainable Cities and Society
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Consumption-driven emissions
  • Process-based LCA
  • Cities
  • Urban policies
  • Random forest
  • Demographic clustering


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