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

Ruchik Patel, Antonino Marvuglia*, Paul Baustert, Yilin Huang, Abhishek Shivakumar, Igor Nikolic, Trivik Verma

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

16 Citations (Scopus)

Abstract

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
Volume86
Early online date27 Jul 2022
DOIs
Publication statusPublished - 1 Nov 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Research Groups and Themes

  • SPS Centre for Urban and Public Policy Research

Keywords

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

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