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Analysing community-level spending behaviour contributing to high carbon emissions using stochastic block models.

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Area of Science:

  • Data Science
  • Environmental Science
  • Network Analysis

Background:

  • Financial transaction datasets are increasingly utilized for estimating carbon emissions from consumer spending.
  • Understanding consumer spending behavior is crucial for effective carbon reduction strategies by policymakers and financial institutions.

Purpose of the Study:

  • To develop a method for identifying consumer spending patterns within large financial transaction datasets.
  • To enable targeted carbon reduction strategies by linking spending patterns to high-emission merchant categories.

Main Methods:

  • Utilized stochastic block modelling for community detection on a bipartite network.
  • Developed a modified weighting system for the bipartite network to maintain constant average community spending across categories.

Main Results:

  • Successfully formed communities of consumers with similar spending patterns.
  • Identified specific merchant categories contributing to high carbon emissions for distinct consumer groups.
  • Demonstrated a framework for analyzing the interplay of spending, energy usage, and transportation habits.

Conclusions:

  • Transaction datasets and stochastic block modelling offer valuable insights for financial institutions aiming to decarbonize.
  • The study provides a controlled framework for researchers to analyze the impact of consumer behavior on carbon reduction efforts.