How do microdata affect greenhouse gas emissions estimates? A comparison of consumption microdata for UK neighborhoods

Authors: Lena Kilian*, University of Leeds, Anne Owen, University of Leeds, Andy Newing, University of Leeds, Diana Ivanova, University of Leeds
Topics: Human-Environment Geography, Quantitative Methods
Keywords: Greenhouse gas emissions, consumption-based footprints, expenditure microdata, neighborhood footprints
Session Type: Virtual Guided Poster
Day: 4/9/2021
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual 53
Presentation File: Download

In light of an increased involvement of local actors in climate change mitigation, understanding local greenhouse gas emission trends is vital. Particularly in countries and cities with high consumption-based footprints, recognizing how local consumption contributes to global and national emissions is key for effective emission reduction. Typically, national emissions are disaggregated using consumption and expenditure microdata. To assess the reliability of such an approach, this research generates emission estimates using three UK microdata datasets from the year 2016 and compares their emission outputs, when levels of spatial and product details are high. These datasets include the Output Area Classification (a publicly available geodemographic classification), the Living Costs and Food Survey (an openly available expenditure survey), and a commercial household expenditure dataset by TransUnion. Whilst these specific data are UK-centric, similar open and commercial sources exist in many international settings. Findings indicate moderate levels of similarity between most emission estimates even at detailed product and spatial levels. Importantly, stronger positive correlations are found between estimates from higher-emission products. This suggests that different microdata generate mostly similar total greenhouse gas footprint estimates at a neighborhood level. Nevertheless, levels of similarity vary by products and geographies, highlighting the importance of understanding the sources of uncertainty in different microdata. We conclude that in order to meaningfully and accurately interpret subnational, product-level emissions, microdata selection must consider limitations and uncertainties from the data generation process, the necessary levels of disaggregation, and the availability of physical consumption units rather than expenditure data for high-emission products.

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