Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption

Abstract

The carbon footprint (CF) linked to electricity consumption in buildings has become a significant environmental issue because of its significant role in greenhouse gas emissions. This study seeks to assess and examine the CF of electricity consumption in buildings across various building types. Additionally, this paper aims to investigate the impact of the COVID-19 pandemic on the CF of buildings. The investigation involves a comparative analysis between the CF values observed and predicted during the years affected by the pandemic. Additionally, the study evaluates the influence of the pandemic on the accuracy of CF model predictions by employing three distinct machine-learning models. Spatial analyses were conducted to identify clustering patterns of CF and identify areas of both high and low CF concentrations within the study area. The findings demonstrate significant disparities in the CF of electricity consumption across distinct building types, with residential buildings emerging as the largest contributors to carbon emissions. Moreover, the pandemic has had a notable impact on CF patterns, leading to alterations in the areas identified as hotspots and cold spots during the pandemic years compared to the pre-pandemic period, based on building types.

Keywords

• Carbon footprint
• Buildings
• Machine-learning models
• Spatial analysis
• COVID-19